Abstract
Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. Improved understanding of the underlying processes and their interactions are needed to infer scaling properties of soil loss and better inform predictive methods. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. The reduction of variability of the geomorphic response is attributed to a ‘compensation effect’: temporal alternation of events that exhibit either source-limited or transport-limited regimes. The rate of reduction is related to environment stochasticity and a novel index is derived to reflect the level of variability of intra- and inter-event hydrometeorologic conditions. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments.
Highlights
Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales
We propose a hypothesis that stems from these data features: the degree of variability of intra- and inter-event dynamics of hydrometeorologic conditions controls how rapidly non-uniqueness is reduced at larger temporal scale
To indicate the overall characteristics of hydrometeorologic conditions at a given location, we propose a stochasticity index, which is defined as the sum of three relative entropy indices for the variables of observed 30-minute rainfall intensity (RI), total rainfall volume (TR), and runoff ratio (RR) that represent rainfall intensity, rainfall amount, and a scaled measure of the hydrological response, respectively
Summary
Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments. The non-uniqueness of soil erosion response can exhibit up to two orders of magnitude difference at various temporal scales[26,27] and significantly impacts predictive uncertainty. It is sorely needed to identify how the environment affects soil erosion and whether there are emerging scaling properties that can be used to inform predictive capabilities across a range of temporal scales
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