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Updated: Sep 4, 2025
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Laboratory and Field Protocol for Estimating Sheet Erosion Rates from Dendrogeomorphology
Published on: January 7, 2019
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Statistical evaluation of proxies for estimating the rainfall erosivity factor
Xiaoqing Ma1,2, Mingguo Zheng3,4
1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences & Natural Resources Research, Chinese Academic of Sciences, Beijing, 100101, People's Republic of China.
Scientific Reports
|July 15, 2022
Summary
This study evaluated 15 rainfall erosivity proxies (PRs) using high-resolution rainfall data. While finer data resolution improved correlation, all proxies showed strong R-factor correlation, making them viable predictors.
Area of Science:
- Earth and Environmental Sciences
- Soil Science
- Hydrology
Background:
- Calculating the Rainfall Erosivity (R) factor requires high-temporal-resolution rainfall data.
- Numerous proxy models (PRs) have been developed to estimate the R-factor using lower-resolution data (daily, monthly, yearly).
Purpose of the Study:
- To evaluate the performance of 15 widely used R-factor proxies (PRs).
- To compare the accuracy of direct estimation versus linear calibration of PRs against the R-factor.
Main Methods:
- Utilized 6-min pluviographic rainfall data from 28 Australian stations.
- Applied Meng's test to rank correlations between R-factor and 15 PRs.
- Assessed direct estimation and linear calibration accuracy using Mean Relative Error (MRE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE).
Main Results:
- All 15 PRs demonstrated high correlation (r > 0.62, p < 0.004) with the R-factor.
- Correlation generally increased with finer temporal resolution of rainfall data (daily > monthly > yearly).
- Linear calibration significantly improved estimation accuracy (MRE: 36.0%, RMSE: 887, NSE: 0.70) compared to direct estimation (MRE: 50.0%, RMSE: 1392, NSE: 0.17).
Conclusions:
- All evaluated PRs are reasonably usable predictors of the R-factor.
- Linear calibration is recommended to enhance the accuracy of R-factor estimation from PRs.
- Recommendations are provided for suitable proxies based on available rainfall data resolution (daily, monthly, or yearly).
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