Hydrologic evaluation of satellite precipitation products over a mid-size basin

Ali Behrangi,Behnaz Khakbaz,Tsou Chun Jaw,Amir Aghakouchak,Kuolin Hsu,Soroosh Sorooshian

doi:10.1016/j.jhydrol.2010.11.043

Ali Behrangi, Behnaz Khakbaz + Show 4 more

Open Access

https://doi.org/10.1016/j.jhydrol.2010.11.043

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Abstract

► We evaluated 5 satellite-based rain products at input level and after streamflow simulation. ► Streamflows were reasonably captured at both 6-hour and monthly time scales. ► We showed the input rain highly influences simulated streamflows for a mid-size basin. ► The bias-adjustment of rain products was found critical for hydrologic applications. Since the past three decades a great deal of effort is devoted to development of satellite-based precipitation retrieval algorithms. More recently, several satellite-based precipitation products have emerged that provide uninterrupted precipitation time series with quasi-global coverage. These satellite-based precipitation products provide an unprecedented opportunity for hydrometeorological applications and climate studies. Although growing, the application of satellite data for hydrological applications is still very limited. In this study, the effectiveness of using satellite-based precipitation products for streamflow simulation at catchment scale is evaluated. Five satellite-based precipitation products (TMPA-RT, TMPA-V6, CMORPH, PERSIANN, and PERSIANN-adj) are used as forcing data for streamflow simulations at 6-h and monthly time scales during the period of 2003–2008. SACramento Soil Moisture Accounting (SAC-SMA) model is used for streamflow simulation over the mid-size Illinois River basin. The results show that by employing the satellite-based precipitation forcing the general streamflow pattern is well captured at both 6-h and monthly time scales. However, satellites products, with no bias-adjustment being employed, significantly overestimate both precipitation inputs and simulated streamflows over warm months (spring and summer months). For cold season, on the other hand, the unadjusted precipitation products result in under-estimation of streamflow forecast. It was found that bias-adjustment of precipitation is critical and can yield to substantial improvement in capturing both streamflow pattern and magnitude. The results suggest that along with efforts to improve satellite-based precipitation estimation techniques, it is important to develop more effective near real-time precipitation bias adjustment techniques for hydrologic applications.

Highlights

Precipitation is the key input for hydrometeorological modeling and applications
After bias adjustment, the over and under-estimations are significantly reduced with overall statistics demonstrating negligible BIAS for TMPA-V6 (1.7%) and PERSIANN-adj (6.2%)
Over a mid-sized basin, 6 years of 5 satellite-based precipitation products namely TMPA-RT, TMPA-V6, CMORPH, PERSIANN and PERSIANN-adj are first evaluated with respect to multi-sensor (NEXRAD and gauge) dataset

Summary

Introduction

Precipitation is the key input for hydrometeorological modeling and applications. Reliable quantification of precipitation data is crucial. In many populated regions of the world including developing countries, ground-based measurement networks (whether from radar or rain gauge) are either sparse in both time and space or nonexistent. This situation restricts these regions to manage water resources and hampers early flood warning systems resulting in massive socioeconomic damages. With suites of sensors flying on a variety of satellites over the last three decades, many satellite-based precipitation estimation algorithms have been developed to make the precipitation data available to the community in quasi-global scale.

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