Extreme precipitation events lead to flash floods, which can trigger soil erosion and landslides. While damages to infrastructure and livelihoods are rapidly assessed on economic terms, damages to natural resources are not estimated due to limited observation record. This study conducted an analysis using remote sensing data to estimate changes in soil erosion rates before, during and after the Kerala 2018 floods, based on the Universal Soil Loss Equation (USLE). The USLE was driven by multiple data including: in situ rainfall data from Indian Meteorological Department (to estimate rainfall erosive factor), soil maps prepared by Food and Agriculture Organization (to estimate the soil erodibility factor from the properties of soil that consists of the percentage of clay, loam and silt), digital elevation model (to estimate topographic slope and length) from Shuttle Radar Topography Mission and multispectral imagery (to estimate cover management factor and conservation practice factor) from Landsat-8 satellite. Data from these sources were analysed using a Geographic Information System (GIS) platform. Results indicate a state-wise average increase of 80% (31–56 metric tons ha−1 year−1) in soil erosion rate during the floods. Of the districts, Idukki showed the highest increase, of 220% and more susceptibility to soil erosion, which is in comparison with government survey records. Results show that the floods and associated erosion were not only due to the rainfall event but also due to the rapid change in land use and land cover, from natural to human settlements. Therefore, government agencies need to protect land cover and reduce unsustainable development in ecologically sensitive environments, which if managed properly can act as a buffer for soil erosion extremes in Kerala.

中文翻译：

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2018 年喀拉拉邦洪水对土壤侵蚀的影响：灾后土壤管理的必要性

极端降水事件会导致山洪暴发，进而引发水土流失和山体滑坡。虽然根据经济条件对基础设施和生计的损害进行了快速评估，但由于观察记录有限，无法估计对自然资源的损害。本研究根据通用土壤流失方程 (USLE)，使用遥感数据进行了一项分析，以估计 2018 年喀拉拉邦洪水之前、期间和之后的土壤侵蚀率变化。USLE 由多种数据驱动，包括：来自印度气象部门的原位降雨数据（用于估计降雨侵蚀因子）、由粮食及农业组织准备的土壤图（用于根据由百分比组成的土壤特性估算土壤侵蚀因子粘土、壤土和淤泥），来自航天飞机雷达地形任务的数字高程模型（用于估计地形坡度和长度）和来自 Landsat-8 卫星的多光谱图像（用于估计覆盖管理系数和保护实践系数）。使用地理信息系统 (GIS) 平台分析来自这些来源的数据。结果表明，洪水期间土壤侵蚀率在各州平均增加了 80%（31-56 公吨 ha-1 年-1）。与政府调查记录相比，在这些地区中，Idukki 显示出最高的增长，达到 220%，并且更容易受到水土流失的影响。结果表明，洪水和相关的侵蚀不仅是由于降雨事件，而且是由于土地利用和土地覆盖从自然到人类住区的快速变化。所以，