A DeepAR-Based Modeling Framework for Probabilistic Mid–Long-Term Streamflow Prediction

Mid–long-term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. The results indicate that the choice of output distribution is the most critical factor for predictive performance. The Gamma distribution consistently outperformed those using Normal and Student’s t distributions, due to its ability to better capture the skewed, non-negative nature of streamflow data. Notably, the magnitude of performance gain from using the Gamma distribution is itself region-dependent, proving more significant in the basin with higher streamflow skewness. For instance, in the more skewed Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE by over 27% compared to its Normal-distribution counterpart (from 1407.77 m³/s to 1016.54 m³/s). Furthermore, the Gamma-based models yield superior probabilistic forecasts, achieving not only lower CRPS values but also a more effective balance between high reliability (PICP) and forecast sharpness (MPIW). In contrast, the relative performance between GRU and LSTM architectures was found to be less significant and inconsistent across the different basins. These findings highlight that the DeepAR-based framework delivers consistent enhancement in forecasting accuracy by prioritizing the selection of a physically plausible output distribution, thereby providing stronger and more reliable support for practical applications.