Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction

Abstract

Ensemble streamflow predictions (ESPs) play an essential role in reservoir operation in terms of flood control, water supply and hydropower generation, etc. However, the working mechanism of dam-regulated ESP is rarely explored even though a large fraction of major waterways is affected by artificial hydraulic infrastructure, let alone its application value in multi-objective multi-reservoir operating system. Here this study develops three-layered hydrologically-relevant long and short-term memory (LSTM) models to implement 1–3 days ahead ESP of cascade reservoirs with the help of numerical weather prediction (NWP), and designs operating policies based on Gaussian radial basis function which can explicitly incorporate ESP information into reservoir operating decision. The framework of our proposed ESP-based operating way is demonstrated on a cascade reservoir system in the upper Hanjiang River basin, China and assessed against two benchmark control ways (i.e., no-forecast and deterministic-forecast operations). Results show that (1) LSTM models have reasonable accuracy in short-term NWP-based ESP of cascade reservoirs, especially for high-flow regimes; (2) compared with the no-forecast operation, our ESP-based operation could harvest additional approximately 36 million kW·h of hydropower generation per year with a slight improvement in water supply over 11 years of operation during the flood season. These findings highlight the application potential of our numerically efficient and skillful NWP-based ESP scheme for multi-objective cascade reservoir operation to address water-energy shortage issues.