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Accurate streamflow forecasting is critical for water resource management and disaster mitigation, yet conceptual models often suffer from systematic biases while machine learning lacks physical interpretability. This study proposes a coupled XAJ-LSTM model to integrate the mechanistic strengths of the Xin’anjiang (XAJ) model with the predictive capability of Long Short-Term Memory (LSTM) networks. Applied in the Woluo River basin using an 11-year daily record from 2012 to 2022, the framework was evaluated with leave-one-year-out validation. Results indicate that XAJ-LSTM achieved the highest full-period NSE of 0.885 and the lowest full-period RMSE of 19.48 m3/s, compared with NSE values of 0.867 for XAJ and 0.724 for LSTM. XAJ-LSTM also performed best during the flood period, with NSE of 0.820 and RMSE of 28.81 m3/s. The SHAP framework indicated that XAJ-simulated flow and precipitation remained the dominant predictors, suggesting that the LSTM relied strongly on the conceptual-model baseline and rainfall information. These findings indicate that XAJ-LSTM can improve selected full-period and flood-period diagnostics for daily streamflow simulation in the Woluo River Basin, while further tests with longer records and additional catchments are needed before broader application.
He et al. (Mon,) studied this question.