Post processing the U.S. National Water Model with a Long Short-Term Memory network

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Jonathan Frame , Grey Nearing , Frederik Kratzert, Mashrekur Rahman 


The U.S. National Water Model (NWM) is a large scale hydrology simulator. Although NWM achieves coupling of multi-scale hydrological processes, its predictability at individual catchments can be improved. Hydrologic post-processing is an approach to reduce systematic simulation errors with statistical models, and has been shown to improve forecast accuracy of both calibrated and uncalibrated models. In this experiment we trained a Long Short-Term Memory (LSTM) network to post-process the NWM output, and tested performance at 531 basins across the continental United States. The LSTM post-processor provided a significant benefit to nearly all aspects of NWM streamflow predictions. The LSTM also benefited from NWM input - in particular, representation of hydrologic signatures improved, which indicates better representation of physical flow patterns.



Earth Sciences, Hydrology, Physical Sciences and Mathematics


Long short-term memory, National Water Model, theory-guided machine learning


Published: 2020-07-01 22:27

Last Updated: 2020-07-27 17:17

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Academic Free License (AFL) 3.0

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