Effective water quality management requires careful consideration of pollutant fate and transport, proper estimation of non-point source loadings, and maximum allowable allocation of point source discharges. A decision support system (DSS) that addresses all these issues is developed in this study by embedding mass-balance expressions, GIS, and a remote-sensing-based non-point source loading scheme into a hybrid goal-programming approach and is applied to the rapidly growing Arroyo Colorado River watershed along the US–Mexico border. The model components were favorably evaluated against field data and previous studies. The DSS was used to evaluate the carrying capacity of the river, defined based on the water quality standards for biochemical oxygen demand, dissolved oxygen, and minimum in-stream flow requirements. The results indicated that on a macro-scale, the current stresses utilize about 40 % of the maximum carrying capacity. However, the most upstream and downstream sub-watersheds are currently over stressed and need to reduce their loadings. The assimilative capacity of the river is not sufficient to carry current flows at their permitted discharge concentrations implying an inequity among discharges with regard to treatment cost burden. Sensitivity analyses indicated that the carrying capacity was more affected by policy choices made for water quality standards then where they were to be enforced (i.e., compliance locations). Urban areas currently cover 13 % of the watershed but contribute nearly 45 % of the total non-point source loadings. Therefore, the urbanization in this watershed must be carefully planned with emphasis on stormwater treatment and management to sustain this valuable resource for future generations.
|Pages (from-to)||1 - 15|
|Journal||Clean Technologies and Environmental Policy|
|State||Published - Nov 2012|