European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy.
European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy.
Sci Total Environ. 2015 Apr 1;511:801-14. doi: 10.1016/j.scitotenv.2015.01.008. Epub 2015 Jan 23.
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.
降雨是土壤侵蚀的主要驱动因素之一。降雨的侵蚀力表现为降雨侵蚀力。降雨侵蚀力考虑了降雨量和强度,最常见的表达方式是 USLE 模型及其修订版 RUSLE 中的 R 因子。在国家和大陆层面,由于数据稀缺,土壤侵蚀模型构建者不得不根据时间分辨率较低的降雨数据(日、月、年平均值)来估计该因子。本研究旨在根据现有最佳数据集,以 RUSLE 的 R 因子形式评估欧洲的降雨侵蚀力。数据来自欧盟所有成员国和瑞士的 1541 个降水站,时间分辨率为 5 至 60 分钟。使用线性回归函数将不同时间分辨率的降水数据计算得出的 R 因子值归一化为时间分辨率为 30 分钟的 R 因子值。降水时间序列最短为 5 年,最长为 40 年。每个降水站的平均时间序列约为 17.1 年,大部分数据集包括 21 世纪第一个十年的数据。使用高斯过程回归(GPR)将 R 因子站点值插值到 1 公里分辨率的欧洲降雨侵蚀力图中。用于 R 因子插值的协变量是气候数据(总降水量、季节降水量、最干燥/最湿润月份的降水量、平均温度)、海拔和纬度/经度。欧盟加瑞士的平均 R 因子为 722 MJ mm ha(-1) h(-1) yr(-1),地中海和阿尔卑斯地区的 R 因子最高(>1000 MJ mm ha(-1) h(-1) yr(-1)),北欧国家的 R 因子最低(<500 MJ mm ha(-1) h(-1) yr(-1))。地中海地区的侵蚀性密度(侵蚀性与年降水量的比值)也最高,这意味着侵蚀性事件和洪水的风险很高。