Livestock and the Environment: Scientific Underpinnings for Policy...

TIAER

LIVESTOCK AND THE ENVIRONMENT:Scientific Underpinnings for Policy Analysis

Analysis of Agricultural Nonpoint Pollution Sources and Land Characteristics

Report No. 1

Anne McFarland and Larry Hauck

September 1995

Texas Institute for Applied Environmental Research · Tarleton State UniversityBox T0410, Tarleton Station · Stephenville, Texas · 76402

(817) 968-9567 · FAX (817) 968-9568

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FOREWORD

Assessing the Policy Implications ofTIAER’s Stream Monitoring Program

by Staci Pratt and Ron Jones1

I. BackgroundAppraising environmental issues requires an open mind and a commitment to scientific principlesof inquiry. Researchers at the Texas Institute for Applied Environmental Research (TIAER) haveexamined water quality concerns in an area encompassing approximately 230,000 acres and adiversity of land uses. The watershed contains 94 dairies with a combined herd size of roughly34,000 cows. The upper North Bosque also hosts a number of other important agriculturalactivities: local land owners raise peanuts, beef cattle, pecan orchards, and hay. In addition, theCity of Stephenville and its population of 16,000 citizens also contribute to the area’s character.Given that the Stephenville Wastewater Treatment Plant possesses a permit to discharge l.85million gallons of effluent per day, its impact merits attention. Thus, the local landscape supportsa variety of pollution sources. While all of these activities and entities influence water quality,some bear a significant share of regulatory attention.

The strength of the area’s dairy industry, located in the number one milk producing county in thestate of Texas, draws attention to its potential influence on water quality. Government officials atthe state level express concern about the potential of confined animal feeding operations to impactwater quality negatively. The Texas Natural Resource Conservation Commission (TNRCC)responded to water quality concerns by promulgating strict regulations on confined animal feedingoperations (CAFOs) and assessing harsh penalties. In 1989, TNRCC's predecessor imposed finestotaling some $490,000 against nine dairy operations in North Central Texas for illegal wastedischarges. 30 T.A.C. § 321.34 and also §§ 321.181-321.198, the new “Subchapter K” rules,detail acceptable best management practices and land application methods for dairy operators.

In reaction to state water quality assessments which revealed that a significant number of waterbodies are now impaired by livestock waste and concerns about potential lawsuits overunpermitted operations, EPA Region 6, headquartered in Dallas, published a general permit forCAFOs.2 Region 6 covers Texas, Oklahoma, New Mexico, Arkansas and Louisiana. The generalCAFO permit essentially applies uniform management criteria to hundreds of operations. Thepermit requires that CAFOs develop a detailed prevention plan and retain the plan on site, whichmust include information concerning the construction, maintenance and dewatering of facilitywaste containment structures. In addition, the permit lists mandatory best management practices(BMPs) for the application of solid and liquid manure to designated agricultural fields, includingapplication only at agronomic rates and on thawed and unsaturated soil.

1Staci Pratt, BA, Dartmouth College, JD, Boston College, LLM, University of London, is a policy analyst with theTexas Institute for Applied Environmental Research (TIAER), Tarleton State University, Stephenville, Texas;Ron Jones, BS, Texas A&M University, MS, Texas A&M University, is director of TIAER.

258 Fed. Reg. 7610 et. seq.

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Responding to all of these strict requirements, the dairy industry in Erath County implementedstructural management practices required by environmental agencies and addressed many of theconcerns identified by TNRCC and EPA. One hundred percent of fully permitted operations in theupper North Bosque River watershed currently contain constructed lagoons. Some dairy operatorsgo beyond TNRCC's lagoon sizing recommendations to insure adequate freeboard. However,issues do remain. Stormwater runoff from dairies and their manure application fields represents aserious problem. Runoff concerns provided the impetus behind Livestock and the Environment: ANational Pilot Project, a study germinating under the auspices of the EPA’s Office of Policy,Planning and Evaluation in Washington, D.C. They also have exposed the necessity of developingnew conceptual approaches for dealing with CAFOs on a watershed basis. Given the economicrealities facing agriculture, any such methodology must take into account the desirability of costeffective measures.

II. TIAER’s Work and ObjectivesThe overall objective guiding the Institute's research in the upper North Bosque River watershedcenters on obtaining a thorough understanding of the nature of the pollution problems associatedwith the river basin. TIAER recognizes the importance of dealing with all potential sources ofpollution in the watershed; however, given the priorities of the regulatory community and localcitizens, the primary emphasis naturally falls upon dairies.

Through a number of studies, including Clean Water Act § 319 research, the EPA sponsoredLivestock and the Environment: A National Pilot Project (NPP), and state funding, TIAER hasestablished a comprehensive monitoring system for examining the upper North Bosque Riverwatershed. The present system incorporates 19 automated stormwater samplers, which monitorstreams and USDA Soil Conservation Service Public Law-566 (PL-566) reservoir spillways.Monthly grab sampling also occurs at seven major stream sites and eight PL-566 reservoirs. Thus,beginning at the base of the watershed and extending through the major tributaries, monitoringsites establish the foundation for understanding the quality of water in an era which followsstructural management practice implementation. This monitoring effort allows TIAER to gainknowledge of the relative contribution of watershed actors in polluting the river. Althoughresearchers possessed suspicions that the dairy industry acted as one of the primary players inpollution loadings, sampling site placement reflected a desire to gather information about a widediversity of sources. For example, sites near urban, industrial and range areas show pollutantloadings attributable to non-dairy actors.

Water quality monitoring for a variety of constituents— including nitrogen, phosphorus, andsuspended solids— represents the primary avenue for isolating appropriate criteria to measuresuccessful intervention. Traditional point source regulation tended to look for the implementationof structural BMPs as the indicator of improved water quality. The difficulty of establishing causalrelationships between the pollution contributions of individual agricultural operations anddegradation in water quality makes focusing on BMPs particularly attractive; implementation ofstructural BMPs is easy to verify and document. In the final analysis, however, efforts to gaugesuccess must focus upon the quality of surface and groundwater and the achievement of in-streamtargets. In this manner, government may establish real progress towards better water quality.

Currently, only screening levels established by TNRCC guide TIAER’s analysis of water qualityconstituents such as nutrients. These levels reflect the point at which water bodies such asreservoirs and streams may become eutrophic. One may question the suitability of applying thesescreening levels to analysis of stormwater runoff in ephemeral streams. In contrast to the relativelysteady conditions of reservoirs, ephemeral streams will naturally reflect nutrient concentrationswhich peak with storm events.

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III. FindingsThe concentrations of various water quality constituents measured through the monitoring systempresented an image of pollution problems that could be correlated with land usage in areasupstream of the sites. This analysis provided the focal point for determining the relativecontributions of watershed actors. The land use of the watershed was determined from LandsatTM imagery classification. Ground truthing was provided to assist in the imagery classificationand to validate the final results. TIAER classified land usage into nine categories: rangeland,improved pasture (or coastal bermudagrass), woodland (trees and heavy brush), wheat and sudan(double cropping), orchards and groves, peanuts, urban, barren, and water. In addition, TIAERexamined the percentage of land occupied by manure application fields and the distribution of cowdensities on land. Land was also classified for "intensive agriculture" and subdivided into dairyand non-dairy. Correlating these usages with TIAER data provided the framework for the study.

TIAER's Scientific Underpinnings for Policy Analysis (the “Monitoring Report”) examines therelationships between TIAER’s water quality data and land uses, land practices and soils in thecontributing basins above each monitoring site. The report analyzes the contributions of micro-watersheds reflecting a diversity of land uses, from rangeland to intensive agriculture to municipaluses. While Stephenville's wastewater treatment plant and various other urban contributionsrepresent important pollution sources for their given locations, when one looks at the watershed asa whole for stormwater runoff, the dairy industry emerges as the major contributor to nutrientloading. Statistical analyses show that "certain land uses and watershed characteristics, mostnotably percent waste application fields, dairy cow density and percent woodland and rangeland inagricultural watersheds, have strong correlations to observed water quality."3 In addition,phosphorus acts as a significant nutrient in the watershed. "[C]omparison of water quality data tonon-regulatory screening levels indicates that some waterborne constituents, especiallyorthophosphate and total phosphorus, exceed these screening levels in both urban and agriculturalwatersheds."4 Data revealed a significant positive association of orthophosphate and totalphosphorus concentrations as the percentage of dairy waste application fields increased in thedrainage basins above reservoir and stream sites. One must proceed on a cautionary note,however. While this strong correlation exists, correlation alone cannot be used to trace the originsof a pollutant to a particular source. Monitoring data expresses current water quality and does notdistinguish between historical sources and present contributors. Although correlation analysishelps to identify areas of concern, more focused research is needed to confirm suspected problems.

The results indicated in the Monitoring Report highlight the importance of targeting areas whichprovide disproportionate pollution loadings to the river system. In these areas, strategies mustevolve to address phosphorus, the nutrient of primary concern. Certain segments, those near wasteapplication fields and with high dairy cow numbers, should garner more attention as potentialphosphorus contributors. Not only in Texas, but also in the Chesapeake Bay area and LakeOkeechobee, Florida, phosphorus has emerged as the major problem.

As the country moves toward focusing on nonpoint sources of pollution, addressing the impact ofphosphorus in runoff from agricultural operations on water quality will constitute an importantstep. Phosphorus (P) runoff represents a complicated phenomenon. "Phosphorus movement inrunoff occurs as particulate P (PP) and dissolved P (DP). In general, PP is the major portion (75 to90%) of P transported in runoff from cultivated land."5 The extent of phosphorus runoff depends

3A. McFarland and L. Hauck, Livestock and the Environment: Scientific Underpinnings for Policy Analysis, ReportNo. 1, September 1995 at 69.

4Id.

5A.N. Sharpley, T.C. Daniel, D.R. Edwards, "Phosphorus Movement in the Landscape", 6 J. PROD. AGRIC. 492, 493n. 4 (1993).

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on a variety of related factors. "PP movement in landscapes is a complex function of rainfall,irrigation application, runoff, and soil management affecting erosion."6 Soil scientists investigatedthe effects of several variables on P transport in runoff. The level of phosphorus found in soil tests"along with runoff volume, depth of soil-runoff interaction, and soil bulk density," all came to"directly influence DP transport."7 Thus, one must examine a number of closely related elementsto obtain fair predictions of phosphorus runoff.

IV. Policy Implications: A Focus on the FieldsOur present state of knowledge at TIAER does not allow us to evaluate the effectiveness ofstructural BMPs in reducing pollution from livestock operations in the upper North Bosque Riverwatershed. We do not possess information on the state of water quality prior to theimplementation of BMPs, and therefore cannot contrast water quality in the two eras. Similarly,we do not have estimates as to how much phosphorus in the water represents historical loadings.In the future, TIAER will utilize modeling to simulate the state of the upper North Bosque River inthe absence of structural BMPs. This will allow us to better address the effectiveness of measuresrequired by TNRCC and EPA.

Future analysis may indicate that structural BMPs alone do not achieve success in the stream.Since dairy operators in the watershed have effectively implemented structural BMPs and yetwater quality remains degraded, one must examine other explanations. The data results containedin this report suggest that nonpoint source problems, such as dairy waste application field runoff,may require bringing attention to management practices. Runoff from manure application fields,rather than discharge from lagoons, may well account for the strong correlation between thenumber of dairy cows in drainage basins and the presence of orthophosphate and total phosphorusin significant quantities.

Management practices on application fields may well play an important role in determiningphosphorus loadings in local water bodies. Current TNRCC and EPA confined animal feedingoperation permitting requirements restrict the application of dairy manure and lagoon effluent tothe agronomic nitrogen requirements of the crop. However, nitrogen based standards encouragethe over application of phosphorus. Under nitrogen based regulations, operators will over applyphosphorus by a factor of 2½ to 3 beyond crop needs. Over application stems from the presenceof plant-unavailable nitrogen and the nitrogen-to-phosphorus ratio in manure and lagoon effluent,coupled with nutrient requirements of crops. New TNRCC rules, however, call for phosphorusbased application rates where local water quality is threatened by phosphorus,8 paralleling therequirements set forth under EPA’s General CAFO permit.9 For example, where phosphorusbuilds up in the top 6 inches of the soil to an extractable level of 200 ppm (mg/kg), phosphoruswill provide the controlling factor.10

Decades of manure over application may saturate soil profiles (0-6 inches) with phosphorus."Sharpley and Smith (1989) observed a close agreement between predicted and measured DP

6Id. at 494.

7Id. at 496.

830 T.A.C. § 321.192(f)(19)(B) and (22), 20 Tex. Reg. 4727, 4737-4738.

958 Fed. Reg. 7610, 7631-2. According to the permit, "Land application rates of wastewaters should be based on theavailable nitrogen content, however, where local water quality is threatened by phosphorus, the permittee shouldlimit the application rate to the recommended rates of available phosphorus for needed cropuptake...”III(2)(f)(2)(I)(ii). Similar provisions also apply for solids. III(2)(f)(2)(J)(i).

10 See TNRCC Standard CAFO Permits; 30 T.A.C. § 321.192(f)(28)(6), 20 Tex. Reg. 4727, 4739.

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concentration in runoff from long-term watershed studies".11 The researchers compared fieldswith varying histories of manure application, including no history of application, historic poultrymanure application and historic swine manure application. "Peak concentrations of 4.5 and 1.8parts per million (ppm) DP were predicted for the runoff from fields having received poultry andswine waste, respectively. These concentrations were 33 and 14 fold greater than from theuntreated area … ."12 Essentially, as extra manure is added to the soil, the soil's capacity to bind upP decreases.

In addition, the absence of incorporation heightens the effects from phosphorus over application.Where manure is applied without soil profile inversion, phosphorus quickly builds up in thesurface layers of soils and may easily wash away.13 Essentially, phosphorus lacks the opportunityto bind adequately with the soil. In a field study using dairy manure and simulated rainfall,Mueller et al. showed that phosphorus loss was five times higher from areas receiving broadcasttreatments of manure than from incorporated manure.14 According to TIAER's data, of the totalupper North Bosque land in waste application fields, 74% is made up of coastal bermudagrass;notably, dairy operators do not incorporate manure on coastal application fields. Current TNRCCpermitting requirements allow operators to choose between incorporation and alternatively,providing a 100 foot buffer zone of grass between the application areas and surface waters. Thus,one may foresee runoff into water bodies stemming from unincorporated manure. In sum,phosphorus runoff presents complex issues which may not be completely understood at this time.Future research will need to prove sensitive to these matters.

V. Issues that Emerged from TIAER's MonitoringTIAER's Stream Monitoring Program Report raises a number of issues which will require furtherattention in the coming years. Even after six years of regulation and strong efforts on the part ofEPA, TNRCC and the regulated industry, loadings beyond the screening levels established byTNRCC persist in surface waters. As policy makers and stakeholders assess the implications ofthe following study, they may wish to keep the following questions in mind:

• Are TNRCC nutrient screening levels adequate as guides for examining waterquality in the upper North Bosque River watershed? How were they developed?TNRCC developed the screening levels as a means of indicating the point ofpotential accelerated eutrophication in water bodies, based on routine monitoring ofreservoirs and streams. One may wonder how appropriate they are for evaluatingstormwater nutrient concentrations in intermittent streams.

• Are the high concentrations observed in some areas attributable to inadequate bestmanagement practices or do they reflect improper management of manureapplication fields? What approach will government take in answering this question?

• How will researchers address dilution issues? Recognizing that pollution loadingsin the upper North Bosque do not translate directly into the water quality observedin Lake Waco, how will policy makers account for the contribution of downstreamtributaries and the settling of nutrients as water travels downstream? In deciding

11Sharpley, supra note 4, at 496.

12Sharpley, supra note 4, at 496.

13"If manure rather than chemical fertilizer is applied, the advantage of reduced tillage may be negated because ofthe leaching of phosphorus from the unincorporated manure, and bioavailable phosphorus losses may be greaterthan those from conventional tillage systems." NRC, Soil and Water Quality, An Agenda for Agriculture, p. 309.

14D.H. Mueller, R.C. Wendt, and T.C. Daniel, Phosphorus Losses Affected by Tillage and Manure Application, 48SOIL SCI. SOC. AM. J. 901, 902- 903 (1984).

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these issues, the public will need to determine which water bodies fall within theirarea of concern. TNRCC regards all water bodies, including ephemeral streams, asdeserving of attention. However, attending to the quality of thousands of streammiles may tax the capacity of TNRCC. Will future efforts simply focus on waterquality in Lake Waco, where the Bosque River empties, or will they also look at "hotspots" upstream? Satisfactory water quality in Lake Waco does not necessarilyimply adequate water quality in upstream areas. So too, degradation problemsupstream do not mean that Lake Waco will possess identical impacts.

• What role will siting issues play in the future? One may envision a time wherenonpoint source contributors within an individual target area will achieve the desiredwater quality objective. They may then choose to resist the introduction of newoperations which may impact water quality negatively and force a new round of BMPimplementation on established local residents.

• What future resources may be directed to examine the role of other nonpoint sourcesand municipal point sources in contributing to pollution in the North Bosque Riverwatershed and Lake Waco?

• Who will pay to inspect manure field applications and enforce management criteria?Few regulators could invest the time necessary to inspect the approximately 400manure application fields existing in the upper North Bosque River watershed, whichis but a small fraction of such fields in the entire state. Nor could their agenciesafford the financial investment required to sustain such an effort. Even whereenforcement mechanisms center on the participation of local stakeholders, resourcesof some kind will be required to build capacity. Determining who will foot the billmay become a politically sensitive and difficult decision. Should individual citizensshare the burden through their contributions to the general revenue or should dairyfarmers pay for the environmental consequences of their operation through permitfees? Alternatively, should downstream cities consider supporting watershedpollution programs as a way to avoid expensive water treatment systems?15

VI. The Next Steps, Priorities for Action:Given the preceding discussion, the following actions appear appropriate in the upper NorthBosque River watershed. They may also serve as a model for other watersheds in the countryencountering nonpoint source pollution problems.

1) Identify Specific Causes of Pollutant Loadings. Policy makers and other stakeholders need todevelop watershed planning strategies that address all land uses as potential sources ofpollution. Loadings may come from a variety of sources.

a) Determine the Impact of Point Sources on Water Quality. Point sources can contributesignificant nutrients to the watershed.

b) Determine the Impact of all Nonpoint Sources on Water Quality. A number of diffusesources exist which may impact nutrient loadings in the watershed.

2) Establish Total Maximum Annual Loads (TMAL) for micro-watersheds, PL-566 structures, andthe main stem of the Bosque. First, policy makers and researchers will need to focus on

15Heavy nutrient loads, and the dangers represented by Giardia and Cryptosporidium, forced New York City toconsider a $5-8 billion filtration system for its water supply. Innovative planners decided to develop and supportfinancially a comprehensive watershed management program focusing on agriculture’s role in nonpoint sourcepollution prevention instead. A successful program will enable the City to avoid enormous expenditure and toretain land in agricultural use. See “Whole Farm Planning in New York City Watersheds,” COASTLINES, Vol. 5,No. 2, Spring 1995, at 1.

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TNRCC screening levels established to identify the point at which water bodies eutrophy.Then, modifications may occur given the designated uses and assimilatory capacity of thereceiving bodies of water. The TMAL will reflect the need to maintain water quality in LakeWaco and also in its tributaries, e.g., the North Bosque River. Notably, the status of LakeWaco as a source of drinking water will affect the development of the TMAL.

3) Model Policy Alternatives. Partners in the NPP have cooperated to develop a series ofproposed policy options, alternative managerial practices and technologies which may alleviatepolluted runoff problems. These policy scenarios focus principally on the management ofmanure application fields and highlight the impact of phosphorus on water quality. NPPactivity has also produced a functional modeling framework for examining and analyzing theeconomic and environmental consequences of proposed policy options. Input-output analysiswill estimate the economic effects of the proposed policies. Then, environmental impacts maybe aggregated from the field to the watershed level by the SWAT mathematical model appliedby Blackland Research Center, a part of the Texas A&M University System. In this manner,economic costs and environmental benefits may be understood. The models will help peopleunderstand the impacts of various measures on downstream water quality. Water qualitymonitoring data serves to calibrate the models and insure their validity.

4) Develop Watershed Planning Strategies. In order to achieve TMALs, policy makers need tofocus on developing the tools to implement watershed planning. Micro-watersheds provide thenatural focal point in this developmental process. Essentially, micro-watersheds are sufficientlysmall and discrete to cope with the complexities inherent in natural systems, allowing fortargeting of limited resources and natural resource problem amelioration. They furnish arealistic forum for addressing phosphorus issues. They also supply a convenient arena for localparticipation in setting priorities and determining treatment measures. By aggregating the inputfrom various micro-watersheds up through the watershed level, policy makers will succeed increating a true agricultural pollution control framework.

Naturally, the Texas Soil and Water Conservation Board (TSSWCB) will play the guiding rolein dealing with nonpoint source pollution issues and developing watershed planning strategies.Pursuant to Senate Bill 503, “The state board is the lead agency in this state for activity relatingto abating agricultural and silvicultural nonpoint source pollution. Other state agencies withresponsibility for abating agricultural and silvicultural nonpoint source pollution shallcoordinate any abatement programs and activities with the state board.”16 Notably, the case ofConcerned Area Residents for the Environment v. Southview Farm, 34 F.3d 114 (2d Cir.1994), recently addressed the status of manure application fields and ruled that rainfall inducedrunoff from manure application fields falls outside the purview of the Clean Water Act’sdefinition of a point source discharge. Thus, runoff from manure application fields fallsdirectly within TSSWCB’s jurisdiction.

5) Using information collected in TIAER's Scientific Underpinnings for Policy Analysis Report,policy makers and other stakeholders should target impaired micro-watersheds. Obtaining ahandle on the source of pollution problems within a larger watershed requires narrowing one’sinquiry to smaller, more manageable units which reduce the complexities attributable tomultiple land uses. TIAER’s examination of water quality data indicates the value of targetingsmall watersheds (micro-watersheds) as priorities for action. Pollution loadings congregate inmicro-watersheds that receive a disproportionate share of intensive agricultural use, particularlydairies and waste application fields. For example, above sampling site NF020, approximately60% of the nearby land is used for waste application fields. In contrast, no land is presentlyused for waste application fields in the region encompassing site SF020. TIAER’s samplingreflects pollution loadings that correspond to the percentage of land occupied by wasteapplication fields; generally, site NF020 has the higher pollution loadings and site SF020, the

16V.T.C.A. Agriculture Code § 201.026; see also 31 T.A.C. § 523.

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unimpacted site, has the lower loadings. Thus, the North Fork micro-watershed should rankhigher as a priority for action than the South Fork micro-watershed.

In targeted micro-watersheds, policy makers and others will be able to evaluate the effectiveness ofBMPs, costs connected with BMP implementation and the necessity of adopting BMPs. Cost-sharing will enable dairy producers to adopt BMPs quickly and provide researchers with anopportunity to observe associated improvements in water quality. This process will test thenonpoint source pollution program set forth in Senate Bill 503 and require significant coordinationbetween the TSSWCB and TNRCC.

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PREFACE

The work described herein was performed by the Texas Institute for Applied EnvironmentalResearch (TIAER) of Tarleton State University, Stephenville, Texas. The monitoring datapresented were collected between March 1, 1991 and March 31, 1994. These data are from threeinterrelated studies. Sources of funding include the State of Texas, the Texas Water DevelopmentBoard, the US Environmental Protection Agency, and the USDA Natural Resources ConservationService through the Texas State Soil and Water Conservation Board.

This work was performed under the general supervision of Mr. Ron Jones, Director, and Mr. LarryHauck, Assistant Director of Environmental Sciences. The data collection program was designedand implemented by Mr. Tim Jones, Research Associate and Mr. Jeff Stroebel, Senior ResearchAssistant. Data analyses were performed by Dr. Anne McFarland, Senior Research Associate, Ms.Teresa Salyer, Research Assistant and Ms. Joan Flowers, Senior Research Associate. Dr.McFarland and Mr. Hauck prepared the report. Ms. Nancy Easterling, Special ProjectsCoordinator, provided technical editing, and Ms. Judy James, Computer Support Assistant,provided final report formatting. Dr. McFarland and Mr. Hauck would also like to acknowledgethe many helpful review comments and suggestions on the manuscript from Dr. Marshall J.McFarland of the Agricultural Experiment Station in Stephenville, Texas and Dr. George Ward ofthe Center for Research in Water Resources at The University of Texas at Austin and otherreviewers from within TIAER. The authors maintain full responsibility for the final presentationand interpretation of all data.

TIAER acknowledges the support from landowners who allowed access to their property formonitoring and provided areas for installation of instruments. Without the willing cooperation ofthese landowners, this study would not have been possible. Further, the authors wish to recognizethe perseverance and long hours of hard work by the field crews and laboratory chemists who areon call seven-days a week, since rainfall does not occur only between Monday and Friday.

Mention of trade names or equipment manufactures in this report does not represent endorsementof the products by TIAER.

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Table of Contents

FOREWORD: Assessing the Policy Implications of TIAER’s Stream Monitoring Program i

PREFACE ix

LIST OF TABLES xv

LIST OF FIGURES xvii

1. INTRODUCTION 1

1.1 Setting..............................................................................................................................................................1

1.2 Surface Water Monitoring ...............................................................................................................................1

1.3 Complexities of Monitoring and Analyzing Nonpoint Source Runoff ............................................................3

1.4 Report Objectives ............................................................................................................................................4

2. SITE DESCRIPTIONS AND OVERVIEW OF MONITORING 5

2.1 Sampling Site Descriptions..............................................................................................................................5

2.1.1 Agricultural Micro-watershed Sites...................................................................................................52.1.2 Urban Micro-watershed Sites ............................................................................................................72.1.3 Major Tributary Subwatershed Sites .................................................................................................72.1.4 Upper North Bosque River Watershed Sites .....................................................................................82.1.5 Monthly and Bi-monthly Sample Sites..............................................................................................82.1.6 Biological Reference Sites ................................................................................................................8

2.2 Methods and Procedures..................................................................................................................................9

2.2.1 Quality Assurance .............................................................................................................................92.2.2 Physical and Chemical Constituents..................................................................................................92.2.3 Biological Parameters .....................................................................................................................112.2.4 Monthly Grab Sampling ..................................................................................................................112.2.5 Automated Stormwater Sampling....................................................................................................122.2.6 Characteristics of Automatically Collected Samples.......................................................................122.2.7 Flow Measurements ........................................................................................................................13

3. WATERSHED CHARACTERISTICS 15

3.1 Watershed Monitoring Sites ..........................................................................................................................15

3.2 Land Use Characteristics ...............................................................................................................................15

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3.3 Soils, Topography and Runoff Potential........................................................................................................17

3.4 Dairy Locations, Milking Herd Distribution, and Major Agricultural Practices ...........................................18

3.5 PL-566 Reservoirs .........................................................................................................................................19

4. STREAMFLOW AT AUTOMATED SAMPLER SITES 21

4.1 Precipitation During Study Period.................................................................................................................21

4.2 Stage-Discharge Relationships ......................................................................................................................23

4.3 Streamflow at Automated Monitoring Sites ..................................................................................................23

4.3.1 Streamflow at Agricultural Micro-watershed Sites .........................................................................244.3.2 Streamflow at Urban Micro-watershed Sites...................................................................................304.3.3 Streamflow at Major Tributary Sites ...............................................................................................314.3.4 Streamflow at North Bosque River Sites.........................................................................................33

5. STATISTICAL ASSESSMENT OF WATER QUALITY BYMONITORING SITE 35

5.1 Data Management Procedures .......................................................................................................................35

5.2 Statistical Analysis Methods..........................................................................................................................36

5.2.1 Vertical Stratification of Reservoir Samples ...................................................................................365.2.2 Data Transformations......................................................................................................................365.2.3 Analysis of Reservoir Water Quality...............................................................................................375.2.4 Analysis of Stream Water Quality ...................................................................................................385.2.5 Reservoir and Stream Water Quality Criteria and Screening Levels...............................................39

5.3 Comparison of Chemical and Physical Water Quality Between Reservoir Sites...........................................40

5.3.1 Results for the Five Long-term PL-566 Reservoir Sites..................................................................405.3.2 Results Comparing All Eight PL-566 Reservoir Sites.....................................................................495.3.3 Summary of Reservoir Site Comparisons........................................................................................50

5.4 Comparison of Water Quality Between Stream Sites ....................................................................................50

5.4.1 Comparison of Stormwater Quality at Micro-Watershed Stream Sites ...........................................515.4.2 Comparison of Water Quality at Major Tributaries and Main Stem Stream Sites ..........................545.4.3 Summary of Stream Site Comparisons ............................................................................................58

6. COMPARISON OF WATER QUALITY WITH LAND CHARACTERISTICS 61

6.1 Correlation and Regression Analysis.............................................................................................................61

6.2 Results of the Correlation and Regression Analyses .....................................................................................62

6.3 Summary of Correlation and Regression Results ..........................................................................................67

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7. INTERPRETATION AND IMPLICATIONS OF WATER QUALITY FINDINGS 69

7.1 Urban Sites ....................................................................................................................................................69

7.2 Rural Sites .....................................................................................................................................................70

7.3 Phosphorus and Confined Animal Feeding Operation Regulations...............................................................73

7.4 Literature Review on Nutrient Losses from Manure Application..................................................................75

7.5 Conclusions for the Upper North Bosque River Watershed ..........................................................................76

8. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 77

8.1 Summary and Conclusions ............................................................................................................................77

8.2 Recommendations for Future Monitoring and Research ...............................................................................79

REFERENCES 81

APPENDIX A

MISCELLANEOUS ISSUES CONCERNING AUTOMATED SAMPLE COLLECTION .................................................................87

APPENDIX B

MONTHLY RESERVOIR WATER QUALITY MEANS AND STANDARD DEVIATIONS.............................................................95

APPENDIX C

STORM WATER QUALITY BY STREAM SITE FOR INDIVIDUAL STORM EVENTS ..............................................................103

APPENDIX D

MULTIPLE REGRESSION EXAMPLE FOR OPO4-P FOR RESERVOIR AND STREAM SITES ................................................113

APPENDIX E

REGRESSION RELATIONSHIPS OF LAND-USE CHARACTERISTICS WITH RESERVOIR WATER QUALITY ...........................123

APPENDIX F

REGRESSION RELATIONSHIPS OF LAND-USE CHARACTERISTICS WITH STORM WATER QUALITY .................................131

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List of Tables

2.1 Monitoring sites historical summary.......................................................................................................................... 7

2.2 In-situ and laboratory methods of analysis .............................................................................................................. 10

2.3 Water quality constituents, abbreviations, units and laboratory method detection limits ........................................ 11

3.1 Land use identification for drainage basins above sampling sites ........................................................................... 16

3.2 Intensive agricultural practices land use separated into dairy and non-dairy categories by percentages ofdrainage basin.......................................................................................................................................................... 17

3.3 Hydrologic soil group, slope, and curve number for drainage basins above sampling sites .................................... 18

3.4 Dairy operation and herd size characteristics by sampling site drainage basin ....................................................... 19

3.5 Percent of each site's drainage basin, dairies and dairy herd size controlled by PL-566 reservoirs......................... 20

4.1 Long-term (1955-1990) monthly average rainfall and individual monthly average rainfall for March 1991 -March 1994 for six National Weather Service sites in Erath and Hamilton Counties, Texas in inches................... 22

4.2 Data history for water level monitoring sites (November 1992 - March 1994) ....................................................... 24

5.1 Relevant water quality criteria and screening levels for the upper North Bosque River watershed......................... 39

5.2 Results of the analysis of variance on PL-566 reservoir data for measured and derived water qualityvariables .................................................................................................................................................................. 41

5.3 Rank ordering of micro-watershed sites based on results of tests of least significant differences for nutrientconstituents .............................................................................................................................................................. 53

5.4 Geometric mean of OPO4-P for reservoir and inflow tributary water quality samples............................................ 54

5.5 Comparison of geometric mean values representing effluent from the City of Stephenville wastewatertreatment plant and baseflow for site BO040 on the upper North Bosque River for samples collectedbetween December 1993 and March 1994 .............................................................................................................. 58

6.1 Correlation coefficients (r) and level of significance for water quality constituents with land characteristicsfor reservoir sites ..................................................................................................................................................... 63

6.2 Correlation coefficients and level of significance for water quality constituents with land characteristics forstream sites .............................................................................................................................................................. 64

6.3 Regression equations for water quality constituents versus land characteristics for reservoir sites......................... 65

6.4 Regression equations for water quality constituents versus land characteristics for stream sites ............................ 65

6.5 Maximum and minimum values for land characteristics used in correlation and regression analyses forstream and reservoir sites ........................................................................................................................................ 67

7.1 Percent of acreage used for waste application on coastal bermudagrass and on other types of forages basedon dairy permit and land use information ................................................................................................................ 74

A-1 Nutrient degradation sutdy; August 27-28, 1993..................................................................................................... 89

A-2 Nutrient degradation study; December 15-16, 1993................................................................................................ 89

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A-3 Nutrient degradation study; August 9-22, 1994....................................................................................................... 90

B-1 Mean water tempertures (ºC) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 95

B-2 Mean dissolved oxygen levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 95

B-3 Mean DO%sat levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 95

B-4 Median BOD5 levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 96

B-5 Mean COD levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 96

B-6 Geometric mean conductivity levels (µmhos/cm) by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994 ............................................................................................. 96

B-7 Mean pH levels (standard units) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 96

B-8 Mean TOC levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 97

B-9 Geometric mean NH3-N levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 97

B-10 Geometric mean NO2-N levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 97

B-11 Geometric mean NO3-N levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 97

B-12 Geometric mean inorganic-N levels (mg/L) by site and by season for monthly samples collected at reservoirsites between March 1991 and March 1994 ............................................................................................................ 98

B-13 Geometric mean OPO4-P by site by season and by season by site for montly samples collected at reservoirsites between March 1994 and March 1994 ............................................................................................................ 98

B-14 Geometric mean ratios of inorganic-N:OPO4-P by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994 ............................................................................................. 99

B-15 Geometric mean chlorophyll-α levels (µg/L) by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994 ............................................................................................. 99

B-16 Mean turbidity levels (NTU) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994 .................................................................................................................................. 99

B-17 Mean ZSD (feet) by site by season and by season by site for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................. 100

B-18 Comparison of mean or geometric mean of water quality variables at eight PL-566 reservoir sites formonthly grab samples collected between August 1993 and March 1994 .............................................................. 101

C-1 Volume-weighted stormwater constituent concentrations by storm event for site AL040..................................... 103

C-2 Volume-weighted stormwater constituent concentrations by storm event for site BO040 .................................... 104

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C-3 Volume-weighted stormwater constituent concentrations by storm event for site BO070 .................................... 104

C-4 Volume-weighted stormwater constituent concentrations by storm event for site DB040 .................................... 105

C-5 Volume-weighted stormwater constituent concentrations by storm event for site GC100 .................................... 105

C-6 Volume-weighted stormwater constituent concentrations by storm event for site IB040...................................... 106

C-7 Volume-weighted stormwater constituent concentrations by storm event for site IC020 ...................................... 106

C-8 Volume-weighted stormwater constituent concentrations by storm event for site MB040.................................... 106

C-9 Volume-weighted stormwater constituent concentrations by storm event for site NF005..................................... 107





C-14 Volume-weighted stormwater constituent concentrations by storm event for site SF020...................................... 109



C-17 Volume-weighted stormwater constituent concentrations by storm event for site SP020...................................... 110

C-18 Geometric mean stormwater quality concentrations for micro-watershed stream monitoring sites for samplescollected between March 1992 and March 1994................................................................................................... 111

C-19 Geometric mean concentrations for storm and baseflow samples collected at major tributary and main stemmonitoring sites ..................................................................................................................................................... 112

D-1 Full model of land characteristics versus OPO4-P concentrations at reservoir sites.............................................. 114

D-2 Regression models for dependent variable OPO4-P with land characteristics above reservoir sites ..................... 115

D-3 Cross correlation of independent variables representing land-use characteristics at reservoir sites ...................... 116

D-4 Full model of land characteristics versus OPO4-P concentrations at stream sites ................................................. 117

D-5 Regression models for dependent variable OPO4-P with land characteristics above stream sites......................... 118

D-6 Cross correlation of independent variables representing land-use characteristics at stream sites.......................... 119

D-7 Model of intensive land-use characteristics with OPO4-P concentrations at reservoir sites .................................. 120

D-8 Regression models for dependent variable OPO4-P with intensive land-use characteristics above reservoirsites........................................................................................................................................................................ 120

D-9 Model of intensive land-use characteristics with OPO4-P concentrations at stream sites...................................... 121

D-10 Regression models for dependent variable OPO4-P with intensive land-use characteristics above streamsites........................................................................................................................................................................ 121

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List of Figures

1.1 Upper North Bosque watershed dairy locations ........................................................................................................ 2

2.1 Upper North Bosque watershed sampling locations .................................................................................................. 6

4.1 Site specific monthly rainfall for six NWS observer sites in Erath and Hamilton Counties .................................... 22

4.2 Mean hourly streamflow at NF005 .......................................................................................................................... 25




4.6 Mean hourly streamflow at SF020........................................................................................................................... 27


4.8 Mean hourly streamflow at IC020 ........................................................................................................................... 28

4.9 Mean hourly streamflow at IC035 ........................................................................................................................... 28

4.10 Mean hourly streamflow at SP020........................................................................................................................... 29

4.11 Mean hourly streamflow at SP035........................................................................................................................... 29

4.12 Mean hourly streamflow at DB040.......................................................................................................................... 30

4.13 Mean hourly streamflow at MB040......................................................................................................................... 30

4.14 Mean hourly streamflow at IB040 ........................................................................................................................... 31

4.15 Mean hourly streamflow at AL040.......................................................................................................................... 31

4.16 Mean hourly streamflow at GC100.......................................................................................................................... 32



4.19 Mean daily streamflow at BO040............................................................................................................................ 33

4.20 Mean daily streamflow at BO070............................................................................................................................ 34

5.1 Arithmetic mean water temperature, DO and DO%sat by site and by season for monthly samples collectedat reservoir sites between March 1991 and March 1994 ......................................................................................... 42

5.2 Median BOD5 and mean COD by site and by season for monthly samples collected at reservoir sites

between March 1991 and March 1994 .................................................................................................................... 43

