| Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations - :15162 | National Weather Service (NWS)
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Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations
  • Published Date:
    2015
  • Source:
    Journal of Hydrology, 529, 779-794.
Filetype[PDF-3.31 MB]


Details:
  • Description:
    This study explores mitigating bias in satellite quantitative precipitation estimates (SQPE) and improving hydrologic predictions at ungauged locations via adjustment of the mean field bias (MFB) in SQPE and data assimilation (DA) of streamflow observations in a distributed hydrologic model. In this study, a variational procedure is used to adjust MFB in Climate Prediction Center MORPHing (CMORPH) SQPE and assimilate streamflow observations at the outlet of Elk River Basin in Missouri into the distributed Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic wave routing models. The benefits of assimilation are assessed by comparing the streamflow predictions with or without DA at both the outlet and an upstream location, and by comparing the soil moisture grids forced by CMORPH SQPE against those forced by higher-quality multisensor quantitative precipitation estimates (MQPE) from National Weather Service. Special attention is given to the dependence of the efficacy of DA on the quality and latency of the SQPE, and the impact of dynamic correction of MFB in the SQPE via DA. The results show that adjusting MFB in CMORPH SQPE in addition to assimilating outlet flow reduces 66% of the bias in the CMORPH SQPE analysis and the RMSE of 12-h streamflow predictions by 81% at the outlet and 34-62% at interior locations of the catchment. Compared to applying a temporally invariant MFB for the entire storm, the DA-based, dynamic MFB correction reduces the RMSE of 6-h streamflow prediction by 63% at the outlet and 39-69% at interior locations. It is also shown that the accuracy of streamflow prediction deteriorates if the delineation of the precipitation area by CMORPH SQPE is significantly different, as measured by the Hausdorff distance, from that by MQPE. When compared with adjusting MFB in the CMORPH SQPE over the entire assimilation window, adjusting the MFB for all but the latest 18 h (i.e., the latency of CMORPH SQPE) within the assimilation window reduces the mean square error (MSE)-based skill score of streamflow predictions at the outlet by up to 0.08 and at interior locations by up to 0.13. (C) 2015 Elsevier B.V. All rights reserved.

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