|DRAFT: Modeling Flow-Salinity Relationships in the Sacramento-San Joaquin Delta Using Artificial Neural Networks|
SUMMARY AND FINDINGS
A fast, accurate model to estimate salinities in the Sacramento-San Joaquin Delta, given flow inputs, is an important tool but not an easy one to develop. Uses for such a model include estimating Marginal Export Cost (MEC, also known as carriage water); a replacement for the MDO routine in the statewide planning model DWRSIM; realtime flow/salinity estimation; and reservoir release optimization studies. Attempts at developing such a model have been made over many years with less than full success. A fairly recent mathematical/programming technique known as Artificial Neural Networks (ANNs) was applied to the problem with considerable success. ANNs offer several advantages over previous methods: they allow multiple, arbitrary inputs, they easily allow "memory" to be incorporated, they are not confined to pre-determined impulse-response function shapes. ANN models are developed by first calibrating the internal coefficients of an ANN with sequences of flows and salinities at a location of interest. Once calibrated, new flow inputs are provided and estimated salinities produced.
There is a strong need to "model a model" of the Delta; in other words, to have available an ANN calibrated on the salinity output of another numerical model such as the DWR Delta Simulation Model (DWRDSM). Ideally, DWRDSM would be incorporated into DWRSIM directly, but this is impractical because of tremendous differences in running time between the two models. Instead, a faithful, fast imitation of DWRDSM can be developed using ANNs and used in DWRSIM.
ANN models were developed at several western and interior Delta locations, and preliminary studies performed about a variety of topics. Major findings are:
Further work needs to be done to incorporate barriers and gates in the ANN, and to estimate the amount of carriage water that occured historically.
The full-length version of this report is available by mail; contact Ralph Finch for more information.
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