喀斯特山地土壤侵蚀和产水量的最优解释力

喀斯特生态系统服务主导影响因子识别是科学治理石漠化的前提，然而环境因子对生态系统服务空间分布的解释力受尺度变换的影响，其尺度效应的定量研究仍需进一步加强。为定量厘定环境因子解释力的尺度效应，本文从多尺度视角出发，利用地理探测器定量分析不同空间分辨率下环境因子对土壤侵蚀和产水量的解释力，并探求其在不同地貌形态类型区的差异性规律。结果表明，坡度和植被覆盖度是影响土壤侵蚀空间分布的主导因子，两者的交互作用对土壤侵蚀空间分布的解释力更强。受研究区地形起伏普遍性和景观破碎化的影响，坡度和土地利用类型在低分辨率下解释力最优。降水、海拔和土地利用类型是产水量空间分异的主导因子。降水和土地利用类型的交互作用对产水量的解释力达95%以上，海拔在不同地貌形态类型区的空间变异性影响其最优解释力水平。具体表现为：在台地、丘陵类型区海拔空间变异性较小，在高分辨率下其解释力最优；在山地类型区，海拔空间变异性较大，在低分辨率下其解释力更强。本文通过多尺度分析定量甄别生态系统服务变量的最优解释力，以期为喀斯特山地生态系统服务的主导因子精准辩识和分区优化提供途径和依据。

The optimal explanatory power of soil erosion and water yield in karst mountainous area

Accurately identifying the dominant factor of karst ecosystem services is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors for the spatial distribution of ecosystem services is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used Geodetector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored their differences in three geomorphological types. Results showed that slope and vegetation coverage were the dominant factors of soil erosion, and the interactive explanatory power between the two factors was stronger. Affected by the universality of topographic relief and landscape fragmentation in the study area, the explanatory of slope and land use type on soil erosion was optimal at a low resolution. Precipitation, elevation, and land use type were the dominant factors for the spatial heterogeneity of water yield, and the interaction between precipitation and land use type could explain more than 95% of water yield. The spatial variability of elevation in different geomorphological types affected its optimal explanatory power, especially in the terrace and hill type areas. The spatial variability of elevation was weak, and its explanatory power was optimal at a high resolution. However, in the mountainous areas, the spatial variability of elevation was strong, and its explanatory power was optimal at a low resolution. This study quantitatively identified the optimal explanatory power of ecosystem service variables through multi-scale analysis, which aimed to provide a way and basis for accurate identification of the dominant factors of karst mountain ecosystem services and zoning optimization.