Combining a River Basin Network Flow Model and Artificial Neural Networks for Salinity Control in an...

7/28/2019 Combining a River Basin Network Flow Model and Artificial Neural Networks for Salinity Control in an Irrigated Valley

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Combining a River Basin Network Flow Model and Artificial Neural Networks

for Salinity Control in an Irrigated Valley

Enrique Triana1, John W. Labadie1 and Timothy K. Gates1

1 Department Civil Engineering, Colorado State University, Fort Collins, CO 80523-

1372; PH (970) 491-7510; email: [email protected]

Abstract

A comprehensive decision support system (DSS) is presented that combines the

MODSIM river basin network flow model with geographic information systems

(GIS) and an artificial neural network (ANN) for basin-wide water management and

salinity control under appropriative water rights and other legal, institutional, and

administrative structures. The MODSIM graphical user interface and/or GIS provide

for display and editing of spatially distributed river basin network topology and other

GIS layers, including basin DEMs, irrigated fields, NEXRAD precipitation data,

basin hydrography, finite-difference groundwater grids, pumping wells, diversion and

water use data, water rights database, water quality data, and hyperlinked display of

GPS-located basin features. MODSIM embeds a VB.NET coded ANN for modeling

of stream-aquifer interactions that is interpreted at runtime rather compiled withMODSIM. The ANN is trained using weekly modeled aquifer responses to historical

events from a calibrated regional-scale finite difference groundwater model that

would be computationally intractable if directly linked with MODSIM for

management studies. A water quality module for characterizing salinity in the basin

is also scripted in MODSIM using VB.net. The DSS is applied to evaluating

remediation strategies in the Lower Arkansas River Basin in Colorado, where

increased salinization in agricultural areas and intensified competition with expanding

urban areas for limited available water supplies have threatened the viability and

sustainability of agriculture in the basin.

Introduction

Water quality degradation, inefficient water use, and increases in upstream and out-

of-basin municipal water demand in irrigated agricultural areas threaten the economy

and sustainability of communities supported by agricultural activities, and make water

less suitable for human consumption, crop production, and aquatic ecosystems.

Design of comprehensive solutions to agro-ecological sustainability of irrigated river

basins requires development of new tools that simultaneously address complex issues

right ASCE 2005 EWImpacts of Global Climate Change


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in water quantity management and water quality control under complex legal and

administrative issues. The increasing stress on water systems has highlighted the

inseparable relationship between surface water and groundwater resources (Winter et

al. 1998). Therefore, adequate representation of stream-aquifer interaction is a major

concern for developing accurate basin scale models, especially in basins where return

flows and depletions make up a considerable portion of the surface water flow.

A limitation in river basin modeling is always the degree of detail that is possible to

achieve. Technologies such as satellite imagery, aerial photography, radar, and

remote sensing make available refined spatial data facilitating regional-scale

modeling. Digital format data is increasingly available and inexpensive, and

geographic information systems (GIS) have evolved and become a powerful and

accessible tool for processing, analyzing and visualizing spatial data. Combined

together, these elements can provide a new dimension in river basin modeling. The

decision support system (DSS) presented herein provides seamless integration

between spatio-temporal river basin data, a comprehensive river basin network flow

model, and an innovative and efficient methodology for modeling basin-scale stream-aquifer interaction. The DSS is applied to the Lower Arkansas River Basin in

Colorado for evaluating a wide variety of water management strategies for salinity

control, including changes in irrigation practice, water use, reservoir operations,

pumping patterns and infrastructure improvements such as canal lining and

subsurface drainage.

Spatio-Temporal Database

The geo-database contains all data for building the basin-scale system model,

including topography, political divisions, hydrography, hydraulic structures (e.g.,

canals, dams, siphons, diversion structures, etc.), irrigated fields, soil types, land usetypes, bed rock scatter points, aerial photos (DOQs), satellite images and spatially

referenced field data. Diversion structures and reservoirs have associated water rights

which are relationally referenced to the GIS features. Dynamic features include

measurements or characteristics that changing time, with relational tables providing

storage and access to spatially-referenced features such as gaging stations, climatic

stations, diversion structures with measurement devices, pumping wells, monitoring

wells, and NEXRAD precipitation data. The database also contains processed data

such as watersheds, slopes, hydrologic networks, geometric networks, and geo-

referenced model results.

