Probabilistic assessment of the satellite rainfall retrieval error translation to hydrologic response

Abstract

Satellite-based precipitation retrieval techniques and algorithms have been developed to estimate precipitation from satellite observation. The realistic characterization of uncertainty in satellite precipitation estimate and the corresponding uncertain hydrologic response can better aid water resources managers in their decision making. In this study, the standard error of satellite-based PERSIANN-CCS rainfall estimates conditioning on the assumed true field (i.e. radar rainfall) is obtained according to a multivariate function considering the spatial and temporal scales. Accepting the multiplicative nature of this error, the Monte Carlo simulation is used to generate the ensemble of precipitation and propagate them into a conceptual hydrologic model to investigate the impact of input error on streamflow simulation. The statistical assessment of the results through probabilistic measures explores the more in-depth quality and reliability of the hydrologic response resulted from input error characterization. © 2010 Springer Science+Business Media B.V.