| dc.description.abstract | Long-term streamflow prediction is important not only to estimate the water storage of a reservoir but also the surface water intakes which supply people′s livelihood, agriculture, and industry. Climatological forecasts of streamflow (e.q., exceedance probability curve of inflow from the historical record) have been traditionally used for water resource management. However, due to the effect of environmental change, the transform of future weather conditions becomes more abnormal, impending effective management faces a greater challenge. Therefore, a long-term weather outlook issued by such agency as the Central Weather Bureau (CWB), which provides a clearer trend of future weather condition can be beneficial for water resource management. The decision-making process based on the weather outlooks with lower forecast accuracy should produce a more conservative decision. But the past approaches doesn′t.
In this study, I assessed the applicability of CWB long-term weather outlooks for determining ``the decision of lifting water rationing (water restriction)＂ first. I used Shimen Reservoir and the drought event in 2011, phase 1 water rationing had executed from March 1 to June 30, as our case study (case study I). By Assuming that the weather outlook reflects the weather forecast accuracy, I studied what effect by the accuracy on the decision-making. According to the weather outlooks (seasonal rainfall outlook) with the accuracy of 100$\%$, I can make a decision in the early March, that the normally supply can start from middle May. With the accuracy of seasonal rainfall outlook of 60$\%$, in the middle and late April, the termination of rationing in middle May can be estimated.
The results show that a more conservative decision is produced by the outlook with lower forecast accuracy (if the outlooks reflects the accuracy).
And next, I applied Bayes′ theorem to derive a method for incorporating the long-term weather accuracy into water resource management based on the weather outlook. The prediction of exceedance probability of Shimen Reservoir inflow is used as the case study (case study II). The results show that our approach can predict the inflow exceedance probability curves (IEPCs) reflecting the tercile probabilistic weather outlooks and the weather forecast accuracy. I employed a forecast skill score, RPSS (rank probability skill score) to show how the improvement of the weather forecast affects the decision. I found the potential problems of making the decision with this kind of categorical weather forecast: for some extreme event in a class, perfect rainfall forecast causes the performance of the decision worse than the decision based on the climatological forecast.
(case study II) Even if I arrange the predicted inflow distribution into categorical inflow forecast, the similar problem may arise, due to the rainfall class does not necessarily coincide with the class of the produced inflow.
Last, I considered the decision against water shortage with different the initial water storages of the reservoir. I assumed that the decision maker applies outlooks from different accuracies rainfall forecast.
If the storage if full, all of assessments (based on different weather accuracies) suggest the shortage happens with little chance. Beginning from the assessment based on the outlook from the rainfall forecast with the lowest rainfall forecast accuracy, as the initial storage decreases, the chance of happening water shortage increases. This approach should be useful for the seasonal planning and management of water resource and their risk assessment. | en_US |