ESTIMATING ANNUAL CADMIUM LOAD VIA SURFACE RUNOFF INTO SONGKHLA LAKE, THAILAND

7/30/2019 ESTIMATING ANNUAL CADMIUM LOAD VIA SURFACE RUNOFF INTO SONGKHLA LAKE, THAILAND

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ESTIMATING ANNUAL CADMIUM LOAD VIA SURFACE RUNOFF

INTO SONGKHLA LAKE, THAILAND

Kitipan Kitbamroong1*

, G. Padmanabhan2,3

, and Penjai Sompongchaiyakul4,5

1Director, Sherwood Group Consulting, Bangkok, Thailand

2 Professor, Civil Engineering Department, North Dakota State University, Fargo, ND 58105, USA3Director, North Dakota water Resources Research Institute, Fargo, ND 58105, USA

4Department of Marine Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand

5Center of Excellence on Hazardous Substance Management, Chulalongkorn University, Bangkok,

Thailand(*e-mail: [email protected])

ABSTRACT

The cadmium transport via surface runoff to Songkhla Lake, Thailand, is the focus of this paper.Songkhla Lake is located in the southern part of Thailand covering approximately an area of 1,042km

2with a drainage area of 7,687 km

2. It is a major producer and nursery for local fisheries.

Predominant land use in the drainage area of the lake is agriculture. Non-point source pollutionloading of cadmium to the lake from the surrounding area is of great environmental concern. Surfacerunoff and associated sediment flow are the major mechanisms of transport of non-point sourcepollutants. A spatially distributed parameter model,Two-dimensional Runoff and Erosion and EXport(TREX), was used in this study to estimate cadmium transport load to the lake under three differentscenarios. The results indicated the critical source areas from which cadmium loads are generated andthe annual loads. This modeling effort was part of a comprehensive study to develop a decision-

making tool for the basin management to minimize non-point source pollutants loading to theSongkhla Lake.

Keywords:

Cadmium, Transport modeling, Non-point source pollution, Distributed models, Songkhla Lake

INTRODUCTION

Songkhla Lake is located in southern Thailand. Intense agriculture-related activities in the lakecatchments have raised concerns about the potential for surface runoff transporting significantamounts of cadmium into the lake. Cadmium is commonly associated with rock phosphate fertilizers

used in the lake catchments. Though not of immediate concern, cadmium was found in the sediment inthe lake between 0-1.25 ppm (Sirinawin et al., 1998). Increasing agricultural activities in thecatchments have the potential to increase cadmium accumulation in the lake (Sae-Eong et al., 2002).Cadmium levels as low as 0.5 ppb are known to kill juvenile fish, levels of 3 ppb are known to kill

aquatic insects while adult fish die at levels as low as 5 ppb (Vital, 2005). Research reported in thispaper is part of a comprehensive study to model the phosphorus and cadmium load to Songkhla Lakefrom its drainage area in order to develop a lake management decision support system (Kitbamroonget al., 2009; Kitbamroong et al., 2010).Songkhla Lake and its basin are spread over 3 provinces of Thailand: Phattalung, Songkhla, and

Nakhon Si Thammarat (Figure 1.). The lake area is approximately 1,042 km2. It has a drainage area of

7,687 km2. The basin is bounded by two mountain ranges. The higher grounds of both the mountain

ranges are covered with rainforests. Further down from the mountain range, from north to south of the

basin parallel to the mountain range, there are undulating plains alternating with low hills. The areatowards the east approaching the lake is a large flat plain, most of which are rice farms. The basin has


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12 sub-basins. The majority of SLB land, 5,660 km2

is used for agriculture. Most of the agriculturalland is used for rubber plantation and rice (60% and 30%, respectively). Forestland occupies 1,164km

2, most of which is rainforest and the remaining is mangrove and swamp forest. The average

rainfall in the basin is 2,043 mm.

Figure 1. Songkhla Lake Basin

TREX WATERSHED MODEL

One of the few available models with the capability of simulating transport of heavy metals via surface

runoff is the Two-dimensional Runoff and Erosion and eXport (TREX) model(Velleux et al., 2005,2006 and 2008). TREX was used in this study. TREX is a fully distributed, physically based numericalmodel to simulate chemical transport and fate at the watershed scale. TREX has three majorsubmodels: hydrology, sediment transport, and chemical transport. A conceptual diagram of thechemical model processes in the TREX is presented in Figure 2.

