Vegetation controls concentrated runoff and erosion in the European loess belt by increasing hydraulic roughness and sediment retention. Studies of plant effects on runoff velocity are usually based on a taxonomical characterisation and do not consider the effects of aboveground plant functional traits in attempts to understand soil erosion by water. This trait-based plant study investigates aboveground plant functional trait effects of herbaceous hedges on the hydraulic roughness to understand soil erosion. Eight aboveground functional traits were measured on fourteen indigenous and perennial plant species (caespitose or comprising dry biomass in winter) from northwest Europe with a high morphological variability. For each trait, density-weighted traits were calculated. ACCEPTED VERSION 2 The effects of functional traits and density-weighted traits were examined using a runoff simulator with four discharges. The leaf density and area, as well as density-weighted stem and leaf areas, stem diameter and specific leaf area were positively correlated with the hydraulic roughness. Generalised linear models defined the best combinations of traits and density-weighted traits: (1) leaf density and leaf area, (2) density-weighted leaf area and density-weighted projected stem area, and (3) density-weighted leaf area and densityweighted stem diameter. Moreover, the effects of leaf density, leaf area and densityweighted specific leaf area, varied depending on the discharge. This study is one of the first characterisation of aboveground trait effects on hydraulic roughness and highlights that vegetation with important stem density, diameter and leaf area plays a significant role in minimising soil erosion. The selection of plant species can derive from these plant trait effects to design reconstructed herbaceous hedges to minimise soil erosion.

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Résumé. - L'auteur étudie l'incidence des orientations polyculturales et des nouvelles pratiques culturales sur le niveau de l'érosion. Il évalue l'efficacité et le coût des aménagements anti-érosifs.

Abstract. Excessive sediment discharge in karstic regions can be highly disruptive to water treatment plants. It is essential for catchment stakeholders and drinking water suppliers to limit the impact of high sediment loads on potable water supply, but their strategic choices must be based on simulations integrating surface and groundwater transfers and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical descriptions for the modelling process, and they can be particularly complex to predict due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new cascade modelling approach with an erosion-runoff geographic information system (GIS) model (WaterSed) and a deep neural network. The model was used in the Radicatel hydrogeological catchment (106 km2 in Normandy, France), where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five design storms under current land use and compared to four land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices, and coupling of eco-engineering/best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes (positive dye tracing) were used as input data for the deep neural network model. The model structure was found by a classical trial-and-error procedure, and the model was trained on 2 significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly backward-chaining nested cross-validation revealed that the model is able to generalize on new datasets. Simulations made for the four land use scenarios suggested that ploughing up 33 % of grasslands would increase sediment discharge at the water treatment plant by 5 % on average. By contrast, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the ranges of 10 %–44 % and 24 %–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 % to 80 %). The cascade modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plants under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

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