Das Subhankar, Jain Manoj Kumar, Auerswald Karl, de Mello Carlos Rogerio, Molnar Peter
Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India.
School of Life Sciences, Technical University of Munich, Freising, Germany.
Sci Rep. 2025 Jul 31;15(1):27940. doi: 10.1038/s41598-025-11992-x.
Rainfall erosivity is a key dynamic factor of water erosion estimation, with a significant spatial and temporal variation. This study presents a comprehensive analysis of the spatial patterns and monthly distribution of rainfall erosivity across India, using data from 261 hourly and 2,525 monthly rainfall stations covering the period from 1969 to 2021. In India, monthly rainfall erosivity and related attributes-such as the kinetic energy of erosive rainfall, the number of erosive events, and peak hourly rainfall intensity-have been systematically examined for the first time. Monthly erosivity estimates derived from hourly data were linked with monthly rainfall, enabling a simplified and efficient estimation approach. To predict monthly erosivity based on rainfall, temperature, and topographic variables, we developed and evaluated three modeling approaches: linear regression, a machine learning-based XGBoost model, and an ensemble model. XGBoost outperformed the others, achieving a median coefficient of determination (R) of 0.97, while the ensemble model also performed well with a median R of 0.96. Additionally, a Geographically Weighted Regression (GWR) approach was applied for spatial interpolation, yielding accurate high-resolution erosivity maps with a median R of 0.90. The results also demonstrate that erosivity peaks during the summer monsoon months (June to September), with July exhibiting the highest value due to intense rainfall and high kinetic energy. Notably, the analysis revealed that nearly 32% of India experiences monthly erosivity exceeding 2,000 MJ mm ha h month in July alone. In contrast, non-monsoon months showed considerably lower erosivity levels across most of the country. A statistically significant long-term increase was detected in January, with an average rise of +0.86 MJ mm ha h month in total erosivity and + 0.1 mm h in maximum 60-min rainfall intensity annually. While acknowledging certain limitations, this study provides valuable insights into erosive rainfall characteristics, enhances rain-driven erosion assessment, and supports the development of timely and location-specific soil conservation strategies across India.
降雨侵蚀力是水蚀估算的关键动态因素，具有显著的时空变化。本研究利用1969年至2021年期间261个小时降雨站和2525个月度降雨站的数据，对印度降雨侵蚀力的空间格局和月度分布进行了全面分析。在印度，首次系统地研究了月度降雨侵蚀力及其相关属性，如侵蚀性降雨的动能、侵蚀事件数量和小时降雨强度峰值。从小时数据得出的月度侵蚀力估算值与月度降雨量相关联，形成了一种简化且高效的估算方法。为了基于降雨量、温度和地形变量预测月度侵蚀力，我们开发并评估了三种建模方法：线性回归、基于机器学习的XGBoost模型和集成模型。XGBoost的表现优于其他模型，决定系数（R）中位数达到0.97，而集成模型也表现良好，R中位数为0.96。此外，采用地理加权回归（GWR）方法进行空间插值，生成了准确的高分辨率侵蚀力图，R中位数为0.90。结果还表明，侵蚀力在夏季季风月份（6月至9月）达到峰值，7月由于降雨量大和动能高而呈现出最高值。值得注意的是，分析显示仅在7月，印度近32%的地区月度侵蚀力超过2000兆焦耳毫米公顷小时。相比之下，在该国大部分地区，非季风月份的侵蚀力水平要低得多。在1月检测到具有统计学意义的长期增长，总侵蚀力平均每年增加+0.86兆焦耳毫米公顷小时，最大60分钟降雨强度平均每年增加+0.1毫米小时。尽管认识到某些局限性，但本研究为侵蚀性降雨特征提供了有价值的见解，加强了降雨驱动的侵蚀评估，并支持在印度制定及时且因地制宜的土壤保护策略。
