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There is an increasing demand for information on organic carbon (OC) in subsurface horizons, because subsurface horizons down to the bedrock can contribute to more than half of soil carbon stocks. In this study, we developed pedotransfer functions (PTFs) for predicting OC content in subsurface horizons of European soils. We used a dataset with a wide geographical coverage in Europe. The dataset was stratified sequentially into land‐cover and soil categories. For each category, PTFs were developed by multiple linear regression with the main soil and climatic factors of soil OC storage as predictor variables: OC in topsoil (0–20 cm), depth of subsurface horizons, texture and bulk density (BD) in subsurface horizons, and mean annual temperature and precipitation. Three land‐cover categories were separated: woodland, a combined category of grassland and non‐permanent arable land, and permanent arable land. For the combined land‐cover category, two soil categories were identified: (i) soils with clay‐rich subsoil and soils with little horizon development and (ii) organic‐rich soils and soils rich in Fe and Al compounds. The adjusted R2 of all PTFs was above 0.62. When PTFs were applied to independent data, the adjusted R2 was above 0.51 for all of them. The PTFs showed good prediction ability, with root mean square error (RMSE) values between 2.43 and 13.82 g C kg−1 soil. The adjusted R2 and RMSE of PTFs were better when BD was used as a predictor variable. The PTFs could be implemented easily for applications at the continental scale in Europe.

https://onlinelibrary.wiley.com/doi/full/10.1111/ejss.12464

Two objectives of the Common Agricultural Policy post-2013 (CAP, 2014–2020) in the European Union (EU) are the sustainable management of natural resources and climate smart agriculture. To understand the CAP impact on these priorities, the Land Use/Cover statistical Area frame Survey (LUCAS) employs direct field observations and soil sub-sampling across the EU. While a huge amount of information can be retrieved from LUCAS points for monitoring the environmental status of agroecosystems and assessing soil carbon sequestration, a fundamental aspect relating to climate change action is missing, namely nitrous oxide (N2O) soil emissions. To fill this gap, we ran the DayCent biogeochemistry model for more than 11’000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. The results showed that current annual N2O emissions followed a skewed distribution with a mean and median values of 2.27 and 1.71 kg N ha-1 yr-1, respectively. Using a Random Forest regression for upscaling the modelled results to the EU level, we estimated direct soil emissions of N2O in the range of 171–195 Tg yr-1 of CO2eq. Moreover, the direct regional upscaling using modelled N2O emissions in LUCAS points was on average 0.95 Mg yr-1 of CO2eq. per hectare, which was within the range of the meta-model upscaling (0.92–1.05 Mg ha-1 yr-1 of CO2eq). We concluded that, if information on management practices would be made available and model bias further reduced by N2O flux measurement at representative LUCAS points, the combination of the land use/soil survey with a well calibrated biogeochemistry model may become a reference tool to support agricultural, environmental and climate policies.

ttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176111

An in-depth analysis of the interannual variability of storms is required to detect changes in soil erosive power of rainfall, which can also result in severe on-site and off-site damages. Evaluating long-term rainfall erosivity is a challenging task, mainly because of the paucity of high-resolution historical precipitation observations that are generally reported at coarser temporal resolutions (e.g., monthly to annual totals). In this paper we suggest overcoming this limitation through an analysis of long-term processes governing rainfall erosivity with an application to datasets available the central Ruhr region (Western Germany) for the period 1701–2011. Based on a parsimonious interpretation of seasonal rainfall-related processes (from spring to autumn), a model was derived using 5-min erosivity data from 10 stations covering the period 1937–2002, and then used to reconstruct a long series of annual rainfall erosivity values. Change-points in the evolution of rainfall erosivity are revealed over the 1760s and the 1920s that mark three sub-periods characterized by increasing mean values. The results indicate that the erosive hazard tends to increase as a consequence of an increased frequency of extreme precipitation events occurred during the last decades, characterized by short-rain events regrouped into prolonged wet spells.

