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As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

Given the high inter-annual variability in soil erosion, representative measurements are best carried out over time scales of a few decades. In this study, a novel approach was tested that relies on diachronic, high-resolution soil mapping. For this test, a cropland-dominated watershed covering 116 ha was selected in central Belgium. An initial soil survey based on augering had been carried out in 1956, with a density of 1.8 auger points/ha. A second survey was carried out in 2015, with a density of 4.5 points/ha. The results were interpreted in terms of changes in the depth of transition between successive horizons. The results indicate that a great proportion of the soils classified as non-eroded in 1956 were strongly eroded in 2015. Strongly eroded soils represented 52% of the watershed area in 2015, whereas they had represented only 8% in 1956. Some colluvial areas have been subjected to erosion and then covered again by colluvium over the 60 years. Only the flatter areas near the watershed boundary appear to still be in the ‘non-eroded soils or soil subject to weak erosion’ category. A mean net erosion rate of 37 t/ha per year is computed for the watershed using this method. The diachronic comparison of the pedological maps over the 60 year time interval within a context of intensive farming is a potential source of information for the calibration of landscape evolution spatial modelling. This method joins the few ones allowing a quantitative spatialization of erosion and deposition phenomena at the catchment scale.

Manual digitizing on aerial photographs is still commonly used for characterizing gully erosion over large areas. Even when automated detection procedures are implemented, manual digitizing is frequently being resorted to in order to constitute reference datasets used for training and validation. In both cases, manual digitizing entails some subjective decisions on behalf of the operator, which introduces uncertainty into the resulting datasets. To assess the magnitude of this uncertainty, 11 experienced operators were asked to digitize and classify ephemeral gullies (EGs) on cropland following a standardized methodology. The resulting 11 datasets were compared in terms of number, type and location of EGs. Furthermore, for EGs located on a welldefined runoff flow concentration axis, the slope versus contributing area topographic thresholds required for initiating gully channels were assessed using four thresholding methods, and compared across the 11 datasets. The operators identified 259 different EGs. However, the number (52–139) and sum total length (8.9–23.7 km) of EGs varied widely across operators. Only 34% of the EGs were digitized by more than half of the operators, and 7% were identified by all. Identification of EGs located on a well-defined flow concentration axis proved least subjective. The longer the EG and the more fields the EG crossed, the larger the number of operators that were able to identify it. EGs were also most easily identified when located in sugar beet fields as compared to other crops. EG classification and topographic threshold lines were also found to be strongly operator-dependent. Quantile regression appeared to be one of the most robust thresholding methods. Operator subjectivity when digitizing EGs on orthophotographs introduces uncertainty that should be taken into account in future remote sensing-based studies of EG erosion whenever they rely, in part or in full, on manual photograph interpretation. Copyright © 2014 John Wiley &

Validation of the erosion models requires field data. To date, many authors continue to highlight the paucity of accurate field observations and long-term enough studies. The fields observations are often put aside because these measures are difficult to obtain: weighty experimental devices, climatic dependence, . . . Hence the models are evolving and propose refined calculation procedures including for instance the calculation of landscape evolution. The need of field data therefore increases and new measuring strategies should arise. In the centre of Belgium we choose an agricultural watershed quite representative of the local context. It covers 124 ha of loamy soil with more than 90% of arable land and a weak proportion of forest and artificial lands. The slope ranges between 0 and 9%. Instrumentation on the watershed includes meteorological observations and discharge measurement coupled with water sampling at different outlets. The weather data (radiation, temperature, wind velocity, relative humidity and rainfall) and discharge measurement (comparison between Doppler and pressure sensors) will allow us to model the hydrological behaviour of the catchment. Rainfall readings (tipping buckets) are completed with erosivity readings (disdrometer). Erosivity, together with soil data, land use and agricultural practices observations on field, will be used as entry in the Landsoil model. The sediment samplings at 3 points in the catchment will give an insight of the sediment delivery of 3 subcatchments. The Landsoil model calculates the evolution of the DTM through time. This cannot be compared to measurements at the outlet and requires further data collection. Older elevation data and/or archaeological data are a possible source of information even if their precision remains scarce in our context. 1950’s soil surveys are on the contrary really informative since they detail the horizons depth in a spatial way and can be compared to new observation across the watershed.

Ephemeral gully erosion is responsible for 20% to 80% of the total soil erosion by water on cropland in Belgium and causes serious on- and off-site damages

L’érosion hydrique des sols pose le problème de la protection de la ressource « sol » mais également de la prévention des impacts environnementaux et sociétaux qui y sont associés tels que la dégradation de la qualité des eaux de surface, l’envasement des retenues d’eau et des bassins d’orage ou encore les inondations boueuses. Afin de cibler au mieux les mesures de lutte anti-érosives, il convient d’identifier les parcelles agricoles les plus à risque d’érosion. Mettant à profit la disponibilité d’importantes bases de données en matière de climat, sol, topographie, parcellaire et occupation du sol, une procédure automatisée de calcul de l’aléa érosion hydrique potentielle à l’échelle parcellaire, adaptée du modèle RUSLE, a été mise au point pour la Wallonie (Belgique). La carte de l’aléa érosion potentielle montre une sensibilité maximale à l’érosion hydrique en Ardenne et Haute-ardenne, en raison du relief accentué et d’une érosivité plus importante des pluies. Pour les principales zones agro-pédologiques de Wallonie, un suivi des principales cultures (céréales d’hiver, maïs, betterave, pomme de terre, colza, lin) a également été réalisé, permettant d’estimer le facteur cultural C des principales successions culturales et ainsi l’érosion effective. La prise en compte de l’occupation du sol fait cette fois ressortir un aléa maximal dans les Régions (sablo-)limoneuses et le Condroz, en raison des superficies importantes de grandes cultures industrielles. En Ardenne et Haute-Ardenne, l’aléa d’érosion effective est faible en raison d’une couverture végétale dominée par les prairies permanentes. Enfin, sur base de la carte numérique des sols de Wallonie, une classification de la vulnérabilité des sols à l’érosion a été établie à partir du volume de sol exploitable par les racines. La vulnérabilité des sols apparaît élevée sur une majorité du territoire wallon, à l’exception des Régions (sablo-)limoneuses et de la Région jurassique. Un indice d’érosion, calculé comme le