5.3 Geometric mean conductivity, mean pH and mean TOC by site and by season for monthly samples collectedat reservoir sites between March 1991 and March 1994 ......................................................................................... 44

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5.4 Geometric mean NH3-N and NO3-N by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994 .................................................................................................................... 45

5.5 Geometric mean OPO4-P by site by season and by season by site for monthly samples collected at reservoirsites between March 1991 and March 1994 ............................................................................................................ 46

5.6 Geometric mean of the ratio inorganic-N:OPO4-P and chlorophyll-α by site and by season for monthlysamples collected at reservoir sites between March 1991 and March 1994 ............................................................ 47

5.7 Arithmetic mean turbidity by site and by season and mean ZSD by site by season and by season by site formonthly samples collected at reservoir sites between March 1991 and March 1994 .............................................. 48

5.8 Arithmetic mean COD, geometric mean conductivity, arithmetic mean pH, arithmetic mean TOC, geometricmean NO3-N, geometric mean OPO4-P, geometric mean chlorophyll-α and arithmetic mean ZSD by site formonthly samples collected at all eight reservoir sites between August 1993 and March 1994................................ 49

5.9 Geometric mean NH3-N, NO2-N, NO3-N, TKN, OPO4-P and total-P by site for storm events monitored atmicro-watershed sites between March 1992 and March 1994................................................................................. 51

5.10 Geometric mean TOC, COD, TSS and VSS by site for storm event monitored at micro-watershed sitesbetween March 1992 and March 1994 .................................................................................................................... 52

5.11 Comparison of geometric mean baseflow water quality to storm event water quality for main stem andmajor tributary monitoring sites for samples collected between March 1992 and March 1994 .............................. 55

5.12 Geometric mean NH3-N, NO2-N, NO3-N, TKN, OPO4-P and total-P by site for storm events and baseflowmonitored at main stem and major tributary sites between March 1992 and March 1994 ...................................... 56

5.13 Geometric mean TOC, COD, TSS and VSS by site for storm events and baseflow monitored at main stemand major tributary sites between March 1992 and March 1994............................................................................. 57

7.1 Estimated septic system density compared with geometric mean OPO4-P concentrations for storm events atselected monitoring sites.......................................................................................................................................... 71

7.2 Ratio of geometric mean of OPO4-P to total-P from storm events versus percent waste application fields inthe drainage basin above stream sites ...................................................................................................................... 74

A-1 Comparison of water sampling procedures at BO040 for TSS................................................................................ 90

A-2 Comparison of water sampling procedures at BO040 for TKN .............................................................................. 91

A-3 Comparison of water sampling procedures at BO040 for NH3-N ........................................................................... 91

A-4 Comparison of water sampling procedures at BO040 for NO3-N ........................................................................... 92

A-5 Comparison of water sampling procedures at BO040 for NO2-N ........................................................................... 92

A-6 Comparison of water sampling procedures at BO040 for Total-P........................................................................... 93

A-7 Comparison of water sampling procedures at BO040 for OPO4-P.......................................................................... 93

A-8 Comparison of water sampling procedures at BO040 for COD .............................................................................. 94

E-1 Relationship of OPO4-P to percent rangeland in the drainage basin above each reservoir site ............................. 123

E-2 Relationship of OPO4-P to percent waste application fields in the drainage basin above each reservoir site ....... 124

E-3 Relationship of OPO4-P to dairy cow density in the drainage basin above each reservoir site ............................. 124

E-4 Relationship of TOC to dairy cow density in the drainage basin above each reservoir site .................................. 125

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E-5 Relationship of COD to dairy cow density in the drainage basin above each reservoir site .................................. 125

E-6 Relationship of chlorophyll-α to percent waste application fields in the drainage basin above each reservoirsite ......................................................................................................................................................................... 126

E-7 Relationship of chlorophyll-α to dairy cow density in the drainage basin above each reservoir site .................... 126

E-8 Relationship of conductivity to percent woodland in the drainage basin above each reservoir site ..................... 127

E-9 Relationship of conductivity to percent waste application fields in the drainage basin above each reservoirsite ......................................................................................................................................................................... 127

E-10 Relationship of conductivity to dairy cow density in the drainage basin above each reservoir site....................... 128

E-11 Relationship of turbidity to percent waste application fields in the drainage basin above each reservoir site....... 128

E-12 Relationship of Secchi depth to percent waste application fields in the drainage basin above each reservoirsite ......................................................................................................................................................................... 129

E-13 Relationship of Secchi depth to dairy cow density in the drainage basin above each reservoir site ...................... 129

F-1 Relationship of NH3-N to percent waste application fields in the drainage basin above each stream site............. 131

F-2 Relationship of NH3-N to dairy cow density in the drainage basin above each stream site................................... 132

F-3 Relationship of NO2-N to percent waste application fields in the drainage basin above each stream site............. 132

F-4 Relationship of NO2-N to dairy cow density in the drainage basin above each stream site................................... 133

F-5 Relationship of TKN to percent waste application fields in the drainage basin above each stream site................ 133

F-6 Relationship of TKN to dairy cow density in the drainage basin above each stream site...................................... 134

F-7 Relationship of OPO4-P to percent woodland in the drainage basin above each stream site................................. 134

F-8 Relationship of OPO4-P to percent waste application fields in the drainage basin above each stream site ........... 135

F-9 Relationship of OPO4-P to dairy cow density in the drainage basin above each stream site ................................. 135

F-10 Relationship of total-P to percent woodland in the drainage basin above each stream site ................................... 136

F-11 Relationship of total-P to percent waste application fields in the drainage basin above each stream site ............. 136

F-12 Relationship of total-P to dairy cow density in the drainage basin above each stream site ................................... 137

F-13 Relationship of TOC to percent waste application fields in the drainage basin above each stream site ................ 137

F-14 Relationship of TOC to dairy cow density in the drainage basin above each stream site ...................................... 138

F-15 Relationship of COD to percent waste application fields in the drainage basin above each stream site................ 138

1. INTRODUCTION

Agricultural and silvicultural nonpoint source (NPS) pollution remains a central concern in effortsto address impaired waters of the United States. The recent assessment of the nation's watersrequired by Clean Water Act Section 305(b) found agriculture to be the pollution source in 72percent of impaired river miles and 56 percent of impaired lake acres (EPA, 1994). State of Texaswater quality assessments conducted by the Texas Water Commission and the Texas State Soil andWater Conservation Board identified the North Bosque River of Central Texas as a knownproblem watershed as the result of dairy waste (TWC & TSSWCB, 1991). The majority of thesedairy operations are located near the headwaters of the North Bosque River in Erath County (seeFigure 1.1), a drainage area referred to as the upper North Bosque River watershed.

Although agricultural operations are thought to be the major contributors to nonpoint sourcepollution in the watershed, they are by no means the only significant contributors. The studies onwhich this report is based were funded, however, to investigate the impact of dairy operations onthe water quality of the upper North Bosque River watershed. Future reports will examine therelative contributions of all sources of pollution to the watershed's streams and reservoirs.

1.1 SettingThe upper North Bosque River watershed, i.e., the watershed of the North Bosque River aboveHico, Texas, encompasses an area of approximately 230,000 acres (359 square miles) and contains94 dairies with a combined milking herd size of approximately 34,000 cows. While dairying is themajor agricultural enterprise in the watershed, peanut, beef cattle, pecan orchard, and hayoperations are also important practices. The City of Stephenville with a population of 16,000 andportions of the smaller cities of Dublin and Hico are contained within the upper North BosqueRiver watershed. The only point source permitted to discharge in the watershed is the StephenvilleWastewater Treatment Plant with an allowable discharge of 1.85 million gallons per day. FortySoil Conservation Service Public Law 566 (PL-566) reservoirs are located in the watershed toprovide flood control.

1.2 Surface Water MonitoringThe Texas Institute for Applied Environmental Research (TIAER) at Tarleton State University hasmonitored agricultural nonpoint source runoff in the upper North Bosque River watershed sinceearly 1991. The watershed has been the focus of three separate TIAER studies documenting thedegree and effects of agricultural nonpoint source pollution. Through an evolving network ofmonitoring sites, a wealth of data has been obtained concerning physical, chemical, and biologicalconditions of the rivers, tributaries, and reservoirs of the watershed. The sampling program haslargely been in place since September 1993 and, despite some slight reduction in sampling sites,the sampling program is expected to remain active through at least December 1995.

2

Figure 1.1 Upper North Bosque watershed dairy locations

3

The monitoring system included 19 automated stormwater samplers, which monitor streams andPL-566 reservoir spillways with contributing drainage areas ranging from a few square miles to theentire watershed. The automated stormwater stream sampling was complemented by monthly grabsampling at seven major stream sites and eight of the PL-566 reservoirs. Bimonthly (i.e., everyother month) benthic macroinvertebrate sampling occurred at 17 stream and reservoir sites,including two least-impacted stream sites located outside the watershed.

Chemical analyses were performed on water samples for total suspended solids, volatile suspendedsolids, total Kjeldahl nitrogen, ammonia, nitrite, nitrate, total phosphorus, orthophosphate, totalorganic carbon, and chemical oxygen demand. These constituents quantify the nutrient, sediment,and oxygen-demanding characteristics of sampled waters. Five-day biochemical oxygen demandanalyses were performed on selected samples. During monthly sampling, the physical parametersof pH, dissolved oxygen, water temperature, and conductivity were also measured. Prior to 1992,analyses were restricted to the basic inorganic forms of nitrogen and phosphorus.

A water-level measurement was obtained every five minutes at the automated sampling sites.These measurements were converted to flow values through stage-discharge relationshipsdeveloped for each site. This information provides a time-history of the streamflow. Also, thecalculation of mass loading of waterborne constituents can be performed as a function ofstreamflow and constituent concentration.

1.3 Complexities of Monitoring and AnalyzingNonpoint Source Runoff

Stormwater runoff is inherently characterized by large spatial and temporal variations in flow andwaterborne constituent concentrations. Each rainfall-induced event contains unique characteristicsrelated to time of year, rainfall duration and intensity, and a broad range of important antecedentconditions concerning time since last rainfall and the conditions of the land, e.g., recentfertilization and crop maturity. The variability of flow and constituent concentrations makesaccurate runoff characterization from a single-grab sample or even a few grab samples virtuallyimpossible. Therefore, TIAER has relied upon automated samplers and their capacity to takemultiple samples to provide data for characterizing rain-induced runoff events in the upper NorthBosque River watershed.

Analysis of the streamflow and water quality data provides even further difficulties. The sheervolume of data collected is a challenge to meaningful analysis. One dilemma faced by theresearcher is how to condense the data into an understandable form without oversimplifying andlosing the inherent dynamics expressed by the data. The common practice of calculating storm-event-average flow and concentrations or storm-event-total flow volumes and total constituentmasses loses much of the data's inherent variability, and this simplifying practice can result in anincomplete understanding of the dynamics of the data. Determination of cause and effectrelationships, e.g., the relationship of land use to observed stream water quality, is made moretedious and difficult by the data's variability.

This data report only initiates the process of understanding the wealth of information obtainedfrom TIAER's continuing monitoring program of the upper North Bosque River watershed.Subsequent reports will delve into the data in more detail, especially as the number of storm eventsand routine samplings grows, allowing improved application of statistical approaches to the data.

4

1.4 Report ObjectivesThe overall goal of this report is to provide initial analyses of the TIAER water quality datagathered from March 1, 1991 through March 31, 1994 and to relate these analyses to the land usesand soils in the contributing watersheds above each monitoring site. Specific tasks include thefollowing:

1. Calculating flow at all automated monitoring sites,2. Comparing chemical and physical water quality between PL-566 reservoir sites,3. Comparing flow-weighted stormwater quality concentrations between stream sites,4. Comparing water quality at low flow with water quality during storm events at major

tributary and main branch sites, and5. Evaluating relationships between land use and soil characteristics with reservoir and

stream water quality constituents.

These analyses establish the response of environmentally important water quality constituents(physical and chemical) to the land-use activities and physical conditions above the in-streammeasurement sites (cause and effect relationships), provide data for testing of mathematical modelsthat predict agricultural nonpoint source runoff, provide information useful in directing land-usepolicy development and implementation, and document the conditions in the North BosqueRiver— a stream segment assessed as having pollution problems (TWC & TSSWCB, 1991). Thisdocumentation of water quality responses in the upper North Bosque River watershed shouldprovide a valuable insight into the regionally contentious subject of agricultural nonpoint sourcepollution. An assessment of rural, urban, point and nonpoint source contributors to North BosqueRiver pollutant loadings will be addressed in a subsequent report. A companion report providespreliminary results of the benthic macroinvertebrate sampling (Coan & Hauck, forthcoming 1995).

This report is written in the spirit of supplying factual information from ongoing intensivemonitoring efforts. Screening values for some waterborne constituents have been established bythe Texas Natural Resource Conservation Commission (TNRCC); for example, see TNRCC(1993a). However, enforceable in-stream numeric criteria or even target concentrations do notexist for the nutrient constituents discussed in this report. In the absence of actual numeric criteriafor in-stream nutrients and biological conditions, no definitive conclusion can be made regardingabsolute levels of pollution. As such, judgment as to the level of pollution, if any, is addressedonly in relative terms, e.g., by comparisons to least-impacted sample sites and screening levels.

This report is organized into sections that build upon the preceding sections. Monitoring sitedescriptions and an overview of water quality monitoring are found in Section 2. A description ofland uses, soils, and flood retardation structures in the drainage basins to each sampling site isprovided in Section 3. Streamflow and precipitation are discussed in Section 4. Section 5 presentsa statistical comparison of water quality at individual sites, while Section 6 presents a comparisonof water quality between sites with land-use characteristics. In Section 7, water quality results arediscussed in the context of existing screening levels and potential pollution sources. Finalconclusions, recommendations and summary are provided in Section 8.

5

2. SITE DESCRIPTIONS AND OVERVIEW OFMONITORING

The monitoring program is designed to characterize the water quality at several different scales ofthe upper North Bosque River watershed, i.e., for the entire watershed, for the primary or majortributary subwatersheds, and for smaller tributary micro-watersheds (less than 6,400 acres or 10square miles). Integrated into the sample site locations is variation in the level of agricultural andurban land use practices (see Section 3). Some sites may be characterized as least impacted, sincerangeland and tree coverage dominate the watershed, while other watersheds represent variousdegrees of impact due to the level of agricultural and urban land uses.

2.1 Sampling Site DescriptionsThe monitoring locations are provided in Figure 2.1, and a summary of each site is provided inTable 2.1. A unique five-digit alphanumeric code identifies each site. The first two-digits specifythe tributary, e.g., IC for Indian Creek and SF for South Fork. The last three-digits give relativelocation, with the lowest numeric value nearer the headwater and the largest numeric value at thefurthest downstream sample point, e.g., BO040 is the North Bosque River below Stephenville,Texas and BO070 is the North Bosque River at Hico, Texas. A brief description of the monitoringsites is provided under several broad categories as follows:

2.1.1 Agricultural Micro-watershed SitesFive agricultural micro-watersheds are monitored, and four contain Soil Conservation Service PL-566 flood retardation reservoirs. Monitoring sites have been established at the principal spillway(outlet) of the PL-566 reservoir and above the reservoir on its tributary (or in one case tributaries).

North Fork of North Bosque River (sites NF005, NF010, NF020, NF030, and NF035): Thismicro-watershed has automated monitoring sites (NF010 and NF020) on the two tributaries abovethe PL-566 reservoir, a manually sampled reservoir site (NF030), and an automatic sampler at theprincipal spillway of the reservoir (site NF035). Unique to the monitoring in this micro-watershed,a second monitoring site (NF005) is located above site NF020.

South Fork of North Bosque River (sites SF020, SF030, and SF035): This micro-watershed hasautomated monitoring sites on the tributary above the reservoir (site SF020) and at the principalspillway (site SF035). Manual monitoring is conducted in the reservoir at site SF030.

Indian Creek (sites IC020, IC030, IC035): Automated samplers are located on the tributary to thePL-566 reservoir (site IC020) and on the principal spillway (site IC035). Manual sampling isconducted in the reservoir at site IC030.

Spring Creek (sites SP020, SP030, SP035): This micro-watershed is monitored with automatedsamplers on the tributary above the reservoir (site SP020) and on the principal spillway (siteSP035), and by manual sampling at reservoir site SP030.

Dry Creek Branch (site DB040): This automated monitoring site is located on the Dry CreekBranch above its confluence with the North Bosque River. Together with major tributarymonitoring sites NF050 and SF075, discussed below, this site measures all agricultural tributariesabove the City of Stephenville.

6

Figure 2.1 Upper North Bosque watershed sampling locations

7

Table 2.1 Monitoring sites historical summary

SiteNumber Watershed

SiteType

Automatic SamplerInstallation Date

BiologicalSite

MonthlyGrab Sample

Date of First Water Sample

AL030 Alarm Creek R X X 04/03/91AL040 Alarm Creek TB 4/1/91 X X 04/18/91BO040 Bosque River MS 8/13/93 X X 04/04/91BO060 Bosque River MS X X 04/04/91BO070 Bosque River MS 4/1/91 X X 04/04/91CC030* Colony Creek TBr X 01/04/94DB040 Dry Branch TB 7/28/93 07/28/93GC020 Green Creek R X X 04/03/91GC100 Green Creek TB 8/1/92 X X 09/01/92IB040 Industrial Branch TB 7/20/93 09/13/93IC020 Indian Creek TB 9/24/93 10/18/93IC030 Indian Creek R X X 08/04/93IC035 Indian Creek S 9/22/93 no sample**MB040 Methodist Branch TB 7/28/93 08/02/93NF005 North Fork North Bosque TB 6/11/92 06/25/92NF010 North Fork North Bosque TB 4/1/92 04/18/91NF020 North Fork North Bosque TB 4/1/92 05/19/92NF030 North Fork North Bosque R X X 04/22/91NF035 North Fork North Bosque S 8/20/92 11/19/92NF050 North Fork North Bosque TB 4/1/91 X X 04/04/91SB030* South Bear Creek TBr X 01/07/93SC030 Simms Creek R X X 08/04/93SF020 South Fork North Bosque TB 4/1/92 05/16/92SF030 South Fork North Bosque R X X 04/30/91SF035 South Fork North Bosque S 8/1/92 02/15/93SF060 South Fork North Bosque R X X 04/30/91SF075 South Fork North Bosque TB 8/6/92 X X 11/19/92SP020 Spring Creek TB 9/23/93 10/20/93SP030 Spring Creek R X X 08/04/93SP035 Spring Creek S 9/9/93 no sample**

*denotes biological reference sites, which are not located in the Upper North Bosque River Watershed,**no releases from reservoirs during study periodTB-Tributary of Bosque RiverTBr - Tributary of Brazos RiverMS - Mainstem of Bosque RiverR - Mainbody of PL-566 ReservoirS - Spillway of PL-566 Reservoir

2.1.2 Urban Micro-watershed SitesMethodist Branch (site MB040): This monitoring site is located within the City of Stephenville.The drainage area above this site is entirely urban and includes the downtown section of the city.

Industrial Branch (site IB040): This monitoring site is situated in a drainage basin that is acombination of urban and rural. The rural portion does not include any dairies, and the urbanportion includes the industrial and major shopping areas of the City of Stephenville.

2.1.3 Major Tributary Subwatershed SitesThe subwatershed sites are located on the four major contributing creeks to the upper NorthBosque River. Sampling at the major tributary sites occurs on an event basis by automatedsamplers, a periodic (monthly) basis by grab sampling, and a bi-monthly basis for biologicalcommunities.

North Branch of North Bosque (site NF050): An automated sampler is located on this tributaryabove the confluence of the North and South Forks of the North Bosque River, which form theNorth Bosque River.

8

South Branch of North Bosque (site SF075): An automated sampler is located on this tributaryabove the confluence of the North and South Forks.

Alarm Creek (site AL040): An automated sampler is located on this creek near the confluencewith the North Bosque River. Because of interchange of lower Alarm Creek and North BosqueRiver waters during flood flows, the site was placed upstream of this complicating factor.

Green Creek (site GC100): An automated sampler is located on Green Creek near its confluencewith the North Bosque River.

2.1.4 Upper North Bosque River Watershed SitesThe sampling on the main stem of the North Bosque River is conducted in a manner similar to thatat the other sites: event sampling occurs from automated samplers, periodic water quality samplingoccurs on a monthly basis, and benthic macroinvertebrate sampling occurs bi-monthly. Theexception is site BO060 which is not equipped with an automated sampler. For this reason siteBO060 is not discussed in the report, but is presented in Coan and Hauck (forthcoming 1995).

North Bosque River below Stephenville Wastewater Treatment Plant (site BO040): An automatedsampler site is located approximately ¼ mile below the Stephenville wastewater treatment plant(WWTP).

North Bosque River above Green Creek (site BO060): This site is located eight miles downstreamof site BO040. The major tributaries entering the river between this site and site BO040 includeAlarm, Indian and Sims Creeks.

North Bosque River at Hico, Texas (site BO070): An automated sampler is located at the U.S.Geological Survey (USGS) streamflow site on the North Bosque River near the US Highway 281bridge. This site reflects the cumulative contribution of the entire watershed.

2.1.5 Monthly and Bi-monthly Sample SitesAt four sites not previously discussed above, monitoring is limited to monthly physicalmeasurements and grab sample analysis and bi-monthly benthic macroinvertebrate sampling.These sites include the following:

⋅ PL-566 Reservoir on Alarm Creek (site AL030),⋅ PL-566 Reservoir on Green Creek (site GC020),⋅ PL-566 Reservoir on South Fork (site SF060), and⋅ PL-566 Reservoir on Sims Creek (site SC030).

The same periodic sampling is performed at stream sites on Alarm Creek (site AL040), GreenCreek (site GC100), North Fork (site NF050), South Fork (site SF075), and the North BosqueRiver (sites BO040, BO060 and BO070). Also, this periodic sampling is performed at agriculturalmicro-watershed PL-566 reservoir sites (IC030, NF030, SF030 and SP030).

2.1.6 Biological Reference SitesTwo biological reference sites are located outside the North Bosque River watershed. ColonyCreek (site CC030) and South Bear Creek (site SB030) are the two nearest TNRCC designatedstreams that are considered as least impacted streams for the Central Oklahoma-Texas PlainsEcoregion (Bayer et al., 1992). These two sites are monitored bi-monthly and serve as referencesfor the biological stream monitoring. The initial results of the biological monitoring are providedin Coan and Hauck (forthcoming 1995).

9

2.2 Methods and ProceduresThe monitoring program methods and procedures are discussed in detail in prior TIAERpublications (Nelson et al., 1992; Coan et al., 1993; Hauck et al., 1994). Therefore, only anoverview of the monitoring program will be provided. The present monitoring program evolvedfrom several research programs, all directed to investigating agricultural nonpoint source issues.The present program is composed of routine monitoring of waterborne parameters and benthicmacroinvertebrates conducted at stream and PL-566 reservoir sites, stormwater runoff eventsampling of waterborne constituents at stream and PL-566 principal spillway sites, measurementsof streamflow, and continuous measurement of water level at all stormwater runoff sample sites.

2.2.1 Quality AssurancePresently all monitoring efforts are being conducted under the approved quality assurance projectplan (QAPP) for the research project entitled Livestock and the Environment: A National PilotProject (TIAER, 1993). Prior to implementation of the National Pilot Project QAPP, allmonitoring was conducted as specified in the QAPP for TIAER's EPA Clean Water Act (CWA)Section 319(h) project. The monitoring under Section 319(h) was very similar in scope,monitoring sites, and laboratory requirements to the National Pilot Project. The Section 319(h)project QAPP was approved by EPA and the Texas Water Commission (now the Texas NaturalResource Conservation Commission). The plan is found in Nelson et al. 1992, Volume V:Appendix 5.

2.2.2 Physical and Chemical ConstituentsA variety of physical and chemical constituents is included in the standard sampling plan (Table2.2). For each water quality constituent, the abbreviation used in this report, units of measurement,and method detection limit (MDL) are provided in Table 2.3.

Field, i.e., in situ, constituents are measured at all sites included in the monthly grab sampling.These constituents include dissolved oxygen, water temperature, specific conductance, pH andSecchi disc depth.

Samples are collected for laboratory analysis during monthly grab sampling and storm eventsampling with automated equipment. Analyses are performed for total Kjeldahl nitrogen,ammonia, nitrite, nitrate, orthophosphate, total phosphorus, total suspended solids, volatilesuspended solids, chemical oxygen demand and total organic carbon. Turbidity, five-daybiochemical oxygen demand, fecal coliforms, and chlorophyll-α are included in the analyses on amore limited basis.

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Table 2.2 - In-situ and laboratory methods of analysis

TEST SOURCE METHOD

IN-SITU CONSTITUENTS:

Dissolved Oxygen APHA, 1989 421 F Membrane Electrode

Temperature APHA, 1989 212

Specific conductance EPA (March 1983) 120.1 Conductivity Meter

pH APHA, 1989 423

Secchi Disc TWC (January 1990)

LABORATORY ANALYSIS:

Nitrite-Nitrogen EPA (March 1983) 353.2 Colorimetric, Automated Cadmium Reduction

Nitrate-Nitrogen EPA (March 1983) 353.2 Colorimetric, Automated Cadmium Reduction

Ammonia-Nitrogen EPA (March 1983) 350.1 Colorimetric, Automated, Phenate

Total Kjeldahl Nitrogen EPA (March 1983) 351.4 Ion Selective Electrode

Orthophosphate-Phosphorus EPA (March 1983) 365.1 Colorimetric, Automated, Ascorbic Acid

Total Phosphorus EPA (March 1983) 365.3 Colorimetric, Automated

BOD5 APHA, 1989 507 5-day BOD

Turbidity EPA (March 1983) 180.1 Nephelometric Hach Turbidimeter, Model 16800

Total Suspended Solids EPA (March 1983) 160.2 Gravimetric

Volatile Suspended Solids EPA (March 1983) 160.4 Gravimetric

Chemical Oxygen Demand EPA (March 1983) 410.4 Colorimetric, Manual

Total Organic Carbon EPA (March 1983) 415.1 Oxidation

Chlorophyll-α APHA, 1989 10200H

*References include Standard Methods for the Examination of Water and Wastewater, 17th Edition, APHA, 1989; Methods for ChemicalAnalysis of Water and Wastes, Revised March 1983, USEPA; and TWC Water Quality Monitoring Procedures Manual, January 1, 1990.

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Table 2.3 Water quality constituents, abbreviations, units and laboratory method detection limits

Parameter Abbreviation Units Method Detection Limit

Orthophosphate OPO4-P mg/L as Phosphorus 0.01

Total Phosphorus Total-P mg/L as Phosphorus 0.01

Ammonia Nitrogen NH3-N mg/L as Nitrogen 0.01

Nitrate Nitrogen NO3-N mg/L as Nitrogen 0.01

Nitrite Nitrogen NO2-N mg/L as Nitrogen 0.01

Total Kjeldahl Nitrogen TKN mg/L as Nitrogen 0.5

5-Day Biochemical Oxygen Demand BOD5 mg/L variable2

Chemical Oxygen Demand COD mg/L 20

Dissolved Oxygen DO mg/L NA

Total Suspended Solids TSS mg/L 4

Volatile Suspended Solids VSS mg/L 10

Total Organic Carbon TOC mg/L 1

Chlorophyll-α CHLA mg/L NA

pH pH standard units NA

Specific Conductance CONDUCT µmhos/cm NA

Turbidity TURB NTU1 NA

Water Temperature TEMPW °C NA

Secchi Disc Transparency ZSD feet NA1Nephelometric Turbidity Units.2Maximum and minimum detection limits change with each test. See APHA (1989) for procedures.NA - Not Applicable

2.2.3 Biological ParametersThough not discussed in this report, at the sites indicated in Table 2.1, benthic macroinvertebrate(biological) sampling is conducted on a bi-monthly basis according to U.S. EnvironmentalProtection Agency rapid bioassessment protocol (Plafkin et al., 1989). Benthic macroinvertebratesare small, invertebrate animals large enough to see with the unaided eye that live at least part oftheir lives within or upon available substrate on a stream or reservoir bottom. These organismsprovide a good indication of localized conditions and are integrators of short-term environmentalvariations. As previously mentioned, Coan and Hauck (forthcoming 1995) provides an in-depthanalysis of the biological sampling.

2.2.4 Monthly Grab SamplingAt the seven major stream sites and eight reservoir sites indicated in Table 2.1, monthly grabsamples are manually collected for laboratory analyses. Stream site collection consists of a single,representative sample. Reservoir site collection consists of near surface, mid-depth and nearbottom sampling.

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2.2.5 Automated Stormwater SamplingEach automated stormwater sampling site (Table 2.1) consists of an ISCO 3230 bubbler-typemeter and an ISCO 3700 automatic sampler. Both are enclosed in a single sheet metal shelter.The ISCO 3230 meter operates by measuring the pressure required to force an air bubble through aone-eighth inch polypropylene tube (bubbler line) and records this pressure as water depth. TheISCO 3230s are programmed to record the stream or spillway water level (stage) and initiatesample retrieval by the ISCO 3700 automatic samplers. Electrical power is provided by marine,deep-cycle batteries with recharge provided by solar cells.

ISCO 3230 meters initiate pre-set sampling programs for the ISCO 3700 automatic samplers whenthreshold water stages are exceeded. Each meter is programmed to record water level at 5-minuteintervals and to actuate the samplers when a stream rise of 0.12 feet above the bubbler datum isregistered. The actuation level was selected by trial-and-error as the lowest level which wouldactuate for rainfall-runoff events and avoid undesired actuation from non-rainfall event causes,e.g., wave action. Once activated, samplers are programmed to retrieve 24 one-liter sequentialsamples. The typical sampling sequence is (1) an initial sample, (2) three samples taken at one-hour intervals, (3) four samples taken at two-hour intervals, and (4) all remaining samples taken atsix-hour intervals.

2.2.6 Characteristics of Automatically Collected SamplesSince all stormwater samples were collected by ISCO automated samplers, the representativenessof the samples to the actual in-stream conditions is an extremely relevant issue. The following twoaspects of automated sample collection have been investigated by TIAER: (1) deterioration ofsamples with holding time in the ISCO sampler and (2) comparison of ISCO sample constituentconcentrations to grab sample concentrations. The details of both these investigations areprovided in Appendix A and are summarized below.

On three occasions, two summer and one winter, samples were left in the ISCO sampler undernatural conditions for periods of many hours to days and analyzed in triplicate for nitrate, nitrite,ammonia, orthophosphate and pH. For two of the tests, the longest holding time was 30 hours,since TIAER protocol requires that samples be collected from the ISCOs in less than 30 hours.The final summer testing was extended to 312 hours and included the additional fieldmeasurements of air and water temperature in the ISCO sampler housing. For the constituentsanalyzed, statistically significant variations from an allowed nutrient concentration variation of 5percent did not occur until after 168 hours . The conclusion from this testing is that the TIAERprotocol of retrieving ISCO samples in less than 30 hours is adequate to avoid significant changesin nutrient concentrations.

To determine the representativeness of ISCO-collected samples to the true in-stream conditions, asa first approximation, near-bottom, near-bank ISCO-collected samples were compared tosimultaneously collected near-surface, center-stream grab samples at site BO040. Least-squaresregressions with a forced zero intercept were applied to near-bottom (ISCO) sampling resultsplotted along the abscissa (x-axis) and near-surface results along the ordinate (y-axis). With thisconvention for analyzing the data, a slope less than 1.0 represents a bias toward higherconcentrations from the ISCO sampler as compared to the grab sample. A slope greater than 1.0represents a bias toward lower concentrations from the ISCO sampler as compared to the grabsample. Dissolved nutrient slopes ranged from 0.99 to 1.06. Total suspended solids showed thegreatest sampling bias with a slope of 0.75. Total Kjeldahl nitrogen, chemical oxygen demand,and total phosphorus had slopes of 1.02, 0.92 and 0.90, respectively. Even under this worst casescenario of comparing near-surface and near-bottom samples, the ISCO provides a veryrepresentative sampling technique for investigating dissolved constituents and an acceptable meansfor constituents, such as total phosphorus, that are associated with solids. This samplingcomparison is continuing in order to provide more data at the less frequent higher water levels.

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2.2.7 Flow MeasurementsAlthough the ISCO automatic samplers are able to measure and record water levels continuously,the parameter needed for water quality analysis is flow. Development of stage-discharge curvesare necessary for stream and spillway sites to determine the site-specific relationship of water levelto water flow. Because the reservoir principal spillways consist of broad-crested weirs and pipes,standard hydraulic equations, e.g., Streeter (1971), are employed to determine the discharge curvesfor spillway sites IC035, NF035, SF035 and SP035. The stage-discharge relationships forreservoir spillways were provided by the USDA Natural Resources Conservation Service (NRCS)(Goertz, 1993). Also, site NF005 is located at a culvert, which allowed TIAER hydrologists tocalculate a stage-discharge curve using established hydraulic equations, e.g., SCS (no date).

Flow measurement procedures for low to moderate stream flow conditions involve wading thestream perpendicular to flow direction and taking velocity and depth measurements at successivedistances along the cross section. A surveyor's tag line, marked in one-foot increments, isstretched across each stream as both a reference for maintaining position at the cross-section andfor accurately measuring increments. Flow measurements were collected at different water levelsusing a Global Water FlowprobeTM calibrated to a Teledyne-Gurley Price flow meter. Flowmeters are subjected to periodic calibration in the Tarleton State University Hydrology Departmentflume.

When water velocity or depth exceed levels which allow safe measurement by wading, less precisemeasurements are the only recourse. At many sites, the absence of a bridge crossing prevents themore traditional and accurate measurement of velocity by cable suspension of velocity meter withattached weight. To measure velocity at unsafe water levels, field personnel time a floating objectover a distance of 100 feet. This is generally repeated multiple times. A second method used athigh flows is to stake the high water marks along a reach of the stream. The stakes are latersurveyed and together with stream cross-sectional area information are used in Manning's equationto estimate flow. This method is referred to as the slope-area method to determine discharge(Dalrymple and Benson, 1967, and SCS, 1985).

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3. WATERSHED CHARACTERISTICS

The upper North Bosque River watershed is defined for this report as the contributing drainagearea above the U.S. Geological Survey streamflow site (Gauge Number 08094800; North BosqueRiver at Hico, Texas; U.S. Highway 281 river crossing). The majority of the 230,000 acrewatershed lies within Erath County, though the southern extremity is in Hamilton County.Climatologically the watershed may be characterized as a Subtropical Subhumid area with hotsummers and dry winters (Larkin and Bomar, 1983). Average annual precipitation isapproximately 30 inches, and average gross lake surface evaporation is nearly 70 inches. Rainfallgenerally follows a bimodal pattern with peaks in the spring and fall. Average wind speed is about13 miles per hour and the prevailing direction is from the southeast.

The watershed is located within the Central Oklahoma-Texas Plains Ecoregion, a regioncharacterized by irregular plains and potential natural vegetation of oaks and bluestem grasses. Ageographic information system (GIS) database comprised of individual land uses, topography andsoil layers was developed for the upper North Bosque River watershed. Through manipulation ofthese data with the GRASS (Geographic Resources Analysis Support System) GIS, characteristicsof the entire watershed and the subwatersheds of each sampling site were determined.

3.1 Watershed Monitoring SitesThe locations of sampling sites are provided previously in Figure 2.1. A description of thelocation of and the monitoring performed at each site is provided as Section 2. The land use, soiltypes, average land-surface slope, dairy locations, and approximate milking herd sizes for thedrainage basin above each monitoring site are described below.

3.2 Land Use CharacteristicsThe land uses of the watershed were determined from Landsat TM imagery classification. Groundtruth was provided to assist in the imagery classification and to validate the final results.Rangeland, improved pasture (or coastal bermudagrass), woodland (trees and heavy brush), wheatand sudan (double cropping), orchards and groves, peanuts, urban, barren, and water were the nineland-use categories specified. The following percent accuracies were guaranteed by the contractor:rangeland— 80 percent; improved pasture— 80 percent; woodland— 90 percent; wheat and summergrain— 80 percent; orchard and groves— 80 percent; peanuts— 70 percent; urban and barren— notspecified; water— 95 percent. Ground truth performed on limited areas by TIAER indicated thatthese percent accuracies were either met or exceeded in every instance. The AgriculturalStabilization and Conservation Service (ASCS) offices provided information in their database andmaps, which allowed high accuracy in determination of peanut field locations and acreages.

The drainage area and land-use characteristics for each monitoring site are provided in Table 3.1.The land-use characteristics of the upper North Bosque River watershed, as represented by thevalues for site BO070 at Hico, are 68 percent woodland plus rangeland, 29 percent improvedpasture, wheat/sudan, peanuts plus orchards and less than 2 percent urban. As designed, muchgreater than average urban land use is found above sites MB040 and IB040. Relatively moreintensive agricultural practices are found above sites NF005, NF010, NF020, NF030, NF035,NF050, SF075, and DB040. In contrast, SF020, SF030, SF035, SP020, SP030, and SP035 haveless intensive agricultural practices and more rangeland and woodland than the watershed average.

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Table 3.1 Land use identification for drainage basins above sampling sites.

