Table of Contents
The purpose of this research is to more tightly integrated GIS and water quality modeling through use of Schematic Network Processing. Two methods for modeling the transport of pollutants through a river system are presented. The basic premise of these methods is the same. Both start with the a geodatabase representation of the basin and a schematic network abstraction of hydrologic transport. The two methods differ in how SchemaLink and SchemaNode features process passed and received loads. The bacteria study uses runoff and expected mean concentration (EMC) grids to estimate the load from a watershed that reaches the river. This load is then decayed as it travels through the river using a decay coefficient documented in literature. The nitrogen study uses statistical regression to optimize parameters describing the proportion of watershed load that is delivered to the river and the portion that is decayed as the load travels downstream. This approach, first introduced in SPARROW (SPAtially Referenced Regressions On Watershed attributes), involves statistically relating observed water-quality data to spatially reference basin characteristics (Smith et al 1997).
Using Schematic Network Processing for Regional Water Quality Modeling
The first step in both of these case studies was to develop a schematic network for the study basin. The Schematic Network for the bacteria study included three types of links and nodes (Figures 1 and 2). Type 1 links and nodes handled land the river transport, type 2 handle along river transport, and type 3 handle river to bay transport. The Schematic Network for the nitrogen study required just two types of links and nodes (Figure 2). Type 1 links and nodes handle land to river transport, and type 2 handle along river transport. The main difference between the two models is the processing DLLs called by each model for each type of link and node. These processing DLLs are documented in the papers describing both case studies, presented at the end of this page. It is important to note that the data development required for both case studies (i.e. the geodatabase and schematic network) are identical. This means that, if one wanted to use the SPARROW DLL for the bacteria model, one would not have to reinvest time building a geodatabase and schematic network. The design of Schematic Network Processing allows for a flexible, easily expandable water quality modeling solution.
Case Study One: Bacteria transport to Galveston Bay, Texas
The purpose of this case study was to estimate the transport of bacteria from non-point sources to the Galveston Bay from a GIS environment with ArcToolbox 9.0 technology. Schematic Network Processing was used to link the land, river, and bay systems. Runoff and Expected Mean Concentration (EMC) grids were used to determine the land to river delivery. The mass traveling down the river was decayed according to first-order decay reaction. The final concentration within the bay was calculated from the non-point loads and significant point loads, assuming CSTR conditions. This entire process was implemented using Model Builder allowing the processes to be linked and implemented with just one click.
WQModelingBacteria.doc - Document describing in detail the Galveston Bay bacteria study
ArcHydro_GalvestonBay.mdb - Geodatabse for Galveston Bay with Schematic Network
WaterQualityProcessors.dll - DLL used to decay loads. NOTE: This DLL must be registered on your machine in order to use this decay processing op with the ProcessSchematic tool.
WaterQualityProcessors.vbp - Source code for WaterQualityProcessors.dll. NOTE: The classes used for this study were clsDECAY and clsCFSTR
Figure 1 Galveston Bay geodatabase with schema links and nodes
Case Study Two: Nitrogen transport through the Guadalupe River at Victoria Basin
The purpose of this case study was to compute nitrogen loadings for river reaches using regression equations generated by the USGS model SPARROW (SPAtially Referenced Regression On Watershed attributes). Instead of using runoff and EMC grids to determine land to river delivery, and instead of using decay coefficients from literature for in river losses, SPARROW uses statistics to optimize these parameters based on observed pollutant levels and watershed attributes. The result is a model with predictions that more closely match observed data. The SAS Bridge for ESRI was used to transfer data from SAS input files to GIS feature classes.
Figure 2 Guadalupe River at Victoria geodatabase with schema links and nodes
Jon Goodall
Graduate Research Assistant
Center for Research in Water Resources
Department of Civil Engineering, University of Texas at Austin
(512) 471-0073
Tim Whiteaker
Graduate Research Assistant
Center for Research in Water Resources
Department of Civil Engineering, University of Texas at Austin
(512) 471-0073
These materials may be used for study, research, and education, but please credit the authors and the Center for Research in Water Resources, The University of Texas at Austin. All commercial rights reserved. Copyright 2003 Center for Research in Water Resources.