Impact of Time Resolution of Rainfall Measurement on Erosivity Factor in Arid Region of India

RESEARCH PAPER

Abstract

Rainfall erosivity is a crucial factor in estimating soil loss through erosion prediction models such as the original and modified Universal Soil Loss Equation (USLE) models. Accurate assessment of rainfall erosivity requires high-resolution rainfall measurements, which are often not widely available across many regions of the world. In this study, a series of conversion factors were developed to translate rainfall erosivity estimates derived from measurements taken at various time intervals into estimates based on 1-minute rainfall data.  This study employed 1-minute rainfall data collected over two years (2020 and 2024) from the western arid region of India—an application conducted for the first time in this region—to compute the total kinetic energy (E), maximum 30-minute rainfall intensity (I30), and the rainfall erosivity factor (R-factor) for individual storm events using the EI30 index method. The results indicated that I30 values were severe for approximately 5% to 10% of the storms, and high to very high for 75% to 80% of the storms. Furthermore, the study revealed that as the measurement interval decreases, the peaks of I30 are more effectively captured, resulting in an increased erosive energy estimate of the rainfall. The derived conversion factors for this arid region were relatively lower compared to those reported for humid and semi-arid regions in previous studies. Additionally, underestimations of the energy component (E) increased with longer measurement intervals—from 5 minutes to 60 minutes—with a relative error within -10%. The R-factors exhibited underestimations of -4.5%, -8.0%, -9.6%, -5.8%, and -96.7% at 5, 15, 30, 60 minutes, and 24 hours, respectively. The relationships developed to accurately estimate E, I30, and R-factors from high-resolution (1-minute) data—based on coarser data at various time intervals (5-min, 15-min, 30-min, 60-min, and 24-h)—demonstrated strong interactions among these factors. These models can be effectively used to produce reliable erosivity estimates from lower-resolution rainfall data, enhancing the precision of soil erosion assessments in similar arid environments.