Statistical evaluation of proxies for estimating the rainfall erosivity factor
Abstract Considering the high-temporal-resolution rainfall data requirements for calculating the Rainfall Erosivity factor (that is, the R-factor), studies have developed a large number of proxies for the R-factor (PR). This study aims to evaluate 15 widely used proxies, which were developed in vari...
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| Format: | Article |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15271-x |
| _version_ | 1818510604727484416 |
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| author | Xiaoqing Ma Mingguo Zheng |
| author_facet | Xiaoqing Ma Mingguo Zheng |
| author_sort | Xiaoqing Ma |
| collection | DOAJ |
| description | Abstract Considering the high-temporal-resolution rainfall data requirements for calculating the Rainfall Erosivity factor (that is, the R-factor), studies have developed a large number of proxies for the R-factor (PR). This study aims to evaluate 15 widely used proxies, which were developed in various countries using daily, monthly, or yearly rainfall data, in terms of correlation and statistical equality with the R-factor by using the 6-min pluviographic data from 28 stations in Australia. Meng’s test was applied to rank the correlations. Although the Meng’s test indicated that the correlation between Rainfall Erosivity (R) and Rainfall Erosivity calculated by the proxy model (PR) generally increased with a finer time resolution of the rainfall data (in the order of year, month, and day), the 15 PRs under examination were all highly correlated with R (r > 0.62, p < 0.004), implying that all of them can be reasonably used as an R predictor. A direct estimation of the R-factor using PRs produced a mean relative error (MRE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE) with a mean of 50.0%, 1392 MJ mm ha−1 h−1 a−1, and 0.17, respectively. The linear calibrations improved the accuracy of the estimation and produced an MRE, RMSE, and NSE with a mean of 36.0%, 887 MJ mm ha−1 h−1 a−1, and 0.70, respectively. Finally, suitable proxies for instances where only daily, monthly, or yearly rainfall data are available were recommended. |
| first_indexed | 2024-12-10T23:22:16Z |
| format | Article |
| id | doaj.art-822e401fe4384a9598a4bc3cba391d27 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-10T23:22:16Z |
| publishDate | 2022-07-01 |
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| spelling | doaj.art-822e401fe4384a9598a4bc3cba391d272022-12-22T01:29:42ZengNature PortfolioScientific Reports2045-23222022-07-0112111110.1038/s41598-022-15271-xStatistical evaluation of proxies for estimating the rainfall erosivity factorXiaoqing Ma0Mingguo Zheng1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences & Natural Resources Research, Chinese Academic of SciencesNational-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Engineering Research Center for Non-point Source Pollution Control, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of SciencesAbstract Considering the high-temporal-resolution rainfall data requirements for calculating the Rainfall Erosivity factor (that is, the R-factor), studies have developed a large number of proxies for the R-factor (PR). This study aims to evaluate 15 widely used proxies, which were developed in various countries using daily, monthly, or yearly rainfall data, in terms of correlation and statistical equality with the R-factor by using the 6-min pluviographic data from 28 stations in Australia. Meng’s test was applied to rank the correlations. Although the Meng’s test indicated that the correlation between Rainfall Erosivity (R) and Rainfall Erosivity calculated by the proxy model (PR) generally increased with a finer time resolution of the rainfall data (in the order of year, month, and day), the 15 PRs under examination were all highly correlated with R (r > 0.62, p < 0.004), implying that all of them can be reasonably used as an R predictor. A direct estimation of the R-factor using PRs produced a mean relative error (MRE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE) with a mean of 50.0%, 1392 MJ mm ha−1 h−1 a−1, and 0.17, respectively. The linear calibrations improved the accuracy of the estimation and produced an MRE, RMSE, and NSE with a mean of 36.0%, 887 MJ mm ha−1 h−1 a−1, and 0.70, respectively. Finally, suitable proxies for instances where only daily, monthly, or yearly rainfall data are available were recommended.https://doi.org/10.1038/s41598-022-15271-x |
| spellingShingle | Xiaoqing Ma Mingguo Zheng Statistical evaluation of proxies for estimating the rainfall erosivity factor Scientific Reports |
| title | Statistical evaluation of proxies for estimating the rainfall erosivity factor |
| title_full | Statistical evaluation of proxies for estimating the rainfall erosivity factor |
| title_fullStr | Statistical evaluation of proxies for estimating the rainfall erosivity factor |
| title_full_unstemmed | Statistical evaluation of proxies for estimating the rainfall erosivity factor |
| title_short | Statistical evaluation of proxies for estimating the rainfall erosivity factor |
| title_sort | statistical evaluation of proxies for estimating the rainfall erosivity factor |
| url | https://doi.org/10.1038/s41598-022-15271-x |
| work_keys_str_mv | AT xiaoqingma statisticalevaluationofproxiesforestimatingtherainfallerosivityfactor AT mingguozheng statisticalevaluationofproxiesforestimatingtherainfallerosivityfactor |