Abstract Understanding how tectonics, climate and lithology interact to control fluvial erosion is complicated because these factors are spatially-variable and they may not be well-represented by mean values. We address these complications using eight new and 54 published 10Be catchment-wide fluvial erosion rates from the south-central Andes. We assess how tectonics, climate, lithology, and topography control erosion through bivariate and multivariate Bayesian regression analysis. We first compare catchment-wide mean values of independent variables compared to other summary statistics and find that metrics that capture extreme values (e.g., 90th percentile) and spatial variability (e.g., 90th minus 10th percentile) produce stronger correlations. This suggests that catchment-wide means may oversimplify the roles of tectonics, climate, and lithology in influencing erosion rates. We find that the overall variability of erosion rates in the south-central Andes is best explained by a combination of lithologic resistance and spatial variability in both vegetation (using the normalized difference vegetation index, NDVI) and topography (using specific stream power). Despite poor bivariate correlations, both lithologic resistance and spatial variability of specific stream power are significant regressors in our multivariate modeling. Lithology influences the relationship (i.e., linearity) between topography and erosion rates. Spatial variability of NDVI produces the strongest correlation with erosion rates of any of the variables we consider. Hence, spatial variability of NDVI both accounts for potential non-uniform vegetation responses to climate and also incorporates the role of both humid climates (high 90th percentile) and large bare regions (low 10th percentile) within a single catchment.

中文翻译：

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安第斯山脉中南部植被中度河流侵蚀的岩性、地形和空间变异性

摘要 了解构造、气候和岩性如何相互作用以控制河流侵蚀是很复杂的，因为这些因素在空间上是可变的，并且它们可能无法用平均值很好地表示。我们使用来自安第斯中南部的 8 个新的和 54 个已发布的 10Be 流域范围内的河流侵蚀率来解决这些并发症。我们通过二元和多元贝叶斯回归分析评估构造、气候、岩性和地形如何控制侵蚀。我们首先将自变量的流域范围平均值与其他汇总统计数据进行比较，并发现捕捉极值（例如，第 90 个百分位数）和空间变异性（例如，第 90 个负 10 个百分位数）的指标产生更强的相关性。这表明流域范围的手段可能会过度简化构造、气候、和岩性对侵蚀速率的影响。我们发现，安第斯山脉中南部侵蚀率的整体变异性最好通过两种植被（使用归一化差异植被指数，NDVI）和地形（使用特定河流功率）的岩性阻力和空间变异性的组合来解释。尽管双变量相关性较差，但在我们的多变量建模中，岩性阻力和特定河流功率的空间变异性都是重要的回归变量。岩性影响地形和侵蚀率之间的关系（即线性）。NDVI 的空间变异性与我们考虑的任何变量的侵蚀率产生最强的相关性。因此，