| dc.description.abstract | Water resource is necessary for human. The drought causes economic losses not only in agriculture but also in industry and commerce. Climate forecasts help us to predict rainfalls before droughts happen. Policymakers can make decisions in advance to reduce the drought-induced impact on agriculture, industry or commerce. However, there are uncertainties in climate forecasts. Quantification of the risk of decisions that are made based on climate forecasts and the corresponding information value of climate forecasts becomes an important issue of water resource management.
In this study, I used the seasonal climate forecasts and the SIA-CWBI system to simulate the agricultural water supplies in Shimen, estimated the amounts of the agricultural water shortages which were calculated from the differences between the agricultural water demands and supplies, found out the compensation costs and the water tariffs, and got the information value of the climate forecasts with the decision trees.
In addition, I used the CROPWAT system to simulate the water demands for rice, estimated the amounts of the water shortages for rice which equal to the amounts of the water demands for rice minus the water supplies for rice, converted the amounts of the water shortages into the economic losses, and got the information value of the climate forecasts with the decision trees.
Finally, I evaluated the expect value of perfect information (EVPI) and the expect value of imperfect information (EVII) of the short-term climate forecast on the fallow decision. The results show that EVII increased as the degree of the accuracy of the forecasts increased. In addition, the maximum EVPI/EVII occurred when the fallow decision based on the initial water storage was difficult to make. I also constructed a framework of agricultural water usage with the help of monthly climate forecasts. By following the framework, decision-makers can reduce the damage caused by droughts and quantify the information value of the monthly climate forecasts.
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