Spatiotemporal Differentiation and Driving Mechanisms of Water Resources Carrying Capacity in the Jialu River Basin Using Combined Weighting and Geodetector

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. This work builds evaluation logic around the Driving-Pressure-State-Impact-Response (DPSIR) framework. Weighted TOPSIS and geographical detector tools are jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010-2022. The results showed that (1) the WRCC exhibited a unique spatial gradient across the basin’s four cities. Zhengzhou outperformed other regions for most surveyed years, with its WRCC score fluctuating between 0.28 and 0.59 and hitting a maximum of 0.59. Kaifeng maintained a chronically weak carrying capacity, its values sitting at 0.10-0.33 and falling under 0.20 for most time points. Xuchang recorded growth starting in 2019, and its annual evaluation indices remained between 0.35 and 0.41 in subsequent years. Zhoukou followed a downward trajectory instead; its initial high value of 0.55 gradually dropped to 0.14-0.28. The average WRCC across the entire basin hovered between 0.18 and 0.35, with most annual averages staying under 0.30. These intercity gaps construct a fixed spatial hierarchy: Zhengzhou > Zhoukou > Xuchang > Kaifeng. (2) Quantitative factor detection makes clear that socioeconomic human activities dominate the basin’s uneven WRCC distribution. R&D expenditure and urbanization rate carry the strongest explanatory power, with respective q statistics of 0.58 and 0.57. Natural background conditions exert far weaker regulating effects. Two typical natural endowment indicators, precipitation and groundwater reserves, only generate tiny q values of 0.11 and 0.09. The large gap between human and natural factor explanatory degrees confirms that human socioeconomic construction acts as the primary trigger for regional WRCC spatial divergence. (3) Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which is 45.0% higher than the average value of the single independent factors, suggesting prominent multi-factor synergistic effects.