Short‐term flood inundation prediction using hydrologic‐hydraulic models forced with downscaled rainfall from global NWP |
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| Authors: | Do Hoai Nam Dang Thanh Mai Keiko Udo Akira Mano |
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| Affiliation: | 1. International Research Institute of Disaster Science, Tohoku University, Sendai, Japan;2. National Centre for Hydrometeorological Forecasting, Ha Noi, Vietnam |
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| Abstract: | A short‐term flood inundation prediction model has been formulated based on the combination of the super‐tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one‐dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically‐based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super‐tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall–runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model. Copyright © 2013 John Wiley & Sons, Ltd. |
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| Keywords: | numerical weather prediction statistical downscaling flow and stage prediction |
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