Sampling Site Woodland(%)

Range(%)

ImprovedPasture (%)

Wheat Sudan(%)

Peanuts(%)

Orchards(%)

Water(%)

Urban(%)

Barren(%)

TotalAcres

NF005 11.0 34.3 52.5 1.7 0.2 0.3 1106NF010 17.7 40.6 30.7 10.8 0.3 1278NF020 13.6 28.6 49.3 8.0 0.3 0.2 1953NF030/NF035 15.4 32.8 40.7 9.5 1.3 0.2 3858NF050 20.2 29.7 40.2 8.2 0.5 0.2 0.7 0.4 20606SF020 35.6 60.5 2.6 1.0 0.2 0.1 2095SF030/SF035 37.4 56.6 4.2 0.9 0.9 0.1 2293SF060 31.7 40.8 23.9 2.8 0.7 0.1 8581SF075 28.3 28.9 34.1 6.1 1.5 0.1 0.8 0.3 30302DB040 22.0 24.5 33.2 8.3 7.1 1.2 0.9 0.1 2.6 6355MB040 100.0 421IB040 18.1 18.0 20.6 8.8 0.6 1.9 0.4 30.8 0.8 3209BO040 23.7 27.7 34.9 6.7 1.6 0.3 0.7 3.7 0.7 63868IC020 16.1 50.2 25.4 7.4 0.4 0.0 0.5 4494IC030/IC035 15.8 51.7 24.1 7.1 0.4 0.6 0.4 4771AL030 19.3 45.1 26.9 4.2 2.5 1.1 0.3 0.8 13392AL040 19.3 45.0 27.0 4.2 2.4 1.0 0.3 0.7 13423SC030 21.8 58.5 16.4 2.2 0.1 0.8 0.2 5594BO060 21.0 39.8 27.9 5.6 1.3 0.5 0.6 2.8 0.6 120936GC020 31.6 43.1 11.1 12.9 1.3 2180GC100 22.4 49.2 20.1 4.8 2.2 0.3 0.5 0.4 0.2 64308SP020 30.6 53.6 10.9 4.5 0.3 0.1 0.1 3924SP030/SP035 29.2 55.9 9.8 4.0 0.2 0.8 0.1 4377BO070 23.3 45.2 22.4 4.8 1.4 0.4 0.5 1.7 0.4 230243

For purposes of analysis in this study, improved pasture and the wheat/sudan land uses werecombined and re-categorized as either dairy waste application or non-dairy (forage) land uses(Table 3.2). The size and location of the animal waste application fields were obtained from theTexas Natural Resource Conservation Commission (TNRCC) dairy permits and available wastemanagement plans. These sources of waste application field data are public information availablefrom the TNRCC Austin, Texas office. For six of the unpermitted dairies, i.e., dairies with lessthan 250 cows in confinement, estimates of application fields were necessary. In these cases, thestandard guidance found in TNRCC permit applications was used to determine application fieldsizes. While the size and location of applications fields is not static, the information from TNRCCwas largely collaborated by the GIS land-use layer. This assessment of dairy waste applicationfield sizes and locations in the upper North Bosque River watershed uses the best availableinformation and is sufficiently current to categorize this land use. Also of note is the location of a20-acre field permitted for land application of septage. This field is located in the drainage basinof sites NF035, NF030 and NF020.

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Table 3.2 Intensive agricultural practices land use separated into dairy and non-dairy categories by percentages ofdrainage basin.

Sampling Site Dairy Waste Application Fields ( % ) Non-Dairy Forage Fields ( % )

NF005 41.7 12.4NF010 3.4 38.0NF020 45.4* 11.9

NF030/NF035 24.2 26.1NF050 10.1 38.4SF020 0.7 2.9

SF030/SF035 1.0 4.1SF060 8.2 18.5SF075 14.6 25.6DB040 13.5 28.0MB040 0.0 0.0IB040 0.1 29.3BO040 11.8 29.8IC020 17.3 15.5

IC030/IC035 16.4 14.8AL030 10.2 20.9AL040 10.1 21.1SC030 5.2 13.4BO060 9.2 24.3GC020 2.5 21.5GC100 6.9 17.9SP020 0.0 15.4

SP030/SP035 0.0 13.8BO070 7.2 19.9

*A 20-acre field permitted for land application of septage is located immediately above site NF020, but is not included in the percentage fordairy waste application fields.

3.3 Soils, Topography and Runoff PotentialDigital information for the GIS soils-type layer were developed by the USDA Natural ResourcesConservation Service (NRCS). Their efforts involved digitizing the appropriate county soilsurveys and having their area soil scientist review and correct the digitized soils information. TheGIS topographic layer was created by using existing 1:24,000 scale U.S. Geological Survey(USGS) digital elevation maps (DEMs) for most of the watershed. A small portion of thewatershed required supplemental digitizing of USGS quadrangle maps which was performed bythe NRCS and Iowa State University.

Soils and topography affect drainage, erosion, and plant cover which in turn influence the quantityand quality of stormwater runoff. Because of these interrelationships, methods of describing thehigh variability of soils and topography within the watershed and each drainage basin areimportant. The hydrologic soil group, average drainage basin slope and land use were consideredthe most important parameters to characterize runoff on a drainage basin scale. Based on rates ofwater infiltration and rates of water transmission, soils are commonly separated into fourhydrologic soil groups. The four hydrologic soil groups are described as follows (SCS, 1973): (1)Group A— soils having a high rate of water transmission and a low runoff potential; (2) Group B—soil having a moderate rate of water transmission and a moderate runoff potential; (3) Group C—soils having a slow rate of water transmission and a high runoff potential; and (4) Group D— soilshaving a very slow rate of water transmission and a very high runoff potential.

The percent contribution of each hydrologic soil group and average slope are provided for eachdrainage basin in Table 3.3. As listed for the entire basin, site BO070, the average slope is 4percent and the approximate percent composition of the hydrologic soil groups are 0, 20, 50 and

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30 percent for groups A, B, C and D, respectively. Also provided in the table is the slope-corrected, area-weighted runoff curve number (CN) for each drainage basin. The runoff curvenumber indicates the runoff potential for the combination of hydrologic soil group and land use.CN values range from 0 to 100 with increasing values indicating higher potential for runoff. CNvalues are factors in the Soil Conservation Service (SCS) curve number method used to determinerunoff volume from precipitation events. A description of the SCS curve number method is foundin SCS (1985), and the slope correction used for this analysis is the same as provided in Sharpleyand Williams (1990). The runoff curve number for each basin was calculated throughmanipulation of the GIS database layers of land use, soils, and topography. The CN values arevery similar for most basins in the study area, with values generally ranging from 73 to 78, and abasin-wide CN of 75. The exceptions are the two urban basins, IB40 and MB40, with higher CNvalues.

Table 3.3 Hydrologic soil group, slope, and curve number for drainage basins above sampling sitesPercent Hydrologic Soil Groups Miscellaneous Area-Weighted Area-Weighted

Sampling Site A B C D Soil Groups * Slope Curve Number **NF005 0.0 2.3 69.0 28.7 0.0 6 76NF010 0.0 5.9 87.6 6.5 0.0 5 75NF020 0.0 4.6 78.1 17.3 0.0 5 76NF030/NF035 0.0 7.7 81.3 11.0 0.0 4 75NF050 0.0 12.9 74.4 12.7 0.1 4 74SF020 0.0 8.3 45.9 45.8 0.0 5 78SF030/SF035 0.0 13.4 45.2 41.4 0.0 5 77SF060 0.0 19.5 51.2 29.0 0.3 4 75SF075 0.0 23.4 56.2 20.3 0.1 4 73DB040 0.0 16.3 53.9 29.8 0.0 3 75MB040 0.0 1.1 85.2 13.8 0.0 3 94IB040 0.0 1.0 80.4 14.9 3.7 3 82BO040 0.0 19.5 61.5 18.9 0.1 4 75IC020 0.0 12.3 37.6 49.8 0.3 4 77IC030/IC035 0.0 11.6 38.1 49.9 0.3 4 78AL030 0.0 15.9 60.8 22.2 1.1 7 75AL040 0.0 16.1 60.6 22.2 1.1 4 75SC030 0.0 10.4 32.7 56.8 0.1 6 78BO060 0.0 18.6 54.7 26.4 0.4 4 75GC020 0.0 32.5 44.3 21.6 1.6 4 73GC100 0.0 25.0 41.8 32.6 0.7 4 75SP020 0.0 14.1 48.9 33.8 3.3 4 76SP030/SP035 0.0 14.1 46.4 36.6 3.0 3 76BO070 0.0 20.6 48.4 30.6 0.5 4 75

*Miscellaneous groups include gullied land and sandy alluvium land.**Condition II (average condition) curve number, slope corrected and determined from GIS land use, soils, and topography.

3.4 Dairy Locations, Milking Herd Distribution, andMajor Agricultural Practices

Erath County contains approximately 200 dairies with an estimated cumulative herd size of 65,000cows, of which 94 dairies and an estimated 34,000 cows are in the upper North Bosque Riverwatershed. The actual numbers for operating dairies and herd size are not static but change inresponse to market demands and other factors. The estimated herd size is based on a compilationof the most recently available information from TNRCC and TIAER surveys on milking herd size.Therefore, the estimated herd size includes only milking cows and does not include dry herd andreplacement heifers. As indicated in Figure 1.1, the dairy locations are not evenly distributedthroughout the watershed, but are concentrated in the northern region. The distribution of dairiesand dairy cows in the watershed above each monitoring site is highly variable (Table 3.4) but issimilar to that for intensive agricultural practices (Table 3.1). Included in the dairy herdinformation of Table 3.4 are the two large calf raising operations in the watershed. The drainage

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basins of sites NF005, NF020, NF030, NF035, SF075, DB040, IC020, IC030 and IC035 have thehigher herd densities, and SF020, SF030, SF035, SP020, SP030, and SP035 have the lower herddensities.

Table 3.4 Dairy operations and herd size characteristics by sampling site drainage basin

Sampling Site Number of Dairies Herd Size by Permit or WasteManagement Plan

Estimated Milking Herd Size Herd Density *(Cows/Acres)

NF005 2 1450 756 0.68NF010 0 0 0 0.00NF020 3 2050 1256 0.64

NF030/NF035 3 2050 1256 0.33NF050 11 4090 2698 0.13SF020 0 0 0 0.00

SF030/SF035 0 0 0 0.00SF060 6 2540 1440 0.17SF075 21 11474 (+ 6000 calves) 8620 (+ 6000 calves) 0.38DB040 8 2159 1612 0.25MB040 0 0 0 0.00IB040 0 0 0 0.00BO040 41 17873 (+ 6000 calves) 12995 (+ 6000 calves) 0.25IC020 7 2750 1442 0.32

IC030/IC035 7 2750 1442 0.30AL030 8 4248 (+ 995 calves) 2868 (+ 995 calves) 0.25AL040 8 4248 (+ 995 calves) 2868 (+ 995 calves) 0.25SC030 4 847 787 0.14BO060 62 26948 (+ 6995 calves) 19172 (+ 6995 calves) 0.19GC020 0 0 0 0.00GC100 24 13701 11116 0.17SP020 0 0 0 0.00

SP030/SP035 0 0 0 0.00BO070 94 45497 (+ 6995 calves) 33748 (+ 6995 calves) 0.16

*Herd densities based on estimated milking herd size (milking herd + (0.5 x calves)) and actual drainage area.

3.5 PL-566 ReservoirsThe SCS PL-566 flood retardation reservoirs in the watershed are important features that influencenot only the hydrology but also the water quality in the upper North Bosque River watershed. The40 PL-566 reservoirs control drainage from almost 130,000 acres or 56 percent of the entiredrainage basin. During rainfall-runoff events these reservoirs and associated spillways reduce peakstreamflows. The reservoirs also act as settling basins to reduce downstream sediment loads andnutrients attached to these sediments. Further, these reservoirs provide permanent waterbodies thatcontain macrophytic and/or planktonic populations that transform nutrients and often provide adegree of biochemical removal of nutrients. The role of these reservoirs in agricultural pollutioncontrol is discussed in Hauck et al. (1994) and McFarland and Hauck (1995).

The percent of each main stem and major tributary site's drainage basin controlled by one or morePL-566 reservoirs is provided in Table 3.5. Also provided in the table are the percentages ofdairies and estimated dairy cows located above reservoirs in each site’s drainage area. Generally,a disproportionately high percentage of dairies and dairy cows are located above PL-566reservoirs. For example, for the entire watershed (site BO070) the reservoirs control only 56percent of the drainage area, but 81 percent of the dairies, representing 84 percent of the milkingcows, are located above the reservoirs.

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Table 3.5 Percent of each site’s drainage basin, dairies and dairy herd size controlled by PL-566 reservoirs.

Sampling Site Drainage Area (%) Dairies (%) Dairy Milking Herd Size (%)

NF005 0 0 0NF010 0 ND NDNF020 0 0 0NF030/NF035 100 100 100NF050 62 91 97SF020 0 ND NDSF030/SF035 100 ND NDSF060 35 100 100SF075 72 86 91DB040 41 75 64MB040 0 ND NDIB040 0 ND NDBO040 60 85 92IC020 0 0 0IC030/IC035 100 100 100AL030 100 100 100AL040 99 100 100SC030 100 100 100BO060 60 90 95GC020 100 100 100GC100 46 58 67SP020 0 ND NDSP030/SP035 100 ND NDBO070 56 81 84

ND - no dairies in watershed.

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4. STREAMFLOW AT AUTOMATEDSAMPLER SITES

Streamflow is an important characteristic that greatly affects stream quality; therefore, itsquantification enhances the value of water quality information. At each automated sampler site,the continuous (5-minute interval) water level data and site-specific stage-discharge relationshipallows the calculation of flow. Streamflow data coupled with the concentration of water qualityconstituents allows the calculation of the mass (e.g., pounds) or mass rate (e.g., pounds per day orpounds per runoff event) for each constituent. For a runoff event, a flow-weighted averageconcentration provides an intuitively more meaningful average than one based solely on the waterquality data independent of flow. The size of a runoff event as quantified by the amount of flow,i.e., runoff volume, also puts into perspective the relative importance of each event.

In this section, general rainfall conditions during the study period are provided, the stage-dischargerelationships (flow-rating curves) are discussed and the streamflow data for each automatedsampler site are presented. The stage-discharge relationship is determined from individualmeasurements of flow and the associated water level or stage at the time of measurement.

4.1 Precipitation During Study PeriodRainfall is the primary variable necessary to produce stormwater runoff, though a myriad of landuse-related variables determine the amount of rainfall that is transformed into surface runoff.During the study period, March 1991 through March 1994, average basin-wide rainfall wasgenerally higher than normal based on comparison to the long-term average. In Table 4.1 themonthly and annual rainfall for the five National Weather Service (NWS) observation sites inErath County, Texas and one in Hamilton County, Texas have been averaged and are provided forthe study period. Also provided in Table 4.1 are the long-term averages of the combined six sitesfor the 36 year period 1955-1990 as obtained from the National Climatic Data Center (NCDC,NOAA-National Weather Service), Asheville, North Carolina. In August 1991, October 1991,December 1991, February 1992 and October 1993, precipitation was well above the long-termaverage. July 1993 was marked by the absence of any measurable precipitation at any site.

Lost in the data averaging of the six NWS sites is the inherent temporal and spatial variability ofrainfall. In Figure 4.1 the monthly average rainfall for each of the six NWS sites is presented.Even as a monthly average, which typically involves the averaging of several events, the spatialand temporal variability in rainfall between observation sites is aptly demonstrated. It is not theintent of this study to investigate the dynamic effects and variability of individual rainfall events.Therefore only this very limited attention will be devoted to issues of rainfall; rather the emphasisis on streamflow and water quality condition.

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Table 4.1 Long-term (1955-1990) monthly average rainfall and individual monthly average rainfall forMarch 1991 - March 1994 for six National Weather Service sites in Erath and Hamilton Counties, Texas in inches

Month Long-term Average 1991 1992 1993 1994

January 1.69 — 3.01 2.68 1.65February 2.03 — 4.63 3.34 1.42March 2.22 0.72 1.53 2.69 1.10April 2.98 2.22 1.69 2.79 —May 4.68 4.33 5.27 1.86 —June 3.07 4.49 4.35 4.02 —July 2.14 0.97 2.41 0.00 —

August 2.13 5.52 2.45 1.74 —September 3.12 4.54 2.26 4.29 —

October 3.10 8.38 0.89 6.04 —November 1.91 0.64 3.87 1.32 —December 1.11 9.22 3.76 1.70 —Annual 30.18 — 36.11 32.46 —

Figure 4.1 Site specific monthly rainfall for six NWS observer sites in Erath and Hamilton Counties

0

2

4

6

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Jan

91

Mar

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91

Jul 9

1

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Prec

ipita

tion

(inch

es)

Huckabay Stephenville Dublin Hico Chalk Mountain Morgan Mill

23

4.2 Stage-Discharge RelationshipsAs discussed briefly in Section 2, manual flow measurements were made at all automated samplerstream sites on an opportunistic basis dependent upon streamflow conditions. Because of theintermittent nature of area streams, their small drainage areas, and the commensurate quickresponse time of flow to rainfall, obtaining flow measurements at the higher water levels wasdifficult. Adding to this difficulty is the general tendency of rainfall to occur during evening hours,which typically results in the higher water levels occurring during hours of darkness when forsafety reasons measurements cannot be made.

The pairs of water level and flow data were used to develop a site specific stage-dischargerelationship for each stream site. For each instrumented PL-566 reservoir spillway site, the USDANational Resources Conservation Service provided the stage-discharge relationship based on theircalculations of spillway hydraulics. Therefore, flow measurements are not necessary at spillwaysites. Because stream site NF005 is located at a road culvert, hydraulic equations were applied byTIAER staff to determine its stage-discharge relationship. For the remaining stream sites, a stage-discharge relationship was developed through the simple plot of stage versus discharge. Typicallysuch relationships are parabolic in shape when plotted on arithmetic (i.e., nontransformed) axes,though irregularities often exist because of abrupt cross section changes and changes indownstream control with variation in flow. Extrapolation of the stage-discharge relationships wasmade by using the surveyed stream cross section at the sample site and an assumed semi-logarithmic relationship of average stream velocity (log) to water level. The semi-log approachgave better results than the log-log relationship of stage and discharge when extrapolations werecompared to new higher stage measurements. This semi-log approach makes use of existing crosssection information at each site and extrapolates the velocity, as opposed to other methods, e.g.,Kennedy (1984), that do not directly account for available cross section information.

4.3 Streamflow at Automated Monitoring SitesIn Table 4.2 the operational history of water level monitoring at each automated monitoring site isprovided. The varying durations of the streamflow record for each site reflect the different dates ofinstrument installation and an early operational history that involved greater than acceptable loss ofdata due to initial adjustments of the instrumentation. Through the implementation of a stricterquality assurance program in September 1993 that requires twice-weekly visits to each site, thetrend has been toward more reliable data and consistently high percentage retrieval of availablewater level records, which is reflected in Table 4.2.

24

Table 4.2 Data history for water level monitoring sites ( November 1992 - March 1994 )

Sampling Site Nov 92 Dec 92 Jan 93 Feb 93 Mar 93 Apr 93 May 93 Jun 93 Jul 93 Aug 93 Sep 93 Oct 93 Nov 93 Dec 93 Jan 94 Feb 94 Mar 94NF005NF010NF020NF035NF050SF020SF035SF075DB040MB040IB040BO040IC020IC035AL040GC100SP020SP035BO070

period of valid data.

4.3.1 Streamflow at Agricultural Micro-watershed SitesThe streamflow records for the four sampling sites in the North Fork micro-watershed (sitesNF005, NF010, NF020, and NF035) are provided in Figures 4.2-4.5, respectively. The highlyintermittent nature of flow at these sites is evidenced by the large periods of time at each site withflow much less than 1.0 cubic feet per second (cfs). This highly intermittent flow is a feature ofsites throughout the study area except the main stem sites BO040 and BO070, which are locatedbelow the Stephenville Wastewater Treatment Plant (WWTP) outfall. Another feature of thisseries of streamflow records is the noticeable reduction in peak flows and extension of the overallhydrograph duration resulting from the storage capacity and restricted spillway outlet of the PL-566 reservoirs. This feature can be discerned by comparing the hydrographs (peak flows anddurations) for the two upstream sites (NF010 and NF020) to those of the reservoir spillway(NF035).

25

Figure 4.2 Mean hourly streamflow at NF005; November 1, 1992 through March 31, 1994.

0

5

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Nov

-92

Dec

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Jan-

93

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-94

Apr

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Date

Flow

(cfs

)

Water Level Operational Nov. 1993

1500

500

0

5

1000

(Note change in scale at 5 cfs.)

Figure 4.3 Mean hourly streamflow at NF010; November 1, 1992 through March 31, 1994

0

5

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Nov

-92

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Jan-

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No Data

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26


0

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Data

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Streamflows for the South Fork micro-watershed are depicted for the upstream site (SF020) andthe PL-566 reservoir spillway (SF035) in Figures 4.6 and 4.7, respectively. A comparison of thestreamflow records for these two sites shows the same intermittent flow and the effects of thereservoir on the shape of the storm event hydrographs as for the North Fork micro-watershed. Ahydrologic response more apparent in these two figures than those for the North Fork micro-watershed is the complete capture of the inflow (Figure 4.6) without any spillway release (Figure4.7) during the late summer of 1993. Due to evaporation and seepage, it is not uncommon forthese reservoirs to be drawn down below the level of the spillway creating sufficient storage tocapture small runoff events.

27

Figure 4.6 Mean hourly streamflow at SF020; November 1, 1992 through March 31, 1994

0

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28

Monitoring equipment installation did not occur until late summer 1993 at the remainingagricultural micro-watershed sites, which greatly reduces the flow record for these sites. Thestreamflow records for sites IC020, IC035, SP020, SP035, and DB040 are provided in Figures 4.8-4.12. Water levels at the PL-566 reservoir spillway sites (IC035 and SP035) were very low priorto installation of monitoring equipment and no spills occurred at either site during the reportingperiod.

Figure 4.8 Mean hourly streamflow at IC020; November 1, 1992 through March 31, 1994

0

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Installed Sept . 1993

1500

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500

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Figure 4.9 Mean hourly streamflow at IC035; November 1, 1992 through March 31, 1994

0

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Installed Sept. 1993

No Spillway Discharge

1500

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29

Figure 4.10 Mean hourly streamflow at SP020; November 1, 1992 through March 31, 1994

0

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Installed Sept. 1993

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0


Figure 4.11 Mean hourly streamflow at SP035; November 1, 1992 through March 31, 1994

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(cfs

) Installed Sept . 1993

No Spillway Discharge

1500

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30

Figure 4.12 Mean hourly streamflow at DB040; November 1, 1992 through March 31, 1994

0

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Installed July 1993

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4.3.2 Streamflow at Urban Micro-watershed SitesThe streamflow record for Methodist Branch (MB040) and Industrial Branch (IB040) are providedin Figures 4.13 and 4.14, respectively. Because of the relatively high percentage of imperviouscover, e.g., streets, parking lots and roofs, above the urban sampling sites, measurable runoffoccurs for all but small rains. Hence the frequency of events is much greater at these sites than forthe agricultural sites.

Figure 4.13 Mean hourly streamflow at MB040; November 1, 1992 through March 31, 1994

0

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Installed July 1993

1500

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31

Figure 4.14 Mean hourly streamflow at IB040; November 1, 1992 through March 31, 1994

0

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Installed July 1993

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4.3.3 Streamflow at Major Tributary SitesStreamflow for the four major tributaries— Alarm Creek (AL040), Green Creek (GC100), NorthFork (NF050) and South Fork (SF075)— are provided in Figures 4.15-4.18, respectively. At allfour of these subwatershed sites, periods of no flow are less frequent than at the micro-watershedsites. For the period of record, the flows at all sites became intermittent each summer and throughthe early fall. With the exception of the site SF075, flows persist from late fall through earlysummer.

Figure 4.15 Mean hourly streamflow at AL040; November 1, 1992 through March 31, 1994

0

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32

Figure 4.16 Mean hourly streamflow at GC100; November 1, 1992 through March 31, 1994

0

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0

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4.3.4 Streamflow at North Bosque River SitesStreamflow records for the North Bosque River below Stephenville (BO040) and North BosqueRiver at Hico (BO070) are presented in Figures 4.19 and 4.20, respectively. Because the flow atboth these sites and in particular BO040 is influenced by the diurnal discharge pattern from theStephenville Wastewater Treatment Plant, average daily discharge is plotted rather than theaverage hourly discharge as for all other sites. The daily averaging somewhat reduces the peakdischarges on the plots from that with hourly values, but avoids the strong diurnal signal thatwould be both indistinguishable at the scale of these plots and result in detrimentally "noisy" plots.For site BO040 and BO070, a base flow persists as a result of the permitted 1.85 million gallon perday (2.9 cfs) discharge from the WWTP.

Figure 4.19 Mean daily streamflow at BO040; November 1, 1992 through March 31, 1994

0

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Installed Aug. 1993

2000

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34

Figure 4.20 Mean daily streamflow at BO070; November 1, 1992 through March 31, 1994

0

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35

5. STATISTICAL ASSESSMENT OF WATERQUALITY BY MONITORING SITE

5.1 Data Management ProceduresThe data in this report were obtained from monthly monitoring at eight reservoir sites, stormwatermonitoring at 19 stream sites and baseflow monitoring at six stream sites in the upper NorthBosque River watershed. All data were collected between March 1991 and March 1994. Thelength of record at each site is variable since sample collections occurred in conjunction with threedifferent projects (see Nelson et al., 1992; Coan et al., 1993; Hauck et al., 1994). Monthlysampling at five PL-566 reservoir sites (AL030, GC020, NF030, SF030, and SF060) began inMarch 1991. Three other reservoir sites (IC030, SC030, and SP030) were added to TIAER'smonitoring program in August 1993. The analysis of water quality within the PL-566 reservoirsemphasizes the five longer term sites with a preliminary analysis comparing water quality of alleight reservoir sites. Twelve of the stream sampling sites were on-line by the spring of 1993 withan additional seven sites (DB040, IB040, MB040, IC020, IC035, SP020 and SP035) installed inthe fall of 1993 (Table 2.1). Because of extremely low reservoir levels during the study period, noreleases occurred at sites IC035 and SP035. Therefore, these two sites provided no data foranalysis. Water quality at each stream site was characterized using the entire period of record foreach stream site to represent runoff water quality from as many different storm events as possible.

The following rules were used on the water quality database:1. Missing values were left as blanks in the database. (No attempt was made to estimate

missing values.)2. Graphical screening of the data was used to highlight questionable data points.

Questionable data were traced through the Chain of Custody Sheets and, as necessary,through laboratory data manuals to make sure data were properly entered. Changes weremade only if an error was found in transcribing the data into the database. No statisticalmethods were used to identify or remove outliers.

3. Left censored data (values determined to be below the laboratory method detection limit)were entered as one-half the method detection limit (MDL) as recommended by Gilliomand Helsel (1986) and Ward et al. (1988). Left censored variables included: OPO4-P,total-P, NH3-N, NO3-N, NO2-N, TKN, BOD5, and COD. Method detection limits forthese variables are listed in Table 2.3.

4. Right censored data (values greater than the MDL) were entered as the maximumdetection limit (Ward et al., 1990). Nonparametric statistics were used to evaluatevariables containing right censored data. For this report, BOD5 was the only waterquality variable containing right censored data.

The following derived water quality variables were included as part of the statistical analysis fortheir environmental relevance:

•Percent of oxygen saturation represented by DO (DO%sat),•Total-N,•Inorganic-N, and•Inorganic-N:OPO4-P Ratio.

36

DO%sat was calculated as the percent of potential oxygen solubility of the water (Os) representedby DO. Os was calculated using the following equation by APHA (1992):

( )ln .. . . .

OT T T Ts = − + ×

− ×

+ ×

− ×

=

139 344111575701 10 6 642308 10 1243800 10 8 621949 105 7

2

10

3

11

4

where O oxygen saturation potential of the water (mg / L) and

T = water temperature ( C).s

o

(5.1)

Total-N was calculated as the sum of TKN, NO2-N, and NO3-N where TKN equals the sum of freeammonia and organic-N compounds. Inorganic-N was calculated as the sum of NH3-N, NO2-N,and NO3-N.

5.2 Statistical Analysis Methods

5.2.1 Vertical Stratification of Reservoir SamplesAn analysis of variance (ANOVA) was performed by reservoir site to determine if measurementswere vertically stratified for all water quality constituents measured at various depths. This wasalso done to determine if depth-composited samples taken in early 1991 could be included in theoverall analysis. In 1991, several monthly reservoir samples were composited across depths due totemporary shortages in available labor and storage space (Nelson et al., 1992). No significantdifferences were found between depths at α=0.051 for any of the water quality constituentsmeasured by depth except DO. DO showed significant differences between depths at α=0.05 butnot at α=0.01. Vertical homogeneity was expected since these reservoirs are relatively shallow,the deepest being about 13 feet near the principle spillway, and subject to wind mixing. All waterquality constituents analyzed by depth were, thus, averaged across depth to characterize themonthly water quality within each reservoir, except for BOD5. BOD5 was the only water qualityconstituent to contain right censored data. For BOD5, data were left intact by depth and analyzedusing nonparametric rank statistics as recommended by Ward et al. (1990).

5.2.2 Data TransformationsWater quality variables often follow a log normal distribution rather than a normal distribution(Spooner, 1994). The log normal distribution can account for occasionally high values or greatdifferences in magnitude between water quality measurements which result in a distribution that isgreatly skewed to the right. A log normal transformation is recommended for data sets with askewed distribution or unequal variances (Little and Hills, 1975; Spooner, 1994). The Shapiro-Wilks Test is generally used to test whether a data set is normally distributed or not, while theHartley's test is used to test for homogeneity of variances (SAS, 1992a; Ott, 1984). If the nullhypothesis of either of these tests is rejected, a natural log (loge) transformation may be needed.

For each water quality constituent, the Shapiro-Wilks and Hartley's tests were performed. If theneed for a loge transformation was indicated, the data were transformed and the Shapiro-Wilks andHartley's tests were re-run on the transformed data. In most cases, the transformed data fit theassumptions of normality and equal variances at α=0.01. Even when the transformed data did notmeet the statistical test for normality, the distribution of the transformed data was considerably

1 α refers to the probability of making a Type 1 error, i.e., rejecting the null hypothesis when it is true. In this case, the null hypothesis is that

water quality at the top depth equals water quality at the middle depth equals water quality at the bottom depth for a given site. The level ofsignificance refers to the probability or α level at which the null hypothesis would be rejected. The smaller the α value, the heavier the weightof evidence needed for rejecting the null hypothesis. An α value of 0.05 is generally used for most statistical tests indicating a 95%probability of not rejecting the null hypothesis when it is true.

37

closer to normal than the distribution of the untransformed data. These minor deviations fromnormality were assumed not to affect the validity of the analysis of variance test, due to therobustness of the test (Spooner, 1994).

The loge transformation does not change the interpretation of the statistical tests but normalizes thedata so the assumptions for parametric analysis can be better met, i.e., the data are independent andnormally distributed and the populations being compared have equal variances. Mean values ofthe loge transformed data are presented in the original scale as the geometric mean, i.e., theexponential (anti-log) of the loge mean value. The standard deviation of the loge transformed datais not symmetrical about the mean when presented in the original scale and is thus presented as arange about the mean, i.e.,

( )std m stdl = −exp ln ln (5.2)

and

( )std m stdu = +exp ln ln (5.3)

where std mean minus the standard deviation in the original scale,std mean plus the standard deviation in the original scale,m mean of the log transformed data, and

std standard deviation of the log transformed data.

l

u

ln e

ln e

====

5.2.3 Analysis of Reservoir Water QualityAn ANOVA was performed to indicate whether differences among sample means for water qualityvariables were large enough to show statistical differences between sites. Since the data at five ofthe reservoir sites covered several years, seasonality was included as a main effect in comparingthese five sites. Aquatic plant growth, seasonal temperature changes, and, to a more limited extent,seasonally anoxic conditions in the deeper reservoir waters are considered to have an importantimpact on water quality measurements within these generally quiescent bodies of water. Reservoirdata were grouped using the following categories for seasons:

Spring March, April and May

Summer June, July and August

Fall September, October, and November

Winter December, January and February.

Comparisons were made between the five long-term reservoir sites for data collected betweenMarch 1991 and March 1994. A preliminary comparison of all eight sites was performed with thedata set restricted to a common period of record from August 1993 through March 1994. Only sitedifferences were considered in the preliminary analysis of all eight reservoirs since only one datapoint was available to represent the spring and summer seasons for sites IC030, SC030, andSP030. This preliminary analysis will be expanded in future reports.

The ANOVA model for the five long-term reservoir sites evaluated the main effects of site andseason and whether a significant site-by-season interaction effect was present, i.e., whetherdifferences between sites varied depending on the season. For the preliminary analysis of all eightreservoirs, only the main effect of site was evaluated in the ANOVA. The ANOVA models wereevaluated as unbalanced designs as recommended by Fruend and Littell (1981) and Pendleton(1985) since the number of observations was unequal between sites and seasons for several of thewater quality constituents.

38

If the null hypothesis of equal means for the ANOVA test is rejected, then at least one site (orseason) is significantly different than the others. A multiple comparison test must then be used toindicate which sites (or seasons) are different from the others. The Least Significance Difference(LSD) Test was used for multiple comparisons to determine specific differences between sites andseasons (Ott, 1984). In the LSD test, pairwise comparisons of all sample means are carried outbased on the respective sample size and variance associated with each mean value using thefollowing formula:

LSD t sn nw

i j=

+

α

2

2 1 1(5.4)

where LSD = least significant difference test statistic,

t the critical t value at 2

s the variance within all n populations,n the number of samples associated with the first mean (Y ), andn the number of samples associated with the second mean (Y ).

2

w2

i i

j j

αα=

===

,

If Yi - Yj ≥ LSD then Yi and Yj are from different populations.

Nonparametric rank statistics were used to analyze BOD5 measurements at reservoir sites. Datafor all sites were combined and ranked by season. These ranks were then analyzed using theANOVA model described above to indicate site, season and site-by-season interactions (SAS,1992b). A Wilcoxon test was used to determine pairwise differences between sites for medianBOD5 values (Gibbons, 1976). The Wilcoxon test is similar to the LSD test outlined above butdistinguishes differences between samples based on median rather than mean values.

5.2.4 Analysis of Stream Water QualitySites were grouped based on drainage basin size and flow characteristics for stream water qualitycomparisons (see Table 3.1 and Section 4.3). The eleven micro-watershed sites (NF005, NF010,NF020, NF035, SF020, SF035, IC020, SP020, DB040, IB040 and MB040) were considered onegroup. The two main stem sites (BO040 and BO070) and the four major tributary sites (AL040,GC100, NF050 and SF075) were placed in a second group. Monthly baseflow water qualityconcentrations were compared directly among the four major tributary and two main stem sites.Since water quality can vary greatly within a given storm event, a volume-weighted mean valuewas calculated for each water quality constituent for each storm event at a given site. Volume-weighted means were calculated by combining the storm hydrograph with the water quality datafor each storm event. The flow hydrograph was divided into intervals based on the date and timewhen water quality samples were taken using a midpoint rectangular method between water qualitysamples (Stein, 1977). The "beginning" of each storm event was set an hour before the first waterquality sample was taken to include any rise in the hydrograph that occurred before the samplerwas initiated. The "end" of each storm event was set either two-hours after the last storm samplewas taken or when the water level reached pre-storm values. Constant flow was assumed betweeneach five-minute level measurement to estimate the water volume associated with each waterquality sample. These volume-weighted mean values for each storm event were then used tocharacterize the stormwater quality at each stream site and to compare water quality betweenstream sites. By using a number of storm events representing a variety of storm sizes, a reasonableaverage was estimated for characterizing watershed runoff water quality at each site (Byron andGoldman, 1989). While season may also impact stream water quality, seasonality was notconsidered in analyzing stream data since the current data-set represents a time period of less thana year for many of the sites (Table 2.1).

39

5.2.5 Reservoir and Stream Water Quality Criteria andScreening Levels

Existing Texas surface water quality standards provide numeric criteria for key water qualityconstituents, such as dissolved oxygen and pH. Nutrient (nitrogen and phosphorus species) andchlorophyll-α criteria do not currently exist, though screening levels are defined. Relevant criteriaand screening levels were compared to mean reservoir and stream water quality values to provide aguide in interpretation.

The North Bosque River from its headwaters to Hico, Texas contains two TNRCC designatedstream segments. Segment 1255 is defined as the North Bosque River from a point immediatelyabove the confluence of Indian Creek to the confluence of the North and South Fork of the NorthBosque River (Figure 1.1). Segment 1226 is defined as the North Bosque River from a pointapproximately 300 feet (100 meters) upstream of FM Road 185 in McLennan County (near LakeWaco) to a point immediately above the confluence of Indian Creek. The designated uses forsegment 1255 include intermediate quality aquatic habitat and contact recreation, and thedesignated uses for segment 1226 include high quality aquatic habitat, contact recreation andpublic water supply. A set of water quality criteria have been established by the TNRCC to protectdesignated uses of each segment. The dissolved oxygen criterion supports the aquatic habitatdesignation. These criteria are outlined in Table 5.1.

Table 5.1 Relevant water quality criteria and screening levels for the upper North Bosque River watershed.

Constituent

Segment1255Criteria

Segment1226Criteria

UnclassifiedIntermittentStreamCriteria

UnclassifiedPerennialWater BodyCriteria

Section305(b)ScreeningLevels

CleanRiversProgramScreeningLevels

EPARecom-mendation

Used inReport

DO, mg/L 4.0 5.0 2.0 5.0 - 5.5 - 5.01

pH, standard units 6.5 - 9.0 6.5 - 9.0 6.5 - 9.0 6.5 - 9.0 - 6.5 - 9.0 - 6.5 - 9.0

NO3 - N, mg/L - - - - 1.0 1.0 - 1.0

NH3 - N, mg/L - - - - 1.0 1.0 - 1.0

Total - N, mg/L - - - - - 3.0 - 3.0

OPO4 - P, mg/L - - - - 0.1 0.2 - 0.1, 0.22

Total - P, mg/L - - - - 0.2 0.2 0.13 0.2

CHLA, µg/L - - - - 30.0 - - 30.01Dissolved oxygen measurements for this study are only available for unclassified perennial PL-566 reservoirs. A DO criteria of 5.0 is used.2For the report, a OPO4 - P screening level of 0.1 mg/L will be used for reservoirs and 0.2 mg/L for streams.3EPA (1986) states that to prevent biological nuisances and to control accelerated eutrophication, total phosphate as P should not exceed 0.05

mg/L in any stream where it enters a lake, nor 0.025 mg/L within the reservoir. Also, a desired goal to prevent plant nuisances in flowingwaters not discharging directly to a reservoir is 0.1 mg/L Total - P.

Most of the streams and all of the reservoirs in this study have not been classified by the TNRCC.The streams have intermittent flow while the reservoirs provide perennial waters. The appropriatecriteria for these unclassified streams and reservoirs remain a case-by-case decision with guidelinesprovided in such documents as TNRCC (1993b). For the purposes of this report, a high qualityaquatic life use for reservoirs and a limited aquatic life use for intermittent streams were assumed.

Also included in Table 5.1 are the screening levels for segments 1226 and 1255 as specified in theState of Texas Water Quality Inventory provided to EPA by the TNRCC in accordance withSection 305(b) of the Clean Water Act (TNRCC, 1994). These screening levels do not representadopted criteria and should not be considered as such, but were developed by experienced TNRCC

40

staff solely to identify areas of concern. If a screening level is exceeded by less than 10 percent ofthe measurements, there is no concern; if a level is exceeded by more than 10 percent but less than25 percent of the measurements, there is potential concern; and if the level is exceeded by morethan 25 percent of the measurements, there is a concern.

Two other sources of relevant information concerning screening levels are the TNRCC CleanRivers Program (TNRCC, 1993a) and the EPA Quality Criteria for Water (EPA, 1986). TheClean Rivers Program screening levels are similar to those of Section 305(b); see Table 5.1. Thesole dissimilarity in levels listed by both is a 0.1 mg/L screening level for OPO4-P under Section305(b) and a 0.2 mg/L level under the Clean Rivers Program. EPA does not provide a criteria forphosphorus, but their recommendation is provided in Table 5.1.

The numeric criteria and screening levels used for comparative purposes in this report are found inthe rightmost column of Table 5.1. From the preceding discussion, the rationale for the selectedlevels is readily apparent, except for OPO4-P. A distinction was made for the OPO4-P screeninglevel of reservoirs and streams in recognition that higher levels of OPO4-P avoid acceleratedeutrophication in moving waters (streams) as opposed to more stagnant waters (reservoirs).Especially for the nutrient screening levels, one must recognize that the levels were established forthe routine monitoring performed by state and federal agencies. Therefore, the application of thesescreening levels to stormwater data must be tempered with the knowledge that the levels may beoverly restrictive for the expected higher nutrient concentrations during runoff events. Thesescreening levels should be appropriate for the routine reservoir and stream sampling results in thisstudy.

5.3 Comparison of Chemical and Physical WaterQuality Between Reservoir Sites

5.3.1 Results for the Five Long-term PL-566 Reservoir SitesResults of the ANOVA tests for the interaction effect of site-by-season and the main effects of siteand season for the five long-term reservoir sites are presented in Table 5.2. Column 4 indicateswhether or not data for a given variable were loge transformed before performing the ANOVAtest. The results for the main effects of site and season (columns 6 & 7) were ignored if theinteraction effect (column 5) was significant (α=0.05). If the site-by-season interaction wassignificant, then LSD tests were conducted to compare seasons by site and sites within seasons. Asignificant site-by-season interaction was indicated for OPO4-P and ZSD. Significant sitedifferences were indicated for all water quality constituents except inorganic-N, NH3-N, NO2-N,NO3-N, and water temperature. Seasonal differences were indicated for all water qualityconstituents except inorganic-N, NO2-N and TOC.

The results of the LSD tests for multiple comparisons are presented using different letters toindicate mean values that are statistically different from one another. 'A' is used to represent thelowest grouping of mean values that are significantly similar at α=0.05, 'B' represents the nexthigher grouping of similar mean values, and so forth. A mean value may have more than one letterassociated with it, indicating that it is similar to several different groupings of mean values. InAppendix B, tables are presented containing the mean and standard deviation for each waterquality constituent as well as the results of the LSD tests. For ease of presentation, a graphicalform of these results is presented with the discussion below.

Water Temperature. Water temperature was found to have significant seasonal differences, but nosignificant differences were indicated between sites at α =0.05 (Figures 5.1a & b). As expected,the warmest temperatures occurred in the summer while the coolest water temperatures occurred inthe winter (Figure 5.1b). Temperatures during the spring and fall were not significantly different.

41

Dissolved Oxygen. Dissolved oxygen (DO) levels were similar at all sites except AL030 (Figure5.1c). To remove the effect of temperature on DO, DO%sat was also compared between sites.DO%sat equals the percentage of potential oxygen saturation met by DO. DO%sat levels at AL030were also significantly lower than those at the four other sites (Figure 5.1e). A ridge with a treerow protects reservoir AL030 from the prevailing wind that would cause vertical mixing withinthis reservoir. There is also some indication that AL030 may be spring or seepage fed. Thesefactors may in part account for the lower DO level at AL030.

Seasonal differences in DO inversely followed water temperature patterns which was expected,since oxygen is more soluble in water at lower temperatures than at higher temperatures (Figures5.1b&d). The lowest mean dissolved oxygen levels occurred in the summer when watertemperatures were the highest, and the highest dissolved oxygen levels occurred in the winter whenwater temperatures were the lowest. In comparing DO%sat between seasons, the lowest valuesoccurred in the summer and fall, while the highest values occurred in the spring and winter (Figure5.1f). Mean DO levels for all sites and seasons were well above 5.0 mg/L, which is therecommended screening level for the maintenance of aquatic life (Table 5.1).

Table 5.2. Results of the analysis of variance on PL-566 reservoir data for measured and derived water qualityvariables. Column (5) indicates the results of the test on site-by-season interactions. The main effects of site andseason are presented in columns (6) and (7), respectively, if the site-by-season interaction was nonsignificant.

Variable # Sites Total # of Obs. Loge Transformed Site-by-season Interaction Site Season(1) (2) (3) (4) (5) (6) (7)

BOD5 5 415§ no ns ** **CHLA 5 97 yes ns ** **COD 5 52 no ns ** **Conductivity 5 159 yes ns ** **DO 5 152 no ns ** **DO%sat 5 152 no ns ** **Inorganic-N 5 75 yes ns ns nsNH3-N 5 159 yes ns ns *NO2-N 5 75 yes ns ns nsNO3-N 5 159 yes ns ns **OPO4-P 5 159 yes * --- ---Inorganic-N:OPO4-P Ratio

5 75 yes ns ** **

TOC 5 32 no ns ** nspH 5 159 no ns ** *Water Temp. 5 159 no ns ns **Turbidity 5 124 no ns ** **ZSD 5 149 no * --- ---

§Monthly samples for BOD5 were not averaged across depth since BOD5 contains both left and right censored data. The results for BOD5represent an analysis of variance on the ranks of the data across sites within each season.

'ns' nonsignificant at α=0.05.*significant at α=0.05.**significant at α=0.01.

42

Figure 5.1 Arithmetic mean a&b) water temperature, b&c) DO, and d&f) DO%sat by site and by season formonthly samples collected at reservoir sites between March 1991 and March 1994. Different letters indicatesignificantly different mean values at α=0.05.

Wat

er T

empe

ratu

re (°

C)

0

5

10

15

20

25

30

AL030 GC020 NF030 SF030 SF060

a)

0

5

10

15

20

25

30

SPRING SUMMER FALL WINTER

A

B

C

B

b)

Dis

solv

ed O

xyge

n (m

g/L)

0

2

4

6

8

10

12

AL030 GC020 NF030 SF030 SF060

BBB

BA

c)

0

2

4

6

8

10

12


D

B

A

C

d)

0

20

40

60

80

100


BA

A

Bf)

DO

sat (

%)

0

20

40

60

80

100

AL030 GC020 NF030 SF030 SF060

BBB

B

A

e)

Five-Day Biochemical Oxygen Demand. BOD5 indicates the oxygen demanding properties ofbiodegradable material in the water. A five-day incubation period relates to the carbonaceousdemand of degradable material and may be used for evaluating organic pollution loadings.Without nitrification inhibitors, incubation beyond five days generally corresponds to a secondstage of the BOD curve indicating the additional oxidation of nitrogen compounds in the water.Significantly higher median BOD5 levels were measured at AL030, NF030 and SF060 than atGC020 or SF030 (Figure 5.2a). A seasonality effect indicated elevated BOD5 levels in thesummer (Figures 5.2b).

Chemical Oxygen Demand. COD is a measure of pollutant loadings in terms of complete chemicaloxidation. COD is often measured in lieu of BOD5 since it is a simpler analysis to complete,although BOD5 is generally considered a better indicator of the oxygen demanding properties ofnatural waters (Brooks et al., 1991). The evaluation of site and seasonal differences for CODclosely followed but did not duplicate the pattern indicated for BOD5 (Figures 5.2a-d). CODlevels were significantly lower at sites GC020 and SF030 than at sites AL030, NF030 and SF060(Figure 5.2c). COD levels were significantly higher during the summer and fall than during thewinter and spring (Figure 5.2d).

43

Figure 5.2 a&b) Median BOD5 and c&d) mean COD by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

CO

D (m

g/L)

0

10

20

30

40

50

AL030 GC020 NF030 SF030 SF060

B

A

B

A

B

c)

0

10

20

30

40

50


A

BB

A

d)

BO

D5

(mg/

L)

0

2

4

6

8

AL030 GC020 NF030 SF030 SF060

C

A

C

A

B

a)

0

2

4

6

8


A

A

B

A

b)

Conductivity. Conductivity is used to indicate the salt content of water and can be used to estimatetotal dissolved solids. Conductivity measurements ranged from a geometric mean value of 373µmhos/cm at SF030 to 887 µmhos/cm at NF030 (Figure 5.3a). Conductivity levels were lowest atGC020 and SF030, intermediate at AL030 and SF060, and highest at NF030. Due to thecarbonaceous soils and limestone parent material in the upper North Bosque River watershed, afairly high background level of natural calcium salts was expected in each of the reservoirs (SCS,1973). A seasonal impact was indicated for conductivity with the lowest levels occurring in thefall and winter and the highest levels occurring in the spring (Figure 5.3b). Minor seasonaldifferences in conductivity may occur naturally, while site differences indicate that factors otherthan background levels may be contributing to the salt content of the water in several of thereservoirs.

pH. pH is a measure of the hydrogen ion activity in a water sample and is important in consideringwater quality since pH can affect the toxicity of many compounds. The toxicity of ammonia tofish, for example, increases with increasing pH. The relatively alkaline pH levels at all reservoirsites reflect the carbonaceous soils and limestone parent material of the area. Significant site andseasonal differences were indicated for pH, although these differences showed a fair amount ofoverlap between groupings (Figures 5.3c&d). Mean pH levels ranged from 8.0 at AL030 to 8.6 atNF030. pH levels in all reservoirs were within a pH range of 6.5 to 9.0, which is recommended formaintenance of most aquatic life (Table 5.1).

Total Organic Carbon. TOC is used as an indicator of the total concentration of organic materialin each reservoir system. The highest TOC levels were indicated at AL030, NF030 and SF060,while the lowest TOC levels were indicated at SF030 and GC020 (Figure 5.3e). No significantdifferences were indicated between seasons, although no summer measurements of TOC wereavailable for comparison (Figure 5.3f). TOC measurements were not added to the laboratoryanalysis of water quality samples at reservoir sites until September 1993, and, thus, represent arelatively small number of samples (Table 5.2).

44

Figure 5.3 a&b) Geometric mean conductivity, c&d) mean pH, and e&f) mean TOC by site and by season formonthly samples collected at reservoir sites between March 1991 and March 1994. Different letters indicatesignificantly different mean values at α=0.05. Asterisk indicates no available data for TOC during the summerseason.

B

A

B

A

B

0

2

4

6

8

10

12

14

16

AL030 GC020 NF030 SF030 SF060

TOC

(mg/

L)

e)

*0

2

4

6

8

10

12

14

16


f)

B

A

C

A

B

0

200

400

600

800

1000

AL030 GC020 NF030 SF030 SF060

Con

duct

ivity

(µm

hos/

cm)

a)

ABA

B

C

0

200

400

600

800

1000


b)

BCAB

C

AA

6

7

8

9

10

AL030 GC020 NF030 SF030 SF060

pH (s

tand

ard

units

)

c)

AAB

BAB

6

7

8

9

10


d)

45

Inorganic Nitrogen Compounds. NO2-N levels showed no significant differences between sites orseasons (Table 5.2). The geometric mean for NO2-N was 0.01 mg/L with a range of minus andplus the standard deviation2 of 0.005 to 0.03 mg/L. NH3-N and NO3-N showed no significantdifferences between sites but a seasonality effect was indicated (Table 5.2). The higher NH3-Nlevels during the winter and NO3-N levels during the spring and winter are probably an indicationof decreased plant uptake during the winter and early spring (Figure 5.4b&d). Inorganic nitrogenforms were fairly low for all sites and seasons indicating that much of the nitrogen in the water ofthese reservoirs may be tied up in plant biomass rather than in a soluble form. Inorganic-Ncalculated as the sum of NO2-N, NH3-N and NO3-N indicated no differences between sites orseasons. The geometric mean of inorganic-N was 0.17 mg/L with a range from minus to plus thestandard deviation of 0.05 to 0.56 mg/L. Geometric mean NH3-N and NO3-N levels for all sitesand seasons were well below the recommended screening level of 1.0 mg/L for identifyingpollutant concerns in freshwater systems in reference to eutrophication (Figures 5.4a-d).

Figure 5.4 Geometric mean a&b) NH3-N and c&d) NO3-N by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

NH

3-N

(mg/

L)

0

0.02

0.04

0.06

0.08

0.1

0.12

AL030 GC020 NF030 SF030 SF060

a)

0

0.02

0.04

0.06

0.08

0.1

0.12

Spring Summer Fall Winter

B

A

AA

b)

NO

3-N

(mg/

L)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

AL030 GC020 NF030 SF030 SF060

c)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07


B

A

A

B

d)

Phosphorus Compounds. A significant site-by-season interaction was indicated for OPO4-P (Table5.2). In the separation of means between seasons for each site, NF030 showed significant seasonaldifferences with higher OPO4-P levels in the summer and fall than in the winter and spring (Figure5.5a). No significant differences were found between seasonal values at any of the other sites.

2 The standard deviation is not symmetrical about the geometric mean (see equations 5.2 and 5.3).

46

Between sites by season, higher OPO4-P were consistently indicated at AL030, NF030 and SF060than at GC020 and SF030 for all seasons (Figure 5.5b).

Figure 5.5 Geometric mean OPO4-P a) by site by season and b) by season by site for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

A

A

A

B

A

B

A

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

AL030 GC020 NF030 SF030 SF060

OPO

4-P

(mg/

L)


a)

BBBBC

AAAA

BC

CC

B

AAAA

C

C

C

C

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


OPO

4-P

(mg/

L)

AL030 GC020 NF030 SF030 SF060

b)

Recommended screening levels for OPO4-P vary from 0.1 to 0.2 mg/L (TNRCC, 1993a & 1994).Geometric mean values for OPO4-P exceeded 0.1 mg/L at AL030, NF030 and SF060 during allfour seasons. These levels indicate that OPO4-P is probably a "concern" in reservoirs AL030,NF030 and SF060 in considering levels for accelerated eutrophication. Geometric mean levels forOPO4-P at GC020 and SF030 were well below the screening level during all seasons.

Nitrogen to Phosphorus Ratio. The ratio of inorganic-N to OPO4-P was calculated to estimate thenutrient balance within each reservoir in relation to plant growth requirements. This simple ratio isoften used to indicate the nutrient that limits plant growth within an aquatic system. Thomann andMueller (1987) provide the following rough guidelines for nitrogen to phosphorus ratios (N/P):

N/P << 10; plant growth is probably nitrogen limited,N/P ≈ 10; neither nutrient can be determined to control plant growth,N/P >> 10; plant growth is probably phosphorus limited, andN/P < 4; blue-green algae (Cyanobacteria) may dominate the system.

Comparing these guidelines to the geometric mean of the ratio of inorganic-N to OPO4-P, neitherphosphorus nor nitrogen appear to be controlling plant growth at SF030 (Figure 5.6a). The

47

geometric mean of the inorganic-N:OPO4-P ratio for GC020 indicates that this site may bephosphorus limited, while the extremely low geometric mean of the inorganic-N:OPO4-P ratios atAL030, NF030 and SF060 indicate that these sites may be nitrogen limited. The presence of blue-green algae has been noted at AL030, NF030 and SF060 (Nelson et al., 1992). Seasonalvariations in the geometric mean of the inorganic-N:OPO4-P ratios reflect the seasonality in NH3-N, NO3-N and OPO4-P concentrations in these reservoirs (Figure 5.6b).

Chlorophyll-α. Chlorophyll-α was monitored as an indicator of phytoplankton, e.g., algaebiomass. Significantly higher values were indicated at AL030, NF030 and SF060 than at GC020or SF030 (Figure 5.6c). A seasonal effect indicated significantly higher chlorophyll-α levels at allsites during the summer and fall than during the winter and spring (Figure 5.6d). Several studieshave indicated a close association between phosphorus levels and chlorophyll-α abundance (Dillonand Rigler, 1974). The pattern of chlorophyll-α levels between sites closely resembles the patternshown for OPO4-P in Figure 5.5b. Geometric mean chlorophyll-α levels at AL030, NF030, andSF060 were above the screening level of 30 µg/L indicating that eutrophication may be a problemin these reservoirs.

Figure 5.6 a&b) Geometric mean of the ratio inorganic-N:OPO4-P and c&d) chlorophyll-α by site and by seasonfor monthly samples collected at reservoir sites between March 1991 and March 1994. Different letters indicatesignificantly different mean values at α=0.05.

BC

A

C

A

B

0

20

40

60

80

100

120

AL030 GC020 NF030 SF030 SF060

Chl

orop

hyll-

a (u

g/L)

c)

A

B

B

A

0

20

40

60

80

100

120


d)

A

C

B

C

B

0

2

4

6

8

10

12

14

AL030 GC020 NF030 SF030 SF060

Inor

gani

c-N

/ O

PO4-

P

a)

C

ABA

BC

0

2

4

6

8

10

12

14


b)

Turbidity. Turbidity is a photometric measure used to indicate water clarity, i.e., the deeper lightcan penetrate, the lower the turbidity. Low turbidity values are generally associated with a greater

48

potential for photosynthesis by submerged plants and higher dissolved oxygen levels. While onlyminor differences in DO were indicated, distinct differences in turbidity occurred betweenreservoir sites. The highest turbidity levels were indicated at NF030 while the lowest turbiditylevels were indicated at GC020 and SF030 (Figure 5.7a). By season, significantly higher turbiditylevels were indicated during the fall compared with all other seasons (Figure 5.7b), which mayreflect increasing phytoplankton levels as indicated in Figure 5.6d.

Secchi Disc Depth. ZSD, like turbidity, is also a measure of water clarity. ZSD is based on avisual measurement of water clarity with depth, i.e., the deeper the measurement, the clearer thewater. A significant site-by-season interaction was indicated for ZSD (Table 5.2). In evaluatingseasonal differences by sites, only SF030 indicated significant differences (Figure 5.7c). Secchidepths were greater in the spring and summer than in the winter at SF030. Differences betweensites varied with season (Figure 5.7d). In the spring and summer, AL030, NF030 and SF060indicated significantly shallower Secchi depths than at GC020 and SF030. In the fall, thisdistinction still occurred but was less pronounced with AL030 showing Secchi depths similar to allsites but NF030. During the winter, no significant differences between sites were indicated forZSD. The damping of differences between sites from spring to winter is probably a function ofplant growth and biological activity as related to season and temperature.

Figure 5.7 a&b) Arithmetic mean turbidity by site and by season and mean ZSD c) by site by season and d) byseason by site for monthly samples collected at reservoir sites between March 1991 and March 1994. Differentletters indicate significantly different mean values at α=0.05.

Turb

idity

(NTU

)

0

5

10

15

20

25

30

35

40

45

AL030 GC020 NF030 SF030 SF060

B

A

C

ABB

a)

0

5

10

15

20

25

30

35

40

45


A

B

AA

b)

ZSD

(fee

t)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

AL030 GC020 NF030 SF030 SF060

C

BC

ABA


c)

ZSD

(fee

t)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5


A A

AB

B B

BC

A A A

B

B

C

AA

A

AL030 GC020 NF030 SF030 SF060

d)

49

5.3.2 Results Comparing All Eight PL-566 Reservoir SitesThe water quality constituents of all eight reservoirs were compared for their common period ofrecord, August 1993 through March 1994. In comparing all eight sites, no significant differenceswere indicated between sites for water temperature, dissolved oxygen, NH3-N, or NO2-N (seeAppendix B, Table B-18). Water temperature averaged 16.3°C + 5.7°C which was comparable tothe fall and winter temperatures found for the five long-term sites (Figure 5.1b). The geometricmean for NH3-N was 0.09 mg/L which most closely reflected the winter values indicated in Figure5.4b. The geometric mean for NO2-N was 0.01 mg/L. The results of the LSD for all othervariables are presented in Figure 5.8. Tabular results showing the mean and standard deviation foreach constituent are presented in Appendix B. Turbidity was not included in the analysis sinceturbidity measurements were not available for two of the sites during this time period.

Figure 5.8 a) Arithmetic mean COD, b) geometric mean conductivity, c) arithmetic mean pH, d) arithmetic meanTOC, e) geometric mean NO3-N, f) geometric OPO4-P, g) geometric mean chlorophyll-α and h) arithmetic meanZSD by site for monthly samples collected at all eight reservoir sites between August 1993 and March 1994.Different letters indicate significantly different mean values at α=0.05.

A

C

AAB

C

D

A

BC

0

20

40

60

80

AL030 GC020 IC030 NF030 SC030 SF030 SF060 SP030

CO

D (m

g/L)

a)

AB

BC

A

CCD

D

AB

C

0

200

400

600

800


Con

duct

ivity

(µm

hos/

cm)

b)

D

AABC

C

ABA

B

ABC

0

1

2

3

4

5


ZSD

(fee

t)

h)

CCBAB

CCC

A

ABC

6

7

8

9

10


pH (s

tand

ard

units

)

c)

A

D

ABB

C

E

AB

CD

0

5

10

15

20


TOC

(mg/

L)

d)

B

B

B

A

B

A

B

B

0

0.02

0.04

0.06

0.08

0.1


NO

3-N

(mg/

L)

e)

A

E

ABAB

E

CB

D

0

0.1

0.2

0.3

0.4

0.5


OPO

4-P

(mg/

L)

f)

A

CD

ABBC

D

E

A

D

0

50

100

150

200


Chl

orop

hyll-

a (µ

g/L)

g)

50

COD and chlorophyll-α levels were highest at IC030 followed by sites NF030, SF060 and AL030(Figure 5.8a&g). Conductivity and pH levels were indicative of the carbonaceous soils andlimestone parent material associated with each site, although the significantly greater conductivitylevels at IC030 and NF030 may indicate increased salt loadings from other sources (Figure5.8b&c). TOC levels were also highest at IC030 with the next highest levels occurring at SF060and AL030 (Figure 5.8d). IC030 and SC030 indicated lower geometric mean concentrations forNO3-N than at all other sites (Figure 5.8e). OPO4-P levels were highest at NF030 and SF060followed by AL030 then IC030 (Figure 5.8f). Secchi depth was greatest at SP030, with the nextdeepest mean values occurring at SC030 and GC020 (Figure 5.8h). In general, higher constituentconcentrations were indicated at sites AL030, IC030, NF030 and SF060 than at sites GC020,SC030, SF030 and SP030.

In comparing mean water quality levels to screening criteria, OPO4-P and chlorophyll-α levelsexceeded screening criteria at several sites. As found in the longer term five-site comparison,AL030, NF030 and SF060 indicated geometric mean OPO4-P levels above the 0.1 mg/L screeninglevel. For chlorophyll-α, geometric mean values at AL030, IC030, NF030 and SF060 exceededthe screening level of 30 µg/L. Since these results cover only a limited timeframe and a relativelysmall number of samples, they should be used only as a guide in ranking water quality between theeight reservoir sites. As more data becomes available, the comparative analysis of the waterquality between all eight reservoirs will be reinvestigated.

5.3.3 Summary of Reservoir Site ComparisonsIn summary, higher levels of water quality constituents (or lower values as is the case for Secchidisc depth) were consistently indicated at sites AL030, NF030 and SF060 than at GC020 andSF030 in comparing the five long-term reservoir sites. In the preliminary comparison of all eightreservoir sites, AL030, IC030, NF030 and SF060 generally indicated higher concentrations ofwater quality constituents and therefore poorer water quality than sites GC020, SC030, SF030 andSP030. The potential for eutrophication was indicated at sites AL030, IC030, NF030 and SF060based on TNRCC screening levels for OPO4-P and/or chlorophyll-α. Seasonal fluctuations inwater quality constituents appeared to be primarily a function of seasonal changes in watertemperature as related to biomass growth dynamics and nutrient cycling, although contributionsfrom outside sources may be contributing to these fluctuations.

5.4 Comparison of Water Quality Between StreamSites

At the stream monitoring sites, a loge transformation was necessary for all water qualityconstituents before comparing mean values for storm events or baseflow samples using ANOVA orLSD tests. All stream results are, thus, presented as geometric means. Stream sites were dividedinto two groups based on drainage basin size and flow characteristics (Table 3.1 and Section 4.3).The first group represents the eleven micro-watershed sites within the watershed: NF005, NF020,NF010, NF035, SF020, SF035, IC020, SP020, DB040, IB040 and MB040. The second grouprepresents the four major tributary sites, AL040, GC100, NF050 and SF075, and the two mainstem sites, BO040 and BO070. Comparisons were also made between water quality values atbaseflow for the major tributary and main stem sites. Seasonality was not considered as a factor inevaluating stream water quality since data from most stream sites represent less than one year ofrecord (Table 2.1). As more data are collected a more intensive investigation of seasonalinfluences will be possible. Individual volume-weighted storm values used to characterize thewater quality at each site are presented in Appendix C.

51

5.4.1 Comparison of Stormwater Quality at Micro-WatershedStream Sites

A comparison of water quality between micro-watershed sites is presented in Figures 5.9 and 5.10.A tabular form of the geometric mean and standard deviation for each water quality constituent ispresented in Appendix C, Table C-18. Because the micro-watershed sites are intermittent, onlystormwater event analyses are available for these sites.

Figure 5.9 Geometric mean NH3-N, NO2-N, NO3-N, TKN, OPO4-P and total-P by site for storm events monitoredat micro-watershed sites between March 1992 and March 1994. Different letters indicate significantly differentmean values at α=0.05.

BAABAABAAABB

CC

0.00

0.50

1.00

NF005 NF020 NF010 NF035 SF020 SF035 IC020 SP020 DB040 IB040 MB040

NH

3-N

(mg/

L)

BCAABCABC

AABCABCD

D

0.00

0.10

0.20


NO

2-N

(mg/

L)

DEABCCABCDEABABCABCDEE

F

0.002.004.006.008.00


NO

3-N

(mg/

L)

DEBBC

ACD

BCDCDCD

FF

0.00

2.004.00

6.00


TKN

(mg/

L)

DEBC

EFA

DEFGABC

FD

FGG

0.00

0.50

1.00


OPO

4-P

(mg/

L)

DEBC

EFG

A

DEF

ABCDED

FG

G

0.00

0.50

1.00

1.50

2.00


Tota

l-P (m

g/L)

52

Figure 5.10 Geometric mean TOC, COD, TSS and VSS by site for storm events monitored at micro-watershed sitesbetween March 1992 and March 1994. Different letters indicate significantly different mean values at α=0.05.

D

BBCD

A

CD

BCDBCCDCD

E

05

101520253035


TOC

(mg/

L)

G

BCCD

A

CDE

AB

DECDE

EF

FGEFG

020406080

100120


CO

D (m

g/L)

EF

BCCDE

AABCAB

DE

BC

DE

F

CDEF

0

200

400

600

800

1000


TSS

(mg/

L)

CDEFG

G

EF

CDDEF

AB BCA

CDEBC

FG

0

20

40

60

80

100


VSS

(mg/

L)

For nutrient constituents, a fairly consistent pattern emerged between sites (Figure 5.9). Thehighest nutrient levels were generally indicated at NF005 and NF020. The lowest nutrient levelswere generally indicated at SP020, SF020, SF035 and IB040. SP020 was consistently groupedwith the lowest concentrations of water quality constituents. A general ranking of all eleven sitesfor nutrient levels was conducted using the highest grouping level associated with each site fromthe LSD test as a rank indicator for each nutrient (for an example see Table 5.3). The followingrank order was established from highest to lowest stormwater nutrient levels for the eleven micro-watershed sites:

NF005>NF020>IC020>MB040>DB040>NF035=NF010>SF020>IB040>SF035>SP020.

53

Table 5.3. Rank ordering of micro-watershed sites based on results of tests of least significant differences fornutrient constituents. Lower rankings correspond to lower constituent concentrations.

Site NH3-N NO2-N NO3-N TKN OPO4-P Total-P Sum Rank

NF005 3 4 6 6 7 7 33 1NF020 3 4 5 6 7 7 32 2IC020 2 3 5 4 7 6 27 3

MB040 2 3 5 5 5 5 25 4DB040 2 3 3 3 6 7 24 5NF010 2 3 5 4 4 4 22 6NF035 2 2 3 4 6 5 22 6SF020 1 3 3 4 3 3 17 7IB040 1 1 3 2 3 3 13 8SF035 1 1 2 2 2 2 10 9SP020 1 1 1 1 1 1 6 10

Max. 3 4 6 6 7 7 33

While differences between sites for nutrient values indicated a fairly consistent pattern, a differentpattern emerged when evaluating TOC, COD, TSS and VSS (Figure 5.10). The highest values forTOC were indicated at NF005, while the highest values for COD, TSS and VSS were indicated atNF005, NF020 and MB040. The lowest values for all four of these constituents were associatedwith SP020. Values at all other sites shifted between moderate to low groupings. A general rankindicator was also developed using the highest grouping level associated with each site for TOC,COD, TSS and VSS. The following rank order was established from highest to lowest forstormwater concentrations:

NF005>NF020=MB040>NF010>SF020>DB040>IC020=NF035>IB040>SF035>SP020

This ranking is very similar to the ranking established using only nutrients, although most notably,MB040, NF010, and SF020 indicate higher levels of water quality constituents and IC020indicates lower levels of water quality constituents based on TOC, COD, TSS and VSS than whenranked using only nutrients.

In both ranking scales, the levels of water quality constituents were lowest at SP020 and highest atNF005. In both cases the ranks for NF035 and SF035, sites representing reservoir spillways, fellbelow the sites representing inflow into the respective reservoirs, i.e., site NF020 and NF010 forreservoir NF030 and site SF020 for reservoir SF030. The effect of reservoirs on mitigating streamwater quality is discussed in more detail in Hauck et al. (1994) and McFarland and Hauck (1995).The effect of inflow water quality on reservoir water quality is clearly demonstrated for OPO4-P inTable 5.4, although the measurement periods differ between reservoir and stream sites. Incomparing primarily agricultural micro-watersheds to urban micro-watersheds, MB040 representedonly urban land while IB040 was 31 percent urban with the rest of the land area divided almostevenly between woodland, rangeland and forage fields (Table 3.1 and 3.2). All other micro-watersheds were predominately used for agricultural purposes. MB040 indicated water qualityconstituent levels similar to areas with a large amount of intensive agricultural practices, e.g.,NF020 and IC020, while water quality at IB040 was more similar to agricultural sites dominatedby less intensive agriculture, e.g., SF035.

54

Table 5.4 Geometric mean of OPO4-P for reservoir* and inflow tributary** water quality samples

Reservoir Site OPO4-P (mg/L) Stream Site OPO4-P (mg/L)

NF030 0.41 NF020 0.54NF010 0.18

IC030 0.06 IC020 0.34SF030 0.01 SF020 0.05SP030 0.01 SP020 0.01

* Monthly grab samples collected at reservoirs between August 1993 and March 1994.** Storm water quality samples collected at stream sites between November 1992 and March 1994 for sites NF020, NF010 and SF020 and

between September 1993 and March 1994 for sites IC030 and SP030.

Geometric mean nutrient levels were compared to TNRCC screening levels to indicate thepotential for eutrophication associated with the water quality at each site. These screening levelsmay be overly restrictive for stormwater samples (see Section 5.2.5) but are presented since theyare the best guidelines available. For NH3-N and NO3-N, a screening level of 1.0 mg/L wasexceeded only at site NF005 (see Table 5.3 for site nutrient values). For total-N, screening level of3.0 mg/L was exceeded at sites NF005 and NF020. For OPO4-P, screening levels of 0.1 mg/L forreservoir spillway sites and 0.2 mg/L for stream sites were exceeded at sites NF005, NF020,NF035, IC020 and DB040. For total-P, screening level of 0.2 mg/L were exceeded at all sitesexcept SF035, SP020 and IB040. These screening levels are only a guide for indicating thepotential for eutrophication. Exceeding these nutrient levels may accelerate eutrophication. Thepotential for eutrophication in receiving waters from tributaries with elevated nutrient levels woulddepend on the size of the receiving body of water and the relative volume of tributary watercontributed compared to the volume of other sources. The water quality in some of the receivingwaters was previously discussed in Section 5.3.

5.4.2 Comparison of Water Quality at Major Tributaries andMain Stem Stream Sites

Although all monitored stream sites, with the exception of the main stem sites, are intermittent,baseflow generally occurred between storm events at major tributary sites (NF050, SF075, AL040,GC100) and at both main stem sites (BO040 and BO070). A comparison of baseflow to stormevent water quality for these sites is presented in Figure 5.11. Significantly higher TOC and CODlevels were generally indicated for storm events compared to baseflow levels. Stormwater TOClevels at AL040 and BO040, however, were greater than but not significantly different frombaseflow values. For nutrient concentrations, a consistent pattern did not appear between the sixsites. At NF050, AL040, GC100 and BO070 nutrient concentrations showed no statisticallysignificant differences between baseflow and storm samples. Both sites SF075 and BO040indicated significantly greater levels of NO3-N during baseflow than during storm events. Becauseof differences in sampling programs at the different sites, values for total-P, TKN, TSS and VSSfor baseflow samples were not available for the timeframe of this report. These values are beinganalyzed under the current monitoring program and will be presented in future reports.

55

Figure 5.11 Comparison of geometric mean baseflow water quality to storm event water quality for main stem andmajor tributary monitoring sites for samples collected between March 1992 and March 1994. Different lettersindicate significantly different mean values at α=0.05.

NF050

0.00

0.50

1.00

1.50

2.00

NH3-N NO2-N NO3-N OPO4-P

(mg/

L)

Baseflow

Storm

NF050

AA

B

B

0

20

40

60

80

TOC COD

(mg/

L)

SF075

B

A

0.000.501.001.502.002.50


(mg/

L)

SF075

A

A

B

B

01020304050

TOC COD

(mg/

L)

AL040

0.000.050.100.150.200.25


(mg/

L)

GC100

0.00

0.10

0.20

0.30


(mg/

L)

BO040

B

A

0.001.002.003.004.005.00


(mg/

L)

AL040

AB

01020304050

TOC COD

(mg/

L)

GC100

AA

B

B

0

10

20

30

40

TOC COD

(mg/

L)

BO040

A

B

01020304050

TOC COD

(mg/

L)

BO070

0.00

0.10

0.20

0.30

0.40


(mg/

L)

BO070

AA

B

B

01020304050

TOC COD

(mg/

L)

56

A tabular form of this information can be found in Appendix C, Table C-19. Comparisonsbetween sites for storm and baseflow samples are presented in Figures 5.12 and 5.13. As with themicro-watershed sites, the major tributary and main stem sites showed a similar pattern betweensites for all nutrient constituents, although these patterns varied between storm and baseflowsamples (Figure 5.12). For storm samples, the highest NO2-N, OPO4-P and total-P levels wereindicated at sites BO040 and NF050. The highest NO3-N levels were indicated at SF075, BO040and NF050. NH3-N levels were significantly greater at BO040 and AL040 than at SF075.Significantly greater storm values for TKN were indicated at NF050 than at all other sites, whilethe highest total-P levels were indicated at NF050 and BO040. For baseflow measurements, thehighest nutrient levels were generally associated with BO040, while the lowest nutrient levels weregenerally associated with GC100. A general ranking of the water quality between these six sitesfor nutrients was developed using the highest LSD grouping of each nutrient constituent as a rankindicator (see Table 5.3 for an example of the methodology). TKN and total-P were excluded incalculating this ranking since these constituents were not measured for baseflow samples at allsites. The following ranking is established in order of highest to lowest nutrient concentrations forstorm and baseflow samples:

BO040=NF050>BO070>SF075>AL040>GC100 Storm Samples

BO040>SF075>NF050>AL040>BO070>GC100 Baseflow SamplesIn comparing the ranking of storm to baseflow samples, BO040 ranked the highest and GC100ranked the lowest in nutrient levels under both circumstances. The relative storm ranking tobaseflow ranking of all other sites shift in their ordering.

The City of Stephenville wastewater treatment plant (WWTP) is a point source discharge into theNorth Bosque River. Site BO040 is located approximately ¼-mile below the WWTP dischargeinto the North Bosque River. Monthly grab samples collected between December 1993 and March1994 of the WWTP effluent and water at BO040 were compared (Table 5.5). Monthly samplingof the WWTP effluent did not commence until December 1993. Daily discharge from the WWTPaveraged 3 cfs while baseflow at BO040 averaged 13 cfs during the sampling period. Therefore,during the period of December 1993 through March 1994, the WWTP effluent comprised less than25 percent of the flow at site BO040. Values were statistically similar for all water qualityconstituents except NO2-N which was slightly higher at BO040. Thus, the data analysis indicatesthe WWTP effluent is mixing with river water of similar quality. Continued monitoring of theWWTP effluent and of sites above the WWTP is needed to more firmly establish the treatmentplant's contribution to the water quality at site BO040.

57

Figure 5.12 Geometric mean NH3-N, NO2-N, NO3-N, TKN, OPO4-P and total-P by site for storm events andbaseflow monitored at main stem and major tributary sites between March 1992 and March 1994. Different lettersindicate significantly different mean values at α=0.05.

A

B

A

AB

B

ABABC

C

AB

BC

A

ABC

0.000.050.100.150.200.250.300.35

NF050 SF075 AL040 GC100 BO040 BO070 NF050 SF075 AL040 GC100 BO040 BO070

NH

3-N

(mg/

L)

STORM BASEFLOW

A

C

AAB

BCBC

AB

C

AA

BBC

0.00

0.02

0.04

0.06

0.08

0.10


NO

2-N

(mg/

L)

D

BABA

CDC

BC

D

BA

DCD

0.00

1.00

2.00

3.00

4.00

5.00


NO

3-N

(mg/

L)

AAAA

A

B

0.00

1.00

2.00

3.00

4.00


TKN

(mg/

L)

B

D

ABCBCCBC

D

ABBC

CD

0.00

0.20

0.40

0.60

0.80


OPO

4-P

(mg/

L)

AB

BC

AAA

C

0.00

0.20

0.40

0.60

0.80

1.00


Tota

l-P (m

g/L)

* TKN and Total-P were not measured in baseflow samples.

