Department of Geosciences, Chair of Physical Geography and Soil Science, University of Tuebingen, Ruemelinstraße 19-23, 72070, Tübingen, Germany,
Environ Sci Pollut Res Int. 2013 Oct;20(10):6917-33. doi: 10.1007/s11356-012-1441-8. Epub 2013 Jan 23.
In densely populated countries like China, clean water is one of the most challenging issues of prospective politics and environmental planning. Water pollution and eutrophication by excessive input of nitrogen and phosphorous from nonpoint sources is mostly linked to soil erosion from agricultural land. In order to prevent such water pollution by diffuse matter fluxes, knowledge about the extent of soil loss and the spatial distribution of hot spots of soil erosion is essential. In remote areas such as the mountainous regions of the upper and middle reaches of the Yangtze River, rainfall data are scarce. Since rainfall erosivity is one of the key factors in soil erosion modeling, e.g., expressed as R factor in the Revised Universal Soil Loss Equation model, a methodology is needed to spatially determine rainfall erosivity. Our study aims at the approximation and spatial regionalization of rainfall erosivity from sparse data in the large (3,200 km(2)) and strongly mountainous catchment of the Xiangxi River, a first order tributary to the Yangtze River close to the Three Gorges Dam. As data on rainfall were only obtainable in daily records for one climate station in the central part of the catchment and five stations in its surrounding area, we approximated rainfall erosivity as R factors using regression analysis combined with elevation bands derived from a digital elevation model. The mean annual R factor (R a) amounts for approximately 5,222 MJ mm ha(-1) h(-1) a(-1). With increasing altitudes, R a rises up to maximum 7,547 MJ mm ha(-1) h(-1) a(-1) at an altitude of 3,078 m a.s.l. At the outlet of the Xiangxi catchment erosivity is at minimum with approximate R a=1,986 MJ mm ha(-1) h(-1) a(-1). The comparison of our results with R factors from high-resolution measurements at comparable study sites close to the Xiangxi catchment shows good consistance and allows us to calculate grid-based R a as input for a spatially high-resolution and area-specific assessment of soil erosion risk.
在中国这样人口密集的国家，清洁水是未来政治和环境规划中最具挑战性的问题之一。由于来自非点源的氮和磷的过度输入导致水污染和富营养化，这主要与农业用地的土壤侵蚀有关。为了防止这种由漫射物质通量引起的水污染，必须了解土壤流失的程度以及土壤侵蚀热点的空间分布。在像长江中上游山区这样的偏远地区，降雨数据稀缺。由于降雨侵蚀力是土壤侵蚀建模的关键因素之一，例如，在修订后的通用土壤流失方程模型中表示为 R 因子，因此需要一种方法来空间确定降雨侵蚀力。我们的研究旨在从长江支流香溪河这个面积大（3200 平方公里）且多山的流域中获取的稀疏数据中进行逼近和空间分区，该流域靠近三峡大坝。由于只有流域中部的一个气候站和周围五个站可以获得每日降雨数据，因此我们使用回归分析结合从数字高程模型中得出的高程带，将降雨侵蚀力近似为 R 因子。平均年 R 因子（Ra）约为 5222 MJ mm ha-1 h-1 a-1。随着海拔的升高，Ra 上升到海拔 3078 米的最大值 7547 MJ mm ha-1 h-1 a-1。在香溪河的出口处，侵蚀力最小，Ra 约为 1986 MJ mm ha-1 h-1 a-1。将我们的结果与香溪河附近可比研究地点的高分辨率测量得到的 R 因子进行比较，结果显示出良好的一致性，并且允许我们计算基于网格的 Ra，作为对土壤侵蚀风险进行空间高分辨率和特定区域评估的输入。