https://www.sciencedirect.com/science/article/pii/S0022169416307296

https://www.frontiersin.org/articles/10.3389/fevo.2017.00097/full

Recent developments in applied mathematics are bringing new tools that are capable to synthesize knowledge in various disciplines, and help in finding hidden relationships between variables. One such technique is topological data analysis (TDA), a fusion of classical exploration techniques such as principal component analysis (PCA), and a topological point of view applied to clustering of results. Various phenomena have already received new interpretations thanks to TDA, from the proper choice of sport teams to cancer treatments. For the first time, this technique has been applied in soil science, to show the interaction between physical and chemical soil attributes and main soil-forming factors, such as climate and land use. The topsoil data set of the Land Use/Land Cover Area Frame survey (LUCAS) was used as a comprehensive database that consists of approximately 20,000 samples, each described by 12 physical and chemical parameters. After the application of TDA, results obtained were cross-checked against known grouping parameters including five types of land cover, nine types of climate and the organic carbon content of soil. Some of the grouping characteristics observed using standard approaches were confirmed by TDA (e.g., organic carbon content) but novel subtle relationships (e.g., magnitude of anthropogenic effect in soil formation), were discovered as well. The importance of this finding is that TDA is a unique mathematical technique capable of extracting complex relations hidden in soil science data sets, giving the opportunity to see the influence of physicochemical, biotic and abiotic factors on topsoil formation through fresh eyes.

https://www.sciencedirect.com/science/article/pii/S0048969717303431

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha−1 h−1 yr−1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

https://www.sciencedirect.com/science/article/pii/S0022169417301439

https://onlinelibrary.wiley.com/doi/full/10.1002/ldr.2588

Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr−1 of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.

https://www.nature.com/articles/s41467-017-02142-7

Biochar can largely contribute to enhance organic carbon (OC) stocks in soil and improve soil quality in forest and agricultural lands. Its contribution depends on its recalcitrance, but also on its interactions with minerals and other organic compounds in soil. Thus, it is important to study the link between minerals, natural organic matter and biochar in soil. In this study, we investigated the incorporation of biochar-derived carbon (biochar-C) into various particle-size fractions with contrasting mineralogy and the effect of biochar on the storage of total OC in the particle-size fractions in an acid loamy soil under Pinus radiata (C3 type) in the Spanish Atlantic area. We compared plots amended with biochar produced from Miscanthus sp. (C4 type) with control plots (not amended). We separated sand-, silt-, and clay-size fractions in samples collected from 0 to 20-cm depth. In each fraction, we analyzed clay minerals, metallic oxides and oxy-hydroxides, total OC and biochar-C. The results showed that 51% of the biochar-C was in fractions < 20 μm one year after the application of biochar. Biochar-C stored in clay-size fractions (0.2–2 μm, 0.05–0.2 μm, < 0.05 μm) was only 14%. Even so, we observed that biochar-C increased with decreasing particle-size in clay-size fractions, as it occurred with the vermiculitic phases and metallic oxides and oxy-hydroxides. Biochar also affected to the distribution of total OC among particle-size fractions. Total OC concentration was greater in fractions 2–20 μm, 0.2–2 μm, 0.05–0.2 μm in biochar-amended plots than in control plots. This may be explained by the adsorption of dissolved OC from fraction < 0.05 μm onto biochar particles. The results suggested that interactions between biochar, minerals and pre-existing organic matter already occurred in the first year.

