A decision-making model for flood warning system based on ensemble forecasts

The purpose of this study is to develop a flood warning system based on Atmospheric Ensemble Forecasts. Although ensemble forecasts are increasingly employed for flood forecasting, developing a flood warning system based on ensemble forecasts has not been adequately addressed yet. In this study, first a Weather Research and Forecasting (WRF) model was used to forecast the heavy precipitation in Kan Basin, Iran. Ensemble storms were forecasted using five cumulus schemes including Kain-Fritsch, Betts-Miller-Janjic, Grell 3D ensemble, Multi-scale Kain-Fritsch and Grell-Devenyi ensemble cumulus scheme. Then, a Bayesian Networks (BN) was developed to estimate the flood peak using the atmospheric ensemble forecasts. Finally, a Fuzzy-TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) model was prepared for making decisions for flood warning scenarios considering all effective factors in flood warning and uncertainty associated with them. Assessment of the proposed flood warning system was examined for various scenarios. It showed that when a significantly high probability was assigned to a warning level, that level had the maximum closeness coefficient and consequently chosen as a warning level. Yet, if the probability was distributed equally between some warning levels, the flood warning system acts cautiously since the decision-making model allocated the highest rank to the stronger warning level. Regarding the reasonable results of this study, applying the Fuzzy-TOPSIS model to develop a flood warning system based on atmospheric ensemble forecasts is recommended to apply in similar catchments for addressing the uncertainties.