Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R² values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R² values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.