https://www.sciencedirect.com/science/article/pii/S0048969717303698

https://www.sciencedirect.com/science/article/pii/S0065211316301249

This report presents the result of WP2 of the RECARE project. One of the objectives of WP2 (Base for RECARE data collection and methods) is to provide an improved overview of existing information on soil threats and degradation at the European scale. The report is written by a group of experts from the RECARE team, coordinated by Bioforsk. In total, 60 persons were included in the process of writing, reviewing and editing the report. Eleven soil threats were identified for the report. These soil threats are soil erosion by water, soil erosion by wind, decline of organic matter (OM) in peat, decline of OM in minerals soils, soil compaction, soil sealing, soil contamination, soil salinization, desertification, flooding and landslides and decline in soil biodiversity.
Editors: Jannes Stolte, Mehreteab Tesfai, Lillian Øygarden, Sigrun Kværnø (NIBIO), Jacob Keizer, Frank Verheijen (University of Aveiro), Panos Panagos, Cristiano Ballabio (JRC), Rudi Hessel (Alterra WUR)
EUR27607

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950–2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios.

https://www.sciencedirect.com/science/article/pii/S0048969716305095

Existing data sets on earthworm communities in Europe were collected, harmonized, collated, modelled and depicted on a soil biodiversity map. Digital Soil Mapping was applied using multiple regressions relating relatively low density earthworm community data to soil characteristics, land use, vegetation and climate factors (covariables) with a greater spatial resolution. Statistically significant relationships were used to build habitat–response models for maps depicting earthworm abundance and species diversity. While a good number of environmental predictors were significant in multiple regressions, geographical factors alone seem to be less relevant than climatic factors. Despite differing sampling protocols across the investigated European countries, land use and geological history were the most relevant factors determining the demography and diversity of the earthworms. Case studies from country-specific data sets (France, Germany, Ireland and The Netherlands) demonstrated the importance and efficiency of large databases for the detection of large spatial patterns that could be subsequently applied at smaller (local) scales.

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300688

The selection of biological indicators for monitoring progress towards policy goals for soil quality should be without bias and in line with individual scenarios of need. Here we describe the prescription of a suite of appropriate indicators for potential application in such monitoring schemes across Europe. We applied a structured framework of assessment and ranking (viz. a ‘logical sieve’), building upon published data and a new survey taken from a wide section of the global soil biodiversity research and policy community.

The top ten indicators included four indicators of biodiversity (three microbial and one meso-faunal) by various methods of measurement, and three indicators of ecological function (Multiple enzyme assay, Multiple substrate-induced respiration profiling, and ‘Functional genes by molecular biological means’). Within the techniques assessed, seven out of the top ten indicators made use of molecular methods.

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300585

Because of the increasing pressures exerted on soil, below-ground life is under threat. Knowledge-based rankings of potential threats to different components of soil biodiversity were developed in order to assess the spatial distribution of threats on a European scale. A list of 13 potential threats to soil biodiversity was proposed to experts with different backgrounds in order to assess the potential for three major components of soil biodiversity: soil microorganisms, fauna, and biological functions. This approach allowed us to obtain knowledge-based rankings of threats. These classifications formed the basis for the development of indices through an additive aggregation model that, along with ad-hoc proxies for each pressure, allowed us to preliminarily assess the spatial patterns of potential threats. Intensive exploitation was identified as the highest pressure. In contrast, the use of genetically modified organisms in agriculture was considered as the threat with least potential. The potential impact of climate change showed the highest uncertainty. Fourteen out of the 27 considered countries have more than 40% of their soils with moderate-high to high potential risk for all three components of soil biodiversity. Arable soils are the most exposed to pressures. Soils within the boreal biogeographic region showed the lowest risk potential. The majority of soils at risk are outside the boundaries of protected areas. First maps of risks to three components of soil biodiversity based on the current scientific knowledge were developed. Despite the intrinsic limits of knowledge-based assessments, a remarkable potential risk to soil biodiversity was observed. Guidelines to preliminarily identify and circumscribe soils potentially at risk are provided. This approach may be used in future research to assess threat at both local and global scale and identify areas of possible risk and, subsequently, design appropriate strategies for monitoring and protection of soil biota.

https://www.sciencedirect.com/science/article/pii/S004896971531247X

The establishment of the range of soil biodiversity found within European soils is needed to guide EU policy development regarding the protection of soil. Such a base-line should be collated from a wide-ranging sampling campaign to ensure that soil biodiversity from the majority of soil types, land-use or management systems, and European climatic (bio-geographical zones) were included. This paper reports the design and testing of a method to achieve the large scale sampling associated with the establishment of such a baseline, carried out within the remit of the EcoFINDERS project, and outlines points to consider when such a task is undertaken.

