Abstract:Even though gauged rainfall data generally provide accurate depth measurements, sparsely spaced, gauging stations cannot effectively account for the spatial variability of precipitation at basin scale. On the other hand, radar data such as the WSR-88D stage III radar rainfall data can generally capture the spatial variability of rainfall fields, but tends to underestimate rainfall depth of stratiform storms, or both convective and stratiform storms if a storm is of low intensity. To take advantage of both the strength of radar data (mapping accurate spatial variability of rainfall) and that of gauge data (accurate depth measurements), the two data sets were merged together by the Statistical Objective Analysis (SOA) scheme. The event-based hydrologic experiments using a semi-distributed, physics-based hydrologic model (distributed physically based hydrologic model using remote sensing, DPHM-RS) revealed that WSR-88D Stage III radar rainfall data simulated more accurate runoff hydrographs than gauged data for convective storms but less accurate runoff hydrograph for stratiform storms, because radars measured slightly more rainfall than gauges for convective storms, but substantially less rainfall for stratiform storms. However, after merging WSR-88D stage III radar data with gauge data by SOA, the radar's underestimation of stratiform storm depth decreased substantially, but the adjustment could be counter productive for convective storms. Results show that rainfall spatial variability, depths, and hydrologic model resolution play a major role on the accuracy of simulated runoff volumes and peak flows.

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Abstract:The infrared-microwave rainfall algorithm (IMRA) was developed for retrieving spatial rainfall from infrared (IR) brightness temperatures (TBs) of satellite sensors to provide supplementary information to the rainfall field, and to decrease the traditional dependency on limited rain gauge data that are point measurements. In IMRA, a SLOPE technique (ST) was developed for discriminating rain/no-rain pixels through IR image cloud-top temperature gradient, and 243K as the IR threshold temperature for minimum detectable rainfall rate. IMRA also allows for the adjustment of rainfall derived from IR-TB using microwave (MW) TBs. In this study, IMRA rainfall estimates were assessed on hourly and daily basis for different spatial scales (4, 12, 20, and 100 km) using NCEP stage IV gauge-adjusted radar rainfall data, and daily rain gauge data. IMRA was assessed in terms of the accuracy of the rainfall estimates and the basin streamflow simulated by the hydrologic model, Sacramento soil moisture accounting (SAC-SMA), driven by the rainfall data. The results show that the ST option of IMRA gave accurate satellite rainfall estimates for both light and heavy rainfall systems while the Hessian technique only gave accurate estimates for the convective systems. At daily time step, there was no improvement in IR-satellite rainfall estimates adjusted with MW TBs. The basin-scale streamflow simulated by SAC-SMA driven by satellite rainfall data was marginally better than when SAC-SMA was driven by rain gauge data, and was similar to the case using radar data, reflecting the potential applications of satellite rainfall in basin-scale hydrologic modelling.

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