Tracing hydrologic model simulation error as a function of satellite rainfall estimation bias components and land use and land cover conditions

Authors

Abebe Gebregiorgis, Tennessee Technological University
Yudong Tian, NASA Goddard Space Flight Center, University of Maryland
Christa Peters-Lidard, NASA Goddard Space Flight Center
Faisal Hossain, Tennessee Technological UniversityFollow

Date of this Version

2012

Citation

WATER RESOURCES RESEARCH, VOL. 48, W11509, doi:10.1029/2011WR011643, 2012Gebregiorgis, A. S., Y. Tian, C. D. Peters-Lidard, and F. Hossain (2012), Tracing hydrologic model simulation error as a function of satellite rainfall estimation bias components and land use and land cover conditions, Water Resour. Res., 48, W11509, doi:10.1029/2011WR011643

Comments

This article is a U.S. government work, and is not subject to copyright in the United States.

Abstract

The key question that is asked in this study is ‘‘how are the three independent bias components of satellite rainfall estimation, comprising hit bias, missed, and false precipitation, physically related to the estimation uncertainty of soil moisture and runoff for a physically based hydrologic model?’’ The study also investigated the performance of different satellite rainfall products as a function of land use and land cover (LULC) type. Using the entire Mississippi river basin as the study region and the variable infiltration capacity (VIC)-3L as the distributed hydrologic model, the study of the satellite products (CMORPH, 3B42RT, and PERSIANN-CCS) yielded two key findings. First, during the winter season, more than 40% of the rainfall total bias is dominated by missed precipitation in forest and woodland regions (southeast of Mississippi). During the summer season, 51% of the total bias is governed by the hit bias, and about 42% by the false precipitation in grassland-savanna region (western part of Mississippi basin). Second, a strong dependence is observed between hit bias and runoff error, and missed precipitation and soil moisture error. High correlation with runoff error is observed with hit bias (~0.85), indicating the need for improving the satellite rainfall product’s ability to detect rainfall more consistently for flood prediction. For soil moisture error, it is the total bias that correlated significantly (~0.78), indicating that a satellite product needed to be minimized of total bias for long-term monitoring of watershed conditions for drought through continuous simulation.