Streamflow Estimation at Ungauged Sections in a Nested Basin Using an HEG-LSTM Model

Abstract

Streamflow estimation at ungauged sections is essential for flood warning, reservoir operation, water allocation, and basin management. Conventional regionalization approaches typically require the construction, calibration, and parameter transfer of conceptual or process-based hydrological models, resulting in high application costs when data are scarce, gauge networks are frequently adjusted, or management sections do not coincide with existing gauging stations. In recent years, regional Long Short-Term Memory (LSTM) networks have provided a data-driven alternative for ungauged streamflow estimation. However, existing studies have focused mainly on spatial generalization across independent catchments, with limited attention to hydrological information transfer among sections with different upstream and downstream relationships within the same nested basin. This study used five nested hydrological stations in the Fujiang River Basin to develop a Hydrological-Efficiency-Guided Long Short-Term Memory framework (HEG-LSTM) and compared it with a baseline LSTM. The model incorporated local meteorological forcing, antecedent precipitation, static basin attributes, and scenario-permitted donor-station discharge as inputs, and leave-one-station-out validation was adopted to assess cross-section applicability. To investigate the effects of different upstream and downstream relationships on streamflow estimation, three gauge-network scenarios were designed: S1 estimated downstream streamflow using upstream stations; S2 was downstream-to-upstream synchronous estimation; and S3 jointly estimated streamflow at an intermediate ungauged section using upstream and downstream stations. HEG-LSTM achieved a mean Nash–Sutcliffe efficiency (NSE) of 0.703, exceeding the 0.603 obtained by the baseline LSTM. The mean NSE values of HEG-LSTM under S1, S2, and S3 were 0.692, 0.695, and 0.730, respectively, with the best performance obtained under S3. Compared with the baseline LSTM, HEG-LSTM increased the coefficient of determination for high flow from 0.617 to 0.737 and that for annual peak flow from 0.731 to 0.770, whereas improvement in low flow correlation remained limited. Shapley Additive Explanations (SHAP) showed that donor-station discharge, drainage area, elevation, slope, and antecedent precipitation jointly influenced cross-section transferability. The proposed HEG-LSTM framework provides a methodological reference for streamflow estimation, historical streamflow estimation, and gauge-network optimization at ungauged sections in nested basins.