Real-time reservoir operation using inflow and irrigation demand forecasts in a reservoir-regulated river basin

Real-time reservoir operation using inflow and irrigation demand forecasts can help reservoir system managers make effective water management decisions. Forecasting of inflow and irrigation demands is challenging, owing to the variability of the weather variables that affect inflows and irrigation demands. In this context, bias-corrected Global Forecasting System (GFS) forecasts are used here in a hybrid approach (reservoir module with Long Short Term Memory (LSTM)) to forecast the reservoir inflows. Concurrently, the bias-corrected GFS forecasts are used in irrigation demand module to forecast the irrigation demands. The 'Scaled Distribution Mapping' method is used to bias-correct the GFS data of 1-5 days lead. The study area is the Damodar river basin, India, consisting of five major reservoirs: Tenughat and Konar located upstream of Panchet, and Tilaya situated upstream of Maithon. With the upstream reservoir outflow forecasts, the inflows are forecasted in Panchet and Maithon reservoirs with NSE values of 0.88-0.96 and 0.78-0.88, respectively, up to a 5-day lead. The irrigation demand module with bias-corrected GFS forecasts forecasted the irrigation demands close to the irrigation demands with the observed weather data. The percentage errors in irrigation demand forecasts of the Kharif (June-October) season at 1-5 days lead are 9.45 %, -15.45 %, -20.52 %, -26.36 %, -27.31 %, respectively. On the contrary, percentage errors in irrigation demand forecasts of Rabi (November-February) and Boro (January-May) are in the range of 8.17-8.79 % and 3.48-8.06 %, respectively. With the inflows and irrigation demand forecasts, the Panchet and Maithon reservoirs satisfied the downstream demands and reduced the floods. The inflow and irrigation demand forecasts, based on the GFS forecasts, have substantial potential for real-time reservoir operation, leading to efficient water management downstream.

Real-time streamflow forecasting in a reservoir-regulated river basin using explainable machine learning and conceptual reservoir module

Real-time streamflow forecasting is essential to manage water resources effectively in a reservoir-regulated basin. However, forecasting becomes challenging without weather and upstream reservoir outflows forecasts in real-time. In this context, a novel hybrid approach is proposed in this study to forecast the streamflows and reservoir outflows in real-time. In this approach, the Explainable Machine Learning model is embedded with a conceptual reservoir module for forecasting streamflows using short-term weather forecasts. Long Short Term Memory (LSTM), a Machine Learning model, is used in this study to predict the streamflow, and the model's explainability is examined by Shapley additive explanations method (SHAP). Panchet reservoir catchment, which contains Tenughat and Konar reservoirs, is selected as a study area. The LSTM model performance is excellent in predicting the streamflows of Tenughat, Konar and Panchet catchments with NSE values of 0.93, 0.87, and 0.96, respectively. The SHAP method identified the high-impact variables as streamflows and precipitation of 1-day lag. In forecasting, bias-corrected Global Forecast System data is used with the LSTM model to forecast the streamflows in three catchments. The inflows are forecasted well up to a 3-day lead in Tenughat and Konar reservoirs with NSE values above 0.88 and 0.87, respectively. The reservoir module performance in forecasting Tenughat and Konar reservoirs' outflows with the inflow forecasts is also promising up to a 3-day lead with NSE values above 0.88 for both reservoirs. The inflows forecasting to Panchet reservoir with reservoirs' outflows as additional inputs is excellent up to 5-day lead (NSE = 0.96-0.88). However, the forecasting error increased from 77 m³/s to 134 m³/s with the lead time. This approach could provide an efficient way to reduce flood risks in the reservoir-regulated basin.