Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model

Upper and middle Yangtze River, China Since measured runoff is no longer natural due to reservoirs, runoff series should be restored (without reservoirs) and regulated (with current reservoirs) for consistency. Restored and regulated runoff from one model benefits from continuously measured data and consistent model conditions. However, current restoration and regulation use two separate models, with data split by reservoir operations. To address this issue, a Reservoir-State Long Short-Term Memory (RS-LSTM) model is proposed to restore and regulate runoff simultaneously by regarding reservoirs as switches. Three schemes are compared: (1) binary states, simulating runoff either with or without reservoirs; (2) dynamic states, beginning from zero and increasing one at each time step after initial impoundment to represent time-dependent operation; (3) no states, namely an LSTM model, as a benchmark. (1) Nash-Sutcliffe efficiencies over test periods are 0.79, 0.67, and 0.75 for the models using binary, dynamic, and no states, respectively. (2) The binary RS-LSTM model can restore the runoff without historical operation data, validated with the water balance model. (3) The RS-LSTM model is used to evaluate the reservoirs’ impact on downstream lakes through restored and regulated runoff. The comparison indicates that reservoirs alleviate downstream droughts instead of causing low water levels. The proposed RS-LSTM model effectively evaluates the reservoirs’ impacts on downstream areas. • Propose a reservoir-state LSTM model to restore and regulate runoff simultaneously. • The reservoir-state LSTM model using binary states performs the best. • The proposed model is validated with a water balance one in runoff restoration. • Reservoirs alleviate drought of downstream lakes in China’s Yangtze River.