Stream-Aquifer Interaction

A common practice in the analysis and modeling of stream-aquifer interaction is an

excessive simplification due to the lack of extensive field data. Analytical

approaches primarily rely upon a number of conceptual simplifications, thereby

increasing the uncertainty and inaccuracy of the results. Common assumptions

include simplified aquifer geometry and significant constraints on aquifer physical

characteristics such as homogeneity, isotropy, time invariance, and infinite (semi-



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infinite) aquifer extent. In contrast, finite difference and finite element numerical

methods can accurately represent the time-variant, heterogeneous physical system, but

are computationally intractable when applied over large areas. A popular method to

model stream-aquifer interactions at basin-scale is the stream depletion factor (SDF)

method (Jenkins 1968). The SDF is a spatially variable system descriptor with time

dimension for predicting volumetric changes in streamflows due to recharge orwithdrawal of water from the aquifer. The SDF method merges spatially-varied

hydraulic properties, aquifer stress locations, and types of boundary conditions.

Sophocleous (1995) compared the finite difference model MODFLOW (McDonald et

al. 1988) and SDF (Jenkins 1968) and noted considerable discrepancies between the

two approaches in representing a real stream-aquifer system. These results were

corroborated by Fredericks et al. (1998), who found significant differences using

groundwater response coefficients developed from SDF as compared to a finite

difference groundwater model. There is a need for a methodology that allows

accurate basin scale stream-aquifer interaction modeling based on field data and

detailed groundwater modeling, but is computationally feasible for integration with

regional water management models.

Triana et al. (2003) showed the potential of training an artificial neural network

(ANN) to represent complex stream-aquifer interactions at the regional scale. The

methodology trains an ANN from detailed, well-calibrated regional quantity-quality

groundwater models to learn relationships between basin-wide quantifiable system

state variables and the regional response of the aquifer (i.e., river return flows, river

depletions, and conservative constituent loading). The relationships learned by the

ANN are used to describe stream-aquifer interactions in areas where detailed

groundwater modeling is not available. GIS is used to build the ANN training and to

test data sets by area-buffer-grouping of explanatory variables and querying of geo-

referenced MODFLOW modeled variables (Triana et al. 2004). Regional scalemodeled scenarios enhance the ANN exposure to stream-aquifer interactions when

changes in the explanatory variables occur.

Accurate surface-groundwater modeling detail is only feasible at field or regional

scales. Triana et al. (2003) showed the potential of training an artificial neural

network (ANN) to represent complex stream-aquifer interactions at a regional scale.

An innovative methodology was developed to extend stream-aquifer interaction

modeling to the basin scale. The methodology is based on detailed, well-calibrated

regional water quantity-quality groundwater models, which during the modeled

period, reveal the relationships between basin-wide quantifiable system state

variables and historical regional responses of the aquifer (i.e., river return flows, river

depletions and water quality constituent loading). An ANN was trained to learn

regional relationships for representing basin-wide stream-aquifer interactions,

especially in areas where detailed groundwater modeling is not available. A custom

GIS interface and the spatio-temporal database are used to build the ANN

training/testing data sets and querying of geo-referenced MODFLOW modeled



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variables (Triana et al. 2004). Figure 1 shows a MODFLOW grid in GIS where

model cells can be associated with geo-referenced system features.

Geo-referenced River Basin Network Model (MODSIM)

MODSIM is a generalized river basin network flow model (Labadie et al. 2002)

employing a network flow structure for representing physical system features and

processes, as well as artificial and conceptual elements for modeling complex

hydraulic, administrative, legal, and institutional/contractual mechanisms. MODSIM

is integrated as an extension in ArcGISTM

(ESRI, Inc.) to construct the river basin

model using a geo-referenced MODSIM network. The topology and infrastructure of

the system is represented using a geometric network. A geometric network contains

the geometry and locations of network edges and nodes, in addition to connectivity

information between edges and junctions. It also defines rules of behavior such as

which classes of edges can connect to a particular class of junction or to which class

of junction two classes of edges must connect. ArcMapTM

(ESRI, Inc.) facilitates theconstruction of the hydro-network and its tools facilitate the setting of flow directions

and checking for connectivity errors and integrity of the hydro-network. The

MODSIM network topology is based on the ArcGIS logical network, with MODSIM

GUI dialogs and output displays brought into ArcMap for entering and editing the

model data and visualizing the model results.

