Using Geographic Information System (GIS) and Multivariate Regression to Model Salt and Selenium Loads in Regions of the Upper Colorado River Basin
This work seeks to quantify GIS information and use it as variable input into regression models that can be used to predict, track, and manage water quality in the Colorado River and its tributaries.
Need and Benefit
Legal issues for water quality include international treaties governing the concentration of salinity at the United States/Mexico border as well as establishing criterion for interstate water delivery. Environmental issues for water quality include salt and selenium contamination resulting from irrigation hinder downstream agricultural and domestic water use and may be affecting endangered fish repopulation efforts and other fish and wildlife. Waters with elevated salinity levels resulting from human activities (irrigation) are also unable to act as dilution water for areas where high salt loading is a natural phenomenon (Glenwood Springs, Colorado, for example).
Output from the proposed model would be used to locate and quantify areas where salt and selenium load reductions may be feasible and to estimate loading scenarios in regions of proposed development or transitional land use. Load reductions for salt and selenium would be modeled using remediation scenarios such as polyacrylamide (PAM) applications in irrigation delivery systems or reducing deep percolation from nonagricultural sources. Examples of possible loading scenarios in regions of proposed development or transitional use might include Native American water rights development or population-growth issues in arid regions. Specifically, the model is designed to represent ambient conditions given input information from the geospatial data. As a result, simulation of loads resulting from remediation and/or development scenarios is feasible when input variables are changed to reflect management ideas.
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