HR: 17:30h
AN: GC44A-07    [Abstracts]
TI: An Investigation of the Impact from Different Rainfall Sources on Flash Flood Prediction using Artificial Neural Networks
AU: * Chiang, Y
EM: chiangym@ntu.edu.tw
AF: National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 106 Taiwan
AU: Hsu, K
EM: kuolinh@uci.edu
AF: University of California, Irvine, E-4150 Engineering Gateway, University of California, Irvine, CA 926 United States
AU: Hong, Y
EM: yanghong@uci.edu
AF: University of California, Irvine, E-4150 Engineering Gateway, University of California, Irvine, CA 926 United States
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: University of California, Irvine, E-4150 Engineering Gateway, University of California, Irvine, CA 926 United States
AU: Chang, F
EM: changfj@ntu.edu.tw
AF: National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 106 Taiwan
AB: An artificial neural network (ANN) based rainfall-runoff model was developed to forecast flash floods by using satellite-derived forcing data during typhoon periods over Keelung watershed, located on northern Taiwan. The satellite rainfall estimates over Taiwan are generated through PERSIANN CCS at grid of 4 km and temporal resolution of half hour. Validation of satellite estimates with gauge measurements shows the PERSIANN CCS captures the heavy rainfall in terms of trend and peak volume but slightly underestimates the light rainfall, in particular the initial stage of the storm events. The major goal of this study is to investigate the impact of rainfall forcing data from different sources on flood forecasting. First, an ANN flood prediction model was calibrated by using datasets of 13 historical gauge-streamflow events. Second, 6 latest flood events were used to investigate the flood prediction results driven by satellite-derived rainfall and gauge observations, respectively. Finally, the realization of uncertainty quantification was examined by propagating satellite-derived precipitation ensemble into the flood prediction model. Our results exemplify the need for a better representation of satellite-derived precipitation error structure and a detailed investigation of the techniques that propagate the input error into hydrological models.
DE: 1821 Floods
DE: 1854 Precipitation (3354)
DE: 1860 Streamflow
DE: 3354 Precipitation (1854)
DE: 3360 Remote sensing
SC: Global Climate Change [GC]
MN: Fall Meeting 2005