58

Figure 5.13 Geometric mean TOC, COD, TSS and VSS by site for storm events and baseflow monitored at mainstem and major tributary sites between March 1992 and March 1994. Different letters indicate significantlydifferent mean values at α=0.05.

A

B

A

B

BBA

ABA

CBCABC

0

5

10

15


TOC

(mg/

L)

STORM BASEFLOW

AB

A

BBB

AAA

AAB

B

0

20

40

60

80


CO

D (m

g/L)

AB

A

AB

AAB

B

0100200300400500600700


TSS

(mg/

L)

AB

A

AB

AA

B

010203040506070


VSS

(mg/

L)

* TSS and VSS were not measured in baseflow samples.

59

Table 5.5 Comparison of geometric mean values representing effluent from the City of Stephenville wastewatertreatment plant (WWTP) and baseflow from site BO040 on the Upper North Bosque River for samples collectedbetween December 1993 and March 1994. 'n' indicates the number samples analyzed for each constituent at eachsite. Numbers in parenthesis equal the mean minus and plus the standard deviation.

WWTP n BO040 n

NH3-N (mg/L) 0.19 (0.05-0.77) 4 0.45 (0.05-3.82) 4

NO2-N (mg/L) ** 0.01 (0.01-0.03) 4 0.20 (0.07-0.59) 4

NO3-N (mg/L) 5.54 (4.94-6.21) 4 5.90 (3.92-8.88) 4

TKN (mg/L) 1.39 (1.26-1.51) 3 2.22 (0.44-11.15) 2

OPO4-P (mg/L) 0.93 (0.44-2.00) 4 0.94 (0.64-1.40) 4

Total-P (mg/L) 0.90 (0.54-1.50) 3 0.92 (0.56-1.49) 2

TOC (mg/L) 7.56 (6.98-8.18) 4 9.33 (7.86-11.08) 4

COD (mg/L) 15.14 (9.33-24.56) 4 16.15 (8.91-29.26) 4

TSS (mg/L) 10.48 (5.54-19.82) 4 8.12 (4.62-14.27) 2

VSS (mg/L) 4.80 (3.35-6.87) 4 3.05 (1.95-4.79) 2

** Indicates significant differences at alpha = 0.05 level of significance

As with the micro-watershed sites, a slightly different picture emerges when comparing TOC,COD, TSS and VSS rather than nutrients between sites (Figure 5.13). Values for storm samplesfor these constituents at BO040 were ranked in the low or moderate groupings based on the resultsof the LSD tests rather than in the highest groupings as indicated for most nutrients (Figures 5.12and 5.13). Storm and baseflow TOC levels followed fairly similar patterns between sites, althoughlower concentrations were measured at baseflow than during storm events. Storm COD valueswere highest at NF050, while baseflow values were significantly greater at NF050, SF075, AL040and BO040 than at GC100 and BO070. TSS and VSS were measured only during storm events.The highest TSS and VSS levels were generally associated with NF050, GC100 and BO070, whilethe lowest values were generally associated with AL040 and BO040.

The geometric mean values at each major tributary and main stem site for storm and baseflowsamples were compared to TNRCC screening levels for nutrients to indicate the potential foreutrophication. As indicated in Section 5.2.5, these screening levels were developed in referenceto baseflow conditions and may not be directly applicable to stormwater quality. Neither storm norbaseflow water quality levels exceeded the 1.0 mg/L screening level for NH3-N at any of the sites.For NO3-N, the screening level of 1.0 mg/L was exceeded at SF075 and BO040 during stormevents and at NF050, SF075 and BO040 during baseflow. For total-N, the screening level of 3.0mg/L was exceeded at NF050 and BO040 during storm events . Using a screening level of 0.2mg/L for OPO4-P, screening levels were exceeded at NF050 and BO040 during storm events andbaseflow. For total-P, the screening level of 0.2 mg/L was exceeded at all sites during stormevents. While this comparison cannot confirm water quality problems due to nutrients, it doesindicate areas of potential concern for waterbodies with nutrient concentrations exceeding thescreening levels.

5.4.3 Summary of Stream Site ComparisonsWater quality in general varied between stream sites depending on the water quality constituentevaluated. Different patterns emerged when analyzing nutrients, i.e., nitrogen and phosphorusconstituents, in comparison to other water quality constituents, TOC, COD, TSS and VSS. For themicro-watershed sites, NF005 and NF020 generally had the highest constituent levels while SF035and SP020 consistently had the lowest constituent levels. For the major tributary and main stemsites, BO040 indicated the highest levels nutrient levels during both storm and baseflow sampling

60

for most constituents measured. Water quality at BO040 was closely related at baseflow toeffluent water quality from the City of Stephenville WWTP and somewhat related to baseflowwater quality at sites NF050 and SF075. However, more data are required to establish theinfluence of the WWTP effluent on water quality in the North Bosque River, since monitoring ofthe WWTP effluent was only initiated in December 1993. Using TNRCC screening levels forfreshwater systems, nitrogen concentrations were indicated as a potential concern at sites NF005,NF020, MB040, SF075, NF050 and BO040. Phosphorus concentrations were indicated as apotential concern at all sites except SF035, SP020 and IB040.

61

6. COMPARISON OF WATER QUALITYWITH LAND CHARACTERISTICS

Attributing specific causes to streamwater quality is difficult, particularly when most constituentsarrive in streams via diffuse sources and are subject to complex kinetic reactions. Ideally,controlled, localized experiments would be conducted to evaluate the mechanisms underlyingsurface runoff and pollutant transport, but such studies are often expensive and time consuming(Osborne and Wiley, 1988). A less expensive, more rapid technique providing reliable results is torelate large-scale land characteristics and land use patterns to stream water quality usingcorrelation and regression analysis (Meals, 1992; Byron and Goldman, 1989; Hirose andKuramoto, 1981). While this technique does not indicate the specific mechanisms causing surfacerunoff and pollutant transport, it can be used to indicate land areas and/or activities that arecontributing to nonpoint source pollution problems.

6.1 Correlation and Regression AnalysisCorrelation analysis was conducted comparing reservoir and stream site water quality separatelywith the land characteristics of the drainage basins above each site. Sites were limited to thoseprimarily impacted by agricultural land uses since only two sites represented urban land uses.Water quality constituents were plotted against the percent of each drainage basin represented byrangeland, woodland, forage fields, waste application fields, peanuts, orchards, water and barren torepresent the different land uses (Table 3.1 and 3.2). Other independent variables used in thecorrelations matrix included dairy cow density3, the percent of land associated with eachhydrologic soil group and the average slope of each drainage basin for reservoir and stream sites(Table 3.3). The percent of land controlled by PL-566 reservoirs was also included as anindependent variable for stream sites (Table 3.5). The dependent variables included NH3-N, NO2-N, NO3-N, OPO4-P, TOC, and COD for both stream and reservoir sites. Chlorophyll-α,conductivity, turbidity and Secchi depth were included as dependent variables for reservoircorrelations. TKN, total-P, VSS and TSS were included as dependent variables for streamcorrelations.

Correlation and regression analyses were conducted using seasonal arithmetic or geometric meanvalues as indicated in Section 5 for each water quality constituent for all eight reservoir sites. Datawere evaluated graphically to determine the need for any transformations. At reservoir sites, logetransformation was required for NH3-N, NO3-N, OPO4-P, chlorophyll-α and conductivity tolinearize the relationship between water quality and land characteristics. The loge transformationsdo not change the interpretation of the correlation coefficients or the significance of therelationships, although the non-transformed relationships are curvilinear rather than linear(Finkelstein and Levin, 1990).

Land use characteristics were not compared to baseflow water quality since baseflow samplingoccurred at only six sites and these sites represent a rather narrow range for most land usecategories (Table 3.1 and 3.2). Stormwater quality was compared with land use characteristics.Agricultural land use areas were emphasized in the correlation analysis since agricultural uses

3 Dairy cow density was calculated using the estimated number of milking cows plus one-half the estimated number

of calves from calf raising operations divided by the number of acres in each drainage basin (Tables 3.1 & 3.4).

62

dominate the majority of stream sites evaluated. Sites MB040, IB040 and BO040 were notincluded in the correlation analysis, since data from these sites reflect urban impacts. For streamsites, the geometric mean of the volume-weighted mean storm event concentrations was used tocharacterize the overall water quality of each stream site as indicated in Section 5. No datatransformations were indicated for stream site correlations.

Correlation analysis indicates the significance and strength of a linear relationship betweenvariables, but regression analysis is needed to identify the specifics of that relationship. Fromregression analysis, the slope of the relationship can be established which can then be used todetermine if the change in water quality with change in land characteristic is large enough toconsider the relationship important. The correlation coefficient (r) represents a measure of thestrength of the linear relationship between dependent and independent variables, while thecoefficient of determination4 (R2) from regression analysis represents the amount of variability inthe dependent variable, 'y', that can be explained by the relationship with the independent variable,'x' (Ott, 1984). An 'r' less than 0 indicates a negative linear relationship, while an 'r' greater than 0indicates a positive linear relationship. The negative and positive signs on the correlationcoefficient are reflected in the sign of the slope in the regression equation. For significantrelationships (α=0.05) with a correlation coefficient greater than 0.71, a linear regression analysiswas conducted to evaluate the slope and intercept defined by these correlations. A correlationcoefficient of 0.71 was chosen as a "break point" for regression analysis since it relates to an R2 of0.50. Significant relationships with R2 values less than 0.50 were considered "weak" relationshipsand, thus, less meaningful in the overall evaluation.

Multiple regression runs were also conducted to evaluate the effect of using more than one landcharacteristic to explain water quality (see Appendix D). Since many of the land characteristicswere interrelated, multicolinearity between independent variables limited the usefulness of theserelationships. Single variable models were determined to be the best approach for the currentdataset in relating land characteristics to water quality.

6.2 Results of the Correlation and RegressionAnalyses

The results of the correlation analyses are presented in Table 6.1 for reservoir sites and Table 6.2for stream sites. Positive correlation coefficients were generally associated with land usesrepresenting more intensive agricultural practices, while negative correlation coefficients weregenerally associated with less intrusive land uses. (Secchi depth correlations should be interpretedinversely to all other water quality constituent correlations, since deeper Secchi depths, i.e., largervalues, indicate increased water clarity.) The regression equations for correlation associationsindicating R2 values greater than 0.50 are presented in Table 6.3 for reservoir sites and Table 6.4for stream sites. Graphs of these regression equations overlaid with individual site data arepresented in Appendix E for reservoir sites and Appendix F for stream sites.

4 (r)2 = R2

63

Table 6.1 Correlation coefficients (r) and level of significance for water quality constituents with land characteristics for reservoir sites. NHOPO4-P, chlorophyll-α and conductivity were transformed using a loge transformation before conducting the correlation analysis.

ln(NH3-N) NO2-N ln(NO3-N) ln(OPO4-P) TOC COD ln(Chlorophyll-α) ln(Conductivity)

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (ug/L) (µmhos/cm)

Woodland (%) -0.20 -0.10 0.17 -0.40 * -0.54 ** -0.48 ** -0.69 ** -0.72 **

Rangeland (%) -0.30 -0.28 -0.40 * -0.79 ** -0.33 -0.38 * -0.39 * -0.46 **

Forage Fields (%) 0.28 0.26 0.31 0.62 ** 0.23 0.27 0.35 0.48 **

Waste Appl. Fields (%) 0.29 0.18 0.00 0.72 ** 0.67 ** 0.66 ** 0.79 ** 0.79 **

Peanuts (%) 0.26 0.11 0.01 0.29 0.27 0.17 0.25 0.20

Orchard (%) 0.15 0.04 0.06 0.15 0.02 -0.06 0.07 0.08

Water (%) -0.20 0.08 0.28 -0.17 -0.43 * -0.28 -0.34 -0.22

Barren (%) 0.28 0.08 -0.17 0.34 0.50 * 0.37 * 0.50 ** 0.42 *

Dairy Cow Density (cows/acre) 0.33 0.15 -0.10 0.74 ** 0.78 ** 0.72 ** 0.87 ** 0.85 **

Soil Group B (%) 0.11 0.16 0.29 -0.11 -0.25 -0.19 -0.46 ** -0.46 *

Soil Group C (%) 0.14 0.17 0.39 * 0.63 ** 0.12 0.18 0.33 0.41 **

Soil Group D (%) -0.20 -0.24 -0.60 ** -0.53 ** 0.05 -0.04 -0.06 -0.15

Avg. Slope (%) 0.01 0.06 -0.30 0.05 0.12 -0.02 0.10 0.09

n 32 32 32 32 30 32 32 32

No. of Sites 8 8 8 8 8 8 8 8

* significant at α = 0.05** significant at α = 0.01n = number of values used in the regression analysis. Seasonal mean values were used for each site.

64

Table 6.2 Correlation coefficients (r) and level of significance for water quality constituents with land characteristics for stream sites.

NH3-N NO2-N NO3-N TKN OPO4-P Total-P TOC COD(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

Woodland (%) -0.67 ** -0.55 * -0.45 -0.67 ** -0.78 ** -0.74 ** -0.60 * -0.69 **

Rangeland (%) -0.39 -0.37 -0.29 -0.52 -0.63 * -0.65 * -0.36 -0.56 *

Forage Fields (%) -0.15 -0.14 -0.13 0.08 0.07 0.13 -0.07 0.15

Waste Appl. Fields (%) 0.86 ** 0.77 ** 0.64 * 0.80 ** 0.91 ** 0.84 ** 0.71 ** 0.78 **

Peanuts (%) -0.21 -0.23 -0.18 -0.26 -0.04 0.03 -0.09 -0.24

Orchard (%) -0.19 -0.28 -0.22 -0.30 -0.12 -0.05 -0.15 -0.28

Water (%) -0.29 -0.24 -0.18 -0.14 -0.03 0.00 -0.19 -0.23

Barren (%) -0.10 -0.11 -0.09 -0.12 0.13 0.19 0.04 -0.10

Dairy Cow Density(cows/acre)

0.81 ** 0.76 ** 0.66 * 0.74 ** 0.88 ** 0.80 ** 0.73 ** 0.70 **

Soil Group B (%) -0.60 -0.54 * -0.43 -0.55 * -0.56 * -0.53 -0.51 -0.61 *

Soil Group C (%) 0.44 0.33 0.24 0.51 0.46 0.48 0.31 0.64 *

Soil Group D (%) -0.22 -0.11 -0.06 -0.31 -0.25 -0.29 -0.07 -0.44

Avg. Slope (%) 0.61 * 0.61 * 0.59 * 0.54 * 0.42 0.38 0.56 0.50

Controlled by PL-566Reservoirs (%)

-0.37 -0.40 -0.30 -0.31 -0.28 -0.26 -0.28 -0.38

n 14 14 14 14 14 14 14 14

No. of Sites 14 14 14 14 14 14 14 14

* significant at α = 0.05** significant at α = 0.01n = the number of values used in the regression analysis

65

Table 6.3 Regression equations for water quality constituents versus land characteristics for reservoir sites.

Water Quality Constituent (y) Land Characteristic (x) Equation R2 Significance Level

OPO4-P (mg/L) Rangeland (%) ln(y)=3.72-0.14x 0.62 0.0001Waste Appl. Fields (%) ln(y)=-4.21+0.14x 0.52 0.0001Dairy Cow Density (cows/ac) ln(y)=-4.32+8.74x 0.55 0.0001

TOC (mg/L) Dairy Cow Density (cows/ac) y=8.13+21.0x 0.61 0.0001

COD (mg/L) Dairy Cow Density (cows/ac) y=18.78+109.12x 0.51 0.0001

Chlorophyll-α (µg/L) Waste Appl. Fields (%) ln(y)=2.67+0.11x 0.63 0.0001Dairy Cow Density (cows/ac) ln(y)=2.51+7.43x 0.76 0.0001

Conductivity (µmhos/cm) Woodland (%) ln(y)=7.18-0.03x 0.51 0.0001Waste Appl. Fields (%) ln(y)=6.04+0.03x 0.62 0.0001Dairy Cow Density (cows/ac) ln(y)=5.99+2.35x 0.72 0.0001

Turbidity (NTU) Waste Appl. Fields (%) y=11.73+1.29x 0.57 0.0001

ZSD (feet) Waste Appl. Fields (%) y=3.32-0.12x 0.51 0.0001Dairy Cow Density (cows/ac) y=3.46-7.92x 0.58 0.0001

Table 6.4 Regression equations for water quality constituents versus land characteristics for stream sites.

Water Quality Constituent (y) Land Characteristic (x) Equation R2 Significance Level

NH3-N (mg/L) Waste Appl. Fields (%) y=0.01+0.013x 0.73 0.0001Dairy Cow Density (cows/ac) y=0.01+0.79x 0.66 0.0005

NO2-N (mg/L) Waste Appl. Fields (%) y=0.006+0.003x 0.60 0.0012Dairy Cow Density (cows/ac) y=0.004+0.16x 0.58 0.0017

TKN (mg/L) Waste Appl. Fields (%) y=0.94+0.08x 0.64 0.0006Dairy Cow Density (cows/ac) y=0.93+4.88x 0.55 0.0025

OPO4-P (mg/L) Woodland (%) y=0.75-0.022x 0.61 0.0009Waste Appl. Fields (%) y=0.06+0.01x 0.82 0.0001Dairy Cow Density (cows/ac) y=0.05+0.89x 0.77 0.0001

Total-P (mg/L) Woodland (%) y=1.41-0.04x 0.55 0.0025Waste Appl. Fields (%) y=0.20+0.03x 0.70 0.0002Dairy Cow Density (cows/ac) y=0.19+1.51x 0.64 0.0006

TOC (mg/L) Waste Appl. Fields (%) y=10.03+0.31x 0.50 0.0046Dairy Cow Density (cows/ac) y=9.49+20.78x 0.54 0.0029

COD (mg/L) Waste Appl. Fields (%) y=30.96+1.38x 0.61 0.0010

66

For reservoir sites, neither NH3-N nor NO2-N indicated significant correlations with any landcharacteristics, indicating that there is probably very little variation in these constituents betweenreservoir sites. This supports the finding of no differences between reservoir sites for NH3-N andNO2-N in Sections 5.3.1 and 5.3.2. For stream sites, neither TSS or VSS indicated significantcorrelations with any land characteristic. While significant differences between stream sites wereindicated for TSS and VSS (see Sections 5.4.3 and 5.4.4), no single land characteristic wascapable of explaining these differences between stream sites.

The percent land area in waste application fields and dairy cow density associated with each siteconsistently showed the highest positive correlations with water quality constituents of any landcharacteristics for both reservoir and stream sites. The similarity in the correlations associatedwith these two independent variables was expected since the amount of land needed for animal-waste application is a function of the number of dairy cows in a given drainage basin5. Thissignificant positive correlation indicates that as the percent of land used for waste application (ordairy cow density) in a drainage basin increases, the concentration of water quality constituents instormwater runoff and downstream reservoirs increases. The proportion of forage fields in adrainage basin also showed fairly high positive correlations with various water quality constituentsfor reservoir sites but not for stream sites. Other significant positive correlations with waterquality constituents occurred with the portion of barren land and soil group C for reservoir sitesand with the average percent slope for stream sites.

The proportion of woodland and/or rangeland in each drainage basin was generally associated withfairly high negative correlations with water quality constituents. The negative correlationsassociated with woodland and/or rangeland generally represent a "trade-off" in each drainage basinbetween relatively intensive and less intensive land use categories, i.e., drainage basins with a highpercentage of woodland generally have less land available for waste application and vice versa.The slopes of the linear regression equations associated with water quality constituents and thepercent rangeland or woodland in a drainage basin were generally of the same magnitude but ofopposite sign in comparison to equations associated with the percent waste application fields orforage fields in a drainage basin (Tables 6.3 and 6.4). Significant negative correlations were alsoassociated with some water quality constituents and the proportion of soil groups B and D forreservoir sites and the proportion of soil group B for stream sites.

Although many other correlations were statistically significant (α=0.05), all correlations should beinterpreted carefully. This is particularly true since the data represent a rather small range ofvalues for several of the land characteristics (Table 6.5). The proportion of land used for peanuts,orchards, water and barren land varies less than three percent between drainage basins. Theaverage slope between drainage basins varies less than four percent between drainage basins.These correlations may not be meaningful if extended beyond the range of the current data set.Correlation and regression relationships, in general, should not be extrapolated beyond theboundaries of the data set from which they were developed.

5 A small portion of the land above NF030 and NF020 is used for septage waste disposal rather than animal waste,

but this area makes up less than three percent of the land used for waste application in these drainage basins (seeSection 3.2).

67

Table 6.5 Maximum and minimum values for land characteristics used in correlation and regression analyses forstream and reservoir sites.

S t r e a m S i t e s R e s e r v o i r S i t e sMinimum

ValueMaximum

ValueDifference Minimum

ValueMaximum

ValueDifference

Woodland (%) 11.0 37.4 26.4 15.4 37.4 21.9Rangeland (%) 24.5 60.5 36.0 32.8 58.5 25.7Forage Fields (%) 2.9 38.4 35.5 4.1 26.1 22.0Waste Appl. Fields (%) 0.0 45.4 45.4 0.0 24.2 24.2Peanuts (%) 0.0 7.1 7.1 0.0 2.5 2.5Orchard (%) 0.0 1.2 1.2 0.0 1.1 1.1Barren (%) 0.1 2.6 2.5 0.0 0.8 0.8Water (%) 0.3 1.3 1.1 0.3 1.3 1.1Dairy Cow Density (cows/ac) 0.0 0.7 0.7 0.0 0.3 0.3Soil Group B (%) 2.3 25.0 22.7 7.7 32.5 24.8Soil Group C (%) 37.6 87.6 50.1 32.7 81.3 48.6Soil Group D (%) 6.5 49.8 43.3 11.0 56.8 45.8Slope (%) 3.0 6.0 3.0 3.0 7.0 4.0

6.3 Summary of Correlation and Regression ResultsFor both stream and reservoir sites, the highest positive correlations with land characteristicsconsistently occurred with the percent land used for waste application and dairy cow density in thedrainage basin above each site. The percent woodland and to a lesser degree percent rangelandhad the highest negative correlations. Soil types and other land uses did not typically provide asignificant correlation to water quality.

Although waste application fields appear to be the predominate source of many water qualityconstituents above background concentrations, other sources to the surface waters of the upperNorth Bosque River watershed should not be ignored. The regression analysis generally explainedonly about 50 percent of the variability in water constituent concentrations. The other 50 percentis unexplained by the current regression models. Other factors that may explain part of theremaining variability include individual management practices, i.e., "good" and "bad" actors, theproximity of different pollutant sources to water courses, timing of BMP implementation, othersources within each drainage basin and general background noise inherent in water qualitysampling.

69

7. INTERPRETATION AND IMPLICATIONSOF WATER QUALITY FINDINGS

The previous two sections provide a battery of statistical analyses showing that 1) the water qualityconstituent concentrations differ between various streams and PL-566 reservoirs as represented byTIAER monitoring sites and 2) certain land uses and watershed characteristics, most notablypercent waste application fields, dairy cow density, percent woodland and percent rangeland inagricultural watersheds, have strong correlations to observed water quality. Further, comparison ofwater quality data to non-regulatory screening levels indicates that some waterborne constituents,especially orthophosphate and total phosphorus, exceed these screening levels in both urban andagricultural watersheds. The purpose of this section is to discuss these results in relation to past,current and future land use activities within the upper North Bosque River watershed.

7.1 Urban SitesWhile the sites monitored in this report emphasize agricultural or rural watersheds, IB040, MB040and BO040 were directly impacted by urban practices. The drainage basins above IB040 andMB040 contain large areas of urban land, while BO040 is directly below the wastewater treatmentplant for the City of Stephenville. Site BO070 represents the cumulative outflow from the upperNorth Bosque River watershed and contains less than two percent urban land, predominatelyrepresented by the City of Stephenville. Although urban land uses are only a small part of thewatershed, their impact on overall water quality is important. The drainage basin above IB040contains about 30 percent urban land with the rest of the land spread almost evenly between usecategories of woodland, rangeland and forage fields. The drainage basin above MB040 contains100 percent urban land within the City of Stephenville. Values for all water quality constituentswere significantly higher at MB040 than at IB040, indicating the large potential for nutrients inrunoff from purely urban land (Figures 5.9 and 5.10). The abundance of paved surfaces in a citygenerally enhances the conveyance of stormwater runoff to receiving streams compared to morerural areas. With respect to potential eutrophication, geometric mean values for total-N and total-Pexceeded TNRCC screening levels at MB040 but not at IB040. The specific sources of nonpointsource pollution from urban sources cannot be pin-pointed without more intensive analysis. Somepotential nutrient sources include the application of fertilizers to lawns and gardens, industrialsources, decomposition of organic matter and waste production from animals within the city limits.

Since site BO040 is located immediately below the WWTP, water quality at site BO040 is directlyimpacted by effluent from the WWTP (see Section 5.4.2). The wastewater treatment plant for theCity of Stephenville is well recognized as a point source contributor to the water quality concernsin the North Bosque River. Prior to the region’s dairy growth which began in the mid-1980s,studies by the Texas Water Commission indicated the City of Stephenville WWTP as the principalsource of nutrient input into the North Bosque River (TWC, 1990). Since that time the City ofStephenville has made continuing efforts to improve its wastewater collection and treatmentfacilities. In the summer of 1993, improvements were completed on the sewage main interceptorlines to prevent unregulated sewage overflow during storm events. As expansion of the WWTPbecame fully operational in 1995, further reductions in nutrient loadings into the North BosqueRiver should occur, which will be of particular importance during baseflow.

70

7.2 Rural SitesMost of the stream and reservoir sites monitored were representative of rural areas with a varietyof dominant agricultural land uses. Based on TNRCC screening levels for the sites studied,nitrogen concentrations were not found to be a concern at reservoir sites and only a possibleconcern at a few of the stream sites (see Section 5). Phosphorus concentrations were consideredelevated at several reservoir and stream sites when compared to TNRCC screening levels. Basedon the results provided in this report, phosphorus from agriculturally dominated watershedsappears to be the main nutrient of concern in the North Bosque River. In the rural subwatershedsof the upper North Bosque River, potential sources of in-stream phosphorus include: (1) runoff anderosion from waste application fields, (2) runoff and erosion from fertilized orchards, peanuts,pastures and forage crops, (3) residual impacts from past phosphorus loadings, (4) deposition ofmanure near watercourses and runoff and erosion from cattle grazing rangelands or pastures, (5)leakage or failure of manure and/or wastewater storage facilities and handling areas, and (6)leakage or failure of rural septic systems.

The leakage of manure storage facilities or rural household septic is generally associated withgroundwater problems rather than surface water problems (Logan, 1990; Perkins and Hanson,1990). Extreme or chronic rainfall events can lead to failure of either of these systems leading tothe flow of partially treated effluent into stream water systems. In reference to lagoon overflowfrom extreme or chronic rainfall events, McFarland et al. (1994) indicated the potential foroverflows, based on current lagoon design criteria, occurring once every six years rather than onceevery 25 years when wet-day sequences were considered rather than single day rainfall events.TIAER compliance survey indicated discharge from several permitted lagoons in February 1992due to abnormally heavy rainfall events from December 1991 through February 1992 (TIAER,1992). TNRCC guidelines for effluent discharges from wastewater lagoons at CAFOs allow forsuch discharges, e.g., Subchapter K. 30TAC§§321.181(b) (Texas Register, March 21, 1995;2032).

While septic system failures do occur, they do not appear to be a widespread problem in the upperNorth Bosque River watershed (Carpenter, 1995). Approximately 3,300 people live in rural areasin the upper North Bosque River watershed (U.S. Dept. of Commerce, 1991). Assuming theextreme case that every resident had his or her own residence and septic system, the maximumseptic tank density in the watershed would be approximately 1 system per 70 acres. This estimateis well below the 40 systems per square mile or 1 system per 16 acres used by the U.S.Environmental Protection Agency in designating regions of potential ground-water contamination(Yates, 1985). To evaluate specific micro-watersheds within the watershed, records and aerialphotographs from the Erath County Tax Assessor's office were reviewed to estimate the number ofhouseholds in the drainage basins above the following sampling sites: IC020, NF005, NF010,NF020, NF035, SF020 and SF035. These drainage basins were selected because their relativelysmall sizes reduced the effort needed to determine the number of households and becausehousehold densities covered a broad range from a low of 1 household per 345 acres above siteSF020 to 1 household per 71 acres above site IC020. A plot of storm event geometric meanOPO4-P concentrations versus household (septic tank) densities shows no discernible pattern(Figure 7.1). The low density of households in the rural subwatersheds and the absence of adiscernible positive correlation of septic tank density to observed OPO4-P concentrations supportsthe premise that septic tanks contribute only a minor portion of the nutrients reaching themonitoring sites of this study.

71

Figure 7.1 Estimated septic system density compared with geometric mean OPO4-P concentrations for storm eventsat selected monitoring sites.

SF035SF020

IC020

NF035

NF020

NF010

NF005

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014

Estimated Septic System Density (systems/acre)

OPO

4-P

(mg/

L)

Another potential source of pollutants in the North Bosque River occurs from grazing beef cattle.Beef cattle can be a significant source of nutrients in stream systems if they are allowed to defecatenear or in stream systems (Larsen et al., 1994). The trampling effect of cattle on riparian areas canalso make significant impacts on the water quality in rangeland areas (Kauffman and Krueger,1984). While beef cattle operations are prevalent throughout the upper North Bosque Riverwatershed, cattle are kept primarily on large portions of the rangeland during most of the year.Because the stocking rate is fairly low on these lands (NRCS locally recommends about 15acres/animal unit on good to excellent rangeland), the impacts of these operations on water qualityis expected to be minor (Wittie, 1995). The minor contribution of beef cattle operations to nutrientlevels of area waterbodies is indicated by the consistently negative correlations of rangeland tonutrients levels (Tables 6.1 and 6.2) and the consistently low nutrient levels at stream monitoringsites (SF020, SF035 and SP020) which have drainage areas containing range cattle operations butfew other agricultural practices (Section 5.4.2).

Most phosphorus losses are generally associated with sediment or erosion losses from agriculturalland uses, since phosphorus adsorbs readily to clay particles in most soils (NRC, 1993). Sedimentcontrol is considered a primary factor in nonpoint source pollution control and is very effective inreducing the loss of particulate phosphorus (Burwell et al., 1977). As the percent land coverincreases, erosion rates and particulate phosphorus losses generally decrease. For land areas withpermanent cover, such as rangelands or woodlands, the primary source of phosphorus is sedimentfrom erosion of the streambank (Sharpley et al., 1993a). The sediment from streambank erosion isgenerally low in phosphorus, since most streambank sediment comes from subsurface soilhorizons. The greatest concentrations of phosphorus are almost always found in the top few inchesof soil, since phosphorus readily transforms from a dissolved to solid phase and rarely leaches tolower soil layers (Nelson and Logan, 1983). This helps explain the relatively high negativecorrelations indicated for OPO4-P and total-P with percent rangeland and/or woodland in thedrainage basins above both stream and reservoir sites, since both these land categories areassociated with a large amount of permanent cover. This also indicates that most backgroundlevels of phosphorus from undisturbed areas are relatively low in phosphorus.

72

Anecdotal comments suggest that some soils in Erath County may have naturally high phosphoruslevels. These comments refer to river bottomland type soils in the Leon River and Paluxy River,located in watersheds adjacent to the North Bosque River. TIAER is not aware of any direct soiltesting results confirming or refuting the presence of such high phosphorus soils in the upper NorthBosque River watershed. The Frio clay loam soil type, a bottomland soil found along many areastreams, is a possible source of naturally occurring phosphorus because its clay content could holdadsorbed phosphorus. The Frio soil is found above many of the stream monitoring sites of thisstudy, including sites such as SF020, SF035 and SP020, which are sites where the lowestphosphorus measurements were obtained. While it is difficult to completely dismiss the existenceof soils naturally high in phosphorus without extensive soils testing, nothing in the existing streamsampling data suggests naturally occurring phosphorus from area soils as the source of theobserved phosphorus differences between water sampling sites.

Intensive agricultural practices are intuitively potential sources of nutrients in stormwater runofffrom soil disturbance and the application of organic and inorganic fertilizers. Peanut fields andorchards comprise only minor portions of the land use in any of the monitored watersheds (Table6.5). Correlations of these land use factors with nutrient concentrations at stream and reservoirsites were generally insignificant or very weak, i.e., low 'r' values (see Section 6). Forage fieldswith and without waste application represent most of the intensive agricultural land use in the ruraldrainage basins studied. Waste application fields occur on 7 percent of the land area in the upperNorth Bosque River watershed, while forage fields without waste application cover 20 percent ofthe total land area (Table 3.2). Correlations of nutrient levels to forage field percentages withoutwaste application were substantially weaker, i.e., smaller 'r' values, than for waste application fieldsfor both reservoir and stream sites (Tables 6.1 and 6.2). While waste application fields are not theonly source of phosphorus in these drainage basins, they appear to be the predominant singlesource explaining up to 52 percent and 82 percent of the variability in OPO4-P in reservoir andstream sites respectively and 70 percent of the variability in total-P in stream sites (Tables 6.3 and6.4). In contrast, the percent forage fields above reservoir sites that were not used for wasteapplication explained only 38 percent of the variability in OPO4-P. Multiple regression with dairywaste application fields, non-dairy forage fields, orchards and peanuts as the independent variableswas performed using stepwise regression procedures to see if a multi-parameter model wouldexplain more of the variability in OPO4-P and total-P levels (see Appendix D). A single parametermodel using percent waste application fields still provided the best fit model with both dependentvariables further indicating the relatively weak relationships between phosphorus levels and otherland use characteristics.

Another nutrient source in these agricultural watersheds may be residual impacts from pastmanagement practices (Clausen et al., 1992). Before 1990, many dairies were without lagoons.Milking parlor and feedlot waste was likely to runoff directly into watercourses. The recycling ofnutrients from the bottom sediment and in the detritus of aquatic plants makes nutrient removalfrom lakes and reservoirs a very slow process (Lebo et al., 1994). Residual nutrients can alsoremain in the sediment of streams, but residual impacts are generally more limited in streamssystems than in lakes and reservoirs since stream sediment can be flushed downstream morereadily. Stormwater runoff in these intermittent streams, thus, is considered a more reliableindicator of current runoff water quality than the water in the reservoirs. The intensive monitoringin the upper North Bosque River watershed has occurred during a transition period for BMPimplementation for nonpoint source pollution in the watershed. The inherent variability in stormevent data requires that a relatively long term record be established before trends can bestatistically evaluated (Spooner et al., 1987). While preliminary analysis of the data as a timeseries shows no trend of decreasing or increasing water quality, we expect continued monitoring toindicate improved water quality with continuing BMP implementation. Since waste applicationfields appear to be the primary source of nutrients in the North Bosque River, the rest of thischapter will focus on regulations governing the application of waste from confined animal feedingoperations and the potential for nutrient losses even when these regulations are followed.

73

7.3 Phosphorus and Confined Animal FeedingOperation Regulations

In the land application of manure or effluent, nutrient and sediment losses can vary greatly.Nutrient and sediment losses from land applied manure or effluent will vary depending on the rateand method of application, the cropping system, season of the year, soil properties, current weatherconditions, and past management practices (Mikkelsen and Gilliam, 1995). The TNRCC confinedanimal feeding operation (CAFO) regulations restrict the application of dairy manure and lagooneffluent to the agronomic nitrogen requirements of the crop. Under these regulations, theallowable nitrogen application is adjusted for losses due to ammonia volatilization and partialmineralization of manure's organic nitrogen into plant available (inorganic) nitrogen during thefirst year after application. Under the nitrogen plant requirement restriction, phosphorus istypically over-applied by a factor of 2½ to 3 times crop requirements. The over application ofphosphorus allowed in the regulations recognizes that phosphorus has a high capacity to bind withmost soils (adsorption of phosphorus to clays) which makes the phosphorus much less available fortransport to surface or groundwaters. Present TNRCC CAFO regulations allow phosphorus build-up in the top 6 inches of the soil to an extractable phosphorus level of 200 ppm (mg/kg), at whichtime the phosphorus requirements of the crop determines manure and lagoon effluent applicationrates. In addition, where local water quality is threatened by phosphorus the operator shall limitapplication rates to the recommended rates of available phosphorus needed for crop uptake, e.g.,20 Tex. Reg. 2041 (1995) (to be codified at 30 TAC § 321.192(19)B). Under current TNRCCwaste application permit provisions, a 100 foot vegetated buffer zone between solid wasteapplication areas and surface water is required when manure is not incorporated. A buffer zone isnot generally required for irrigated effluent except around private or public wells. The above arefound in present CAFO permits in Erath County pursuant to the Texas Administrative Code (30TAC §§ 321.34-321.46) and Subchapter K Rules. 20 Tex. Reg. 2028-2045 (1995) (to be codifiedat 30 TAC §§ 321.181-321.198)

Since OPO4-P and total-P were highly correlated and TSS and VSS were not significantlycorrelated at stream sites with any of the land characteristics evaluated, it appears that a largepercentage of the phosphorus associated with the stream sites is probably in a soluble rather thanparticulate form. Although OPO4-P does not represent all of total-P that is in dissolved form, ageneral increase in the percent of total-P associated with OPO4-P occurs as the percent wasteapplication fields increases up to about twenty percent (Figure 7.2). For basin areas containingbetween 20 and 45 percent waste application fields, this ratio appears to hold fairly steady atapproximately 0.55, indicating that at least 55 percent of total-P is in a soluble form at this level.This same relationship cannot be as readily applied to the reservoir site measurements, since thewind mixing of these shallow reservoirs can easily resuspend settled particles, plants and algaeuptake and release phosphorus with growth and decay, and benthic sediments release inorganicphosphorus over time. Based on information from TNRCC permits, approximately 74 percent ofthe land used for animal waste application in the upper North Bosque River watershed isdesignated for surface application on coastal bermudagrass (Table 7.1). While surface applicationof manure and effluent on coastal bermudagrass seems to have controlled sediment losses in thewatershed and, thus, particulate phosphorus losses, soluble phosphorus may still be contributingsignificantly to surface water nutrient loadings.

74

Figure 7.2 Ratio of the geometric mean of OPO4-P to total-P from storm events versus percent waste applicationfields in the drainage basin above stream sites.

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30 35 40 45 50

Waste Application Fields (%)

Rat

io O

PO4-

P/To

tal-P

Table 7.1 Percent of acreage used for waste application on coastal bermudagrass and on other types of foragesbased on dairy permit and land use information.

