A stochastic approach for seasonal water-shortage probability forecasting based on seasonal weather outlook

This study proposed a stochastic approach to forecast water-shortage probabilities for the coming three months in central Taiwan. Monte Carlo method is used to repeat random sampling from the seasonal weather outlook. For each Monte Carlo trial, the monthly rainfalls and monthly mean temperatures for one to three months ahead in eleven upstream catchments of central Taiwan can be obtained. Further, the disaggregation model is used to convert the monthly values into daily rainfall and temperature series. The HBV-based hydrological model uses the daily series to simulate daily inflows for each catchment as the input of system dynamic model for simulating the water budget of water resources system. After all the Monte Carlo trails, the monthly water-shortage probabilities for one to three months ahead can be calculated. The results reveal that the proposed approach can reasonably forecast the watershortage conditions for one to three months ahead, which are beneficial for regional drought warning and decision support of drought-disaster prevention.