Figure 1. GMS Modeled River Cells for ANN Output Variables

Groundwater

Model grid

Groundwater

Model grid



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Integration of MODSIM and GIS provides greater detail and realism in representing

the river system than is possible to achieve with the MODSIM GUI alone. It allows a

close relationship between spatial water distribution and the nodes and links that

represent the system, which is especially important for representing return flows and

identifying system gains and losses. The MODSIM geo-referenced network is ready

for coupling with ArcHydroTM

(ESRI, Inc.) tools to establish watershed associatedwith the network nodes, thereby facilitating the addition of hydrologic runoff

modeling. In addition, the geo-referenced model representation allows having

information such as aereal photos, satellite images, spatial varied information and the

MODSIM network available in the same environment. ArcMap visualization tools

allow easy exploration of small areas or areas with closely proximate nodes and

displaying node and link properties. Figure 1 shows a MODFLOW grid in GIS where

model cells can be associated with geo-referenced system features.

MODSIM and Artificial Neural Network Integration

As depicted in Figure 2, an ANN module was developed to incorporate trained ANNpredictions in the MODSIM solution. The ANN module utilizes MODSIM flow

solutions within the iterative process to update the input data set and predict variables

for which it was trained. The ANN can be used to predict water quantity and water

quality for aquifer stream interactions, system gains and losses, runoff predictions and

user diversion patterns as function of observable/measured system variables. The

module is attached to MODSIM using its customized option that allows MODSIM to

be integrated with any .NET technology program.

MODSIM and Water Quality

Module Integration

A surface water quality module was

developed to carry out conservative

constituent concentrations along with

the MODSIM solution. The quality

module traces the constituent

movement in the network,

performing mass conservation

calculations from the most upstream

nodes in the network to the network

sinks:

( ) SxQC

xCQ =

+

(1)

where C is concentration, Q is the

flow rate, x is the distance in the

direction of the flow, and S are

external sources or sinks. The

module takes into account the

concentration of the water entering

MODSIM

Initialize

TrainedANN

GISHydro-

network

ANNInputs

DB

MODSIMSOLVER

ANN Inputvariables

Predicted ANNFlows as MODSIM

inflows

MODSIM

Initialize

TrainedANN

GISHydro-

network

ANNInputs

DB

MODSIMSOLVER

ANN Inputvariables

Predicted ANNFlows as MODSIM

inflows

Figure 2. ANN Prediction in MODSIM

Solver Schema



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the network from both surface water and groundwater and the flow estimated in the

MODSIM solution to route the solutes. The module is attached to the MODSIM

solver using a customized programming code in.NET technology.

Case Study: Lower Arkansas River Valley

The Arkansas River is the primary municipal water supply for most of the 170.000

people living in the five counties that comprise the lower Arkansas River Valley in

Colorado. Irrigation water supply is primarily based on Arkansas River streamflow

diversions and groundwater pumpage due to the limited natural precipitation. Water

in the system is reused several times along the river whereby irrigation water in the

system seeps through the soil, recharging the alluvial aquifer which returns water to

the surface water system. Returned water augments the system streamflow for users

downstream, especially in the late irrigation season. Although the users benefit from

reusing water, the quality of the water degrades significantly while moving

downstream.

There are many issues that need to be addressed in this river basin such as

waterlogging in several areas with the associated problems of salinization (Gates et al.

2002; Burkhalter et al. 2005a). Selenium contamination induced by agricultural

irrigation practices threaten the health and safety of humans, animals and aquatic life

(Donelly 2005), and the increase in population in cities along the Colorado Front

Range has generated a desperate search for new sources of municipal water. If no

actions or provisions are taken promptly, these factors together threat to permanently

harm the already debilitated Lower Arkansas Valley. A decision support system

(DSS) is being developed to aid in basin-wide management and salinity control in the

Lower Arkansas River basin. It is designed to encompass the necessary system

elements, as well as legal, administrative and institutional rules to accuratelyreproduce the conjunctive use of surface water and groundwater in the river basin.

