Literature DB >> 30319160

Estimating Basin-Scale Water Budgets with SMAP Soil Moisture Data.

Randal D Koster1, Wade T Crow2, Rolf H Reichle1, Sarith P Mahanama1,3.   

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 two 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 RMSE) 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.

Entities:  

Keywords:  1855 Remote sensing (1640, 4337); 1866 Soil moisture; 1876 Water budgets

Year:  2018        PMID: 30319160      PMCID: PMC6179158          DOI: 10.1029/2018WR022669

Source DB:  PubMed          Journal:  Water Resour Res        ISSN: 0043-1397            Impact factor:   5.240


  5 in total

1.  Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation.

Authors:  Randal D Koster; Qing Liu; Sarith P P Mahanama; Rolf H Reichle
Journal:  J Hydrometeorol       Date:  2018-05-01       Impact factor: 4.349

2.  Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals.

Authors:  Randal D Koster; Luca Brocca; Wade T Crow; Mariko S Burgin; Gabrielle J M De Lannoy
Journal:  Water Resour Res       Date:  2016-09-06       Impact factor: 5.240

3.  A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture.

Authors:  Randal D Koster; Rolf H Reichle; Sarith P P Mahanama
Journal:  J Hydrometeorol       Date:  2017-03-09       Impact factor: 4.349

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

Authors:  W T Crow; F Chen; R H Reichle; Y Xia; Q Liu
Journal:  Geophys Res Lett       Date:  2018-05-04       Impact factor: 4.720

5.  L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

Authors:  W T Crow; F Chen; R H Reichle; Q Liu
Journal:  Geophys Res Lett       Date:  2017-05-10       Impact factor: 4.720

  5 in total
  1 in total

1.  Inferring causal relations from observational long-term carbon and water fluxes records.

Authors:  Emiliano Díaz; Jose E Adsuara; Álvaro Moreno Martínez; María Piles; Gustau Camps-Valls
Journal:  Sci Rep       Date:  2022-01-31       Impact factor: 4.379

  1 in total

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