Site

Total Land AreaUsed for Waste

Application(Acres)

Waste Applied Solely toForage Crops Other

than CoastalBermudagrass (Acres)

Waste Appliedto Coastal

Bermudagrass*(Acres)

Percent of WasteApplication Area in

Other Crops(%)

Percent of WasteApplication Area

in Coastal Bermudagrass(%)

NF005 462 0 462 0.0 100.0NF010 44 0 44 0.0 100.0NF020 886 88 798 9.9 90.1NF035 932 88 844 9.4 90.6NF050 2074 187 1887 9.0 91.0SF020 14 14 0 100.0 0.0SF035 24 17 6 72.9 27.1SF075 4435 1309 3126 29.5 70.5DB040 856 111 745 13.0 87.0MB040 0 0 0 0.0 0.0IB040 2 0 2 0.0 100.0BO040 7529 1627 5901 21.6 78.4BO070 16665 4352 12314 26.1 73.9AL040 1358 303 1054 22.3 77.7IC020 778 73 705 9.4 90.6IC035 781 73 707 9.4 90.6SP020 0 0 0 0.0 0.0SP035 0 0 0 0.0 0.0GC100 4455 1641 2814 36.8 63.2SF060 704 179 525 25.5 74.5GC020 54 54 0 100.0 0.0BO060 11092 2518 8574 22.7 77.3AL030 1372 310 1062 22.6 77.4SC030 293 45 248 15.5 84.5*Coastal fields may be over-seeded with other crops such as winter wheat as a double cropping system.

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7.4 Literature Review on Nutrient Losses from ManureApplication

Using the Erosion Productivity Impact Calculator (EPIC) and Agricultural Non-Point Source(AGNPS) models to evaluate sediment and nutrient losses under different manure managementpractices on corn-oat-alfalfa crop sequences, Sugiharto et al. (1994) predicted reduced erosion butincreased phosphorus loss when chisel plowing or no tillage were compared with fall moldboardplowing. Several field studies also indicate increased levels of phosphorus and nitrogen in surfacerunoff as the degree of incorporation of fertilizer or manure is decreased (Timmons, et al., 1973,Baker and Laflen, 1982; Römkens and Nelson, 1974; Mueller et al., 1984). The highest nutrientlevels in runoff generally occur when fertilizer or manure are surface applied just prior to anintensive rainfall event (Edwards and Daniel, 1993; Westerman et al., 1985 & 1987). Irrigatedwastewater has a lower potential for surface runoff nutrient losses compared to manure applicationsince irrigated effluent infiltrates the soil much more rapidly.

The potential for nutrient loss in surface runoff appears to be greatest when manure is surfaceapplied since surface application minimizes the potential for nutrients to bind with the soil (SCS,1992). In "no-till" systems, decaying organic matter and shallow nutrient application lead to anincrease in the proportion of phosphorus that is bioavailable in both particulate and dissolvedforms (Sharpley et al., 1993a). Occasional plowing of "no-till" soils may be necessary toredistribute surface phosphorus so it may bind with sediment in deeper soil layers andconsequently be more readily available in the root zone for plant uptake. Care should be taken inthe timing of the application of manure to optimize plant uptake and thus minimize surface runoffof nutrients. Weather conditions should also be monitored to avoid, if possible, the application ofmanure immediately prior to a major rain event. Vegetated filter strips are also recommended tohelp "trap" sediment and uptake soluble nutrient fractions in field runoff, although theeffectiveness of vegetated filter strips varies depending on the type of vegetation, the slope of thefield and the length of the filter strip (SCS, 1992; Johnson, 1995).

Long-term land application of manure may also increase nitrogen and phosphorus to levels in thesoil that exceed plant uptake capacity. Nutrient application rates of manure are generally based onnitrogen levels. Phosphorus application from manure, thus, often exceeds plant nutrientrequirements, although it is generally assumed that excess phosphorus binds with the soil into an"unavailable" form that is relatively stable. Phosphorus binds readily to most soil types except invery sandy soils or soils high in organic matter (Nelson and Logan, 1983; Harris et al., 1994). In along-term experiment in which cattle manure was applied and incorporated in eleven annualapplications, Chang et al. (1991) found that most of the applied NH4-N was nitrified, volatilized orfixed while the accumulation of organic matter, total-N, NO3-N, total-P, available-P and saltcontent of the soil increased with increasing manure application rates. Accumulated NO3-N wasfound to leach and accumulate in lower soil layers, while available-P accumulated mostly in thesurface soil. Sharpley et al. (1993b) evaluated the impact of long-term application of poultry litteron coastal bermudagrass and found significantly higher nitrogen and phosphorus contents in thesurface 5 cm of soil on treated coastal pastures compared to untreated native grassland. A slightincrease in NO3-N was observed between 50 and 100 cm and no movement of phosphorus below30 cm was observed on the coastal pastures. A three-year study of dairy manure and lagooneffluent application on coastal bermudagrass and bermudagrass overseeded with cool-seasonforages showed increased soil NO3-N and extractable-phosphorus levels with increasing dairywaste application rates (Chasteen et al., 1994). Most of the residual phosphorus from manure andeffluent application accumulated in the top six inches of soil in this study. This build-up inphosphorus levels is expected and is not a problem unless the phosphorus level increases beyondthe adsorption capacity of the soil.

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Long-term application of manure can become a surface water quality problem because thepotential for phosphorus movement in runoff increases when the adsorption capacity of the soil forphosphorus is exceeded (Reddy et al., 1979; Sharpley et al., 1993b). As additional manure isadded to the soil, a decrease in the phosphorus adsorption capacity of the soil occurs (Singh andJones, 1976). As the adsorption capacity of the soil becomes saturated, phosphorus can still beloosely bound to soil particles as labile-P. Labile-P remains in equilibrium with solublephosphorus in the soil, thus, as the labile-P concentration of the soil increases so does the solublephosphorus concentration of the soil. Soluble phosphorus (also called available or dissolvedphosphorus) is the form used by plants, but is also the form that is subject to leaching (SCS, 1992).Control of particulate phosphorus may actually lead to increases in soluble or bioavailablephosphorus losses (Sharpley et al., 1993a). Identification of critical soil surface phosphorusconcentrations should help to maintain phosphorus concentrations in runoff below critical levelsfor nutrient control (Daniel et al., 1993). While the idea of establishing a simple relationship topredict the potential for increased dissolved phosphorus runoff is appealing, the complexity ofadsorption/desorption relationships in the soil will not make this an easy task (Nelson and Logan,1983; Raven and Hossner, 1993).

7.5 Conclusions for the Upper North Bosque RiverWatershed

While there are many potential sources of nutrients in the upper North Bosque River watershed,runoff from waste application fields appears to be the predominant source impacting surface waterquality in stormwater runoff. Soil test results available to TIAER are insufficient for a properassessment of phosphorus levels in the soils of dairy waste application fields. Anecdotalinformation suggests at least some occurrences of elevated phosphorus in soils, though not alwaysin excess of the 200 ppm extractable phosphorus level. Further complicating the issue ofphosphorus is the surface application of manure without incorporation. The use of wasteapplication fields of coastal bermudagrass or coastal bermudagrass overseeded in the winter withwheat or rye restricts the ability to incorporate the manure. With surface application, soil erosionis minimized, but soluble runoff of nutrients is increased from direct exposure of the manure torainfall and restricted adsorption of manure phosphorus to soil particles occurs.

Intensive efforts have been made to implement BMPs for manure application (Upper NorthBosque River Hydrologic Unit Project, 1993 & 1994). While these efforts have been quitesuccessful, phosphorus levels still appear to be a potential concern in the watershed. Phosphorusrunoff is dependent on three factors: 1) sediment transport, 2) the capacity of the soil to adsorbphosphorus into the solid phase and 3) the dissolved phosphorus concentration of the soil surfacelayer. All three factors need to be considered in controlling phosphorus levels in surface andsubsurface runoff from waste application fields. Based on rough estimates of soluble phosphorusto total-P losses, most phosphorus losses appear to be in soluble form. Many proven erosioncontrol technologies exist to manage particulate losses of phosphorus. The control of dissolvedphosphorus forms may be a greater management challenge than the control of particulatephosphorus forms since factors controlling the concentration of soluble phosphorus in runoff arenot as well understood as those controlling particulate phosphorus. Few economically feasiblemanagement options are available to minimize phosphorus losses in the soluble form (Daniel et al.,1993). Continuing efforts are needed to develop and implement nonpoint source pollution controltechnologies emphasizing the control of soluble and insoluble forms of phosphorus.

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8. SUMMARY, CONCLUSIONS ANDRECOMMENDATIONS

This report presents the analysis of data and information obtained and developed from threeinterrelated studies investigating surface water quality in the upper North Bosque River watershed.Funding for these projects was provided by the State of Texas, Texas Water Development Board,U.S. Environmental Protection Agency and the USDA Natural Resources Conservation Servicethrough the Texas State Soil and Water Conservation Board. Extensive surface water monitoringwas conducted on both agriculturally dominated subwatersheds and some urban subwatersheds.Major areas of discussion in this report include land use and soils information from a geographicinformation system, flow data developed for sites at a variety of stream sizes, statistical analysis ofwater quality data from several stream and reservoir sites in the watershed, and development ofcorrelations of water quality to land characteristics. Summary, conclusions and recommendationsfor future research and data analysis efforts follow.

8.1 Summary and Conclusions(1) Monthly water quality sampling was conducted at eight PL-566 reservoirs and six stream

sites in the upper North Bosque River watershed from March 1, 1991 through March 31,1994. During this same period, storm event sampling and continuous streamflowmonitoring were conducted at 19 stream and reservoir spillway sites. Water samples wereanalyzed for total phosphorus, orthophosphate, total Kjeldahl nitrogen, ammonia, nitrite,nitrate, total suspended solids, volatile suspended solids, total organic carbon andchemical oxygen demand. During monthly sampling, the physical parameters of pH,dissolved oxygen, water temperature and conductivity were also measured.

(2) Land use characteristics, i.e., rangeland, improved pasture, woodland, wheat and sudan,orchards and groves, peanuts, urban, barren and water, were identified from Landsat TMimagery for the upper North Bosque River watershed and entered as a geographicinformation system (GIS) layer. Through TNRCC records of dairy permits, complianceaudits and waste management plans, TIAER determined dairy waste application fields,dairy locations, permitted dairy herd size and estimated milking dairy herd sizes. GIStopographic and soil type layers were also generated. These GIS layers were used todetermine the land characteristics above each stream and PL-566 reservoir monitoringsite. With the exception of two monitoring sites located specifically in the urban settingof Stephenville, Texas, the characteristics of the watersheds above each site were ruralwith a relatively wide range in density of dairy cows and percentage of dairy wasteapplication field acreage. Less than 2 percent of the upper North Bosque River watershedis urban, while 45 percent is rangeland, 23 percent is woodland, 22 percent is improvedpasture, and nearly 8 percent includes the intensive agricultural land uses of peanut,orchard and wheat/summer grain production.

(3) Streamflow records for the automated sampling and water level monitoring sites indicatea watershed with intermittent streamflow, especially during the late summer months.Smaller watershed sites may be characterized as highly intermittent where significant flowoccurs only after rainfall runoff events. Even the North Bosque River would beintermittent at times if not for the discharge from the Stephenville wastewater treatmentplant.

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(4) Application of statistical techniques to the reservoir water quality data indicates that sitesAL030, IC030, NF030 and SF060 have higher concentrations of water quality indicators(poorer water quality) than sites GC020, SC030, SF030 and SP030 (see Figure 2.1 forsite locations). At AL030, IC030, NF030 and SF060, potentially elevated levels oforthophosphate and chlorophyll-α were indicated based on TNRCC non-regulatoryscreening levels. Seasonal fluctuations in water quality constituents, when present,seemed to be related primarily to seasonal fluctuations in water temperature.

(5) Water quality during storm events varied significantly between the various stream andreservoir spillway sites. In comparison to TNRCC non-regulatory screening levels,nitrogen concentrations were indicated as a potential concern at stream sites NF005,NF020, MB040, SF075, NF050 and BO040. Phosphorus concentrations were indicatedas a potential concern at all 19 stream sites except SF035, SP020 and IB040.

(6) Water quality at the North Bosque River site BO040 during baseflow was very similar toeffluent quality of the Stephenville wastewater treatment plant (WWTP). During thesampling period, the flow at BO040 was about 13 cfs, flow from the WWTP was about 3cfs, and WWTP effluent appeared to be mixing with river water of similar quality.Monitoring of the WWTP effluent did not begin until December 1993, limiting the periodof analysis to December 1993 through March 1994.

(7) The water quality concentrations at the rural reservoir and stream monitoring sites weregenerally positively correlated to dairy cow density and percent waste application fields inthe drainage area above each site, and negatively correlated to the percent woodland andto a lesser degree percent rangeland. Soil types and other land uses did not typicallyprovide a significant correlation to water quality. At stream sites, ammonia,orthophosphate and total phosphorus concentrations were highly correlated to the percentwaste application fields and dairy cow density in a drainage basin. Linear regressionsresulted in coefficients of determination (R2) ranging from 0.64 to 0.82 for theserelationships. These R2 values are extremely high considering the inherent variability"lumped" into the independent variables of waste application fields and cow density, e.g.,timing of nutrient applications, amount of nutrient applications, existing nutrient values insoil and crop type vary on a field-by-field basis and other dairy operator practices,including cow feed rations.

(8) While the high correlations and significant linear regressions of in-stream nutrientconcentrations to dairy operations, i.e., application fields and number of cows, do not ofthemselves demonstrate a causal relationship of dairies to increased in-stream nutrientlevels (only the formidable task of monitoring all application fields could accomplishthat), elimination of all other potential sources support this causal relationship. Thepossibility that other sources - including beef cattle operations, rural household septictank malfunctions, naturally occurring high nutrient levels in area soils, and otherintensive agricultural practices in the watershed - result in the observed in-stream nutrientlevels is not supported by either statistical analysis nor evaluation of the other sources.

(9) The study's results show elevated levels of nutrients in streams and reservoirs downstreamof dairy operations when compared to waterbodies without upstream dairy operations. Atthe same time, but on an appreciably smaller scale, the urban stormwater runoff alsoprovides elevated levels of nutrients. Further, the effluent from the Stephenvillewastewater treatment plant represents an important point-source of nutrients to the NorthBosque River, particularly at baseflow.

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8.2 Recommendations for Future Monitoring andResearch

While this report reflects an intensive effort in water quality monitoring in the North Bosque River,it represents a fairly limited timeframe. Continuing efforts are needed to develop the long-termdatabase needed to answer some of the more pressing issues dealing with water quality in theNorth Bosque River. In recognizing the potential limitations of this report, TIAER proposes thefollowing:

(1) As soon as possible, TIAER should report on additional monitoring data to see if theseadditional monitoring data strengthen or weaken the linkage between dairy operations andelevated nutrient levels in the streams and PL-566 reservoirs of the upper North BosqueRiver watershed.

(2) Trend analyses of the monitoring data should be conducted to determine whether nutrientlevels are changing (increasing or decreasing) in area streams and reservoirs.

(3) A high level of routine and storm event sampling in the upper North Bosque Riverwatershed should be continued to address environmental concerns in the watershed.

(4) Biomonitoring and efforts toward development of analyses of the biomonitoring datashould be continued to determine the impacts, if any, of dairy operations on biologicalconditions in the upper North Bosque River watershed. Coan and Hauck (forthcoming1995) is the initial report on TIAER's biological studies.

(5) Mass loadings of nutrients from major sources, e.g., dairy waste application fields and theStephenville Wastewater Treatment Plant, should be estimated as to their contributioninto the upper North Bosque River system.

(6) The assimilative capacity of the North Bosque River and Lake Waco for nutrient loadsshould be determined.

(7) Research efforts are needed to assist the dairy community in the management of nutrientson their fields and the regulatory community in formulating proper rules for dairyoperations. These diverse, but interrelated, research areas include a) dietary or rationmanagement to control nutrient production by cows without reducing milk production(which has particular promise with phosphorus), b) phosphorus adsorption potential oflocal soils, c) the appropriateness of the 200 ppm extractable phosphorus limit for varioussoil types in protecting against unreasonable phosphorus runoff, d) evaluation of the costsand environmental trade-offs associated with the implementation of a phosphorusstandard for manure application, e) evaluation of the trade-offs of surface application andincorporated application for sediment and nutrient control, f) evaluation of different cropsfor phosphorus uptake, g) evaluation of liquid and solid manure management practices,e.g. storage practices and solids separators, relative to their impacts on the nitrogen-phosphorus balance in manure, h) evaluation of effectiveness of filter strips and otherrelatively inexpensive BMPs, i) the use of wetlands, both at the farm level and basinscale, for the amelioration of pollutants from nonpoint sources, and j) development ofinnovative approaches to control the potentially excessive phosphorus levels in dairyapplication field runoff.

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TWC, Texas Water Commission. 1990. Waste Load Evaluation for the Bosque River System in the BrazosRiver Basin: Segment 1226 - North Bosque River and Segment 1246 - Middle Bosque/SouthBosque River. WLE 90-02, Texas Water Commission, Austin, Texas, February, 1990.

Upper North Bosque River Hydrologic Unit Project. 1993. Annual Project Report Fiscal Year 1993.

Upper North Bosque River Hydrologic Unit Project. 1994. Annual Project Report Fiscal Year 1994.

U.S. Department of Commerce, Bureau of the Census. 1991. 1990 Census of Population and Housing, p.81.

Ward, R.C., J.C. Loftis, H.P. DeLong, and H.F. Bell. 1988. Groundwater quality: A data analysis protocol.Journal of the Water Pollution Control Federation 60:1938-1945.

Ward, R.C., J.C. Loftis, and G.B. McBride. 1990. Design of Water Quality Monitoring Systems. VanNostrand Reinhold, New York, New York.

Westerman, P.W., M.R. Overcash, R.O. Evans, L.D. King, J.C. Burns, and G.A. Cummings. 1985. SwineLagoon effluent applied to 'Coastal' bermudagrass: III. Irrigation and rainfall runoff. Journal ofEnvironmental Quality 14:22-25.

Westerman, P.W., L.D. King, J.C. Burns, G.A. Cummings, and M.R. Overcash. 1987. Swine manure andlagoon effluent applied to a temperate forage mixture: II. Rainfall runoff and soil chemicalproperties. Journal of Environmental Quality 16:106-112.

Wittie, R. 1995. Personal Communication, Associate Professor, Range and Ranch Management, TarletonState University 16 May 1995.

Yates, M.V. 1985. Septic tank density and ground-water contamination. Ground Water 23:586-591.

87

APPENDIX A

Miscellaneous issues concerning automated samplecollection.

TIAER conducted two studies to address the following:(1) Degradation of nutrients in sample containers held in an automated sampler. This study

investigated potential detrimental water quality changes for various sample holding timesin the uncontrolled environment of the automated sampler, and

(2) Water quality comparison of near-bottom, automated collected samples to near-surface,grab samples. This study investigated the representativeness of the water quality fromISCO-obtained samples to the water quality of near-surface, mid-stream samples.

1. Degradation Study: From August 27-28, 1993, a 30-hour degradation study was conducted; fromDecember 15-16, 1993, a 30-hour study was conducted; and from August 9-22, 1994, a 312-hourstudy was conducted. The same procedures were used for all three periods of testing.

Water from the North Bosque River was obtained and spiked with orthophosphate, ammonia,nitrite and nitrate to ensure the presence of detectable quantities of these inorganic nutrient forms.The water was well mixed and poured into individual automated sampler containers (one-literHDPE containers). A typical automated sampler installation was set up in the TIAER parking lot.The sample-filled containers were placed in the automated sampler, which itself is housed in asheet metal shelter. The shelter was left exposed to the weather. At predetermined times, samplecontainers were removed from the shelter three at a time. Laboratory analyses were performed forpH, nitrate, nitrite, ammonia and orthophosphate on each of the three samples.

The average of the triplicate results of each test period for nitrate, nitrite, ammonia andorthophosphate are provided in tabular form in Tables A-1, A-2 and A-3. For each constituent, themean value at time 0 was compared to the mean at each subsequent time using the Student-t testfor comparison of two means. The test was applied as a two-tailed test with a level of significance, α, of 0.05 with the null hypothesis that the means are equal. The test was performed a second timewith the mean value at time zero deviated by ±5.0 percent. With the mean deviated 5.0 percent,the Student-t indicates whether the mean concentration at a time other than zero is statisticallysimilar to the mean concentration at time equal zero with an allowed degradation of 5.0 percent.For the purposes of this study, a degradation over time of 5.0 percent was considered acceptable.

For the August 1993 and December 1993 tests, results indicate no statistically significant changesin mean concentration over time with the 5.0 percent deviation and a only few significantdifferences for nitrite and orthophosphate with no deviation. Results from the long-term testing ofAugust 1994 showed no significant differences in mean values until hour 72 of the test.

TIAER's protocol requires that samples be retrieved within 30 hours. Within required sampleretrieval times, no significant changes— that is, statistically significant changes for α = 0.05 at anallowable deviation of 5 percent— in water quality were detected by these studies. Therefore,based on available testing, TIAER's sample retrieval protocol is acceptable and provides thedesired level of sample integrity.

2. Representativeness of Automated Samples: Ongoing efforts at North Bosque River site BO040 arebeing conducted to compare the water quality of samples obtained from a TIAER ISCO sampler towater quality from grab samples obtained mid-stream, near-surface. During elevated flow,

88

approximately simultaneous samples were obtained by the ISCO sampler and manually from thevantage of a bridge 100 feet upstream of the sampler. The grab sample was obtained mid-streamand near the surface with an alpha-style sampler. The ISCO intake sample tube was positionednear-bottom and near one of the stream banks. The sampling and subsequent analysis weredesigned to provide a preliminary investigation of the degree of vertical homogeneity of thestream's water quality during stormwater runoff events and the degree of bias imposed throughsampling near the bottom of the water column.

If the water quality is similar from these two extremes (near-bottom and near-surface) of samplingthe water profile, then the ISCO sampling may be assumed to provide a representative (unbiased)sample of the stream water quality. Under the experimental design the converse can not beassumed. That is, if there is an appreciable difference between the near-bottom and near-surfacesamples, what has been proven is that there is a difference in water quality with depth. Furthersampling with depth (or a depth-composite sample) and transversely across the stream are requiredto determine in-stream water quality and how the ISCO-sampler water quality compares to therepresentative sample of water quality. Depth-composite sampling was beyond the scope of thisinitial experiment.

The installation at site BO040 is typical of other installations throughout the upper North BosqueRiver watershed. The sample intake tube is anchored just off the streambed near the bank with theintake tube running into the sampler shelter. A small filter screen is placed in the intake tube. Thesampler and shelter are located on a high bank above most flood water levels. Site BO040 wasselected for this study for the following reasons: (1) this site is near the TIAER office so thattravel time is minimized, (2) a bridge crossing of the North Bosque River 100 feet upstream of theautomated sampler provides access for mid-stream sampling, (3) tributaries enter upstream of thesampler and the Stephenville Wastewater Treatment Plant effluent discharge is approximately ¼mile upstream, providing a reasonable worst-case scenario to evaluate transverse and verticalmixing of inflows with the river waters, and (4) it is typical of other TIAER installations.

Nearly simultaneous sampling from the bridge and by the ISCO were conducted. In practice, thesamples could not always be collected at exactly the same time. When only one person went to thesite, the samples were taken as close to simultaneously as physically possible. The samples werereturned to the lab and submitted for routine analyses for total suspended solids (TSS), totalKjeldahl nitrogen (TKN), ammonia (NH3), nitrate (NO3), nitrite (NO2), total phosphorus (TP),orthophosphate (OPO4) and chemical oxygen demand (COD).

To investigate any bias in the water quality of the ISCO samples as compared to the mid-stream,near-surface samples, the simultaneous pairs of water quality data were plotted. For each waterquality constituent, a separate plot was created with the ISCO sample along the abscissa (x-axis)and the mid-stream sample along the ordinate (y-axis). A least-squares fit was forced through theorigin. The slope of the least-squares equation provides a measure of any bias in the sampling. Aslope less than one indicates that the ISCO samples are higher in concentration than the mid-streamsamples, and conversely a slope greater than one indicates a higher concentration for the mid-stream samples as compared to the ISCO samples. A slope of one indicates no bias. Theregression plots with data for TSS, TKN, NH3, NO3, NO2, TP, OPO4 and COD are found onFigures A-1 through A-8, respectively. Only TSS with a slope of 0.75 showed a large bias. TPhad a moderate bias with a slope of 0.90, as did COD with a slope of 0.92. While no exactperformance guidelines exist, a slope in the range of 0.90 to 1.10 was considered to indicate noappreciable bias in the ISCO samples as compared to mid-stream, near-surface samples. Only theTSS slope was outside the acceptance range. However, TP and COD are sufficiently close to thenon-acceptance range, that further evaluation is warranted. The scatter in the plotted data for TP,TKN and, to a lesser extent, COD possibly indicate high variability in these constituents'concentrations within the water column.

At the time of this report, this sampling program continues in order to provide data at higher waterlevels. If the new data do not alter the present results, the indication is that ISCO samplers providerepresentative sampling of those constituents found in the dissolved form. Those associated with

89

suspended sediments, i.e., TP, TKN and COD, show some bias, although the bias is most likely inthe acceptable range. Only TSS seems to be highly biased by the near-bottom sampling from theISCO samplers. Positioning of sampler intake further from the streambed may remove some ofthis bias. Comparison of the ISCO samples to integrated or depth-composite samples, rather thannear-surface samples, would be necessary to quantify the actual bias in ISCO samples for TSS andconstituents related to TSS.

Table A-1 Nutrient degradation study; August 27-28, 1993

Two-Tailed Student-TResults (alpha = .05)




TimeFromStart(hrs)

MeanNitrateas N

(mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

MeanNitriteas N

(mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

Mean Ortho-Phosphate as

P (mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

MeanAmmonia as

N (mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means0 0.16 0.31 0.26 0.302 0.16 NS NS 0.32 NS NS 0.25 NS NS 0.25 NS NS

5 0.16 NS NS 0.35 NS NS 0.25 NS NS 0.27 NS NS


11 0.15 NS NS 0.35 S NS 0.24 NS NS 0.35 NS NS

21 0.15 NS NS 0.32 NS NS 0.22 S NS 0.27 NS NS


30 0.15 NS NS 0.36 S NS 0.24 NS NS 0.33 NS NS

NS = Not Significant at alpha=0.05S = Significant at alpha=0.05

Table A-2 Nutrient degradation study; December 15-16,1993





TimeFromStart(hrs)

MeanNitrateas N

(mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

MeanNitriteas N

(mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

Mean Ortho-Phosphate as

P (mg/L)

With 0%Deviation of

Means

With 5%Deviation of

Means

MeanAmmonia as

N (mg/L)

With 0%Deviation of

Means

With 5%Deviation of








NS = Not Significant at alpha=0.05S = Significant at alpha=0.05

90

Table A-3 Nutrient degradation study; August 9-22, 1994

Two-Tailed Student-T Results

(alpha = .05)

Two-TailedStudent-T Results

(alpha = .05)


(alpha = .05)


(alpha = .05)

Time From Start (hrs)

Mean Nitrate as N (mg/L)

With 0% Deviation of

Means


MeansMean Nitrite as N (mg/L)


Means


Means

Mean Ortho-Phosphate as P

(mg/L)


Means


MeansMean Ammonia

as N (mg/L)


Means
















72 0.49 S NS 0.25 NS NS 0.29 NS NS 0.45 NS NS



192 * * 0.38 S NS 0.48 NS NS

216 0.57 S S 0.28 NS NS 0.32 NS NS 0.45 NS NS

312 1.68 S S 0.68 S S 0.30 NS NS *

* = Missing Data PointNS = Not Significant at alpha=0.05S = Significant at alpha=0.05

Figure A-1 Comparison of water sampling procedures at BO040 for TSS.

0

200

400

600

800

1000

1200

1400

1600

1800

0 200 400 600 800 1000 1200 1400 1600 1800

Near-Bottom (ISCO) Sample (mg/L)

Nea

r-S

urfa

ce S

ampl

e (m

g/L)

TSS 1:1 relationship regression

y=0.746x

91

Figure A-2 Comparison of water sampling procedures at BO040 for TKN.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 1 2 3 4 5


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

TKN 1:1 relationship regression

y=1.024x

Figure A-3 Comparison of water sampling procedures at BO040 for NH3--N.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 1 2 3 4 5


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

NH3-N 1:1 relationship regression

y=1.000x

92

Figure A-4 Comparison of water sampling procedures at BO040 for NO3-N.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 1 2 3 4 5


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

NO3-N 1:1 relationship regression

y=0.994x

Figure A-5 Comparison of water sampling procedures at BO040 for NO2-N.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

NO2-N 1:1 relationship regression

y=1.050x

93

Figure A-6 Comparison of water sampling procedures at BO040 for Total-P.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.5 1 1.5 2


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

Total-P 1:1 relationship regression

y=0.896x

Figure A-7 Comparison of water sampling procedures at BO040 for OPO4-P.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.5 1 1.5 2


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

O-PO4-P 1:1 relationship regression

y=1.063x

94

Figure A-8 Comparison of water sampling procedures at BO040 for COD.

0

20

40

60

80

100

120

0 20 40 60 80 100 120


Nea

r-S

urfa

ce S

ampl

e (m

g/L)

COD 1:1 relationship regression

y=0.922x

95

APPENDIX B

Monthly reservoir water quality means and standarddeviations.

Table B-1 Mean water temperatures (ºC) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.

Site n Mean + Std Season n Mean + Std

AL030 25 19.0 + 7.5 Spring 47 18.0 + 4.4 B

GC020 31 17.8 + 7.0 Summer 34 26.5 + 2.1 C

NF030 36 18.1 + 6.7 Fall 38 18.9 + 5.7 B

SF030 36 17.8 + 6.9 Winter 40 10.1 + 2.1 A

SF060 31 17.8 + 6.7

Table B-2 Mean dissolved oxygen levels (mg/L) by site and by season for monthly samples collected at reservoirsites between March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 25 6.7 + 3.5 A Spring 47 8.5 + 2.4 C

GC020 30 8.0 + 2.5 B Summer 34 5.7 + 2.4 A

NF030 34 8.8 + 2.5 B Fall 38 7.5 + 2.0 B

SF030 34 8.2 + 2.4 B Winter 33 10.3 + 1.6 D

SF060 29 8.0 + 2.3 B

Table B-3 Mean DO%sat levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 25 68.5 + 28.9 A Spring 47 89.7 + 23.7 B

GC020 30 82.7 + 21.7 B Summer 34 71.4 + 29.1 A

NF030 34 92.8 + 20.7 B Fall 38 79.9 + 19.5 A

SF030 34 85.2 + 20.7 B Winter 33 91.3 + 13.4 B

SF060 29 84.4 + 20.8 B

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Table B-4 Median BOD5 levels (mg/L) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different median values at α=0.05.

Site n Median Season n Median

AL030 77 5.3 B Spring 147 3.4 A

GC020 97 1.4 A Summer 106 5.9 B

NF030 109 7.1 C Fall 120 4.8 A

SF030 113 1.4 A Winter 119 3.8 A

SF060 96 7.4 C

Table B-5 Mean COD levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 10 38.0 + 7.9 B Spring 10 29.5 + 13.7 A

GC020 10 22.6 + 14.2 A Summer 15 45.1 + 18.6 B

NF030 11 44.7 + 14.6 B Fall 12 43.9 + 19.3 B

SF030 11 24.8 + 17.6 A Winter 15 24.4 + 11.3 A

SF060 10 49.4 + 19.6 B

Table B-6 Geometric mean conductivity levels (µmhos/cm) by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

Site n Geometric Mean (*) Season n Geometric Mean (*)

AL030 25 658 (418-1037) B Spring 47 744 (446-1241) C

GC020 31 391 (259-590) A Summer 34 558 (328-949) B

NF030 36 887 (600-1312) C Fall 38 456 (274-757) A

SF030 36 373 (286-486) A Winter 40 522 (335-812) AB

SF060 31 719 (459-1127) B

* Numbers in parenthesis represent the geometric mean minus and plus one standard deviation.

Table B-7 Mean pH levels (standard units) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 25 8.0 + 0.5 A Spring 47 8.3 + 0.4 AB

GC020 31 8.1 + 0.5 A Summer 34 8.5 + 0.7 B

NF030 36 8.6 + 0.4 C Fall 38 8.3 + 0.6 AB

SF030 36 8.2 + 0.6 AB Winter 40 8.1 + 0.4 A

SF060 31 8.4 + 0.5 BC

97

Table B-8 Mean TOC levels (mg/L) by site and by season for monthly samples collected at reservoir sites betweenMarch 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 6 12.4 + 1.2 B Spring 5 11.4 + 3.0

GC020 6 8.2 + 1.3 A Summer 0 —

NF030 7 11.9 + 2.0 B Fall 12 11.4 + 3.2

SF030 7 9.2 + 2.1 A Winter 15 10.9 + 2.6

SF060 6 14.2 + 2.1 B

Table B-9 Geometric mean NH3-N levels (mg/L) by site and by season for monthly samples collected at reservoirsites between March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.

Site n Geometric Mean (*) Season n Geometric Mean

AL030 25 0.09 (0.03-0.28) Spring 47 0.05 (0.01-0.19) A

GC020 31 0.07 (0.02-0.22) Summer 34 0.05 (0.01-0.18) A

NF030 36 0.06 (0.01-0.26) Fall 38 0.06 (0.02-0.20) A

SF030 36 0.04 (0.01-0.12) Winter 40 0.11 (0.03-0.38) B

SF060 31 0.07 (0.02-0.36)


Table B-10 Geometric mean NO2-N levels (mg/L) by site and by season for monthly samples collected at reservoirsites between March 1991 and March 1994. Analysis of variance indicated no significant differences between sitesor seasons at α=0.05.


AL030 15 0.01 (0.005-0.04) Spring 20 0.02 (0.008-0.04)

GC020 15 0.01 (0.006-0.03) Summer 15 0.01 (0.004-0.02)

NF030 15 0.01 (0.005-0.03) Fall 15 0.01 (0.004-0.03)

SF030 15 0.01 (0.005-0.02) Winter 25 0.01 (0.005-0.02)

SF060 15 0.01 (0.004-0.03)


Table B-11 Geometric mean NO3-N levels (mg/L) by site and by season for monthly samples collected at reservoirsites between March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 25 0.03 (0.01-0.13) Spring 47 0.04 (0.01-0.18) B

GC020 31 0.05 (0.01-0.25) Summer 34 0.01 (0.01-0.03) A

NF030 36 0.04 (0.01-0.16) Fall 38 0.02 (0.01-0.07) A

SF030 36 0.02 (0.01-0.06) Winter 40 0.07 (0.02-0.34) B

SF060 31 0.03 (0.01-0.16)


98

Table B-12 Geometric mean inorganic-N levels (mg/L) by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Analysis of variance indicated no significant differencesbetween sites or seasons at α=0.05.


AL030 15 0.17 (0.05-0.59) Spring 20 0.18 (0.06-0.35)

GC020 15 0.24 (0.11-0.56) Summer 15 0.12 (0.04-0.36)

NF030 15 0.19 (0.06-0.58) Fall 15 0.14 (0.06-0.34)

SF030 15 0.10 (0.04-0.25) Winter 25 0.23 (0.06-0.84)

SF060 15 0.18 (0.04-0.80)


Table B-13 Geometric mean OPO4-P by site by season and by season by site for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

Season Site n Geometric Mean (*) Site Season n Geometric Mean (*)

Spring AL030 7 0.20 (0.12-0.31) BC AL030 Spring 7 0.20 (0.12-0.31)

GC020 10 0.02 (0.01-0.03) A Summer 6 0.14 (0.06-0.31)

NF030 10 0.13 (0.04-0.41) B Fall 6 0.13 (0.04-0.41)

SF030 10 0.01 (0.01-0.02) A Winter 6 0.12 (0.05-0.28)

SF060 10 0.37 (0.17-0.80) C

GC020 Spring 10 0.02 (0.01-0.03)

Summer AL030 6 0.14 (0.06-0.35) B Summer 6 0.03 (0.01-0.06)

GC020 6 0.03 (0.01-0.06) A Fall 7 0.01 (0.00-0.02)

NF030 8 0.48 (0.27-0.85) C Winter 8 0.02 (0.01-0.04)

SF030 8 0.01 (0.01-0.03) A

SF060 6 0.68 (0.57-0.81) C NF030 Spring 10 0.13 (0.04-0.41) A

Summer 8 0.48 (0.27-0.85) B

Fall AL030 6 0.13 (0.04-0.41) B Fall 9 0.47 (0.33-0.67) B

GC020 7 0.01 (0.00-0.02) A Winter 9 0.16 (0.05-0.49) A

NF030 9 0.47 (0.33-0.67) C

SF030 9 0.01 (0.01-0.03) A SF030 Spring 10 0.01 (0.01-0.02)

SF060 7 0.56 (0.23-1.36) C Summer 8 0.01 (0.01-0.03)

Fall 9 0.01 (0.01-0.03)

Winter AL030 6 0.12 (0.05-0.28) B Winter 9 0.01 (0.01-0.03)

GC020 8 0.02 (0.01-0.04) A

NF030 9 0.16 (0.05-0.49) BC SF060 Spring 10 0.37 (0.17-0.80)

SF030 9 0.01 (0.01-0.03) A Summer 6 0.68 (0.57-0.81)

SF060 8 0.33 (0.20-0.52) C Fall 7 0.56 (0.23-1.36)

Winter 8 0.33 (0.20-0.52)


99

Table B-14 Geometric mean ratios of inorganic-N:OPO4-P by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.


AL030 15 1.2 (0.4-3.4) B Spring 20 2.7 (0.2-9.9) BC

GC020 15 13.2 (5.7-30.5) C Summer 15 0.9 (0.2-5.3) A

NF030 15 0.9 (0.2-3.8) B Fall 15 1.4 (0.2-9.9) AB

SF030 15 7.9 (3.8-16.4) C Winter 25 3.6 (0.7-18.7) C

SF060 15 0.4 (0.1-2.1) A


Table B-15 Geometric mean chlorophyll-α levels (µg/L) by site and by season for monthly samples collected atreservoir sites between March 1991 and March 1994. Different letters indicate significantly different mean valuesat α=0.05.

