Monthly Reservoir Inflow Forecasting for Dry Period Using Teleconnection Indices: A Statistical Ensemble Approach

Abstract

Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used to develop monthly reservoir inflow forecasting models. These models have been verified using leave-one-out cross-validation with expected error S as a measure of forecast skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52 and the SVM from 0.21 to 0.56 for different months. When used as an ensemble, Bayesian model averaging was more accurate than simple model averaging and naïve forecast for four target years tested. These were considered to be decent prediction skills, indicating that teleconnection-based models have the potential to be used as a tool to make a decision for reservoir operation in preparing for droughts.