修订的通用土壤流失方程(RUSLE)的侵蚀因子——系统综述

Shaheemath Suhara K K, Anu Varughese, Anjaly C Sunny, Anjitha Krishna P R
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摘要

修正通用土壤流失方程(RUSLE)是全球公认的侵蚀模型,具有良好的可接受性。RUSLE法估算土壤侵蚀的5个影响因子中,侵蚀力因子(R)代表降雨事件产生侵蚀的能力。主要受降雨强度和降雨动能的影响。以EI30为代表的侵蚀指数是最常用的r因子估计方法。由于许多流域缺乏降雨强度数据,研究人员开发了利用降雨深度估算侵蚀力的方法。修正傅里叶指数法得到了广泛的应用。最近,使用机器学习技术和人工神经网络的不同模型也被建立起来,以建立土壤流失估算的r因子。这些模型可以快速准确地估计r因子。他们甚至可以预测未来的r因子来预测土壤流失并制定相应的保护措施。本文试图对全球科学家提出的利用RUSLE模型估算土壤流失r因子的不同方法进行综述。
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Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review
The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The Modified Fournier Index method has gained popularity. Recently, different models using machine learning techniques and ANN are also being set up to establish the R-factor for soil loss estimation. These models can estimate the R-factor quickly and more accurately. They can even predict the R-factor for the future to predict soil loss and plan conservation measures accordingly. An attempt has been made here to review different methodologies proposed by scientists across the globe for arriving at the R-factor for soil loss estimation using RUSLE model.
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