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Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.


ABSTRACT: 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 pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA 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 pre-storm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation depth 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 sub-surface stormflow or saturation-excess runoff generation processes.

SUBMITTER: Crow WT 

PROVIDER: S-EPMC6140354 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.

Crow W T WT   Chen F F   Reichle R H RH   Xia Y Y   Liu Q Q  

Geophysical research letters 20180504 10


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 pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonst  ...[more]

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