Applying a GIS spatial selection process, a sampling campaign was undertaken by 13 EcoFINDERS partners across 11 countries providing data on the range of indicators of biodiversity and ecosystem functions including; micro and meso fauna biodiversity, extracellular enzyme activity, PLFA and community level physiological profiling (MicroResp™ and Biolog™). Physical, chemical and bio-geographical parameters of the 81 sites sampled were used to determine whether the model predicted a wide enough range of sites to allow assessment of the biodiversity indicators tested.

Discrimination between the major bio-geographical zones of Atlantic and Continental was possible for all land-use types. Boreal and Alpine zones only allowed discrimination in the most common land-use type for that area e.g. forestry and grassland sites, respectively, while the Mediterranean zone did not have enough sites sampled to draw conclusions across all land-use types. The method used allowed the inclusion of a range of land-uses in both the model prediction stage and the final sites sampled. The establishment of the range of soil biodiversity across Europe is possible, though a larger targeted campaign is recommended. The techniques applied within the EcoFINDERS sampling would be applicable to a larger campaign

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300342

The idea of offsetting anthropogenic CO2 emissions by increasing global soil organic carbon (SOC), as recently proposed by French authorities ahead of COP21 in the ‘four per mil’ initiative, is notable. However, a high uncertainty still exits on land C balance components. In particular, the role of erosion in the global C cycle is not totally disentangled, leading to disagreement whether this process induces lands to be a source or sink of CO2. To investigate this issue, we coupled soil erosion into a biogeochemistry model, running at 1 km2 resolution across the agricultural soils of the European Union (EU). Based on data‐driven assumptions, the simulation took into account also soil deposition within grid cells and the potential C export to riverine systems, in a way to be conservative in a mass balance. We estimated that 143 of 187 Mha have C erosion rates <0.05 Mg C ha−1 yr−1, although some hot‐spot areas showed eroded SOC >0.45 Mg C ha−1 yr−1. In comparison with a baseline without erosion, the model suggested an erosion‐induced sink of atmospheric C consistent with previous empirical‐based studies. Integrating all C fluxes for the EU agricultural soils, we estimated a net C loss or gain of −2.28 and +0.79 Tg yr−1 of CO2eq, respectively, depending on the value for the short‐term enhancement of soil C mineralization due to soil disruption and displacement/transport with erosion. We concluded that erosion fluxes were in the same order of current carbon gains from improved management. Even if erosion could potentially induce a sink for atmospheric CO2, strong agricultural policies are needed to prevent or reduce soil erosion, in order to maintain soil health and productivity.

http://www.eemj.icpm.tuiasi.ro/pdfs/vol15/no12/10_91_Toth_14.pdf

Understanding spatial and temporal patterns in land susceptibility to wind erosion is essential to design effective management strategies to control land degradation. The knowledge about the land surface susceptible to wind erosion in European contexts shows significant gaps. The lack of researches, particularly at the landscape to regional scales, prevents national and European institutions from taking actions aimed at an effective mitigating of land degradation. This study provides a preliminary pan‐European assessment that delineates the spatial patterns of land susceptibility to wind erosion and lays the groundwork for future modelling activities. An Index of Land Susceptibility to Wind Erosion (ILSWE) was created by combining spatiotemporal variations of the most influential wind erosion factors (i.e. climatic erosivity, soil erodibility, vegetation cover and landscape roughness). The sensitivity of each input factor was ranked according to fuzzy logic techniques. State‐of‐the‐art findings within the literature on soil erodibility and land susceptibility were used to evaluate the outcomes of the proposed modelling activity. Results show that the approach is suitable for integrating wind erosion information and environmental factors. Within the 34 European countries under investigation, moderate and high levels of land susceptibility to wind erosion were predicted, ranging from 25·8 to 13·0 M ha, respectively (corresponding to 5·3 and 2·9% of total area). New insights into the geography of wind erosion susceptibility in Europe were obtained and provide a solid basis for further investigations into the spatial variability and susceptibility of land to wind erosion across Europe. 

