European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy.
Environmental Geosciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland.
Sci Total Environ. 2014 May 1;479-480:189-200. doi: 10.1016/j.scitotenv.2014.02.010. Epub 2014 Feb 21.
The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 thahha(-1)MJ(-1)mm(-1) with a standard deviation of 0.009 thahha(-1)MJ(-1)mm(-1). The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.
在较大的空间尺度上进行土壤侵蚀建模的最大障碍是缺乏土壤特性数据。土壤可蚀性是建模土壤侵蚀的关键参数之一，以 K 因子表示，广泛应用于土壤侵蚀模型——通用土壤流失方程（USLE）及其修订版（RUSLE）中。K 因子表示土壤易受侵蚀的程度，与土壤有机质含量、土壤质地、土壤结构和渗透性等土壤特性有关。2009 年，通过土地利用/覆盖面积框架调查（LUCAS）进行了首次泛欧土壤调查，该调查涵盖了欧盟 25 个成员国的约 20000 个点。本研究的目的是为 25 个欧盟成员国生成一个协调一致的高分辨率土壤可蚀性图（网格单元大小为 500 m）。使用 Wischmeier 和 Smith（1978）的列线图为 LUCAS 调查点计算土壤可蚀性。应用 Cubist 回归模型来关联空间数据，如纬度、经度、遥感和地形特征，以开发高分辨率土壤可蚀性图。欧洲的平均 K 因子估计值为 0.032 thahha(-1)MJ(-1)mm(-1)，标准偏差为 0.009 thahha(-1)MJ(-1)mm(-1)。生成的土壤可蚀性数据集与已发表的局部和区域土壤可蚀性数据相比表现良好。然而，考虑到通常不用于土壤可蚀性计算的表面石覆盖的保护作用，将其纳入后，K 因子平均降低了 15%。在 K 因子计算中排除这种影响可能导致土壤侵蚀的高估，特别是在观察到表面石覆盖比例最高的地中海国家。
