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

被引:1
|
作者
Gao, Jiangbo [1 ]
Zhang, Yibo [1 ,2 ]
Zuo, Liyuan [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
optimal explanatory power; scale effect; soil erosion; water yield; karst mountainous areas; ECOSYSTEM SERVICES; RUSLE;
D O I
10.1007/s11442-023-2166-y
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Accurately identifying the dominant factor of karst ecosystem services (ESs) is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors on the spatial distribution of ESs is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used the geographical detector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored its differences in three geomorphological-type areas. 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 power of slope and land use type on soil erosion was optimal at 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 explained more than 95% of water yield. The spatial variability of elevation in different geomorphological-type areas affected its optimal explanatory power, specifically, in the terrace and hill-type areas, the spatial variability of elevation was weak, its explanatory power was optimal at high resolution. While in the mountain-type areas, the spatial variability of elevation was strong, and its explanatory power was optimal at low resolution. This study quantitatively identified the optimal explanatory power of ES variables through multi-scale analysis, which aims to provide a way and basis for accurate identification of the dominant factors of karst mountain ESs and zoning optimization.
引用
收藏
页码:2077 / 2093
页数:17
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