https://onlinelibrary.wiley.com/doi/full/10.1002/ldr.2318

Nearly all of Europe is affected by soil erosion. A major policy response is required to reverse the impacts of erosion in degraded areas, particularly in light of the current climate change and water crisis. Soil loss occurs not because of any lack of knowledge on how to protect soils, but a lack in policy governance. The average rate of soil loss by sheet and rill erosion in Europe is 2·46 Mg ha−1 yr−1. To mitigate the impacts of soil erosion, the European Union's Common Agricultural Policy has introduced conservation measures which reduce soil loss by water erosion by 20% in arable lands. Further economic and political action should rebrand the value of soil as part of ecosystem services, increase the income of rural land owners, involve young farmers and organize regional services for licensing land use changes. In a changing World of 9 billion people with the challenge of climate change, water scarcity and depletion of soil fertility, the agriculture economy should evolve taking into account environmental and ecological aspects.

https://onlinelibrary.wiley.com/doi/full/10.1002/ldr.2538

Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland–Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.

https://www.sciencedirect.com/science/article/pii/S0301479716300548

Soil erosion by water is a serious threat for the Mediterranean region. Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation. The potential ability for rainfall to cause soil loss is expressed as rainfall erosivity, a key parameter required by most soil loss prediction models. In Italy, rainfall erosivity measurements are limited to few locations, preventing researchers from effectively assessing the geography and magnitude of soil loss across the country. The objectives of this study were to investigate the spatio-temporal distribution of rainfall erosivity in Italy and to develop a national-scale grid-based map of rainfall erosivity. Thus, annual rainfall erosivity values were measured and subsequently interpolated using a geostatistical approach. Time series of pluviographic records (10-years) with high temporal resolution (mostly 30-min) for 386 meteorological stations were analysed. Regression-kriging was used to interpolate rainfall erosivity values of the meteorological stations to an Italian rainfall erosivity map (500-m). A set of 23 environmental covariates was tested, of which seven covariates were selected based on a stepwise approach (mostly significant at the 0.01 level). The interpolation method showed a good performance for both the cross-validation data set ( = 0.777) and the fitting data set (R2 = 0.779)

https://www.tandfonline.com/doi/full/10.1080/17538947.2016.1148203

Soil contamination is one of the greatest concerns among the threats to soil resources in Europe and globally. Despite of its importance there was only very course scale (1/5000 km2) data available on soil heavy metal concentrations prior to the LUCAS topsoil survey, which had a sampling density of 200 km2. Based on the results of the LUCAS sampling and auxiliary information detailed and up-to-date maps of heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn, Sb, Co and Ni) in the topsoil of the European Union were produced. Using the maps of heavy metal concentration in topsoil we made a spatial prediction of areas where local assessment is suggested to monitor and eventually control the potential threat from heavy metals. Most of the examined elements remain under the corresponding threshold values in the majority of the land of the EU. However, one or more of the elements exceed the applied threshold concentration on 1.2 M km2, which is 28.3% of the total surface area of the EU. While natural backgrounds might be the reason for high concentrations on large proportion of the affected soils, historical and recent industrial and mining areas show elevated concentrations (predominantly of As, Cd, Pb and Hg) too, indicating the magnitude of anthropogenic effect on soil quality in Europe.

https://www.sciencedirect.com/science/article/pii/S0048969716310452

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.

https://www.mdpi.com/2073-4441/8/4/119