TY - JOUR
T1 - Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
AU - Konapala, Goutam
AU - Kao, Shih Chieh
AU - Painter, Scott L.
AU - Lu, Dan
N1 - Publisher Copyright:
© 2020 The Author(s). Published by IOP Publishing Ltd
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Long short-term memory (LSTM) network
KW - Machine learning (ML)
KW - Process-based hydrologic models
KW - Sacramento soil moisture accounting model (SAC)
UR - http://www.scopus.com/inward/record.url?scp=85092553757&partnerID=8YFLogxK
U2 - 10.1088/1748-9326/aba927
DO - 10.1088/1748-9326/aba927
M3 - Article
AN - SCOPUS:85092553757
SN - 1748-9318
VL - 15
JO - Environmental Research Letters
JF - Environmental Research Letters
IS - 10
M1 - 104022
ER -