Deep learning for compute-efficient modeling of BMP impacts on stream- aquifer exchange and water law compliance in an irrigated river basin. (December 2019) - Rohmat, Faizal I.W.; Labadie, John W.; Gates, Timothy K.
Abstract: Inefficient irrigation practices in the alluvial Lower Arkansas River Basin (LARB) of Colorado are contributing to salinization, waterlogging, reduced crop yields, and harmful concentrations of pollutants in the stream-aquifer system. Intensive data collection and modeling efforts in the LARB over the past 20 years have resulted in development of the GIS-based basin-scale decision support system River GeoDSS. Parallel efforts in regional-scale calibration and application of the MODFLOW-SFR2-RT3D-OTIS stream-aquifer system model permit evaluation of best management practices (BMPs) designed to mollify adverse environmental impacts. Since BMP implementation is allowable only if water laws are not violated, a deep learning model is developed to serve as an accurate, compute-efficient surrogate of MODFLOW-SFR2 and is imbedded in River GeoDSS for assessing basin-scale impacts of BMP implementations on stream-aquifer exchange and water rights. It is shown that BMPs can be implemented while maintaining reasonable water law compliance with development of a new reservoir storage account. Highlights: Inefficient irrigation practices risk Lower Arkansas River Basin sustainability. Data and modeling have resulted in a river basin decision support system. Deep neural network model is developed as a surrogate stream-aquifer model. River basin-scale impacts of best management practices (BMP) are assessed. The BMP assessment ensures protection of water rights and policies in the basin.