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Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements
Author(s) -
Akbar Ruzbeh,
Short Gianotti Daniel J.,
Salvucci Guido D.,
Entekhabi Dara
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr027307
Subject(s) - streamflow , precipitation , environmental science , surface runoff , drainage , evapotranspiration , drainage basin , hydrology (agriculture) , hydrological modelling , climatology , geology , meteorology , geography , ecology , cartography , biology , geotechnical engineering
Microwave brightness temperature observations from the NASA Soil Moisture Active Passive (SMAP) mission and gauge‐based precipitation data over the United States are used to reconstruct the soil water loss function and then historical (1979–2019) hydrological fluxes in the form of evapotranspiration (ET) and drainage (D) are quantified. Over the period of study, with the exception of snowy and hyper‐arid regions, we observe a correlation of R 2 > 0.6 between SMAP‐precipitation derived drainage estimates and streamflow measurements from the U.S. Geological Survey (USGS). There is a bias between estimated drainage and USGS streamflow with an underestimation of about 1 mm day −1 in southwest United States to 3 mm day −1 in parts of the eastern United States. SMAP‐derived sensitivities of drainage and ET partitioning with respect to precipitation anomalies are also calculated. In parts of the Great Plains the drainage partitioning exhibits a near‐linear response, while in the southeast United States, the response is nonlinear. Partitioning also is examined for 6 four‐digit hydrologic unit basins wherein year‐to‐year variations in drainage partitioning are shown to be key mediators in translating precipitation anomalies into streamflow anomalies. Observation‐driven drainage and ET estimates are obtained without relying on full hydrologic and Land Surface Models (LSMs). This independence (isolation from model parameterization assumptions) provides a path toward using satellite‐derived landscape hydrological diagnostics to assess hydrologic models and LSMs as well as to guide their further development.