Figure 2. Generalized Conceptual Model Framework in TREX (Velleux et al., 2005)

CONCEPTUALIZATION OF SLB FOR MODELING

For this study, the SLB was divided into8 sub-watersheds (Figure 1): Klong Pa Payom & Thanae sub-watershed with one discharge point on the upper lake, Nathom sub-watershed with one discharge point


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to the upper lake, Tachiad sub-watershed with three discharge points to the upper lake and onedischarge point to the middle lake, Pa Bon sub-watershed with one discharge point to the middle lake,Phru Poh and Rattaphum sub-watershed with 3 discharge points to the middle lake and two dischargepoints to the lower lake, U-Tapao and Eastern Coast Sub Basin 4 Sub-watershed with four dischargepoints to the lower lake, Eastern Coast Sub Basin 2 and 3 Sub-watershed with two discharge points tothe middle lake and one discharge point to the middle lake, and Eastern Coast Sub Basin 1 Sub-watershed with four discharge points to the upper lake.

DATA AND INPUT PREPARATION

The study used 2004 data assembled into a GIS database. Topographic data in the Digital ElevationModel (DEM) format with 30mx30m resolution was provided by the Southern Regional Geo-Informatics and Space Technology Centre, Prince of Songkla University. The Land Development

Department of Thailand had the soils and land use data already formatted for GIS usage. LANDSATsatellite image was used to confirm the landuse of the area.Crop, fertilizer and management data on

field, schedule and operation were collected from field survey and from farmers, fertilizer resellers,cooperatives, and farmers in Phatthalung, Nakhon Si Thammarat and Songkhla provinces. Non-cropdata was obtained from field surveys and literature reviews. The cadmium data available for transport

was estimated using Substance Flux Analysis (SFA),. Rainfall and temperature data were obtainedfrom the Thai Meteorological Department in text format and had to be converted into GIS format.

Data from both primary and secondary sources were compiled to establish a database of sources andamount of cadmium in the system products and processes. The model was calibrated and verified withactual cadmium levels. Non-availability of different categories of data for same periods has been aproblem. For example, the concentration of cadmium in phosphate fertilizer was investigated in 2004,but the corresponding figures for the concentration in animal feed were only available for the year2001. The predominant land use in the study area is agriculture . TREX, being a spatially distributedmodel, requires large amount of data. All data was input in the program in ASCII format. Watersheddelineation was implemented using GIS. However, additional massive amount of work was necessaryto create links and node maps. Delineation of watershed to obtain cell and reach data is achieved byusing DEM 100m x 100m to create watershed boundary and cells, avoiding generating grid cellsbeyond 50,000 grid cells per model.Locations of channel cells and the stream channel network wereestimated using ArcMAP 9.3 (ESRI, 2005) and TauDEM (Tarboton, 2002). One of the most difficultaspects of using TREX on large watersheds is the development of connectivity relationships requiredfor modeling channels. The basic problem is zero (flat) slopes in channel (stream network) cells withinthe watershed. The result is the model grid will not properly drain. This can be an important issue onlarge watersheds (> 1,000 km2) when using larger (> 100m) grid cell sizes or due to problems withDEM quality in complex terrain. With larger grid cells, there can be many contiguous locations withinthe defined stream channel that have zero slopes (Tarboton, 2002).

GIS PLATFORM

TREX is designed to be compatible with data from raster GIS sources. In particular, data describing

elevation, soil types, land use, and contaminant distributions can be processed in a GIS and used asmodel inputs. Details of input data preparation are given below:

1) Map and boundary of Phatthalung, Nakhon Si Thammarat and Songkhla province.2) Digital Elevation Model (DEM) of Phatthalung, Nakhon Si Thammarat and Songkhla

province with resolution 30mx30m and 100mx100m3) Accumulate rain fall, number of rainy days, and monthly average rain intensity for the past 30

years (1974 2003) of every weather station located in Phatthalung, Nakhon Si Thammaratand Songkhla province from Thai Meteorological Department.

4) Land use of Phatthalung, Nakhon Si Thammarat and Songkhla province year 2000 from LandDevelopment Department.

5) Soil and Soil shape files of Phatthalung, Nakhon Si Thammarat and Songkhla province fromLand Development Department.


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6) LANDSAT satellite image with 30x30m of Phatthalung, Nakhon Si Thammarat and Songkhlaprovince year 2000

7) Rivers and streams of Phatthalung, Nakhon Si Thammarat and Songkhla province year 2000,from Southern Regional Geo-Informatics and Space Technology Centre, Prince of SongklaUniversity

8) Calibration sampling points (Sea-Eong et al., 2002)9) Validation sampling points (Leekpai, 2006)

MODEL CALIBRATION AND VALIDATION

The data for calibration was taken from Sae-Eong et al., (2002). A total of 20 soil samples werecollected randomly covering SLB to study the metal concentrations. The validation data was takenfrom Leekpai (2006). A total of 212 soil samples were collected randomly to study the spatial

variability of heavy metal concentrations in surface soil of SLB.

