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Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models
Author(s) -
Crow W. T.,
Chen F.,
Reichle R. H.,
Xia Y.,
Liu Q.
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2018gl077193
Subject(s) - surface runoff , environmental science , infiltration (hvac) , streamflow , water content , storm , hydrology (agriculture) , precipitation , runoff curve number , soil science , subsurface flow , antecedent moisture , moisture , water balance , soil water , groundwater , drainage basin , geology , meteorology , ecology , oceanography , physics , cartography , geotechnical engineering , biology , geography
Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in prestorm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the National Aeronautics and Space Administration Soil Moisture Active Passive Level‐4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between prestorm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation volume converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration‐excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent subsurface stormflow or saturation‐excess runoff generation processes.