Estimating Basin‐Scale Water Budgets With SMAP Soil Moisture Data

Abstract

Soil Moisture Active Passive (SMAP) Level‐2 soil moisture retrievals collected during 2015–2017 are used in isolation to estimate 10‐day warm season precipitation and streamflow totals within 145 medium‐sized (2,000–10,000 km2) unregulated watersheds in the conterminous United States. The precipitation estimation algorithm, derived from a well‐documented approach, includes a locally calibrated loss function component that significantly improves its performance. For the basin‐scale water budget analysis, the precipitation and streamflow algorithms are calibrated with 2 years of SMAP retrievals in conjunction with observed precipitation and streamflow data and are then applied to SMAP retrievals alone during a third year. While estimation accuracy (as measured by the square of the correlation coefficient, r2, between estimates and observations) varies by basin, the average r2 for the basins is 0.53 for precipitation and 0.22 for streamflow. For the subset of 22 basins that calibrate particularly well, the r2 increases to 0.63 for precipitation and to 0.51 for streamflow. The magnitudes of the estimated variables are also accurate, with sample pairs generally clustered about the 1:1 line. The chief limitation to the estimation involves large biases induced during periods of high rainfall; the accuracy of the estimates (in terms of r2 and root‐mean‐square error) increases significantly when periods of higher rainfall are not considered. The potential for transferability is also demonstrated by calibrating the streamflow estimation equation in one basin and then applying the equation in another. Overall, the study demonstrates that SMAP retrievals contain, all by themselves, information that can be used to estimate large‐scale water budgets.

Mapped Hydroclimatology of Evapotranspiration and Drainage Runoff Using SMAP Brightness Temperature Observations and Precipitation Information

“…While such estimates reflect surface‐only soil moisture conditions, they found that remotely sensed soil moisture dose indeed convey and retain statistically significant amount of information regarding total storage dynamics. Additionally, Koster et al () demonstrated that SMAP soil moisture estimates can be used, to a reasonable degree, to estimate medium‐size basin precipitation and streamflow quantities and quantify large‐scale water budgets.…”

“…In the limit of gravity D, the flow rate approaches the unsaturated hydraulic conductivity, which is a scaled power law in soil volumetric moisture content (e.g., Clapp & Hornberger, ). Following Koster et al () and Jalilvand et al (), we also invoke a D function that is inspired by this physical consideration but has parameters that are estimated using the soil moisture and precipitation data input streams and : The c parameter controls the maximum rate of loss, and the d parameter controls the reduction of this loss by the level of soil moisture saturation. With flexible c and d parameters, the D function can assume a variety of forms, allowing for a wide range of water balance partitioning spanning from D dominated to ET dominated.…”

“…In the limit of gravity D, the flow rate approaches the unsaturated hydraulic conductivity, which is a scaled power law in soil volumetric moisture content (e.g., Clapp & Hornberger, 1978). Following Koster et al (2018) and Jalilvand et al (2018), we also invoke a D function that is inspired by this physical consideration but has parameters that are estimated using the soil moisture and precipitation data input streams and (1):…”

“…Recently, basin-scale water balance and closure studies have assessed the suitability of SMAP and other remotely sensed soil moisture estimates to track terrestrial water storage dynamics (Crow, Chen, et al, 2017;Crow, Han, et al, 2017), estimating precipitation (Brocca et al, 2013;Koster et al, 2016), enhancing remote sensing-based rainfall products (W. Crow et al, 2011;Pellarin et al, 2008), and improve streamflow forecasting via direct usage of SMAP soil moisture or as inputs into land data assimilation models (Crow, Chen, et al, 2017;Crow, Han, et al, 2017). Furthermore, a number of studies have made estimates of the total hydrologic loss function (sum of ET and runoff) using precipitation and SMAP information on surface soil moisture dynamics (Akbar et al, 2018b;Jalilvand et al, 2018;Koster et al, 2018Koster et al, , 2017. A key challenge is to partition this total loss function among its constituent ET and D losses components.…”

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

“…In recent years, the surface-reflected Global Navigation Satellite System (GNSS) signals have also been evaluated for SM estimations, which applies a different source of signals from the active/passive microwave sensors to observe the Earth's surface [14]. Moreover, the Advanced Scatterometer (ASCAT), which is an active microwave remote sensing instrument, provides global SM data sets derived from the backscatter measurements [15,16].SM products obtained from active/passive microwave remotely-sensed data have been applied in wide spectra of contexts [17][18][19][20][21][22][23][24][25][26]. However, SM data derived from most of the satellite sources provide the near surface moisture that needs to be converted in Root Zone Soil Moisture (RZSM) estimations [27,28].…”