Surface runoff change is significantly influenced by both human activities and climate change. Decoupling their respective contributions to runoff change represents a critical frontier in hydrological research and a pressing challenge for water resource management. This study focuses on the Dawen River Basin, a strategic area for ecological conservation and high-quality development in the lower Yellow River region. By integrating three methodological approaches—empirical models (Precipitation–Runoff Double Mass Curve), conceptual models (elasticity coefficient methods), and hydrological models (Soil and Water Assessment Tool, SWAT)—we systematically quantify the impacts of climate change and human activities on runoff change. A correlation analysis was first applied to screen independent runoff drivers and basin characteristic factors, followed by a random forest algorithm to rank their relative importance. This process informed the establishment of a comprehensive framework for runoff attribution analysis. Results demonstrate that hydrological modeling (SWAT) is the most appropriate method for the Dawen River Basin, revealing human activities as the dominant driver of runoff changes, accounting for 70% to 82%. These findings provide critical insights for guiding sustainable water resource planning and management in anthropogenically stressed basins under a changing environment.

Temporal and spatial changes in non-point source pollution, driven by significant alterations in land use due to increased human activity, have considerably affected the quality of groundwater, surface water, and soil environments in the region. This study examines the Weihe River basin in greater detail, an area heavily impacted by human activity. The study developed the River Section Potential Pollution Index (R-PPI) model using the Potential Non-Point Source Pollution Index (PNPI) model in order to investigate the dynamic changes in River Section Potential Pollution (R-PP) over a 31-year period and its associated risks, especially those related to land use and land cover change (LUCC). The predominant land uses in the Weihe River Basin are cropland, grassland, and forest, making up around 97% of the basin’s total area. The Weihe River Basin underwent a number of soil and water conservation initiatives between 1990 and 2020, which significantly decreased the potential pollution risk in the river segment. The research separated the R-PP risk values in the area into five different categories using a quantile classification technique. According to the data, there is a polarization of R-PP risk in the area, with downstream parts especially having an increased risk of pollution in river segments impacted by human activity. On the other hand, river segments in the middle and upper reaches of the basin showed a discernible decline in possible pollution risk throughout the study period. The Weihe River Basin’s rapid urbanization and land degradation are to blame for the current increase in R-PP risk. The substantial influence of LUCC on the dynamic variations in R-PP risk in the Weihe River Basin is highlighted by this study. Additionally, it offers crucial information for upcoming conservation initiatives and urban planning guidelines meant to enhance the area’s ecological well-being and environmental standards.

Human activities and climate change have significantly influenced the water cycle, impacting flood risks and water security. This study centers on the Jing River Basin in the Chinese Loess Plateau, analyzing hydrological patterns and flood progression using the HEC-HMS model under changing conditions. The findings indicate that climate change substantially affects flood predictions, increasing peak flows and volumes by up to 10.9% and 11.1%, respectively. It is essential to recognize that traditional flood models may underestimate the risks posed by these changes, emphasizing the necessity for updated methods incorporating climatic and human factors. Changes in land use, such as the expansion of grasslands and forests, have reduced peak discharges and flood volumes. Consequently, the combined impacts of climate and land use changes have intensified flood frequencies, necessitating updated strategies to manage risks effectively. The dynamics of flooding are significantly impacted by changes in climate and land use, particularly in minor floods that occur frequently, highlighting the influence of climate change on flooding trends. Within the Jing River Basin, hydrological patterns have been shaped by both climatic variations and human activities, leading to an increase in extreme hydrological events and concerns regarding water security. Using the HEC-HMS model, this study examines the hydrology of the Jing River Basin, focusing on the design of storm events and analyzing various flood characteristics under different scenarios. Climate change has resulted in higher peak discharges and volume surges ranging from 6.3% to 10.9%, while shifts in land use, such as decreases in farmland and the expansion of grasslands, have caused declines ranging from 7.2% to 4.7% in peak flows and volumes. The combined effects of climate variation and land utilization have complex implications for flood patterns, with milder to moderate floods showing a more significant impact and shorter return periods facing increased consequences. These findings underscore the interconnected nature of climate change, land use, and flooding dynamics in the Jing River Basin, highlighting the need for comprehensive strategies to address these challenges and ensure sustainable water management in the region.

Projecting runoff and extreme hydrological events in human-modified basins requires climate, land-use and reported water-consumption forcings to be represented within a single, traceable scenario framework. Here, the Dawen River, the largest tributary of the lower reaches of the Yellow River, was selected as the study area. We coupled a physically based model (SWAT model) with the CEEMDAN–Attention–LSTM model to forecast future social water consumption and runoff and then diagnosed extreme hydrological events and decoupled the main driving factors driving runoff dynamics using multiple remote-sensing and hydrometeorological datasets. Five global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected, and the Delta statistical downscaling method was adopted to enhance the future climate data. The GeoSOS-FLUS model was applied to simulate the future LULC distribution, and then all the climate data, the LULC data and other remote-sensing data were input into the CEEMDAN–Attention–LSTM–SWAT model. The results confirmed that the coupled model had reasonable performance in social water consumption and runoff simulation; the Nash–Sutcliffe efficiency (NSE) values for the measured and simulated runoff ranged from 0.80 to 0.84. Future runoff in all scenarios showed varying degrees of increasing trends. Future droughts display longer durations with lower severity compared to floods, while pluvial flood events are characterized by shorter durations with higher severity. The frequency of the drought–pluvial seesaw increased from upstream to downstream in the SSP126, SSP245, and SSP585 scenarios. Finally, the results of decoupling runoff dynamics revealed that the contribution of human activities showed a slight overall increasing tendency from upstream to downstream, accounting for 70% to 82% of runoff alterations, and social water consumption and the LULC factors were the dominant anthropogenic factors.