Application of a spatially referenced water quality model to predict E. coli flux in two Texas river basins

Water quality models are applied to assess the various processes affecting the concentrations of contaminants in a watershed. SPAtially Referenced Regression On Watershed attributes (SPARROW) is a nonlinear regression based approach to predict the fate and transport of contaminants in river basins. In this research SPARROW was applied to the Guadalupe and San Antonio River Basins of Texas to assess E. coli contamination. Since SPARROW relies on the measured records of concentrations of contaminants collected at monitoring stations for the prediction, the effect of the locations and selections of the monitoring stations was analyzed. The results of SPARROW application were studied in detail to evaluate the contribution from the statistically significant sources. For verification of SPARROW application, results were compared to 303 (d) list of Clean Water Act, 2000. Further, a methodology to maintain the monitoring records of the highly contaminated areas in the watersheds was explored with the application of the genetic algorithm. In this study, the importance of the available scale and details of explanatory variables (sources, land-water delivery and reservoir/ stream attenuation factors) in predicting the water quality processes were also analyzed. The effect of uncertainty in the monitored records on SPARROW application was discussed. The application of SPARROW and genetic algorithm were explored to design a monitoring network for the study area. The results of this study show that SPARROW model can be used successfully to predict the pathogen contamination of rivers. Also, SPARROW can be applied to design the monitoring network for the basins.

Author dc:creator

• , Deepti

Contributors dc:contributor

• Karthikeyan, Raghupathy

• SPARROW
• water quality
• model selection
• genetic algorithm

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1969.1/ETD-TAMU-2878

OAI record oai:identifier

https://tdl-ir.tdl.org/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Atdl-ir.tdl.org%3A1969.1%2FETD-TAMU-2878