Application of Multi-Model Superensemble technique to flood forecasting through distributed hydrologic models

HR: 12:05h
AN: H22A-08 [Abstracts]
TI: Application of Multi-Model Superensemble technique to flood forecasting through distributed hydrologic models
AU: * Ajami, N K
EM: nkhodata@uci.edu
AF: University of California, Irvine, Civil and environmental Eng. E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AU: Duan, Q
EM: qduan@llnl.gov
AF: Lawrence Livermore National Laboratory, P.O. Box 808, L-103 7000 East Avenue , Livermore, CA 94551 United States
AU: Gao, X
EM: gaox@uci.edu
AF: University of California, Irvine, Civil and environmental Eng. E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: University of California, Irvine, Civil and environmental Eng. E/4130 Engineering Gateway, Irvine, CA 92697-2175 United States
AB: Streamflow forecasts are generally produced through the use of a single hydrologic model. In spite of the existence of a wide range of hydrologic models, it is hard to claim that any single model among them performs better than the rest, for all type of watersheds under all conditions. This is because hydrologic models, lumped or distributed, introduce many assumptions and simplifications in their structure. Since various model structures capture different aspects of the watershed processes, one way of exploiting the strength of different models and compensating for their weaknesses is to obtain consensus predictions by combining their results using model combination techniques such as Multi Model SuperEnsmble (MMSE). MMSE is a special case of ensemble techniques, which consider the model outputs as ensemble members. This study surveys the performance of MMSE for flood forecasting by using the simulation results from various distributed models participated in the Distributed Model Intercomparison Project (DMIP), an international project sponsored by National Weather Service. The key questions addressed in this study are: (1) What is the skill level of the consensus forecast compared to those of individual forecasts? (2) How many models do we need to produce accurate consensus forecasts? (3) Can model combination techniques compensate for the inadequacy of model calibration? Simulations for the Illinois River Basin at Watts from 7 uncalibrated DMIP models are combined and the results are compared to the calibrated model results.
DE: 1800 HYDROLOGY
DE: 1821 Floods
DE: 1860 Runoff and streamflow
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting