The selection of suitable tree species and the reasonable allocation of planting areas are important measures for improving soil quality. This study aimed to investigate the characteristics of typical vegetation type soil quality differences and their dominant factors in loess hilly–gully areas after returning farmland to the forest (grassland). The soil quality status and dominant factors of arbors, shrubs and grasslands in the study area were comprehensively analyzed using the soil quality index (SQI) and structural equation modeling (SEM). The results showed the following: (1) In the study area, the shrub forest had a high capacity for air permeability, water retention and nitrogen fixation. (2) The soil quality of the three vegetation types improved with increasing precipitation, and the soil quality indicator of shrubs was the highest, indicating a better soil quality improvement. However, the soil quality of the arbors and grasslands showed a greater percentage increase. In the precipitation range of 400–410 mm, the soil quality of shrub forests was significantly higher than that of arbors and grasslands. (3) Structural equation modeling analysis indicated that precipitation, vegetation and soil factors are closely related to soil quality. Further analysis showed that soil bulk density, porosity, capillary water-holding capacity, soil organic carbon and total phosphorus were the dominant factors affecting the soil quality in the study area. The purpose of this study was to evaluate quantitatively the soil quality after different vegetation types under different precipitation gradients, to clarify the variation trend of soil quality at different vegetation types with different precipitation gradients and to provide a scientific basis and data support for the quantitative evaluation of vegetation restoration and selection of tree species and vegetation configuration within different precipitation gradients in loess hilly and gully regions in the future.

The Loess Plateau frequently endures droughts, and the propagation process has grown more intricate due to the interplay of climate change and human activities. This study developed the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSMI) on a 3-month scale and examined the spatiotemporal characteristics and driving mechanisms of drought propagation from meteorological to agricultural drought utilizing cross-wavelet analysis, grey relational analysis, and the optimal parameter-based geographical detector (OPGD) model. The results demonstrate a substantial seasonal correlation between meteorological and agricultural droughts in spring, summer, and autumn, as evidenced by cross-wavelet coherence analysis (wavelet coherence > 0.8, p < 0.05). Lag analysis utilizing grey relational degree (>0.8) indicates that drought propagation generally manifests with a temporal delay of 1–3 months, with the shortest lag observed in spring (average 1.2 months) and the longest in winter (average 3.1 months). Distinct spatial heterogeneity is seen within geomorphological divisions: the loess wide valley hills and loess beam hills divisions exhibit the highest propagation rates (0.64 and 0.59), whereas the loess tableland and soil–stone hills divisions have lower propagation (around 0.50). The OPGD results reveal that precipitation, soil moisture, and temperature are the principal contributing factors, although their effects differ among geomorphological types. Interactions among components exhibit synergistic enhancement effects. This study improves our comprehension of seasonal and geomorphological heterogeneity in drought propagation from meteorological to agricultural droughts and provides quantitative evidence to support early drought warnings across various divisions, agricultural risk assessment, and water security strategies in the Loess Plateau.

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.

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.

During severe drought conditions, water supply risks tend to be concentrated at critical water intake nodes and vulnerable river segments, while the conventional total balance method is inadequate for depicting the spatial evolution of these risks. This study developed a node-based water supply security assessment framework for the Feng River Basin, a representative watershed in the piedmont transition zone. The framework coupled SWAT-simulated runoff with a supply–demand balance model and evaluated water supply reliability at both the node and basin scales. Two scenarios were compared: an artificial water system scenario and an artificial water system with an engineering-based water resource allocation scenario. Dry (P = 75%) and extremely dry (P = 95%) conditions were considered to examine the performance of artificial regulation under drought stress. The results showed that basin-scale water supply reliability remained relatively stable, ranging from 0.833 to 0.853 under different scenarios. However, the node-scale results revealed strong spatial heterogeneity. Nodes 1 and 3 maintained full or nearly full reliability, whereas Nodes 4, 6, and 7 showed relatively low reliability and higher water shortage risk. Under the P = 75% scenario, engineering-based water resource allocation increased basin-scale reliability from 0.848 to 0.853, indicating a slight improvement in supply–demand balance. In contrast, under the P = 95% scenario, reliability decreased from 0.839 to 0.833 after introducing water resource allocation, suggesting that transfer-based interventions may have limited effectiveness when natural inflow is severely reduced. In particular, Node 7 showed a marked decline in reliability under the allocation scenario, indicating that water supply risks may be redistributed and concentrated at specific intake nodes under extreme drought conditions. The scenario comparison further indicates that water diversion strategies may produce a dual effect of local improvement and global reconfiguration. Insufficient supply intensity may result in engineering interventions that cause downstream water reduction impacts, leading to a spatial redistribution and shifting of risks rather than their systemic eradication. This study seeks to transition the evaluation paradigm from “total safety” to “node safety,” establishing a scientific foundation for enhancing the emergency response system in critical areas of the basin.