The model parameters subject to the calibration were hydraulic conductivity, surface roughness, soilerosivity, land cover factor and chemical distribution coefficients. Calibrated model parameters valuesare shown in table 1.

Parameter Range Description

Hydraulic conductivity (Kh) (m/s) 1.5 x 10-6 Sandy loams

1.5 x 10- 2.0 x 10- Gravelly sandy loams

1.5 x 10-62.8 x 10-6 Pits and dumps

1.0 x 10-61.5 x 10-6 Diggings and tailings

Soil erodibility (K) (ton/acre) 0.02 - 0.03 Sandy loams

0.05 - 0.15 Gravelly sandy loams

0.02 Pits and dumps

0.02 - 0.64 Diggings and tailings

Manning's roughness coefficient 0.45 Forest

(Manning, n) 0.300.45 Shrub and grassland

0.15 Bare rock/sand

0.050.15 Urban/commercial

0.080.18 Channel bed

Crop Management Factor (C) 0.0-0.001 Fertile Forest

0.001-0.02 Meadow, Dry Forrest, Peat

0.02-0.03 Mangroves and rice fields

0.03-0.05 Deciduous forest

0.05-0.10 Orchards

0.10-0.20 Horticultural and Field

0.25-0.30 Pine tree

0.30-0.60 Housing/Mixed farming/beach

0.60-1.00 Abandon land/mine/open

Table 1. Summary of calibrated model parameter values

Model sensitivity was explored by parameter perturbation as part of calibration efforts. The mostsensitive parameters in the hydrologic model were the effective hydraulic conductivity (Kh) and flowresistance (Manning n). The most sensitive parameters for the sediment transport model were typicallythe soil erodibility (K) and land cover factor (C). The land management practice factor (P) was notconsidered to be uncertain because lands in the watershed are not managed for agriculture or asrangelands. The most sensitive parameter for the chemical transport model was the chemical partitioncoefficient (Kd). During calibration, these parameters were varied within accepted ranges representing

the uncertainty of each parameter. Overall model uncertainty envelope bounds were estimated fromthe combination of individual parameter values that cause the largest increase (upper bound) or

decrease (lower bound) in model response. Upper bound conditions occur for maximum surfacerunoff, maximum soil erosion, and minimum chemical partitioning. Lower bound conditions occur forminimum runoff, minimum erosion, and maximum partitioning.


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SCENARIOS MODELED

The model could be used to develop the decision support tool for the Songkhla Lake environmentalmanagement decision making process under practical scenarios. The following three scenarios weretested in this study:

1) Significant incremental and decrease of fertilizer usage. This provides an understanding of therelative impact of fertilizer usage to phosphorous and cadmium distribution in the basin andrunoff from surrounding watershed into the lake.

2) Types of fertilizer applied. Providing an alternate choice for types of fertilizer that decreasesload of phosphorous and cadmium to the basin.

3) Types of crops applied and application rate of fertilizer for certain crops. High contribution ofphosphorous and cadmium from certain areas in the basin could be removed or transferred tolower contribution areas.

RESULTS AND DISCUSSION

An overall increase of 10%, 50%, -10%, and -50% fertilizer usage within the area were simulated.Results for each scenario are shown below. Figure 3 for 10% increase shows that the amount ofcadmium contamination increases especially in mixed orchards near the rivers.

Figure 3. Comparison of TREX results after increase 10% fertilizer

Figure 4 for 50% increase shows that the amount of cadmium contamination significantly increasesthroughout the sub-watershed.

Figure 4. Comparison of TREX results after increase 50% fertilizer


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Figure 5 for 10% decrease shows that the amount of cadmium contamination significantly decreases.The cadmium content near the rivers and near the lake is still high.

Figure 5. Comparison of TREX results after decrease 10% fertilizer

Figure 6 for 50% decrease shows that the amount of cadmium contamination significantly decreases.The cadmium content near therivers and near the lake has appreciably decreased.

Figure 6. Comparison of TREX results after decrease 50% fertilizer

The result of changing to low cadmium containing fertilizer, less than 1.5 mg-Cd/kg, is shown inFigure 7. The amount of cadmium contamination reduced significantly by more than 5 times.

Figure 7. Comparison of TREX results after changing fertilizer formula


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A change from horticultural crops to rubber crop was simulated. Figure 8 shows that the amount ofcadmium contamination significantly decreases.