The Lower Arkansas Valley has been grouped into areas for stream-aquifer

interaction modeling. The areas have been defined as the adjacent alluvial valley for

river segments of 15 km length (Figure 3). The explanatory variables extracted for

each region are the representative river flow for the region, average elevation of the

river section calculated from a Digital Elevation Model, length of the river section,

Figure 2. Grouping-areas for ANN predictions and groundwater modeling

Future Ground

Water Modeled

Area

River SectionsAdjacent Area for

River Section 7

Current Ground

Water Modeled

Area

Future Ground

Water Modeled

Area

River SectionsAdjacent Area for

River Section 7

Current Ground

Water Modeled

Area



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and scenarios magnitude indicators. Explanatory variables processed for each area-

buffer in the regions include total land area, area of irrigated land, average land

elevation, total length of the canals, average elevation of the canals, total area of

water bodies, average elevation of water bodies, average pumpage, number of

pumping wells, fraction of average diversion to the irrigated fields, canal seepage

indicator, and aquifer recharge indicator, as a function of the total diversion andirrigated land area.

Artificial Neural Network Training and Testing

Gates et al. (2002) applied the GMS software package (Brigham Young University

1999) to model steady state groundwater flow and salt transport in a portion of the

Lower Arkansas River Basin in Colorado (area at the center ofFigure 2). The model

was developed to analyze and predict water table elevations and salinity and to

simulate the interaction between the shallow aquifer, the river, and the irrigation-

drainage system. The model was calibrated based on an extensive and detailed data

collection effort. The initially-developed steady-state model has been furthercalibrated as a transient model over a 133 week period (encompassing three

consecutive irrigations seasons of data gathering from 1999 to 2001). The transient

model provides an invaluable resource for understanding stream-aquifer interaction

and evaluating salinity control strategies (Burkhalter et al. 2005a, 2005b).

Using the calibrated scenarios of the weekly transient model, a radial basis ANN was

trained to represent the quantity and quality of the stream-aquifer interaction learning

relationships between the explanatory variables and the amount and concentration of

river water return/depletion. The explanatory variables used in the training include

for each grouping-area: stream length, average stream elevation, and representative

river flow. They also include for each buffer in each grouping area: average pumping,the number of active pumps, total length of lined canals and their average elevation,

the area covered by water bodies and their average elevation, the buffer area, average

elevation and the irrigated land-area, the average diversion for the canal companies

that irrigate the buffer, the average percentage of land irrigated in the buffer out of the

total land irrigated by each canal company, a drainage intensity indicator, average

seepage indicator and estimate of recharge as the average diversion times the area

irrigated. The methodology described in Triana et al. 2004 is used to group the

explanatory variables. Fifteen-km river segments serve to divide the valley in

prediction grouping-areas. These segments extend north and south to the river

approximately to the outlying boundaries of the alluvial aquifer. In addition, inside

each grouping area, 1500m-area-buffers are created to consolidate the explanatoryvariables. Figure 2 shows the modeled Lower Arkansas Valley, the ANN prediction

grouping areas and the groundwater modeled areas. The ANN is trained using 400

randomly selected datasets from the more than 20,000 available datasets. The ANN

is trained to predict net return flow from the groundwater and its salinity load. Figure

3 shows the ANN performance during the training and testing stages, with the latter

using inputs not employed during the training process.



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Performance of the ANN when applied to the vicinity of the area used for training is

evaluated using scenarios in which datasets from a grouping-area are not used for

training. The ANN is trained using the remaining grouping-areas. The resulting

coefficients of determination generally exceed 0.80 for explanatory variables in the

center of the training dataset. Cases when most of the explanatory variables are close

to the boundaries of the training dataset produce predictions that are visibly biasedwith coefficients of determination as low as 0.40. Figure 55 shows an example of

ANN testing outside of the training area.

Datasets : 400MSE = 0.01

r2 = 0.95


r2 = 0.95

Training and Validation Testing


r2 = 0.95


r2 = 0.95

Training and Validation Testing

Figure 3. ANN training and testing performance comparison between predicted

and modeled net water return to the Arkansas River.

