Combining Copula-Based Bayesian Network Method

Especially for drought periods, the higher the accuracy of reservoir inflow forecasting, the 9 more reliable the water supply from a dam. The article focuses on probabilistic forecasting of 10 seasonal inflow to reservoirs and determines estimates from the probabilistic seasonal inflow 11 according to drought forecast results. The probabilistic seasonal inflow was forecasted by a copula12 based Bayesian network employing a Gaussian copula function. Drought forecasting was 13 performed by calculation of the standardized streamflow index value. The calendar year is divided 14 into four seasons; the total inflow volume of water to a reservoir for a season is referred to as the 15 seasonal inflow. Seasonal inflow forecasting curves conforming to drought stages produce estimates 16 of probabilistic seasonal inflow according to the drought forecast results. The forecasted estimates 17 of seasonal inflow were calculated by using the inflow records of Soyanggang and Andong dams in 18 the Republic of Korea. Under the threshold probability of drought occurrence ranging from 50 to 55 19 %, the forecasted seasonal inflows reasonably matched critical drought records. Combining the 20 drought forecasting with the seasonal inflow forecasting may produce reasonable estimates of 21 drought inflow from the probabilistic forecasting of seasonal inflow to a reservoir. 22