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Developing the Remote Sensing-based Early Warning System for Monitoring TSS Concentrations in Lake Mead
Citation:
Imen, S., N. Chang, AND J. Yang. Developing the Remote Sensing-based Early Warning System for Monitoring TSS Concentrations in Lake Mead. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, 160:73-89, (2015).
Impact/Purpose:
This journal article is to communicate to technical and scientific community a satellite-based early warning system developed for assisting water utilities to better adapt to climate-related source water changes.
Description:
Adjustment of the water treatment process to changes in the water quality has been an area of focus for engineers and managers of water treatment plants. This desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. In the forecasts, water quality variation at the water intake can be updated daily. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada in the water-stressed western U.S. The daily TSS concentration maps can support not only water treatment operations, but also the analysis of TSS sources and origins of its variability associated with erosion impact at the watershed scale. The results demonstrated the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake.
