Data-Driven Reservoir Simulation in a Large-Scale Hydrological and Water Resource Model

Large-scale hydrological and water resource models (LHMs) are used increasingly to study the vulnerability of human systems to water scarcity. These models rely on generic reservoir release schemes that often fail to capture the nuances of operations at individual dams. Here we assess whether empirically derived release-availability functions tailored to individual dams could improve the simulation performance of an LHM. Seasonally varying, linear piecewise relations that specify water release as a function of prevailing storage levels and forecasted future inflow are compared to a common generic scheme for 36 key reservoirs of the Columbia River Basin. When forced with observed inflows, the empirical approach captures observed release decisions better than the generic scheme—including under conditions of drought. The inclusion of seasonally varying inflow forecasts used by reservoir operators adds further improvement. When exposed to biases and errors inherent in the LHM, data-driven policies fail to offer a robust improvement; inclusion of forecasts deteriorates LHM reservoir simulation performance in some cases. We perform sensitivity analysis to explain this result, finding that the bias inherent in LHM streamflow is amplified by a reservoir model that relies on forecasts. To harness the potential of interpretable, data-driven reservoir operating schemes, research must address LHM flow biases arising from inaccuracies in climate input, runoff generation, flow routing, and water withdrawal and consumption data.