Hydrological modeling is a crucial tool in hydrology and water resource management for analyzing runoff evolution patterns. In this study, the process-driven soil and water assessment tool (SWAT) model and data-driven machine learning techniques (XGBoost, random forest, LSTM, BILSTM, and GRU) were employed to simulate runoff at monthly and daily intervals in the Fenhe River basin, situated in the middle reaches of the Yellow River, respectively. The SWAT model demonstrated effective performance in simulating runoff at various scales, with the coefficient of determination (R²) exceeding 0.80 and the Nash–Sutcliffe efficiency (NSE) surpassing 0.79. Sensitivity analysis reveals varying degrees of sensitivity among the model parameters. Furthermore, the deep learning techniques (LSTM, BILSTM, and GRU) exhibited superior simulation generalization capabilities compared to the SWAT model across various scales. Additionally, the generalization abilities of traditional machine learning techniques (XGBoost and random forest) were comparable to the SWAT model. This indicates that deep learning techniques demonstrate remarkable stability and generalization capabilities across various scales. This analysis was motivated by the use of external continuous time series data as input and the application of deep learning techniques to internal mechanisms. Moreover, an integrated modeling approach was used to enhance simulation accuracy by combining the SWAT model with machine learning techniques. The results indicate that the integrated modeling approach improves simulation performance across various scales compared to the single-model approach. This research is significant for improving the efficiency of water resource utilization and management in the Fenhe River basin.

The Huaihe River Basin is an important ecological function conservation area in China, and it is also an important production area for national food, energy, minerals, and manufacturing. The groundwater storage and groundwater drought in this region are of great significance for ecological maintenance and water resources management. In this study, based on GRACE data and GLDAS data, a dynamic calculation method for groundwater storage in the Huaihe River Basin was developed, and a groundwater drought index (GRACE-GDI) was derived. By coupling GRACE-GDI with run theory, the quantitative identification of groundwater drought events, as well as their duration, intensity, and other characteristics within the basin, was achieved. The spatiotemporal changes in groundwater storage and groundwater drought in the Huaihe River Basin were analyzed using the developed method. The results showed that GRACE data are highly applicable in the Huaihe River Basin and is capable of capturing the spatiotemporal variations in groundwater storage in this region. Over the study period, mainly affected by rainfall, the terrestrial water storage and surface water storage in the Huaihe River Basin showed a decreasing trend, while groundwater storage showed a slight increasing trend. The duration of groundwater drought events in the basin ranged from 78 to 152 months, with an intensity of 82.77 to 104.4. The duration of drought gradually increased from north to south, while the intensity increased from south to north.