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Data-based analysis about the influence on erosion rates of the Tibetan Plateau
[Display omitted] •Data-based analysis confirms former empirical studies about the complicated mechanisms towards erosion.•Clustering of the dataset is consistent with the separation of Inner and Peripheral of Tibetan Plateau.•Fitting of quadratic polynomials suggests non-linear and multi-factor cou...
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Published in: | Journal of Asian earth sciences 2022-08, Vol.233, p.105246, Article 105246 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | [Display omitted]
•Data-based analysis confirms former empirical studies about the complicated mechanisms towards erosion.•Clustering of the dataset is consistent with the separation of Inner and Peripheral of Tibetan Plateau.•Fitting of quadratic polynomials suggests non-linear and multi-factor coupling relation with erosion.
Understanding the mechanisms and influential parameters of erosion rates is of great significance to comprehend landscape evolution and surface process. For years, many scientists have dedicated themselves into investigating the controlling factors of erosion rates over different spatial–temporal scales and various erosion mechanisms have been proposed, such as tectonics, climate, lithology, vegetation and etc., solely or coupled. Here, we exert data-based analysis of the catchment-wide 10Be erosion rates around the Tibetan Plateau (TP) (n = 289) to establish the correlation between erosion rates and other geological and topographic indices with bivariate analysis (Pearson and Spearman correlation), polynomial fitting (linear and quadratic) and data-based clustering (K-medoids). The results show that it is better to cluster the whole TP into two parts (the Inner Group (n = 176) and the Peripheral Group (n = 113)) where the quadratic fitting polynomials can reach an acceptable fitting (R2 > 0.6 and RMSE |
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ISSN: | 1367-9120 1878-5786 |
DOI: | 10.1016/j.jseaes.2022.105246 |