HR: 09:30h
AN: H11G-06    [Abstracts]
TI: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response
AU: * Hong, Y
EM: yanghong@uci.edu
AF: Center for Hydrometeorology and Remote Sensing Department of Civil and Environmental Engineering University of California, Irvine , E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AU: Moradkhani, H
EM: moradkha@uci.edu
AF: Center for Hydrometeorology and Remote Sensing Department of Civil and Environmental Engineering University of California, Irvine , E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AU: Hsu, K
EM: kuolinh@uci.edu
AF: Center for Hydrometeorology and Remote Sensing Department of Civil and Environmental Engineering University of California, Irvine , E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: Center for Hydrometeorology and Remote Sensing Department of Civil and Environmental Engineering University of California, Irvine , E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AB: A general framework to quantify the error associated with satellite-based precipitation estimates, at various spatial and temporal scales is presented. In addition, the impact of using such precipitation data as input to a hydrologic rainfall-runoff model is examined. The uncertainty in the satellite-based precipitation estimates, as a function of the space (A), time (T), sampling frequency (Dt), and spatio-temporal average of precipitation estimates (R), using two years of high resolution PERSIANN-CCS* precipitation data over Southwest U.S, is determined. Parameter sensitivity analysis is conducted at 5o x 5o latitude-longitude grids for 16 selected areas. The eventual goal of this latter step is to obtain a generalization of the error function. The influence of spatio-temporal precipitation errors on hydrologic response is examined using a Monte Carlo approach. By this approach, an ensemble of precipitation data is generated, as forcing to the hydrologic model, and the resulting uncertainty in the forecasted streamflow is estimated. The applicability and usefulness of this procedure is demonstrated in the case of the Leaf River Basin, located north of Collins, Mississippi. It is shown that the current strategy offers a more realistic uncertainty assessment of precipitation estimates and the correspondingly streamflow forecasts. *Hong, Y., K. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network--Cloud Classification System, Journal of Applied Meteorology, in press.
DE: 1854 Precipitation (3354)
DE: 1860 Runoff and streamflow
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting