[Objective]Long Short-Term Memory(LSTM)networks have shown strong forecasting capabilities in hydrological re-search,but they typically rely on a large amount of training data.In order to better adapt LSTM models to watersheds with limited data and introduce some physical mechanisms during the forecasting process,[Methods]this study applies recession curves to impose physical constraints on LSTM models,proposes a hybrid model for low flow prediction.[Results]The hybrid model is tested in three different watersheds in southwest China.The results are as follows:(1)as the forecast horizon increases,the ac-curacy of the hybrid model slightly decreases,but the accuracy can exceed 90％for a forecast horizon within 10 days;(2)the hybrid model significantly outperforms LSTM in terms of prediction accuracy and mitigates the effects of error accumulation;(3)the hybrid model performs better than LSTM when reducing the number of training samples and the dimensions of prediction fac-tors.[Conclusion]The results indicate that the introduction of recession curves can reduce the training data requirement of the hybrid model,extend the forecast horizon,which can provide a new approach for deep learning in low flow prediction,and offer technical support for drought mitigation planning and other related fields.
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