Artificial Neural Network for Sacramento–San Joaquin Delta Flow–Salinity Relationship for CalSim 3.0
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 4
Abstract
The California State Water Project (SWP) along with the Central Valley Project (CVP), under various environmental regulations, manage California’s complex water storage and delivery system. An important part of the water system regulations strictly limit salinity intrusion into the Sacramento–San Joaquin Delta, which is a complex tidal estuary with many factors influencing its salinity. The nonlinear relationship of these factors on salinity make the system operations challenging. Operational models [e.g., California Water Resources Simulation Model (CalSim) and CalSim Lite (CalLite)] are used to provide guidelines to decision makers for efficient planning and management of the system. But these operational models are not designed to directly simulate the salinity. The hydrodynamic and water quality model, California Department of Water Resources (DWR) Delta Simulation Model II (DSM2), is needed to simulate the salinity. Because of a linking problem and longer simulation time of DMS2, it is impractical to use DSM2 directly in operational models. This paper presents the development, improvement, and successful application of an artificial neural network (ANN). The ANN, when fully integrated into CalSim and CalLite, emulates the Delta salinity so that the operational models, when coupled with the ANN, can simulate the salinity management in the Delta. The newly developed and improved ANN reported in this research, when used in the CalSim model, provides more accurate insights on the salinity regime in the Delta, which is conducive to more efficient use of the freshwater in the Delta resulting in the more efficient overall operation of the SWP and CVP.
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Data Availability Statement
The following models used during the study are available online: CalSim 3.0 Model (https://water.ca.gov/Library/Modeling-and-Analysis/Central-Valley-models-and-tools/CalSim-3); DSM2 Model (http://baydeltaoffice.water.ca.gov/modeling/deltamodeling/models/dsm2/dsm2.cfm); DCD Model (https://water.ca.gov/Library/Modeling-and-Analysis/Bay-Delta-models-and-tools/DCD). The following data generated or used during the study are available from the corresponding author by request: input data used in artificial neural network training; output salinity data from DSM2 Model and CalSim 3.0 model.
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Information
Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 4 • April 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 26, 2019
Accepted: Oct 12, 2019
Published online: Feb 8, 2020
Published in print: Apr 1, 2020
Discussion open until: Jul 8, 2020
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