中国水蚀区土壤可蚀性因子更新方法与应用  

Updating method of soil erodibility factor in water-erosion areas of China and its application

作　　者：田芷源 梁音[1] 赵院[2] 曹龙熹 赵艳 武逸杭 TIAN Zhiyuan;LIANG Yin;ZHAO Yuan;CAO Longxi;ZHAO Yan;WU Yihang(State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,210008,Nanjing,China;The Center of Soil and Water Conservation Monitoring,Ministry of Water Resources of the People s Republic of China,100053,Beijing,China;College of Ecology and Environment,Chengdu University of Technology,610059,Chengdu,China;University of Chinese Academy of Sciences,Nanjing,211135,Nanjing,China)

机构地区：[1]土壤与农业可持续发展国家重点实验室,中国科学院南京土壤研究所,南京210008 [2]水利部水土保持监测中心,北京100053 [3]成都理工大学生态环境学院,成都610059 [4]中国科学院大学南京学院,南京211135

出　　处：《中国水土保持科学》2023年第6期63-70,共8页Science of Soil and Water Conservation

基　　金：水利部水土保持监测中心项目“2022年度全国土壤可蚀性K值率定与更新”;国家自然科学基金“土壤水分对红壤土壤可蚀性时间变异的影响及机制研究”(42207403)。

摘　　要：土壤可蚀性因子K值是土壤侵蚀监测的必要因子,反映土壤在降雨侵蚀力作用下被分散和搬运的难易程度。然而现有全国K值图基于土种志数据,调查时间距今已有近40 a。基于2008—2018年的土系调查项目对全国K值进行更新。先收集4327个样点的土壤机械组成及有机质数据,利用诺模图(Nomo)方程计算K因子,其中极细砂质量分数运用三次样条函数结合自然对数进行插值;当土壤有机质质量分数>12%,则使用校正后的EPIC公式计算K值。然后采用随机森林模型对样点K值进行训练,将环境因素作为预测变量,包括气候、地表温度、植被指数、地形和母岩类型,并利用遥感影像开展空间制图。更新结果显示:全国K值变化范围为0.0051~0.0745 t·hm^(2)·h/(MJ·mm·hm^(2)),平均值为0.0298 t·hm^(2)·h/(MJ·mm·hm^(2))。土壤可蚀性分布呈现出黄土高原与华北平原K值居高、南方与东北地区K值居中、青藏高原最低的宏观规律,这与各地区土壤类型有关。分布于西北地区的黄绵土和北方的潮土K值较大,南方红壤和东北暗棕壤的K值较小,青藏地区高山土的K值最小。以上方法进行实际应用时仍需根据径流小区实测资料进行校正。其结果可为水土流失调查与监测提供方法与数据支撑。[Background]Soil erodibility(named K factor)is one of the key parameters of the soil erosion equation.The K is the basic data for soil erosion monitoring,reflecting the difficulty of soil dispersion and transportation under the action of rainfall erosion,and its size is related to the characteristics of the soil.However,the existing national K map was made based on soil species data,and the survey was conducted nearly 40 years again.Besides,the soil-polygon linked method was used to produce the legacy map,which cannot reflect the K variability existing in the same soil polygon.[Methods]This article updated the national K-value map based on the soil series survey(completed from 2008 to 2018)and the random forest regression model.Firstly,the soil texture and organic matter content of 4327 sample points were collected,and the K was calculated using the nomograph equation;when the soil organic matter content was>12%(mass fraction),the corrected EPIC formula was used to calculate the K.Secondly,the random forest regression model was used to train the K of the sample points,and a variety of environmental factors were used as prediction variables,including climate,surface temperature,vegetation index,terrain and parent rock type,and then remote sensing images were used to carry out spatial mapping.[Results]The cubic spline function combined with natural logarithm interpolated the content of very-fine sand content(≥0.050-0.100 mm)with high R 2 and reasonable value.An exponential equation was built between the Nomo-K and EPIC-K values(R 2=0.8071).The updated map showed that the range of national K values was 0.0051-0.0745 t·hm^(2)·h/(MJ·mm·hm^(2)),with an average value of 0.0298 t·hm^(2)·h/(MJ·mm·hm^(2)).The map of soil erodibility in China showed the macro rule that the K of the Loess Plateau and North China Plain was the largest,that of the southern and northeastern regions was the middle,and that of the Qinghai Tibet Plateau was the lowest.The spatial difference of the K factor was related to the distribution

关 键 词：土壤可蚀性因子 中国土壤流失方程 水力侵蚀 随机森林 数字土壤制图 EPIC模型 Nomo公式 

分 类 号：S157.1[农业科学—土壤学]