Investigation of an Ensemble Inflow-Prediction System for Upstream Reservoirs in Sai River, Japan

Abstract

In this study, an ensemble inflow-prediction system was developed for a hydropower-generation dam in the upper Sai River basin, and the accuracy of ensemble inflow prediction, which is important for efficient dam operation, was investigated. First, the Water and Energy Based Distributed Hydrological Model for Snow (WEB-DHM-S), a hydrological model developed for the Sai River basin, can represent the hydrological process from warm to cold seasons. Next, a system was developed on the Data Integration and Analysis System (DIAS) to predict inflows into the dam by inputting real-time meteorological data and ensemble rainfall forecast data into WEB-DHM-S. The WEB-DHM-S was calibrated and validated over a 3-year period from August 2015 to July 2018, and showed good agreement with observed inflows from base flow to peak flow and snowmelt runoff in each year. The results of inflow forecasting during frontal rainfall in August 2021 by inputting ensemble rainfall forecasts up to 39 h ahead showed that at the Inekoki Dam site, the total inflow (volume) to the peak was predicted with an accuracy of within 20% at 30 h, 24 h, 18 h, 12 h, and 6 h before the peak. These ensemble inflow forecasts can help optimize dam operations.

Keywords

• WEB-DHM-S
• ensemble inflow prediction
• lead-time
• reservoir
• DIAS