Predicting future exceedance probability of streamflow using the long-term weather outlook and logistic regression: a case study in Northern Taiwan

Abstract In Taiwan, the Long-Term Weather Outlook, which is provided by the Central Weather Administration (CWA) and indicates the likelihood of different weather categories in the future, has been applied to assess streamflow. However, the previous approaches had not addressed the uncertainty arising from both intrinsic uncertainty and the mismatch between the stations targeted by the weather outlook and the locations where it is applied. In order to address this issue, this study used the monthly weather outlook by the CWA for Northern Taiwan, along with its past prediction performance, to estimate the future exceedance probability of streamflow at the Shimen Reservoir. Logistic regression was employed to calculate the posterior probabilities of future weather categories based on the CWA’s weather outlook. Results suggest that the posterior probabilities of weather classes—the modified outlook—differ from those indicated by the original outlook and enhance inflow prediction performance, highlighting the importance of estimating these probabilities. Using the streamflow exceedance probability based on this modified outlook, water resource decisions can be made more appropriately compared with traditional approaches. Our study presents a novel approach for enhancing the utility of existing public weather forecast products in Taiwan, supporting decision-making under uncertainty.