article · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · 2025
Rainfall erosivity variability based on updated rainfall records
Chen, Mengting
Cited by 0Abstract and details only
Abstract
Rainfall erosivity (R-factor) is an essential input dataset used in soil erosion models such as USLE, RUSLE, and RUSLE2, as it quantifies the combined effects of rainfall and runoff in causing soil erosion. Accurate estimation of rainfall erosivity is critical for predicting soil loss, evaluate the risk of water pollution from construction sites, and supporting land management decisions under changing climate conditions. However, the determination of erosivity depends on rainfall data, and due to changing precipitation patterns, erosivity datasets must be updated periodically. The main objectives of this dissertation are to:(1) evaluate the applicability of published regression equations for estimating annual rainfall erosivity based on annual precipitation in EPA Region 6; (2) estimate monthly rainfall erosivity using high-temporal-resolution (5-minute interval) data from a dense gauge network in Oklahoma; (3) assess the performance of gridded precipitation datasets when applied in the erosivity density approach to estimate rainfall erosivity; (4) quantify discrepancies between the estimates generated in this dissertation and those from published global datasets (GloREDa 1.2 and GloRESatE); and (5) identify changes in rainfall erosivity and erosivity density with variations in rainfall through trend analysis from 1995 to 2023. The results demonstrate that commonly used regression equations introduce substantial bias, highlighting the need for region-specific models. High-resolution gauge observations revealed significant spatial and seasonal variability in erosivity across Oklahoma, with increasing trends particularly evident in the eastern regions. Comparisons with global datasets uncovered considerable discrepancies, underscoring the need to update these products. Additionally, significant upward trends in both erosivity and erosivity density were observed, indicating intensifying rainfall events. This dissertation emphasizes the need to revise existing rainfall erosivity databases in response to changing climate conditions. There is a critical need for robust estimation approaches that integrate gridded precipitation data and leverage machine learning techniques. Gauge-derived erosivity estimates are essential for training and evaluating these emerging methods.
Cite this paper
Chen, M. (2025). Rainfall erosivity variability based on updated rainfall records. SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University).
Chen, Mengting. “Rainfall erosivity variability based on updated rainfall records.” SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University), 2025.
Chen, Mengting. 2025. “Rainfall erosivity variability based on updated rainfall records.” SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University).
Chen, M. (2025) ‘Rainfall erosivity variability based on updated rainfall records’, SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University).
@article{chen2025rainfall,
title = {{Rainfall erosivity variability based on updated rainfall records}},
author = {Chen, Mengting},
journal = {SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)},
year = {2025}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.