Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model

Abstract

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.