Site n Geometric Mean (*) Season n Geometric Mean (*)

AL030 17 57 (28-115) B Spring 24 28 (6-22) A

GC020 16 10 (4-26) A Summer 19 47 (10-214) B

NF030 25 105 (46-239) C Fall 23 60 (20-178) B

SF030 22 13 (5-38) A Winter 31 28 (10-82) A

SF060 17 68 (26-175) BC


Table B-16 Mean turbidity levels (NTU) by site and by season for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.


AL030 24 23.9 + 12.5 B Spring 40 18.9 + 15.8 A

GC020 30 21.2 + 20.9 AB Summer 27 22.5 + 14.4 A

NF030 18 41.2 + 21.0 C Fall 47 33.5 + 22.3 B

SF030 22 13.5 + 11.8 A Winter 28 23.7 + 16.2 A

SF060 30 25.2 + 13.4 B

100

Table B-17 Mean ZSD (feet) by site by season and by season by site for monthly samples collected at reservoir sitesbetween March 1991 and March 1994. Different letters indicate significantly different mean values at α=0.05.

Season Site n Mean + Std Season n Mean + Std

Spring AL030 7 1.6 + 0.7 A AL030 Spring 7 1.6 + 0.7

GC020 9 3.8 + 0.9 B Summer 6 1.5 + 0.5

NF030 10 1.1 + 0.3 A Fall 5 1.0 + 0.3

SF030 10 4.3 + 1.8 B Winter 6 2.0 + 0.7

SF060 9 1.4 + 0.6 A

GC020 Spring 9 3.8 + 0.9

Summer AL030 6 1.5 + 0.5 A Summer 5 3.7 + 2.0

GC020 5 3.7 + 2.0 B Fall 6 2.2 + 1.9

NF030 8 1.0 + 0.4 A Winter 6 2.2 + 1.2

SF030 8 3.7 + 2.3 B

SF060 6 1.1 + 0.3 A NF030 Spring 10 1.1 + 0.3

Summer 8 1.0 + 0.4

Fall AL030 5 1.0 + 0.3 AB Fall 7 1.0 + 0.3

GC020 6 2.2 + 1.9 BC Winter 8 1.5 + 0.7

NF030 7 1.0 + 0.3 A

SF030 8 2.3 + 1.1 C SF030 Spring 10 4.3 + 1.8 C

SF060 7 0.9 + 0.3 A Summer 8 3.7 + 2.3 BC

Fall 8 2.3 + 1.1 AB

Winter AL030 6 2.0 + 0.7 Winter 8 2.0 + 0.6 A

GC020 6 2.2 + 1.2

NF030 8 1.5 + 0.7 SF060 Spring 9 1.4 + 0.6

SF030 8 2.0 + 0.6 Summer 6 1.1 + 0.3

SF060 7 1.5 + 0.4 Fall 7 0.9 + 0.3

Winter 7 1.5 + 0.4

101

Table B-18 Comparison of mean or geometric mean of water quality variables at eight PL-566 reservoir sites formonthly grab samples collected between August 1993 and March 1994. Different letters in a given column indicatesignificantly different values between sites at α=0.05. Numbers in parenthesis represent the mean minus and plusone standard deviation. 'n' equals the number of observations at each site.

COD Conductivity * pH TOC(mg/L) (µmhos/cm) (standard units) (mg/L)

AL030 37 (28-46) BC 580 (410-821) C 8.20 (7.9-8.5) ABC 12 (11-13) CD

GC020 20 ( 9-31) A 438 (373-513) AB 7.80 (7.5-8.1) A 8 (7-9) AB

IC030 71 (59-83) D 770 (732-811) D 8.60 (8.2-9.0) C 19 (16-22) E

NF030 41 (28-53) C 645 (447-932) CD 8.50 (8.0-9.0) C 12 (10-14) C

SC030 25 (10-40) AB 577 (542-615) C 8.40 (8.3-8.5) C 10 (9-11) B

SF030 23 (12-34) A 387 (305-492) A 8.00 (7.7-8.3) AB 9 (7-11) AB

SF060 47 (24-70) C 536 (381-753) BC 8.30 (7.6-9.0) BC 14 (12-16) D

SP030 12 ( 9-15) A 424 (360-498) AB 8.40 (8.0-8.8) C 7 (6-8) A

n 8 8 8 5-7

NO3-N * OPO4-P * Chlorophyll-α * ZSD(mg/L) (mg/L) (µg/L) (feet)

AL030 0.03 (0.01-0.14) B 0.18 (0.09-0.33) D 67 (37-119) D 1.5 (0.8-2.2) ABC

GC020 0.09 (0.01-0.58) B 0.02 (0.01-0.04) B 11 (4- 28) A 2.4 (0.6-4.2) B

IC030 0.01 (0.00-0.02) A 0.06 (0.03-0.12) C 193 (131-285) E 1.0 (0.7-1.3) A

NF030 0.06 (0.01-0.27) B 0.41 (0.19-0.89) E 73 (27-195) D 1.4 (1.0-1.8) AB

SC030 0.01 (0.00-0.02) A 0.01 (0.01-0.02) AB 32 (17-61) BC 2.6 (1.5-3.7) C

SF030 0.03 (0.01-0.15) B 0.01 (0.01-0.03) AB 20 (7-57) AB 1.6 (1.0-2.2) ABC

SF060 0.05 (0.01-0.37) B 0.42 (0.26-0.68) E 58 (15-222) CD 1.2 (0.7-1.7) A

SP030 0.04 (0.01-0.11) B 0.01 (0.01-0.02) A 13 (7-24) A 4.2 (2.4-6.0) D

n 8 8 8 8

NH3-N * NO2-N * Water Temp. DO(mg/L) (mg/L) ( C) (mg/L)

AL030 0.09 (0.02-0.38) 0.01 (0.00-0.04) 16.79 (7.86-25.72) 7.04 (3.41-10.67)

GC020 0.10 (0.03-0.27) 0.02 (0.01-0.04) 16.03 (7.87-24.19) 7.31 (4.06-13.56)

IC030 0.09 (0.04-0.22) 0.01 (0.00-0.02) 16.06 (7.96-24.16) 8.05 (4.97-14.13)

NF030 0.14 (0.04-0.52) 0.02 (0.01-0.05) 16.16 (8.16-24.16) 8.67 (5.79-11.55)

SC030 0.05 (0.02-0.10) 0.01 (0.00-0.02) 16.52 (7.93-25.11) 8.96 (6.46-11.46)

SF030 0.09 (0.05-0.18) 0.01 (0.00-0.03) 15.82 (7.68-23.96) 7.44 (4.69-10.19)

SF060 0.25 (0.05-1.23) 0.02 (0.01-0.04) 16.26 (8.01-24.51) 7.78 (5.22-10.34)

SP030 0.05 (0.02-0.12) 0.01 (0.00-0.02) 16.89 (8.32-25.46) 8.75 (6.22-11.28)

n 8 8 8 7-8* Indicates geometric mean values.

103

APPENDIX C

Storm water quality by stream site for individual stormevents.

Table C-1 Volume-weighted stormwater constituent concentrations by storm event for site AL040.

Elapsed Volume NH3-N NO2-N NO3-N TKN OPO4-P Total-P TOC COD VSS TSSSite Begin hh:mm (cubic ft.) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

AL040 25FEB93:08:50 26:35 2807507 0.27 0.02 0.31 . 0.19 . . . . .

AL040 27FEB93:15:05 26:35 1934718 0.19 0.02 0.32 . 0.17 . . . . .

AL040 28MAR93:21:10 26:35 1231841 0.09 0.01 0.02 . 0.07 . . . . .

AL040 07APR93:15:15 26:35 2912484 0.05 0.01 0.02 . 0.12 . . . . .

AL040 14APR93:04:05 26:55 4051360 0.20 0.02 0.15 . 0.16 . . . . .

AL040 25APR93:15:00 26:05 814904 1.09 0.01 0.03 . 0.17 . . . . .

AL040 29APR93:06:20 26:35 1860961 0.11 0.01 0.06 . 0.17 . . . . .

AL040 12MAY93:11:00 34:15 238677 0.26 0.10 0.27 1.77 0.20 0.31 . 34 5 24

AL040 22JUN93:21:50 26:35 2693977 0.10 0.04 0.18 . 0.26 . . . . .

AL040 01JUL93:08:55 26:35 283285 0.04 0.03 0.09 . 0.28 . . . . .

AL040 25SEP93:12:45 3:05 3280 6.38 0.01 0.75 1.70 0.21 0.51 12 43 270 2270

AL040 18OCT93:01:00 110:00 12307284 0.06 0.06 0.19 1.58 0.35 0.45 12 39 9 56

AL040 16NOV93:06:45 68:20 149699 0.12 0.01 0.05 0.97 0.20 0.42 23 57 2 5

AL040 03DEC93:06:40 101:00 561513 0.10 0.01 0.04 0.87 0.13 0.37 16 43 13 47

AL040 13DEC93:04:45 27:45 156019 1.66 0.01 0.09 0.25 0.10 0.32 15 42 14 37

AL040 22JAN94:07:45 75:05 2027059 0.23 0.01 0.07 1.53 0.14 0.27 13 35 7 31

AL040 21FEB94:17:50 68:25 1530375 0.42 0.02 0.15 4.68 0.24 0.46 17 58 25 153

104

Table C-2 Volume-weighted stormwater constituent concentrations by storm event for site BO040.


BO040 13SEP93:04:50 80:05 38895187 0.79 0.08 1.51 2.30 0.54 1.35 11 65 105 1002

BO040 03OCT93:10:25 32:05 4822344 0.77 0.06 2.03 2.06 0.62 0.76 11 53 17 113

BO040 12OCT93:22:40 62:05 53700889 0.22 0.04 0.45 3.81 0.41 1.23 11 72 70 773

BO040 19OCT93:11:00 68:05 94040536 1.36 0.03 0.36 2.31 0.51 0.82 12 47 38 495

BO040 14NOV93:01:50 6:05 916038 0.31 0.05 0.28 1.44 0.37 0.55 14 41 5 13

BO040 16NOV93:06:25 10:05 1360646 0.64 0.06 0.58 1.61 0.36 0.52 14 42 4 13

BO040 02DEC93:22:05 26:05 2992967 0.36 0.09 1.54 1.39 0.59 0.71 12 32 7 41

BO040 12DEC93:19:15 17:10 1209142 0.10 0.04 3.11 1.25 0.51 0.68 12 32 6 27

BO040 21DEC93:22:30 21:25 1772968 0.05 0.40 3.83 1.25 0.58 0.72 11 24 1 15

BO040 21JAN94:08:15 98:30 15885667 0.26 0.10 2.40 2.03 0.44 0.69 12 34 10 54

BO040 21FEB94:21:45 85:25 15429930 0.55 0.14 2.26 4.00 0.32 0.44 15 45 12 60

BO040 21MAR94:10:55 14:45 1375516 0.16 0.13 1.71 2.22 0.47 0.71 15 45 11 26

Table C-3 Volume-weighted stormwater constituent concentrations by storm event for site BO070.


BO070 11JUN93:17:10 39:05 4871194 0.04 0.01 0.29 1.20 0.09 0.22 . 28 4 21

BO070 13SEP93:16:15 70:20 62235566 0.33 0.06 0.86 4.01 0.26 1.04 10 80 172 1861

BO070 04OCT93:00:25 29:30 2830132 0.39 0.01 0.15 0.95 0.10 0.23 13 42 81 481

BO070 13OCT93:10:20 54:20 39465225 0.13 0.05 0.83 4.83 0.35 1.31 11 83 100 885

BO070 18OCT93:05:45 102:00 1.49E+08 0.03 0.05 0.32 3.30 0.39 0.74 11 44 65 610

BO070 21JAN94:21:45 86:35 18867931 0.09 0.05 1.44 1.71 0.30 0.47 11 31 14 100

BO070 20FEB94:00:10 131:00 63209662 0.37 0.04 0.78 2.01 0.23 0.29 11 26 13 81

BO070 26MAR94:22:50 55:45 7713366 0.14 0.01 0.01 0.76 0.08 0.23 9 26 34 150

105

Table C-4 Volume-weighted stormwater constituent concentrations by storm event for site DB040.


DB040 03AUG93:04:45 26:05 94399 0.02 0.01 0.12 0.25 0.05 4.45 . 11 3 6

DB040 13SEP93:05:30 80:05 6240563 0.21 0.05 0.66 1.76 0.61 1.02 11 60 121 1315

DB040 03OCT93:06:55 80:05 2004163 0.37 0.03 0.57 2.62 0.75 1.13 10 48 187 1612

DB040 12OCT93:23:45 38:05 2839008 0.24 0.04 0.82 1.07 0.47 0.60 11 23 13 85

DB040 17OCT93:21:10 86:05 11987643 0.09 0.01 0.17 1.86 0.49 0.48 12 42 34 288

DB040 16NOV93:12:10 14:05 144670 0.09 0.01 0.01 1.29 0.37 0.53 38 103 6 30

DB040 03DEC93:02:45 14:05 125358 0.04 0.02 0.06 1.06 0.28 0.48 17 47 7 20

DB040 21JAN94:16:35 50:05 846804 0.07 0.04 0.87 1.46 0.41 0.64 13 27 13 116

DB040 21FEB94:23:40 11:35 181393 0.08 0.03 0.55 1.84 0.19 0.41 11 41 19 134

Table C-5 Volume-weighted stormwater constituent concentrations by storm event for site GC100.


GC100 28MAR93:09:00 14:55 4659954 0.10 0.03 0.52 . 0.01 . . . . .

GC100 14APR93:07:40 26:35 25225712 0.11 0.04 0.54 . 0.13 . . . . .

GC100 29APR93:04:05 26:35 5184057 0.08 0.01 0.52 . 0.05 . . . . .

GC100 09MAY93:19:25 16:05 2143201 5.20 0.01 0.77 . 0.04 . . . . .

GC100 11JUN93:02:35 55:35 1276254 0.25 0.01 0.09 . 0.02 . . . . .

GC100 16JUN93:20:35 42:05 11053371 0.10 0.04 0.46 . 0.13 . . . . .

GC100 22JUN93:18:10 26:35 12300598 0.07 0.01 0.24 . 0.20 . . . . .

GC100 26JUN93:03:50 26:40 14932003 0.01 0.02 0.44 . 0.13 . . . . .

GC100 13SEP93:21:50 21:45 13975449 0.13 0.07 0.47 3.27 0.17 0.92 18 87 161 1399

GC100 18OCT93:01:00 63:10 33529562 0.11 0.02 0.22 2.91 0.32 0.56 14 60 60 1683

GC100 22JAN94:17:35 11:10 1710588 0.03 . . 0.53 0.02 0.14 8 20 10 51

GC100 22FEB94:04:10 33:05 6326425 0.11 0.02 0.35 1.67 0.06 0.25 11 26 17 122

GC100 27MAR94:02:30 33:45 3031982 0.12 0.01 0.01 0.49 0.03 0.09 8 17 8 33

106

Table C-6 Volume-weighted stormwater constituent concentrations by storm event for site IB040.


IB040 13SEP93:05:30 80:45 4730200 0.07 0.04 0.50 1.68 0.16 0.40 13 43 51 832

IB040 12OCT93:21:15 38:05 1199435 0.07 0.02 0.29 1.54 0.09 0.30 13 39 21 149

IB040 17OCT93:23:55 81:05 3737736 0.08 0.01 0.18 1.57 0.20 0.40 13 36 15 103

IB040 25OCT93:12:10 6:05 27754 0.11 0.01 0.19 0.31 0.07 0.18 9 43 4 16

IB040 02DEC93:21:00 75:30 755692 0.03 0.01 0.02 0.75 0.02 0.13 11 25 8 40

IB040 07DEC93:17:15 10:00 40026 0.14 0.01 0.02 0.25 0.02 0.06 9 22 3 5

IB040 21JAN94:17:15 86:05 2229371 0.03 0.01 0.28 0.95 0.09 0.15 10 25 8 49

IB040 21FEB94:16:40 38:05 828997 0.06 0.02 0.33 0.73 0.01 0.12 10 21 7 36

IB040 28FEB94:10:10 7:05 75015 0.04 0.01 0.18 1.01 0.01 0.04 7 11 4 9

IB040 20MAR94:12:00 10:05 140898 0.04 0.01 0.01 0.54 0.01 0.10 7 22 7 49

Table C-7 Volume-weighted stormwater constituent concentrations by storm event for site IC020.


IC020 18OCT93:01:55 80:05 577119 0.07 0.03 0.29 2.43 0.67 0.75 17 63 14 96

IC020 22JAN94:08:00 74:05 312520 0.05 0.03 0.32 1.30 0.30 0.43 14 34 5 24

IC020 21FEB94:22:05 43:20 534075 1.24 0.18 2.34 4.47 0.76 1.03 31 81 16 57

IC020 26MAR94:17:10 4:35 19656 0.08 0.01 0.02 0.88 0.09 0.20 11 28 6 21

Table C-8 Volume-weighted stormwater constituent concentrations by storm event for site MB040.


MB040 24AUG93:15:35 3:25 93861 0.16 0.04 0.43 3.69 0.13 0.75 17 129 121 1028

MB040 13SEP93:23:20 3:25 3699 0.06 0.01 0.29 1.20 0.17 0.55 5 21 64 606

MB040 12OCT93:21:20 4:10 147450 0.08 0.04 0.64 3.30 0.16 0.77 22 175 142 1131

MB040 17OCT93:17:30 44:35 35736 0.20 0.02 0.34 2.11 0.41 0.45 10 63 71 583

MB040 14NOV93:00:00 3:05 32864 0.36 0.06 1.34 8.00 0.09 1.00 35 309 178 1302

MB040 16NOV93:03:55 11:50 153742 0.20 0.03 0.39 1.39 0.47 0.62 47 139 22 101

MB040 02DEC93:20:00 18:55 93718 0.10 0.01 0.21 1.62 0.19 0.61 20 128 78 479

MB040 12DEC93:19:30 4:05 29517 0.06 0.04 0.74 2.00 0.17 0.62 30 115 42 212

MB040 21DEC93:20:50 8:20 52091 0.09 0.02 0.59 1.24 0.26 0.45 18 70 14 62

MB040 21JAN94:08:45 32:50 320600 0.07 0.03 0.35 1.46 0.17 0.39 10 54 36 249

MB040 21FEB94:21:35 5:05 399100 0.14 0.03 0.66 3.21 0.16 0.64 16 130 84 611

MB040 28FEB94:15:55 4:05 42406 0.48 0.08 1.15 3.12 0.12 0.46 25 103 103 767

MB040 08MAR94:14:20 3:35 14837 0.79 0.12 1.83 4.37 0.17 0.40 28 167 87 551

107

Table C-9 Volume-weighted stormwater constituent concentrations by storm event for site NF005.


NF005 22JAN94:04:55 38:05 675959 0.17 0.12 6.90 4.76 1.14 1.72 31 88 27 143

NF005 21FEB94:21:45 22:15 486810 3.00 0.20 9.61 9.36 1.03 2.32 40 123 35 200

NF005 28FEB94:15:10 12:55 16754 1.01 0.29 4.81 4.34 0.51 1.13 29 76 39 489



NF010 13DEC92:15:00 50:05 993315 0.25 . 0.47 1.97 0.39 0.75 . . 34 2243

NF010 19JAN93:16:20 27:25 37798 0.08 0.02 0.93 1.16 0.06 0.15 . . 14 85

NF010 11MAR93:18:50 9:55 17082 0.66 0.14 2.20 1.95 0.11 0.12 . . 21 131

NF010 19MAR93:12:45 25:05 116058 0.20 0.05 1.58 2.78 0.21 0.06 . . 27 203

NF010 28MAR93:05:25 26:05 83266 0.20 0.09 1.69 2.05 0.30 0.27 . . 25 155

NF010 03APR93:20:40 19:15 31520 0.15 0.11 2.80 0.76 0.10 0.45 . . 17 215

NF010 14APR93:05:40 8:15 9585 0.07 0.01 1.62 1.15 0.07 0.31 . . 15 84

NF010 29APR93:03:20 8:05 10968 0.09 0.01 0.19 0.43 0.13 0.28 . . 15 84

NF010 01MAY93:22:05 12:05 23373 0.20 0.12 1.01 1.53 0.13 0.33 . . 18 106

NF010 22MAY93:08:20 11:05 9548 0.31 0.03 0.11 1.23 0.18 0.42 . . 13 30

NF010 09JUN93:21:40 8:05 8925 0.41 0.04 0.39 6.66 0.26 1.02 . 109 119 508

NF010 22JUN93:17:20 14:05 40699 0.35 0.07 0.68 3.15 0.29 0.78 . 74 79 517

NF010 25JUN93:20:00 59:15 3357109 0.13 0.04 0.48 3.18 0.34 0.85 . 92 144 2517

NF010 24AUG93:15:40 12:05 133742 0.07 0.10 0.68 6.40 0.28 1.33 19 121 225 2466

NF010 13SEP93:05:10 38:05 751864 0.37 0.06 0.28 2.47 0.30 0.70 13 71 76 936

NF010 03OCT93:04:40 12:05 34181 0.87 0.03 0.31 5.68 0.28 0.99 17 130 205 1724

NF010 12OCT93:20:55 14:10 2344617 0.14 0.03 0.31 6.33 0.25 0.22 11 99 171 2539

NF010 19OCT93:04:35 49:55 8042671 0.02 0.02 0.23 2.65 0.77 0.79 10 42 103 1744

NF010 13NOV93:23:55 8:05 5501 0.13 0.01 0.01 0.81 0.08 0.55 17 52 14 52

NF010 16NOV93:03:55 12:05 2968 0.24 0.01 0.07 0.73 0.08 0.43 16 48 8 31

NF010 02DEC93:21:50 14:00 3209 0.03 0.01 0.02 0.25 0.07 0.24 9 36 7 35

NF010 22JAN94:06:10 44:05 460318 0.08 0.04 1.16 2.78 0.38 0.50 19 75 43 329

NF010 21FEB94:21:30 38:05 660696 0.94 0.06 1.43 6.77 0.28 0.75 29 108 50 380

NF010 28FEB94:16:15 4:05 518 0.73 0.01 0.22 0.97 0.09 0.23 11 12 11 68

NF010 26MAR94:14:35 12:50 15548 0.48 0.03 0.30 1.47 0.07 0.25 15 45 20 113

108



NF020 29APR93:01:50 14:05 35523 0.34 0.06 0.41 0.88 0.31 0.47 . . 45 411

NF020 01MAY93:22:00 14:05 48081 0.39 0.10 0.55 2.50 0.33 0.59 . . 41 346

NF020 09JUN93:21:35 14:05 11539 1.34 0.07 0.56 4.49 0.36 0.77 . 71 137 1582

NF020 22JUN93:17:45 26:05 22375 1.78 0.12 0.48 2.21 0.91 1.55 . 104 160 1155

NF020 25JUN93:19:55 5:10 9314117 0.66 0.07 0.30 22.00 0.50 2.17 . 1027 717 6958

NF020 24AUG93:16:30 6:45 271808 0.12 0.12 0.65 5.14 0.43 1.03 13 105 187 2066

NF020 13SEP93:05:15 36:30 1410859 0.97 0.07 1.05 7.63 0.68 1.64 11 149 327 4632

NF020 12OCT93:21:25 20:05 3969750 0.15 0.02 0.51 2.64 0.39 0.88 9 52 275 6190

NF020 19OCT93:04:25 38:15 10576554 0.08 0.03 0.81 4.20 1.17 1.77 17 77 100 1406

NF020 22JAN94:05:05 38:05 511596 1.62 0.10 4.35 4.30 1.17 1.21 28 62 49 358

NF020 21FEB94:21:35 32:05 296259 1.59 0.13 4.14 6.21 0.54 0.96 26 82 41 344

NF020 28FEB94:22:50 6:05 2985 1.63 0.63 6.92 3.28 0.46 0.49 21 46 5 34



NF035 19NOV92:05:10 113:00 1619850 0.06 . 0.28 1.59 0.50 0.68 . . 17 64

NF035 13DEC92:23:25 98:05 4678518 0.34 . 0.45 1.87 0.63 0.68 . . 9 68

NF035 28MAR93:09:25 50:05 220290 0.07 0.01 0.01 1.59 0.11 0.29 . . 40 55

NF035 04APR93:00:35 62:05 51360 0.05 0.01 0.03 1.69 0.13 0.33 . . 24 41

NF035 25JUN93:21:20 140:00 6901857 0.08 0.03 0.41 2.10 0.53 0.85 . 65 25 106

NF035 03OCT93:11:05 32:05 34496 0.26 0.01 0.01 1.57 0.65 0.77 14 55 24 41

NF035 12OCT93:20:55 50:05 4325936 0.15 0.03 0.21 2.51 0.62 0.89 13 43 15 80

NF035 19OCT93:09:00 74:05 14685992 0.21 0.05 0.23 2.14 0.84 1.00 11 46 24 257

NF035 02DEC93:20:45 32:05 75591 0.57 0.02 0.25 1.49 0.53 0.74 12 32 5 21

NF035 22JAN94:07:30 74:05 1189541 0.05 0.02 0.27 1.54 0.20 0.38 14 35 7 19

NF035 21FEB94:21:30 62:05 1006412 0.14 0.02 0.26 2.39 0.12 0.54 14 39 12 24

NF035 27MAR94:16:10 21:05 42444 0.03 0.01 0.01 1.95 0.15 0.43 15 49 19 38

109



NF050 15FEB93:07:05 26:35 3148696 0.03 0.21 0.89 . 0.06 . . . . .

NF050 25FEB93:20:35 3:05 593757 3.99 0.04 1.45 . 0.46 . . . . .

NF050 20MAR93:01:35 19:05 550748 0.12 0.06 2.35 . 0.10 . . . . .

NF050 28MAR93:06:25 26:35 3085020 0.28 0.06 1.13 . 0.27 . . . . .

NF050 03APR93:23:45 26:35 1700611 0.41 0.04 1.30 . 0.32 . . . . .

NF050 29APR93:02:15 26:35 2753899 1.16 0.14 2.45 . 0.59 . . . . .

NF050 11JUN93:14:15 42:05 189616 0.28 0.02 3.13 . 0.57 . . . . .

NF050 26JUN93:00:45 26:35 17920220 0.03 0.06 0.71 . 0.32 . . . . .

NF050 13SEP93:05:20 29:25 7744726 0.31 0.10 1.15 3.55 0.95 2.43 16 215 154 2256

NF050 03OCT93:09:40 3:05 660386 0.11 0.01 0.04 4.70 0.25 1.07 14 99 125 2131

NF050 12OCT93:19:35 26:40 7192724 0.09 0.03 0.34 5.20 0.44 1.03 13 92 127 1686

NF050 19OCT93:02:40 23:15 40764382 0.01 0.02 0.21 5.06 0.35 0.61 11 66 155 1758

NF050 22OCT93:13:25 3:05 880749 0.25 0.04 0.23 1.67 0.65 0.91 12 31 7 53

NF050 22JAN94:08:50 3:05 197684 0.03 0.03 0.69 5.00 0.47 0.49 10 59 200 1863

NF050 22FEB94:04:30 42:15 4215016 0.23 0.05 0.72 2.90 0.27 0.52 17 50 13 51

Table C-14 Volume-weighted stormwater constituent concentrations by storm event for site SF020.


SF020 14APR93:07:35 20:05 103089 0.11 0.01 0.01 0.45 0.04 0.12 . . 5 35

SF020 29APR93:06:00 103:00 365564 0.08 0.01 0.14 1.33 0.03 0.12 . . 25 166

SF020 09JUN93:21:25 68:05 8685 0.33 0.04 0.49 2.63 0.07 0.35 . 47 29 197

SF020 25JUN93:20:15 56:05 1807185 0.07 0.06 0.18 3.14 0.13 0.70 . 102 194 2186

SF020 03AUG93:07:20 14:05 158130 0.07 0.08 0.32 1.47 0.05 0.26 . 57 66 687

SF020 24AUG93:15:35 14:05 186230 0.03 0.04 0.27 1.89 0.09 0.36 13 52 61 478

SF020 13SEP93:05:00 48:30 807977 0.16 0.06 0.22 1.52 0.09 0.40 15 61 81 619

SF020 12OCT93:20:40 38:05 5706523 0.03 0.07 0.12 1.75 0.08 0.35 18 60 111 1615

SF020 17OCT93:18:45 56:20 4819859 0.01 0.01 0.04 1.94 0.12 0.17 13 43 42 599

SF020 21JAN94:22:45 80:05 723341 0.02 0.01 0.23 1.23 0.05 0.09 15 36 15 163

SF020 21FEB94:21:30 38:05 806619 0.04 0.02 0.36 1.95 0.04 0.49 17 78 41 657

SF020 28FEB94:18:20 16:05 45729 0.08 0.01 0.06 0.62 0.02 0.07 12 16 3 20

SF020 26MAR94:15:35 35:35 196760 0.04 0.01 0.03 1.19 0.01 0.11 14 36 15 121

110



SF035 01MAY93:23:40 38:05 275345 0.04 . 0.01 0.59 0.03 0.03 . . 1 2

SF035 12OCT93:22:15 62:05 5399023 0.05 0.02 0.12 1.19 0.02 0.22 11 35 18 131

SF035 19OCT93:11:55 74:05 7037078 0.03 0.03 0.07 1.03 0.07 0.19 11 38 7 46

SF035 22JAN94:11:15 92:35 835479 0.03 0.01 0.22 0.70 0.02 0.14 9 12 3 17

SF035 21FEB94:21:20 50:05 1074167 0.07 0.01 0.09 0.70 0.02 0.26 9 15 2 11

SF035 28FEB94:18:10 68:05 164668 0.11 0.01 0.12 0.66 0.01 0.09 10 10 2 8

SF035 26MAR94:17:40 61:35 243717 0.06 0.01 0.01 0.50 0.01 0.06 9 19 3 8



SF075 01MAR93:10:00 26:35 2829300 0.17 0.04 1.31 . 0.20 . . . . .

SF075 18MAR93:18:05 26:35 2489520 0.03 0.02 1.38 . 0.15 . . . . .

SF075 28MAR93:08:35 26:35 3035708 0.11 0.06 1.62 . 0.44 . . . . .

SF075 03APR93:21:30 26:35 3553273 0.13 0.04 0.76 . 0.27 . . . . .

SF075 14APR93:04:15 26:35 3389607 0.15 0.07 0.84 . 0.24 . . . . .

SF075 17MAY93:11:35 26:35 807581 0.08 0.08 2.19 . 0.18 . . . . .

SF075 22JUN93:20:45 26:35 4882086 0.23 0.02 0.57 . 0.41 . . . . .

SF075 25JUN93:22:20 26:35 41629103 0.01 0.04 0.40 . 0.32 . . . . .

SF075 02DEC93:18:55 15:40 464680 0.06 0.09 1.73 0.80 0.19 0.36 17 55 17 97

SF075 21JAN94:00:40 104:00 6789917 0.10 0.04 1.37 2.00 0.26 0.53 16 46 11 54

SF075 10MAR94:08:15 23:45 985917 0.18 0.03 1.03 2.76 0.13 0.30 15 48 11 46

SF075 19MAR94:17:55 57:20 2993176 0.04 0.03 0.59 1.83 0.04 0.28 13 37 17 61

Table C-17 Volume-weighted stormwater constituent concentrations by storm event for site SP020.


SP020 20OCT93:00:30 10:05 29172 0.05 0.02 0.18 0.25 0.02 0.05 6 19 2 9

SP020 22JAN94:09:05 26:05 226362 0.02 0.01 0.01 0.25 0.01 0.06 3 10 1 2

SP020 20FEB94:07:20 8:05 104333 0.09 0.01 0.03 0.25 0.01 0.08 3 10 3 5

SP020 21FEB94:22:25 24:40 507901 0.04 0.01 0.04 0.36 0.01 0.10 5 10 4 45

SP020 28FEB94:22:05 22:20 252980 0.06 0.01 0.03 0.39 0.01 0.06 5 12 1 4

111

Table C-18 Geometric mean stormwater quality concentrations for micro-watershed stream monitoring sites for samples collected between March 1992 andMarch 1994. Numbers in parenthesis represent the mean minus and plus the standard deviation and 'n' represents the number of storm samples analyzed foreach constituent at each site.

SiteNH3-Nmg/L n

NO2-Nmg/L n

NO3-Nmg/L n

TKNmg/L n

OPO4-Pmg/L n

NF005 0.80 (0.19-3.41) 3 0.19 (0.12-0.30) 3 6.83 (4.83-9.70) 3 5.78 (3.80-8.80) 3 0.84 (0.54-1.31) 3

NF020 0.57 (0.19-1.77) 12 0.09 (0.04-0.20) 12 0.96 (0.34-2.75) 12 4.03 (1.85-8.78) 12 0.54 (0.34-0.87) 12

NF010 0.19 (0.07-0.51) 25 0.03 (0.01-0.08) 24 0.41 (0.10-1.64) 25 1.88 (0.78-4.51) 25 0.18 (0.09-0.35) 25

NF035 0.12 (0.05-0.28) 12 0.02 (0.01-0.03) 10 0.10 (0.02-0.49) 12 1.84 (1.54-2.21) 12 0.33 (0.15-0.72) 12

SF020 0.06 (0.02-0.14) 13 0.02 (0.01-0.06) 13 0.12 (0.04-0.39) 13 1.45 (0.85-2.47) 13 0.05 (0.03-0.11) 13

SF035 0.05 (0.03-0.08) 7 0.01 (0.01-0.02) 6 0.06 (0.02-0.20) 7 0.74 (0.54-1.00) 7 0.02 (0.01-0.04) 7

IC020 0.14 (0.03-0.60) 4 0.04 (0.01-0.12) 4 0.26 (0.04-1.81) 4 1.88 (0.92-3.83) 4 0.34 (0.13-0.91) 4

SP020 0.05 (0.03-0.08) 3 0.01 (0.01-0.02) 5 0.04 (0.01-0.10) 5 0.29 (0.24-0.37) 5 0.01 (0.01-0.02) 5

DB040 0.10 (0.04-0.24) 9 0.02 (0.01-0.02) 9 0.23 (0.05-1.04) 9 1.27 (0.65-2.50) 9 0.33 (0.15-0.74) 9

IB040 0.06 (0.03-0.10) 10 0.01 (0.01-0.02) 10 0.11 (0.03-0.46) 10 0.80 (0.41-1.52) 10 0.04 (0.01-0.13) 10

MB040 0.15 (0.07-0.35) 13 0.03 (0.02-0.07) 13 0.57 (0.03-1.07) 13 2.40 (1.36-4.25) 13 0.18 (0.12-0.29) 13

SiteTotal-Pmg/L n

TOCmg/L n

CODmg/L n

TSSmg/L n

VSSmg/L n

NF005 1.65 (1.15-2.37) 3 33 (28-40) 3 94 (73-120) 3 241 (128-455) 3 33 (28-40) 3

NF020 1.01 (0.61-1.67) 12 16 (11-26) 7 101 (42-246) 10 962 (208-4456) 12 94 (26-347) 12

NF010 0.41 (0.19-0.86) 25 15 (11-20) 12 65 (35-120) 15 260 (58-1162) 25 34 (12-98) 25

NF035 0.59 (0.39-0.89) 12 13 (12-15) 7 44 (35-56) 8 51 (24-107) 12 16 (9-29) 12

SF020 0.22 (0.11-0.46) 13 14 (13-17) 8 49 (30-79) 11 295 (74-1176) 13 32 (10-103) 13

SF035 0.11 (0.05-0.25) 7 10 (9-11) 6 19 (11-33) 6 15 (4-56) 7 3 (1-9) 7

IC020 0.60 (0.25-1.45) 4 17 (11-27) 4 47 (28-78) 4 41 (20-84) 4 9 (5-16) 4

SP020 0.07 (0.05-0.09) 5 4 (3-6) 5 12 (9-16) 5 7 (2-22) 5 2 (1-4) 5

DB040 0.77 (0.37-1.62) 9 14 (9-22) 8 38 (20-71) 9 113 (18-718) 9 19 (5-75) 9

IB040 0.15 (0.07-0.32) 10 10 (8-13) 10 27 (18-41) 10 45 (10-193) 10 9 (4-21) 10

MB040 0.57 (0.43-0.76) 13 19 (11-34) 13 104 (53-202) 13 438 (173-1107) 13 65 (31-136) 13

112

Table C-19 Geometric mean concentrations for storm and baseflow samples collected at major tributary and main stem monitoring sites. Numbers inparenthesis represent the mean minus and plus the standard deviation and 'n' represents the number of observations used to calculate each mean.