Figure 4. ANN performance testing comparison inside the training areas and

outside the training areas

Areas

Outside trainingTesting Data



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MODSIM Network in ArcMap

The MODSIM network is constructed from a geometric network that is built in

ArcGIS using stream and canal features to represent the network edges and four

feature classes to represent the different types of nodes (i.e., reservoirs, demands,

sinks and gaging stations). Special constructs for the reservoirs are built in whereedges are created from the point where the reservoir intersect the stream or canal to

the node that represent the reservoir that is located in the interior of the reservoir. All

the edges are assigned with flow direction and sink nodes created at the required

locations.

The creation of the geometric network allows a richer detail in water paths and

connections between the features. The basic topology of the system is complemented

with aerial photography and geo-referenced field observations achieving excellent

water path details. This detail is useful to predict and estimate system gains and

losses. Figure 5 shows a portion of the Arkansas River basin geometric network.

Figure 5. ArcGIS geometric network/MODSIM network representing a portion

of the Lower Arkansas River Basin.

Model Calibration

Sites where water flow is measured are represented by MODSIM flow-through

demand nodes. A VB.NET Interface is developed to calculate and import flowmeasurements for each time step into the demand nodes. It also queries and imports

decreed water rights into MODSIM.

The trained ANN is loaded and coupled with the MODSIM solver using the ANN

module. The objective during the calibration stage is determining unmeasured gains

and losses in the system. A special construct is used to facilitate the quantification of

total gains and losses after the water return/depletion ANN prediction is placed in



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groundwater modeling to other areas in the river basin to increase confidence in the

predictions basin-wide.

The fully implemented DSS will be an invaluable tool for river basin salinity

management and control. In addition, it will support decision making in selecting

management strategies for contamination issues, agricultural to municipal water usechanges, and legal disputes.

References

Brigham Young University. (1999). The department of defense groundwater modeling

system: GMS v3.0 reference manual, Environmental Modeling Research

Laboratory, Provo, Utah.

Burkhalter, J., and Gates, T. (2005a). "Agroecological Impacts from Salinization and

Waterlogging in an Irrigated River Valley." journal of Irrigation and Drainage

Engineering, 131(2), (in press).

Burkhalter, J., and Gates, T. (2005b). "Evaluating Regional Solutions to Salinization

and Waterlogging in an Irrigated River Valley." journal of Irrigation and

Drainage Engineering, 131(in press).

Donelly, J. P. (2005). "Assessing Irrigation Induced Selenium and Iron in the Lower

Arkansas River Valley in Colorado," Colorado State University, Fort Collins,

CO.

Fredericks, J. W., Labadie, J. W., and Altenhofen, J. M. (1998). "Decision Support

System for Conjunctive Stream Aquifer Management." Journal of Water

Resources Planning and Management, ASCE, 124(2), 69--78.Gates, T., Burkhalter, J., Labadie, J., Valliant, J., and Broner, I. (2002). "Monitoring

and Modeling Flow and Salt Transport in a Salinity-threatened Irrigated

Valley." Journal of Irrigation and Drainage Engineering, 128(2), 87-89.

Jenkins, C. T. (1968). "Techniques for computing rate and volume of stream depletion

by wells." Ground Water, 6(2), 37-46.Labadie, J., Baldo, M. and Larson, R., (2002). "MODSIM: Decision Support System

for River Basin Management, Documentation and User Manual," Colo. StateUniv. and U.S. Bureau of Reclamation, Ft. Collins, CO, 2002.

McDonald, M. G., and Harbaugh, A. W. (1988). Techniques of Water-Resources

Investigations of teh U.S. Geological Survey, U.S. Printing Office,

Washington, D.C.

Sophocleous, M., Koussis, A.,Martin, J. L., and Perkins S.P. (1995). "Evaluation of

simplified stream-aquifer depletion models for water rights administration."

Ground Water, 33(4), 579-588.

Triana, E., Labadie, J. W., and Gates, T. K. "Stream-Aquifer Interaction Modeling

Using Artificial Neural Networks." World Water and Environmental

Resources Congress, Philadelphia.



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Triana, E., Labadie, J. W., and Gates, T. K. "Basin-Scale Stream-Aquifer Modeling of

the Lower Arkansas River, Colorado." Hydrology Days, Colorado State

University, Fort Collins, CO.

Winter, T. C., Harvey, J. W., Franke, O. L., and Alley, W. M. (1998). "Ground water

and surface water-- A single resource." USGS.

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