Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Akbar, Ruzbeh; Short Gianotti, Daniel J.; Salvucci, Guido D.; Entekhabi, Dara
• Is Part Of: Water resources research. Volume 56:Issue 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-01
• Subjects: hydrology -- remote sensing -- SMAP -- evapotranspiration -- drainage -- hydroclimate; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR027307
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: A large amount of hydrologic information is encoded within satellite remote sensing observations of soil moisture SMAP‐era soil moisture and precipitation information, without reliance on models, can characterize landscape hydrological partitioning Historical remote sensing and precipitation‐based drainage estimates closely track streamflow measurements across the United States