As an important indicator for assessing regional water resources, the study of the spatiotemporal evolution and driving factors of water resources carrying capacity (WRCC) is essential for achieving sustainable water resource utilization. This study focuses on Yulin City, a typical arid region located on the Loess Plateau in northwestern China. By constructing an evaluation index system for regional WRCC and combining an improved fuzzy comprehensive evaluation model with the TOPSIS evaluation model, a comprehensive WRCC evaluation model is established. Additionally, Geodetector is used to explore the main driving factors behind the evolution of regional WRCC. This multidimensional analytical framework aims to deeply analyze the dynamic evolution trends of WRCC and the driving mechanisms of different factors in its spatiotemporal changes. The results indicate that (1) from 2011 to 2020, the overall WRCC of Yulin City showed a trend of positive improvement, with Shenmu, Yuyang, and Fugu areas performing the best, and by 2020, more than half of the counties had achieved Grade 3 or above; (2) the spatial variability of WRCC in Yulin City was more significant than its temporal changes; and (3) in terms of driving mechanisms, the northern six counties gradually shifted from traditional economic-driven factors to ecological and environmental drivers, whereas the southern six counties remained constrained by economic factors. Overall, water resource factors remain the primary driving force for the socio-economic development and environmental sustainability of the entire Yulin City. The study provides valuable information for water resource allocation and differentiated management in arid regions.

Horizontal resistance can be significantly different for gravelly soil slope sites with different gradients. The impact of slope gradient on the horizontal resistance of soil based on a three-dimensional physical simulation test has been investigated, and the displacement of the pile top and soil pressure for four piles under various gradients (slope gradient 0–45°) was analyzed. The research reveals that ① The soil resistance exhibits a linear increase stage, non-linear steep increase stage, and gradually stabilizing stage with the increase in load. ② The ultimate soil resistance is significantly affected by depth and displacement, and hyperbolic variation characteristics related to the displacement stage. It has a slope weakening effect, and the steeper the slope gradient, the lower the ultimate soil resistance of the pile sides, which effect is negligible when the depth exceeds 0.6–0.7 times that of the pile buried depth. ③ Based on the characteristics of horizontal ultimate resistance-displacement-depth variation in soil, a gradient factor, which is only related to the slope gradient, was used to describe the impact of gradient on the soil resistance modulus (k_ini) and ultimate resistance (p_u). k_ini is reduced close to the ground surface and gradually increases with depth until it becomes equal to the value of level ground. The ratio between p_u in slope ground and level ground was determined for slope angles. The above horizontal resistance parameters were expressed as a function based on the test data to calculate the lateral ultimate bearing capacity of gravelly soil considering the slope gradient factor. The research results developed the theory of foundation design for building structures, promoting the reliability evaluation of pile soil systems towards a more scientific and rigorous direction.

China’s Yangtze and Yellow River Basins exhibit divergent drought patterns, yet the underlying mechanisms driving these differences remain underexplored. This study compares their drought characteristics from 1961 to 2022 using the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Palmer Drought Severity Index, and identifies their drivers through attribution models and interpretable machine learning. Our results reveal two distinct paradigms: the Yangtze Basin is characterized by high-frequency, over 14% in all seasons, short-duration droughts, reflecting a rapid hydrological response, while the Yellow River Basin experiences low-frequency, long-duration events indicative of strong soil moisture memory. Quantitative attribution demonstrates that atmospheric evaporative demand (VPD) plays a significantly greater role in the Yellow River Basin, contributing over 20% to soil drought, far exceeding its 14.4% contribution in the Yangtze Basin. Furthermore, their large-scale drivers differ fundamentally: the Yangtze Basin responds primarily to the Atlantic Multidecadal Oscillation (AMO) and Arctic Oscillation (AO), whereas the Yellow River Basin is mainly influenced by solar activity and the El Niño-Southern Oscillation (ENSO). These findings reveal that Yangtze drought is primarily driven by precipitation deficits, while Yellow River drought is a composite phenomenon amplified by evaporative demand. This distinction underscores the need for basin-specific water management strategies.