SampleType

NH3-N(mg/L) n

NO2-N(mg/L) n

NO3-N(mg/L) n

TKN(mg/L) n

OPO4-P(mg/L)

Storm NF050 0.16 (0.03-0.73) 15 0.05 (0.02-0.10) 15 0.72 (0.23-2.28) 15 3.76 (2.47-5.72) 7 0.34 (0.17-0.68)SF075 0.08 (0.03-0.20) 12 0.04 (0.03-0.07) 12 1.02 (0.61-1.72) 12 1.69 (1.00-2.86) 4 0.20 (0.11-0.38)

AL040 0.21 (0.06-0.81) 17 0.02 (0.01-0.04) 17 0.10 (0.04-0.29) 17 1.28 (0.56-2.94) 8 0.17 (0.12-0.26)

GC100 0.11 (0.03-0.08) 13 0.02 (0.01-0.04) 12 0.27 (0.08-0.90) 12 1.33 (0.53-3.30) 5 0.07 (0.02-0.19)

BO040 0.33 (0.13-0.85) 12 0.08 (0.01-0.16) 12 1.24 (0.51-3.03) 12 2.00 (1.35-2.94) 12 0.47 (0.38-0.58)

BO070 0.13 (0.05-0.36) 8 0.03 (0.01-0.06) 8 0.32 (0.07-1.56) 8 1.93 (0.97-3.82) 8 0.19 (0.10-0.37)

Baseflow NF050 0.12 (0.02-0.66) 20 0.05 (0.02-0.14) 13 1.54 (0.25-9.60) 20 0.21 (0.10-0.46)

SF075 0.29 (0.03-2.80) 10 0.05 (0.03-0.11) 10 2.32 (1.37-3.92) 10 0.18 (0.07-0.48)

AL040 0.14 (0.05-0.41) 13 0.02 (0.01-0.07) 12 0.09 (0.06-0.33) 13 0.19 (0.09-0.38)

GC100 0.05 (0.02-0.16) 12 0.01 (0.01-0.05) 12 0.12 (0.02-0.74) 12 0.05 (0.01-0.17)

BO040 0.23 (0.05-1.12) 23 0.09 (0.02-0.41) 13 4.56 (2.47-8.46) 23 0.70 (0.35-1.40)

BO070 0.06 (0.02-0.18) 25 0.02 (0.01-0.03) 16 0.30 (0.06-1.41) 25 0.11 (0.04-0.217

SampleType

TOC(mg/L) n

COD(mg/L) n

TSS(mg/L) n

VSS(mg/L) n

Storm NF050 13 (11-16) 7 74 (40-136) 7 686 (117-4012) 7 68 (17-267) 7

SF075 15 (13-17) 4 46 (40-54) 4 62 (45-85) 4 14 (11-18) 4

AL040 15 (12-19) 7 43 (35-52) 8 58 (10-338) 8 13 (3-55) 8

GC100 11 (8-16) 5 34 (17-70) 5 217 (34-1377) 5 27 (7-95) 5

BO040 12 (11-14) 12 42 (31-58) 12 68 (14-329) 12 12 (3-42) 12

BO070 11 (10-12) 7 40 (25-65) 8 243 (55-1070) 8 35 (10-124) 8

Baseflow NF050 10 (10-11) 5 26 (17-40) 9

SF075 11 (9-13) 5 24 (13-41) 9

AL040 14 (12-15) 5 32 (29-36) 9

GC100 6 (5-7) 5 14 (8-25) 9

BO040 11 (8-15) 14 22 (13-39) 18

BO070 7 (5-10) 12 14 (8-23) 17

113

APPENDIX D

Multiple regression example for OPO4-P for reservoirand stream sites

Multiple regression analysis was conducted for OPO4-P using all land-use characteristics asindependent variables to represent the full model (Tables D-1 and D-4). Thirteen characteristicswere used for stream sites and fourteen characteristics were used for reservoir sites. Step-wiseselection was then used to determine the number of variables needed to define the "best fit" model(Tables D-2 and D-5). Because of multicollinearity or interdependence between independentvariables, a single variable model was determined to be most meaningful way to describe therelationships between land-use characteristics and water quality parameters. No "best fit" multipleregression model was indicated for stream sites based on the Cp statistic. For reservoir sites, afour parameter model using the percent orchard, range, water and soil group B was the "best fit"model based on the Cp statistic, but since the variables for the full model are interrelated, it isdifficult to attach a meaning to the individual parameters in a multiple regression model.

Indicators of multicollinearity include the Variance Inflation Factor and the Cp Statistic (Fruendand Littell, 1986). The Variance Inflation Factor is presented in the Analysis of Variance tables(Tables D-1 and D-4). The Cp Statistic is presented in Tables D-2 and D-5. Multicollinearity isindicated when the Variance Inflation Factor is greater than (>) 1/(1-R2) or when Cp values arehigh. The Cp Statistic can be used as a guide to determine the "best fit" model (Fruend and Littell,1986). For the "best fit" model, Cp should be approximately equal to the number of parameters (p)in the model plus one. Cp is a measure of the error variance in the subset model which includes anestimate of the bias in the subset model introduced by failing to include other variables from thefull model in the subset model. Thus, Cp will equal (p+1) when the full model is specified. WhenCp > (p+1) for subsets of the full model, there is an indication that the model may beunderspecified. When Cp < (p+1) for subsets of the full model, there is an indication that themodel may be overspecified. Multicollinearity can greatly increase the measure of error in the CpStatistic. Another indicator to determine the "best model" includes the increase in R2 betweenmodels or the partial R2 which indicates the loss in model R2 by dropping a variable from themodel (Fruend and Littell, 1986). A correlation matrix is presented for both reservoir and streamsites showing the interdependence of the various land-use characteristics (Tables D-3 and D-6).

Multiple regression models using the intensive land-use variables of percent waste applicationfields, percent forage, percent orchards and percent peanuts were also run to determine if theaddition of these variables could be used to significantly explain more of the variability in OPO4-Plevels than a one variable model. The Analysis of Variance for the full model with all fourvariables is presented in Table D-7 for reservoir sites and Table D-9 for stream sites. The multiplevariable models using intensive agriculture variables did improve the fit of the model based on theCp Statistic for both reservoir and stream sites, but the R2 increased only slightly in explaining thevariability in OPO4-P compared to a one parameter model using percent waste application fields(Tables D-8 and D-10). Multiple intensive land-use variables did not, therefore, warrant inclusionin the model.

114

Table D-1 Full model of land characteristics versus OPO4-P concentrations at reservoir sites. Output from SASregression analysis.Model: MODEL1Dependent Variable: OPO4 the mean, OPO4

Analysis of VarianceSource DF Sum of

SquaresMean Square F Value Prob>F

Model 7 69.98149 9.99736 73.5 0.0001

Error 24 3.26445 0.13602

C Total 31 73.24594

Root MSE 0.36881 R-square 0.9554

Dep Mean -3.02453 Adj R-sq 0.9424

C.V. -12.1939

NOTE: Model is not full rank. Least-squares solutions for the parameters are not unique. Some statistics will be misleading. A reported DF of 0or B means that the estimate is biased. The following parameters have been set to 0, since the variables are a linear combination of othervariables as shown.

PBARREN = +100.0000 * INTERCEP -1.0000 * PFORAGE -1.0000 * PWASTE -1.0000 * PPEANUTS -1.0000 * PORCHARD -1.0000 *PWOOD -1.0000 * PRANGE -1.0000 * PWATER

SOILB = -11328 * INTERCEP +114.4591 * PFORAGE +112.9820 * PWASTE +164.0370 * PPEANUTS +49.4920 * PORCHARD+112.9790 * PWOOD + 113.9968 * PRANGE +103.3311 * PWATER

SOILC = -10461 * INTERCEP +104.0457 * PFORAGE +108.1831 * PWASTE +107.0415 * PPEANUTS +180.4768 * PORCHARD+105.5538 * PWOOD + 104.8685 * PRANGE +109.8594 * PWATER

SOILD = +30702 * INTERCEP -306.7373 * PFORAGE -309.9103 * PWASTE -380.7946 * PPEANUTS -310.8047 * PORCHARD -306.2439 * PWOOD -307.5595 * PRANGE -291.2736 * PWATER

SLOPE = +11289 * INTERCEP -112.9207 * PFORAGE -113.7713 * PWASTE -140.3485 * PPEANUTS -100.3837 * PORCHARD -112.3716 * PWOOD -113.5344 * PRANGE -99.0146 * PWATER

Parameter Estimates

Variable DF ParameterEstimate

StandardError

T for HO:Parameter=0

Prob > |T| VarianceInflation

VariableLabel

INTERCEP B 1595.0266 946.0281 1.6860 0.1048 0 Intercept

PFORAGE B -15.8848 9.4617 -1.6790 0.1062 831047.84

PWASTE B -15.9454 9.5371 -1.6720 0.1075 1297199.51

PPEANUTS B -20.5503 11.9224 -1.7240 0.0976 21691.32

PORCHARD B -14.3192 8.3785 -1.7090 0.1003 1921.14

PWOOD B -15.7764 9.4186 -1.6750 0.1069 1245781.69

PRANGE B -16.1488 9.5194 -1.6960 0.1027 1530629.88

PWATER B -17.7063 8.3879 -2.1110 0.0454 1877.81

PBARREN 0 0 . . . .

SOILB 0 0 . . . .

SOILC 0 0 . . . .

SOILD 0 0 . . . .

SLOPE 0 0 . . . .

115

Table D-2 Regression models for dependent variable OPO4-P with land use characteristics above reservoir sites.SAS output from PROC RSQUARE selecting best four subsets.

N = 32 Regression Models for Dependent Variable: OPO4

Number inModel

R-square C(p) Variables in Model

1 0.6187 177.4 PRANGE1 0.5174 231.9 PWASTE1 0.3933 298.7 SOILC1 0.3824 304.6 PFORAGE

------------------------------2 0.8499 54.8 PRANGE PWATER2 0.7266 121.2 PRANGE SOILD2 0.7200 124.8 PRANGE PBARREN2 0.6906 140.6 PWASTE PRANGE

-------------------------------------3 0.9399 8.4 PORCHARD PRANGE PWATER3 0.9322 12.5 PPEANUTS PRANGE PWATER3 0.9167 20.9 PRANGE PWATER SOILD3 0.8749 43.4 PRANGE PWATER SOILB

----------------------------------------------4 0.9506 4.6 PORCHARD PRANGE PWATER SOILB4 0.9485 5.7 PORCHARD PRANGE PWATER SOILC4 0.9437 8.3 PWASTE PPEANUTS PRANGE PWATER4 0.9436 8.4 PORCHARD PRANGE PWATER SLOPE

-------------------------------------------------------5 0.9540 4.8 PWOOD PRANGE PWATER SOILB SOILC5 0.9536 5.0 PWOOD PRANGE PWATER SOILB SOILD5 0.9535 5.0 PORCHARD PRANGE PWATER SOILC SLOPE5 0.9531 5.2 PPEANUTS PWOOD PRANGE PWATER PBARREN

----------------------------------------------------------6 0.9554 6.0 PFORAGE PRANGE PWATER PBARREN SOILB SOILD6 0.9554 6.0 PWASTE PPEANUTS PBARREN SOILB SOILC SOILD6 0.9554 6.0 PWASTE PPEANUTS PWATER SOILB SOILD SLOPE6 0.9554 6.0 PFORAGE PPEANUTS PRANGE PWATER PBARREN SLOPE

------------------------------------------------------------------7 0.9554 8.0 PFORAGE PPEANUTS PORCHARD PBARREN SOILB SOILC SOILD7 0.9554 8.0 PFORAGE PWASTE PPEANUTS PORCHARD SOILB SOILC SOILD7 0.9554 8.0 PFORAGE PPEANUTS PORCHARD PBARREN SOILB SOILC SLOPE7 0.9554 8.0 PFORAGE PWASTE PORCHARD PWOOD PBARREN SOILB SOILD

NOTE: Models not of full rank are not included.

116

Table D-3 Cross correlation of independent variables representing land-use characteristics at reservoir sites.r = Pearson Correlation Coefficient and p = Prob >R under Ho = 0. Number of observations = 32.

PWOOD PRANGE PFORAGE PWASTE PPEANUTS PORCHARD PWATER PBARREN COWAC SOILB SOILC

PWOOD r 1 0.29 -0.54 -0.81 -0.37 -0.27 0.21 -0.63 -0.88 0.52 -0.35p 0 0.11 0.00 0.00 0.04 0.14 0.26 0.00 0.00 0.00 0.05

PRANGE r 0.29 1 -0.86 -0.66 -0.11 -0.02 -0.35 -0.03 -0.51 -0.19 -0.81p 0.11 0 0.00 0.00 0.56 0.91 0.05 0.87 0.00 0.30 0.00

PFORAGE r -0.54 -0.86 1 0.61 0.24 0.21 0.26 0.19 0.54 0.20 0.65p 0.00 0.00 0 0.00 0.19 0.26 0.15 0.29 0.00 0.27 0.00

PWASTE r -0.81 -0.66 0.61 1 0.15 -0.02 0.05 0.37 0.93 -0.47 0.65p 0.00 0.00 0.00 0 0.41 0.93 0.77 0.04 0.00 0.01 0.00

PPEANUTS r -0.37 -0.11 0.24 0.15 1 0.95 -0.70 0.92 0.37 -0.02 0.24p 0.04 0.56 0.19 0.41 0 0.00 0.00 0.00 0.04 0.91 0.19

PORCHARD r -0.27 -0.02 0.21 -0.02 0.95 1 -0.66 0.82 0.20 -0.03 0.24p 0.14 0.91 0.26 0.93 0.00 0 0.00 0.00 0.27 0.89 0.19

PWATER r 0.21 -0.35 0.26 0.05 -0.70 -0.66 1 -0.77 -0.28 0.27 0.25p 0.26 0.05 0.15 0.77 0.00 0.00 0 0.00 0.12 0.13 0.17

PBARREN r -0.63 -0.03 0.19 0.37 0.92 0.82 -0.77 1 0.62 -0.31 0.18p 0.00 0.87 0.29 0.04 0.00 0.00 0.00 0 0.00 0.09 0.34

COWAC r -0.88 -0.51 0.54 0.93 0.37 0.20 -0.28 0.62 1 -0.52 0.50p 0.00 0.00 0.00 0.00 0.04 0.27 0.12 0.00 0 0.00 0.00

SOILB r 0.52 -0.19 0.20 -0.47 -0.02 -0.03 0.27 -0.31 -0.52 1 -0.23p 0.00 0.30 0.27 0.01 0.91 0.89 0.13 0.09 0.00 0 0.22

SOILC r -0.35 -0.81 0.65 0.65 0.24 0.24 0.25 0.18 0.50 -0.23 1p 0.05 0.00 0.00 0.00 0.19 0.19 0.17 0.34 0.00 0.22 0

SOILD r 0.06 0.88 -0.75 -0.37 -0.23 -0.24 -0.38 -0.01 -0.19 -0.30 -0.86p 0.73 0.00 0.00 0.04 0.21 0.19 0.03 0.96 0.29 0.09 0.00

SLOPE r -0.22 0.15 -0.06 0.00 0.71 0.67 -0.54 0.70 0.23 -0.15 -0.02p 0.22 0.40 0.76 1.00 0.00 0.00 0.00 0.00 0.21 0.42 0.90

117

Table D-4 Full model of land characteristics versus OPO4-P concentrations at stream sites. Output from SASregression analysis.

Model: MODEL1Dependent Variable: OPO4

Analysis of Variance

Source DF Sum of Squares MeanSquare

F Value Prob>F

Model 13 0.78682 0.06052 . .

Error 0 0 .

C Total 13 0.78682

Root MSE . R-square 1

Dep Mean 0.30323 Adj R-sq .

C.V. .

NOTE: Model is not full rank. Least-squares solutions for the parameters are not unique. Some statistics will be misleading. A reported DF of 0or B means that the estimate is biased. The following parameters have been set to 0, since the variables are a linear combination of othervariables as shown.

RES = -5016 * INTERCEP + 28.9666 * PWOOD + 32.3144 * PRANGE + 29.4314 * PFORAGE + 32.4855 * PWASTE -39.6172 * PPEANUT+ 162.3629 * PORCHARD + 100.6383 * PWATER + 171.0676 * PBARREN -118.6292 * COWD + 28.4067 * SOILB + 17.0013 * SOILC+ 15.3863 * SOILD + 3237 * SLOPE

Parameter Estimates

Variable DF ParameterEstimate

StandardError

T for H0:Parameter=0

Prob > |T| VarianceInflation

INTERCEP B 3.9755 . . . 0.00PWOOD B -0.0469 . . . 899.19PRANGE B -0.0623 . . . 2156.38PFORAGE B -0.0254 . . . 1634.77PWASTE B -0.0258 . . . 3832.29PPEANUT B -0.0311 . . . 108.83PORCHARD B 0.2931 . . . 23.37PWATER B -0.0550 . . . 3.81PBARREN B -0.2712 . . . 313.55COWD B -0.6242 . . . 126.87SOILB B -0.0088 . . . 247.39SOILC B -0.0043 . . . 754.66SOILD B 0.0199 . . . 521.76SLOPE B 20.4479 . . . 23.66RES 0 0.0000 . . . .

118

D-5 Regression models for dependent variable OPO4-P with land characteristics above stream sites. SAS outputfrom PROC RSQUARE selecting best four subset.N = 14 Regression Models for Dependent Variable: OPO4Numberin Model R-square C(p) Variables in Model

1 0.8125 987.10 PWASTE1 0.6612 1792.00 PWOOD1 0.4218 3065.00 PRANGE1 0.2983 3722.30 SOILB

------ --------- --------- ------2 0.8594 739.70 PWOOD PWASTE2 0.8508 785.50 PFORAGE PWASTE2 0.8361 863.90 PBARREN PWASTE2 0.8290 901.20 PRANGE PWASTE

------ --------- --------- --------------3 0.9128 457.90 PFORAGE SOILD PWASTE3 0.8819 622.40 PBARREN PPEANUT PWASTE3 0.8730 669.20 PWOOD PBARREN PWASTE3 0.8695 688.10 PWOOD SOILB PWASTE

------ --------- --------- -------------------4 0.9525 248.40 PFORAGE SOILC SOILD PWASTE4 0.9503 260.10 PFORAGE SOILB SOILD PWASTE4 0.9457 284.60 PFORAGE SOILB SOILC PWASTE4 0.9263 387.80 PWOOD PRANGE SOILC SOILD

------ --------- --------- -------------------------5 0.9580 221.50 PWOOD PRANGE PBARREN SOILC SOILD5 0.9565 229.50 PWOOD PRANGE PBARREN SOILB SOILD5 0.9548 238.50 RES PFORAGE SOILC SOILD PWASTE5 0.9539 243.40 PWOOD PRANGE PPEANUT SOILC SOILD

------ --------- --------- ---------------------------------6 0.9632 195.70 PWOOD PRANGE PBARREN PORCHARD SOILC SOILD6 0.9596 215.10 PWOOD PRANGE PBARREN RES SOILC SOILD6 0.9593 216.50 PWOOD PRANGE PBARREN PORCHARD SOILB SOILD6 0.9592 216.80 PWOOD PRANGE PBARREN RES SOILB SOILD

------ --------- --------- -------------------------------------7 0.9678 173.10 PWOOD PRANGE PBARREN PFORAGE SOILB SOILD PWASTE7 0.9649 188.70 PWOOD PRANGE PBARREN PFORAGE SOILB SOILC PWASTE7 0.9648 189.40 PWOOD PRANGE PBARREN PFORAGE SOILC SOILD PWASTE7 0.9642 192.70 PWOOD PRANGE PBARREN PORCHARD SOILB SOILC SOILD

------ --------- --------- ------------------------------------------------8 0.9740 142.20 PWOOD PRANGE PBARREN PFORAGE PORCHARD SOILC SOIL PWASTE8 0.9733 146.10 PWOOD PRANGE PBARREN PFORAGE PORCHARD SOILB SOIL PWASTE8 0.9723 151.30 PWOOD PRANGE PBARREN PWATER PFORAGE SOILB SOILD PWASTE8 0.9692 167.80 PWOOD PRANGE PBARREN PWATER PFORAGE SOILC SOILD PWASTE

------ --------- --------- -------------------------------------------------9 0.9818 102.60 PWOOD PRANGE PBARREN PWATER RES PFORAGE SOILB SOILD PWASTE9 0.9781 122.30 PWOOD PRANGE PBARREN PWATER RES PFORAGE SOILB SOILC PWASTE9 0.9768 129.60 PWOOD PRANGE PBARREN PWATER PFORAGE PORCHARD SOILC SOILD PWASTE9 0.9762 132.70 PWOOD PRANGE PBARREN PFORAGE PPEANUT PORCHARD SOILB SOILD PWASTE

------ --------- --------- -------------------------------------------------10 0.9857 84.01 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT SOILB SOILD PWASTE10 0.9856 84.62 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT SOILB SOILC PWASTE10 0.9833 96.59 PWOOD PRANGE PBARREN PWATER RES PFORAGE PORCHARD SOILB SOILC PWASTE10 0.9820 103.60 PWOOD PRANGE PBARREN PWATER RES PFORAGE PORCHARD SOILB SOILD PWASTE

------ --------- --------- -------------------------------------------------11 0.9920 52.62 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT PORCHARD SOILB SOILC PWASTE11 0.9872 77.86 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT SOILB SOILC SOILD PWASTE11 0.9858 85.57 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT PORCHARD SOILB SOILD PWASTE11 0.9834 98.54 PWOOD PRANGE PBARREN PWATER RES PFORAGE PORCHARD SOILB SOILC SOILD PWASTE

------ --------- --------- -------------------------------------------------12 0.9998 13.00 PWOOD PRANGE PBARREN PWATER RES PFORAGE PPEANUT PORCHARD SOILB SOILC SOILD

PWASTE

119

Table D-6 Cross correlation of independent variables representing land-use characteristics at stream sites.r = Pearson Correlation Coefficient and p = Prob > |R| under Ho: Rho=0. Number of observations = 14.

PWOOD PRANGE PFORAGE PWASTE PPEANUT PORCHARD PWATER PBARREN COWD SOILB SOILC SOILD SLOPE RES

PWOOD r 1 0.60 -0.41 -0.74 -0.02 -0.02 0.12 -0.13 -0.71 0.39 -0.57 0.48 -0.18 0.20p 0 0.02 0.15 0.00 0.95 0.96 0.69 0.67 0.00 0.17 0.03 0.08 0.53 0.50

PRANGE r 0.60 1 -0.61 -0.61 -0.37 -0.24 -0.42 -0.45 -0.61 0.10 -0.62 0.69 0.13 -0.12p 0.02 0 0.02 0.02 0.19 0.42 0.14 0.11 0.02 0.73 0.02 0.01 0.66 0.69

PFORAGE r -0.41 -0.61 1 -0.09 0.27 0.25 0.19 0.29 -0.09 0.12 0.56 -0.75 -0.37 0.17p 0.15 0.02 0 0.77 0.35 0.38 0.50 0.31 0.77 0.67 0.04 0.00 0.20 0.55

PWASTE r -0.74 -0.61 -0.09 1 -0.11 -0.18 0.04 0.01 0.96 -0.50 0.45 -0.27 0.43 -0.21p 0.00 0.02 0.77 0 0.72 0.54 0.90 0.98 0.00 0.07 0.11 0.35 0.12 0.47

PPEANUT r -0.02 -0.37 0.27 -0.11 1 0.88 0.33 0.94 0.01 0.47 -0.23 0.04 -0.72 0.20p 0.95 0.19 0.35 0.72 0 0.00 0.25 0.00 0.97 0.09 0.43 0.89 0.00 0.49

PORCHARD r -0.02 -0.24 0.25 -0.18 0.88 1 0.17 0.82 -0.06 0.41 -0.16 -0.03 -0.69 0.31p 0.96 0.42 0.38 0.54 0.00 0 0.57 0.00 0.83 0.15 0.58 0.93 0.01 0.29

PWATER r 0.12 -0.42 0.19 0.04 0.33 0.17 1 0.29 0.05 0.25 0.11 -0.24 -0.18 0.74p 0.69 0.14 0.50 0.90 0.25 0.57 0 0.32 0.88 0.38 0.71 0.40 0.54 0.00

PBARREN r -0.13 -0.45 0.29 0.01 0.94 0.82 0.29 1 0.07 0.20 -0.09 0.02 -0.61 0.08p 0.67 0.11 0.31 0.98 0.00 0.00 0.32 0 0.81 0.50 0.75 0.94 0.02 0.80

COWD r -0.71 -0.61 -0.09 0.96 0.01 -0.06 0.05 0.07 1 -0.30 0.31 -0.20 0.30 -0.13p 0.00 0.02 0.77 0.00 0.97 0.83 0.88 0.81 0 0.29 0.29 0.50 0.29 0.65

SOILB r 0.39 0.10 0.12 -0.50 0.47 0.41 0.25 0.20 -0.30 1 -0.60 0.19 -0.74 0.44p 0.17 0.73 0.67 0.07 0.09 0.15 0.38 0.50 0.29 0 0.02 0.51 0.00 0.11

SOILC r -0.57 -0.62 0.56 0.45 -0.23 -0.16 0.11 -0.09 0.31 -0.60 1 -0.90 0.42 0.01p 0.03 0.02 0.04 0.11 0.43 0.58 0.71 0.75 0.29 0.02 0 0.00 0.13 0.98

SOILD r 0.48 0.69 -0.75 -0.27 0.04 -0.03 -0.24 0.02 -0.20 0.19 -0.90 1 -0.11 -0.23p 0.08 0.01 0.00 0.35 0.89 0.93 0.40 0.94 0.50 0.51 0.00 0 0.71 0.43

SLOPE r -0.18 0.13 -0.37 0.43 -0.72 -0.69 -0.18 -0.61 0.30 -0.74 0.42 -0.11 1 -0.22p 0.53 0.66 0.20 0.12 0.00 0.01 0.54 0.02 0.29 0.00 0.13 0.71 0 0.46

RES r 0.20 -0.12 0.17 -0.21 0.20 0.31 0.74 0.08 -0.13 0.44 0.01 -0.23 -0.22 1p 0.50 0.69 0.55 0.47 0.49 0.29 0.00 0.80 0.65 0.11 0.98 0.43 0.46 0

120

Table D-7 Model of intensive land-use characteristics with OPO4-P concentrations at reservoir sites. Output fromSAS regression analysis.Model: MODEL1Dependent Variable: OPO4 the mean, OPO4



Model 4 44.80777 11.20194 10.635 0.0001Error 27 28.43817 1.05327

C Total 31 73.24594Root MSE 1.02629 R-square 0.6117Dep Mean -3.02453 Adj R-sq 0.5542

C.V. -33.9322

Parameter EstimatesVariable DF Parameter

EstimateStandard

ErrorT for H0:

Parameter=0Prob > |T| Variance

InflationVariable

Label

INTERCEPT 1 -5.0065 0.528733 -9.469 0.0001 0 InterceptPFORAGE 1 0.0788 0.039973 1.970 0.0591 1.915525

PWASTE 1 0.0763 0.037454 2.037 0.0516 2.58356PPEANUTS 1 1.4516 0.950196 1.528 0.1382 17.79282

PORCHARD 1 -2.8884 2.264847 -1.275 0.2131 18.12855

Table D-8 Regression models for dependent variable OPO4-P with intensive land-use characteristics abovereservoir sites. SAS output from PROC RSQUARE selecting best four subsets.

N = 32 Regression Models for Dependent Variable: OPO4

Number in Model R-square C(p) Variables in Model

1 0.5174 5.6 PWASTE1 0.3824 14.9 PFORAGE1 0.0834 35.7 PPEANUTS1 0.0218 40.0 PORCHARD

---------------------------------------------2 0.5679 4.0 PFORAGE PWASTE2 0.5508 5.2 PWASTE PPEANUTS2 0.5426 5.8 PWASTE PORCHARD2 0.4034 15.5 PFORAGE PPEANUTS

------------------------------------------------------3 0.5884 4.6 PFORAGE PWASTE PPEANUTS3 0.5782 5.3 PFORAGE PWASTE PORCHARD3 0.5559 6.9 PWASTE PPEANUTS PORCHARD3 0.5521 7.1 PFORAGE PPEANUTS PORCHARD

--------------------------------------------------------------4 0.6117 5.0 PFORAGE PWASTE PPEANUTS PORCHARD

121

Table D-9 Model of intensive land-use characteristics with OPO4-P concentrations at stream sites. Output fromSAS regression analysis.Model: MODEL1Dependent Variable: OPO4



Model 7 0.68963 0.0985 6.082 0.0214Error 6 0.09719 0.0162C Total 13 0.78682

Root MSE 0.12728 R-square 0.8765Dep Mean 0.30323 Adj R-sq 0.7324C.V. 41.97366

Parameter EstimatesVariable DF Parameter

EstimateStandard

ErrorT for H0:

Parameter=0Prob > |T| Variance

InflationINTERCEP 1 -0.481 0.770 -0.625 0.555 0.00PRANGE 1 0.008 0.012 0.656 0.536 14.90PWATER 1 0.080 0.241 0.330 0.752 7.07RES 1 -0.001 0.002 -0.554 0.599 5.48PFORAGE 1 0.009 0.008 1.176 0.284 6.00PPEANUT 1 0.015 0.058 0.266 0.799 10.01PORCHARD 1 -0.002 0.278 -0.005 0.996 9.57PWASTE 1 0.019 0.007 2.901 0.027 7.32

D-10 Regression models for dependent variable OPO4-P with intensive land-use characteristics above stream sites.SAS output from PROC RSQUARE selecting best four subsets.N = 14 Regression Models for Dependent Variable: OPO4Numbe

r inModel

R-square C(p) Variables in Model

1 0.8125 1.52 PWASTE1 0.0140 50.60 PFORAGE1 0.0137 50.61 PORCHARD1 0.0004 51.43 PPEANUT

------ --------------- -------------2 0.8508 1.17 PFORAGE PWASTE2 0.8182 3.17 PPEANUT PWASTE2 0.8146 3.39 PORCHARD PWASTE2 0.0425 50.84 PPEANUT PORCHARD

------ --------------- -------------3 0.8514 3.13 PFORAGE PPEANUT PWASTE3 0.8508 3.17 PFORAGE PORCHARD PWASTE3 0.8200 5.06 PPEANUT PORCHARD PWASTE3 0.0609 51.71 PFORAGE PPEANUT PORCHARD

------ --------------- -------------4 0.8535 5.00 PFORAGE PPEANUT PORCHARD PWASTE

123

APPENDIX E

Regression relationships of land-use characteristicswith reservoir water quality.

Figure E-1 Relationship of OPO4-P to percent rangeland in the drainage basin above each reservoir site(R2=0.62). Reservoir values represent seasonal geometric means for OPO4-P for samples taken between March1991 and March 1994.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

30 35 40 45 50 55 60

Rangeland (%)

OPO

4-P

(mg/

L)

y=exp(3.72-0.14x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

124

Figure E-2 Relationship of OPO4-P to percent waste application fields in the drainage basin above each reservoirsite (R2=0.52). Reservoir values represent seasonal geometric means for OPO4-P for samples taken between March1991 and March 1994.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 5 10 15 20 25


OPO

4-P

(mg/

L)

y=exp(-4.21+0.14x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-3 Relationship of OPO4-P to dairy cow density in the drainage basin above each reservoir site (R2=0.55).Reservoir values represent seasonal geometric means for OPO4-P for samples taken between March 1991 andMarch 1994.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Dairy Cow Density (cows/ac)

OPO

4-P

(mg/

L)

y=exp(-4.32+8.74x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

125

Figure E-4 Relationship of TOC to dairy cow density in the drainage basin above each reservoir site (R2=0.61).Reservoir values represent seasonal arithmetic means for TOC for samples taken between March 1991 and March1994.

0

5

10

15

20

25

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35


TOC

(mg/

L)

y=8.13+21.0x

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-5 Relationship of COD to dairy cow density in the drainage basin above each reservoir site (R2=0.51).Reservoir values represent seasonal arithmetic means for COD for samples taken between March 1991 and March1994.

0

10

20

30

40

50

60

70

80

0 0.1 0.2 0.3 0.4


CO

D (m

g/L)

y=18.78+109.12x

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

126

Figure E-6 Relationship of chlorophyll-α to percent waste application fields in the drainage basin above eachreservoir site (R2=0.63). Reservoir values represent seasonal geometric means for chlorophyll-α for samples takenbetween March 1991 and March 1994.

0

50

100

150

200

250

0 5 10 15 20 25


Chl

orop

hyll-

a (u

g/L)

y=exp(2.67+.11x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-7 Relationship of chlorophyll-α to dairy cow density in the drainage basin above each reservoir site(R2=0.76). Reservoir values represent seasonal geometric means for chlorophyll-α for samples taken betweenMarch 1991 and March 1994.

0

50

100

150

200

250

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Dairy Cow Denstiy (cows/ac)

Chl

orop

hyll-

a (u

g/L)

y=exp(2.51+7.43x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

127

Figure E-8 Relationship of conductivity to percent woodland in the drainage basin above each reservoir site(R2=0.51). Reservoir values represent seasonal geometric means for conductivity for samples taken between March1991 and March 1994.

0

200

400

600

800

1000

1200

15 20 25 30 35 40

Woodland (%)

Con

duct

ivity

(um

hos/

cm)

y=exp(7.18-0.03x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-9 Relationship of conductivity to percent waste application fields in the drainage basin above eachreservoir site (R2=0.62). Reservoir values represent seasonal geometric means for conductivity for samples takenbetween March 1991 and March 1994.

0

200

400

600

800

1000

1200

0 5 10 15 20 25


Con

duct

ivity

(um

hos/

cm)

y=exp(6.04+0.03x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

128

Figure E-10 Relationship of conductivity to dairy cow density in the drainage basin above each reservoir site(R2=0.72). Reservoir values represent seasonal geometric means for conductivity for samples taken between March1991 and March 1994.

0

200

400

600

800

1000

1200

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35


Con

duct

ivity

(um

hos/

cm)

y=exp(5.99+2.35x)

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-11 Relationship of turbidity to percent waste application fields in the drainage basin above each reservoirsite (R2=0.57). Reservoir values represent seasonal arithmetic means for turbidity for samples taken betweenMarch 1991 and March 1994.

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25


Turb

idity

(NTU

)

y=11.73+1.29x

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

129

Figure E-12 Relationship of Secchi depth to percent waste application fields in the drainage basin above eachreservoir site (R2=0.51). Reservoir values represent seasonal arithmetic means for Secchi depth for samples takenbetween March 1991 and March 1994.

0

1

2

3

4

5

6

0 5 10 15 20 25


ZSD

(fee

t)

y=3.32-0.12x

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

Figure E-13 Relationship of Secchi depth to dairy cow density in the drainage basin above each reservoir site(R2=0.59). Reservoir values represent seasonal arithmetic means for Secchi depth for samples taken betweenMarch 1991 and March 1994.

0

1

2

3

4

5

6

0 0.1 0.2 0.3 0.4


ZSD

(fee

t)

y=3.46-7.92x

AL030

GC020

IC030

NF030

SC030

SF030

SF060

SP030

131

APPENDIX F

Regression relationships of land-use characteristicswith storm water quality.

Figure F-1 Relationship of NH3-N to percent waste application fields in the drainage basin above each stream site(R2=0.73). Stream values represent geometric means of stormwater samples taken between March 1992 and March1994.

AL040 BO070

DB040 GC100 IC020

NF005

NF010

NF020

NF035 NF050

SF020 SF035

SF075

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 5 10 15 20 25 30 35 40 45 50


NH

3-N

(mg/

L)

y=0.01+0.013x

SP020

132

Figure F-2 Relationship of NH3-N to dairy cow density in the drainage basin above each stream site (R2=0.66).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040

BO070 DB040 GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


NH

3-N

(mg/

L)

y=0.01+0.79x

SP020

Figure F-3 Relationship of NO2-N to percent waste application fields in the drainage basin above each stream site(R2=0.60). Stream values represent geometric means of stormwater samples taken between March 1992 and March1994.

AL040 BO070

DB040 GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 5 10 15 20 25 30 35 40 45 50


NO

2-N

(mg/

L)

y=0.006+0.003x

SP020

133

Figure F-4 Relationship of NO2-N to dairy cow density in the drainage basin above each stream site (R2=0.58).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040BO070

GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


NO

2-N

(mg/

L)

y=0.004+0.16x

DB040SP020

Figure F-5 Relationship of TKN to percent waste application fields in the drainage basin above each stream site(R2=0.64). Stream values represent geometric means of stormwater samples taken between March 1992 and March1994.

AL040

BO070

DB040 GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020

SF035

SF075

SP0200

1

2

3

4

5

6

0 5 10 15 20 25 30 35 40 45 50


TKN

(mg/

L)

y=0.94+0.08x

134

Figure F-6 Relationship of TKN to dairy cow density in the drainage basin above each stream site (R2=0.55).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040

BO070

GC100

NF005

NF010

NF020

NF035

NF050

SF020

SF035

SF075

SP0200

1

2

3

4

5

6

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


TKN

(mg/

L)

y=0.93+4.88x

IC020

DB040

Figure F-7 Relationship of OPO4-P to percent woodland in the drainage basin above each stream site (R2=0.61).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040 BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

SP0200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 15 20 25 30 35 40

Woodland (%)

OPO

4-P

(mg/

L)

y=0.75-0.022x

135

Figure F-8 Relationship of OPO4-P to percent waste application fields in the drainage basin above each streamsite (R2=0.82). Stream values represent geometric means of stormwater samples taken between March 1992 andMarch 1994.

AL040 BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035 NF050

SF020 SF035

SF075

SP0200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 5 10 15 20 25 30 35 40 45 50


OPO

4-P

(mg/

L)

y=0.06+0.01x

Figure F-9 Relationship of OPO4-P to dairy cow density in the drainage basin above each stream site (R2=0.77).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040 BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035 NF050

SF020 SF035

SF075

SP0200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


OPO

4-P

(mg/

L)

y=0.05+0.89x

136

Figure F-10 Relationship of total-P to percent woodland in the drainage basin above each stream site (R2=0.55).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040 BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

SP0200

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

10 15 20 25 30 35 40

Woodland (%)

Tota

l-P (m

g/L)

y=1.41-0.04x

Figure F-11 Relationship of total-P to percent waste application fields in the drainage basin above each streamsite (R2=0.70). Stream values represent geometric means of stormwater samples taken between March 1992 andMarch 1994.

AL040 BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020 SF035

SF075

SP0200

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 5 10 15 20 25 30 35 40 45 50


Tota

l-P (m

g/L)

y=0.20+0.03x

137

Figure F-12 Relationship of total-P to dairy cow density in the drainage basin above each stream site (R2=0.64).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040

BO070

DB040

GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020

SF035

SF075

SP0200

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


Tota

l-P (m

g/L)

y=0.19+1.51x

Figure F-13 Relationship of TOC to percent waste application fields in the drainage basin above each stream site(R2=0.50). Stream values represent geometric means of stormwater samples taken between March 1992 and March1994.

AL040 BO070

DB040 GC100

IC020

NF005

NF010

NF020

NF035

NF050

SF020

SF035

SF075

SP020

0

20

40

60

80

100

120

0 5 10 15 20 25 30 35 40 45 50


CO

D (m

g/L)

y=30.96+1.38x

138

Figure F-14 Relationship of TOC to dairy cow density in the drainage basin above each stream site (R2=0.54).Stream values represent geometric means of stormwater samples taken between March 1992 and March 1994.

AL040

BO070

DB040

IC020

NF005

NF010 NF020

NF035 NF050

SF020

SF035

SF075

SP020

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35 40 45 50


TOC

(mg/

L)

y=10.03+0.31x

GC100

Figure F-15 Relationship of COD to percent waste application fields in the drainage basin above each stream site(R2=0.61). Stream values represent geometric means of stormwater samples taken between March 1992 and March1994.

AL040 DB040

GC100

IC020

NF005

NF010 NF020

NF035 NF050 SF020

SF035

SF075

SP020

0

5

10

15

20

25

30

35

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


TOC

(mg/

L)

y=9.49+20.78x

BO070

Livestock and the Environment: Scientific Underpinnings for Policy...

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