Sensitivity Analyses of Satellite Rainfall Retrieval and Sampling Error on Flood Prediction Uncertainty

Abstract

The Global Precipitation Measurement mission planned jointly by the United States, Japanese, and European space agencies envisions providing global rainfall products from a constellation of passive microwave (PM) satellite sensors at time scales ranging from 3-6 h. In this paper, a sensitivity analysis was carried out to understand the implication of satellite PM rainfall retrieval and sampling errors on flood prediction uncertainty for medium-sized (/spl sim/100 km/sup 2/) watersheds. The 3-h rainfall sampling gave comparable flood prediction uncertainties with respect to the hourly sampling, typically used in runoff modeling, for a major flood event in Northern Italy. The runoff prediction error, though, was magnified up to a factor of 3 when rainfall estimates were derived from 6-h PM sampling intervals. The systematic and random error components in PM retrieval are shown to interact with PM sampling introducing added uncertainty in runoff simulation. The temporal correlation in the PM retrieval error was found to have a negligible effect in runoff prediction. It is shown that merging rain retrievals from hourly infrared (IR) and PM observations generally reduces flood prediction uncertainty. The error reduction varied between 50% (0%) and 80% (50%) for the 6-h (3-h) PM sampling scenarios, depending on the relative magnitudes of PM and IR retrieval errors. Findings from this paper are potentially useful for the design, planning, and application assessment of satellite remote sensing in flood and flash flood forecasting.