Joris Eekhout

Dimensionless morphological ratios (DMR) generally are used in systemic proposals for stream classification and river restoration projects. Often, such morphometric parameters, including field data from channel cross sections, develop into a template for a given geomorphic reference site. In this study, high-resolution Digital Terrain Models (HRDTMs), combined with orthophotographs from 2018 and ground-based surveys, were used to analyze the spatial variability of DMR in a semi-arid ephemeral stream subject to changes in stream power under bankfull conditions. In particular, a channel reach of 2.7 km in length along the Upper Mula stream in southeastern Spain was chosen to test the relationships between the two types of variables. Rosgen’s DMR (width to depth ratio, entrenchment ratio, bank height ratio) and hydraulic data at bankfull stage (flow velocity, Froude number, shear stress, mean stream power and energy gradient, among others) were calculated by 1D hydrodynamic modeling and HRDTMs prior entry of field information. The resulting maps allowed comparison of stream power with DMR in relation to the channel stretch class and bed stability. The results showed similar spatial patterns for the width to depth ratio and the bank height ratio. The average values estimated in bend stretches were lower than along straight and slightly sinuous stretches and very similar to those of sub-reaches from the inflection point to the meander bend apex. However, the entrenchment ratio followed a different pattern, according to which the straight and slightly sinuous stretches were the most entrenched and the bend stretches presented a more moderate average entrenchment ratio. In addition, the energy balance and power gradient also experienced spatial variations in relation to the bed stability and DMR. Only in highly incised sub-reaches were such relationships not significant. The lack of a significant correlation between excess energy and bank height ratio or width to depth ratio over short lag distances was also verified, regardless of the affected bedforms. An ANOVA showed important differences between the straight and slightly sinuous stretches and bend stretches, which were strongly influenced by the incision and entrenchment ratios and the maximum bankfull depth.

Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. Different soil erosion model concepts are used to assess the impact of climate change on soil erosion at large spatial scales, including models forced by precipitation and by runoff. However, there is little knowledge of the implications of soil erosion model conceptualization on projected soil erosion rates under climate change. Here we assessed the impact of climate change with the three most widely used soil erosion model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). We applied the models to two contrasting Mediterranean catchments (SE Spain), where climate change is projected to decrease the annual precipitation sum and increase extreme precipitation, based on the RCP8.5 climate change scenario. Depending on the model, soil erosion is projected to decrease (RUSLE) or increase (MUSLE and MMF) in the study area. While it is difficult to validate future model projections, the differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion.

Climate change will most likely cause an increase of extreme precipitation and consequently an increase of soil erosion in many locations worldwide. In most cases, climate model output is used to assess the impact of climate change on soil erosion, however, there is little knowledge of the implications of bias‐correction methods and climate model ensembles on projected soil erosion rates. Using a soil erosion model, we evaluated the implications of three bias‐correction methods (delta change, quantile mapping and scaled distribution mapping) and climate model selection on regional soil erosion projections in two contrasting Mediterranean catchments. Depending on the bias‐correction method, soil erosion is projected to decrease or increase. Scaled distribution mapping best projects the changes in extreme precipitation. While an increase in extreme precipitation not always results in increased soil loss, it is an important soil erosion indicator. We suggest to first establish the deviation of the bias‐corrected climate signal with respect to the raw climate signal, in particular for extreme precipitation. Furthermore, individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that the impact of climate change on soil erosion can only accurately be assessed with a bias‐correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.