Figure 8. Comparison of TREX results after changing crops

Sub watershed Cadmium (Kg/Year) Cadmium Contribution (%)

Klong Pa Payom and Thaae 42 15.38

Nathom 27 9.89

Tachiad 28 10.25

Pa Bon 12 4.39

Phru Poh and Rattaphum 41 15.01

U-Tapao and Eastern Coast Sub Basin 4 93 34.06

Eastern Coast Sub Basin 2 and 3 12 4.39

Eastern Coast Sub Basin 1 18 6.59

Total 273 100

Table 2. Cadmium contribution

For the west side of the watershed the error between cadmium observed and model prediction wasfound to be between 15.9 to 64.1 %. Considered together with the physical factors of U-Tapaoincluding runoffhigh runoff, crop managementhorticultural crops, elevationsteep slope, organic

matter - high, soil erodibility high erosion, pHacidity, clayhigh percentage, and total metal high, it is clearly seen that why the area has the highest potential. The cadmium contribution from

each sub-watershed is shown in Table 2. Almost one third of the cadmium contribution occurred fromU-Tapao and Eastern Coast Sub Basin 4 sub watershed, followed by Klong Pa Payom and Thaae, PhruPoh and Rattaphum sub watershed.

CONCLUSIONS

By changing the fertilizer formula alone from high to low cadmium-contaminated type leads to asignificant decrease of cadmium contribution from the watershed, especially in the U-Tapao subwatershed where the overall physical conditions including high runoff, horticultural crops, steep slope,high organic matter, high erosion, acidity, high clay percentage, and high total metal could promote

the transport of cadmium through the watershed. However, with other scenarios, by either changingthe types of crops grown in the area or by lowering the fertilizer rate could also lead to lower cadmium


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contribution to the SLB. Methods used and approximations made in previous studies on estimatingcadmium loads to the Lake were often crude and therefore be considered only as a starting point. Thewide range of cadmium content reported in the past studies suggests the need for furthermeasurements specific to the SLB situation so as to describe it more accurately. This study is anattempt to estimate cadmium transport to the Songkhla Lake via surface runoff using TREX, a metaltransport model.

ACKNOWLEDGEMENTThe authors acknowledge the National Research Center for Environmental and Hazardous WasteManagement, Chulalongkorn University, Thailand for sponsoring this research, the department ofCivil Engineering, North Dakota State University, USA for hosting the first author as a visitingscholar during the studyand the Faculty of Environmental Management, Prince of Songkla Universityfor providing research materials and facilities.

REFERENCES

Environmental Systems Research Institute (ESRI). 2005. ArcGIS/ArcInfo 9.3. ESRI, Redlands, CA.

Kitbamroong, K., Sompongchayakul P., and Padmanabhan, G. (2009). Improving Non-Point SourceModel Input Parameters Using Substance Flux Analysis, Journal of Applied Sciences, ISSN1812-5654, 2009.

Kitbamroong, K., Sompongchayakul P., and Padmanabhan, G. 2010. Modeling Phosphorus Transportvia Surface Runoff in Songkhla Lake Basin, Thailand, EWRI/ASCE conference, 3rdInternational Perspective on Current and Future State of Water Resources and the Environment,January 5-7, 2010, Chennai, India.

Leekpai, K. 2006. Spatial Variability of Heavy Metal Concentrations in the Songkhla Lake CatchmentSurface Soil. Master of Science in Environmental Management, Prince of Songkla University

Sae-Eong, T., Tjell, C., Mosbaek, H. and Sompongchaiyakul, P. 2002. Metals Concentration inSongkhla Lake Basin's Soils. The 28th Congress on Science and Technology of Thailand, 24-26

October.

Sirinawin, W., Turner, D., Westerlund, S. and Kanatharana, P. 1998. Trace metals study in the OuterSongkla Lake, Thales Sap Songkla, a southern Thai estuary, Mar. Chem. 62:175-183

Tarboton, D.G. 2002. TauDEM: Terrain Analysis Using Digital Elevation Models. Utah StateUniversity, Department of Civil and Environmental Engineering,

http://moose.cee.usu.edu/taudem/taudem.html, October 2002.Velleux, M.L., Julien, P.Y. and England, J.F. 2005 TREX Watershed Modeling Framework User's

Manual: Model Theory and Description. Department of Civil Engineering, Colorado StateUniv., Fort Collins, August, 83 p.

Velleux, Mark L., Pierre Y. Julien, Rosalia Rojas-Sanches, William H. Clements, and John F. EnglandJr. 2006. Simulation of Metals Transport and Toxicity at a Mine-Impacted watershed: CaliforniaGulch, Colorado, Environmnetal Science and Technology, Vol. 40, No. 22, 2006

Velleux, M. L., England, J. F., and Julien, P. Y 2008 TREX: spatially distributed model to assess

watershed contaminant transport and fate, Science of the Total Environment, 404(1):113-28,2008 Oct 1; doi: 10.1016/j.scitotenv.2008.05.053. Epub 2008 Jul 22.

ESTIMATING ANNUAL CADMIUM LOAD VIA SURFACE RUNOFF INTO SONGKHLA LAKE, THAILAND

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