Predictive analysis of minimum inflow using synthetic inflow in reservoir management: a case study of Seomjingang Dam

Abstract

Climate change has been intensifying drought frequency and severity. Such prolonged droughts reduce reservoir levels, thereby exacerbating drought impacts. While previous studies have focused on optimizing reservoir operations using historical data to mitigate these impacts, their scope is limited to analyzing past events, highlighting the need for predictive methods for future droughts. This research introduces a novel approach for predicting minimum inflow at the Seomjingang dam which has experienced significant droughts. This study utilized the Stochastic Analysis Modeling and Simulation (SAMS) 2007 to generate inflow sequences for the same period of observed inflow. Then we simulate reservoir operations to assess firm yield and predict minimum inflow through synthetic inflow analysis. Minimum inflow is defined as the inflow where firm yield is less than 95% of the synthetic inflow in many sequences during periods matching observed inflow. The results for each case indicated the firm yield for the minimum inflow is on average 9.44 m³/s, approximately 1.07 m³/s lower than the observed inflow's firm yield of 10.51 m³/s. The minimum inflow estimation can inform reservoir operation standards, facilitate multi-reservoir system reviews, and assess supplementary capabilities. Estimating minimum inflow emerges as an effective strategy for enhancing water supply reliability and mitigating shortages.