Identifying monthly rainfall erosivity patterns using hourly rainfall data across India.

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.