期刊
ENVIRONMENTAL RESEARCH LETTERS
卷 15, 期 10, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1748-9326/aba927
关键词
process-based hydrologic models; machine learning (ML); sacramento soil moisture accounting model (SAC); long short-term memory (LSTM) network
资金
- ExaSheds project within the US Department of Energy, Office of Science, Biological and Environmental Research Program
- US Department of Energy [DE-AC05-00OR22725]
Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with an ML algorithm known as long short-term memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash-Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e. NSE < 0). However, all models performed poorly in catchments with extended low flow periods, suggesting need for additional research.
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