Mapping monthly rainfall erosivity in Europe

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

Projected mid-century rainfall erosivity under climate change over the southeastern United States

Recent observations and climate change projections indicate that changes in rainfall energy, intensity, duration, and frequency, which determine the erosive power of rainfall, will amplify erosion rates around the world. However, the magnitude and scope of these future changes in erosive power of rainfall remain largely unknown, particularly at finer-resolutions and local scales. Due to a lack of available projected future sub-hourly climate data, previous studies relied on aggregates (hourly, daily) rainfall data. The erosivity for the southeastern United States in this study was calculated using the RUSLE2 erosivity calculation method without data limitation and a recently published 15-min precipitation dataset. This precipitation data was derived from five NA-CORDEX climate models' precipitation products under the Representative Concentration Pathway (RCP) 8.5 scenario. In this dataset, hourly climate projections of precipitation were bias-corrected and temporally downscaled to 15-min resolution for 187 locations with collocated 15-min precipitation observations. Precipitation, erosivity (R-factor), and erosivity density (ED) estimations were provided for historical (1970-1999) and future (2030-2059) time periods. Ensemble results for projected values (as compared to historical values) showed increase in precipitation, erosivity, and erosivity density by 14 %, 47 %, and 29 %, respectively. The future ensemble model showed an average annual R-factor of 11,237±1299 MJ mm ha^-1h^-1yr^-1. These findings suggest that changes in rainfall intensity, rather than precipitation amount, may be driving the change in erosivity. However, the bias correction and downscaling limitations inherent in the original precipitation dataset and this study's analyses obscured this particular result. In general, coastal and mountainous regions are expected to experience the greatest absolute increase in erosivity, while other inland areas are expected to experience the greatest relative change. This study offers a novel examination of projected future precipitation characteristics in terms of erosivity and potential future erosion.

Global assessment of storm disaster-prone areas

Background

Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm^-2 h^-1 yr^-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events.

Methods and findings

Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm^-2 h^-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively.

Conclusion

RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected).

Production-Based and Consumption-Based Accounting of Global Cropland Soil Erosion

The effective control of cropland soil erosion is urgent for all countries because of its threat to global food security. Cropland soil erosion is caused by agricultural production and driven indirectly by consumption. Analyzing the causes and preventive strategies from the consumption side is essential for soil erosion control. However, there is not yet sufficient research or practice. In this study, we estimated global cropland soil erosion with the revised universal soil loss equation, allocated it to specific types of crops, and quantified the cropland soil erosion footprint of the economies with a multiregional input-output analysis model. Our results showed that developed economies, usually importing cropland soil erosion from developing or agriculturally developed economies, are the beneficiaries in the current crop trading system. The European Union is the largest net importer, while Brazil is the largest exporter. The indirect and induced sectors are the main contributors, consuming approximately 70.48% of the total cropland soil erosion. Our results revealed the region- and product-specific contributors that could inform the reduction of global cropland soil erosion for sustainable food production and consumption.

Evaluating spatio-temporal soil erosion dynamics in the Winam Gulf catchment, Kenya for enhanced decision making in the land-lake interface

Soil erosion accelerated by poor agricultural practices, land degradation, deprived infrastructure development and other anthropogenic activities has important implications for nutrient cycling, land and lake productivity, loss of livelihoods and ecosystem services, as well as socioeconomic disruption. Enhanced knowledge of dynamic factors influencing soil erosion is critical for policymakers engaged in land use decision-making. This study presents the first spatio-temporal assessment of soil erosion risk modelling in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation (RUSLE) within a geospatial framework at a monthly resolution between January 2017 and June 2020. Dynamic rainfall erosivity and land cover management factors were derived from existing datasets to determine their effect on average monthly soil loss by water erosion. By assessing soil erosion rates with enhanced temporal resolution, it is possible to provide greater knowledge regarding months that are particularly susceptible to soil erosion and can better inform future strategies for targeted mitigation measures. Whilst the pseudo monthly average soil loss was calculated (0.80 t ha^-1 month^-1), the application of this value would lead to misrepresentation of monthly soil loss throughout the year. Our results indicate that the highest erosion rates occur between February and April (average 0.95 t ha^-1 month^-1). In contrast, between May and August, there is a significantly reduced risk (average 0.72 t ha^-1 month^-1) due to the low rainfall erosivity and increased vegetation cover as a result of the long rainy season. The mean annual gross soil loss by water erosion in the Winam Gulf catchment amounts to 10.71 Mt year^-1, with a mean soil loss rate of 9.63 t ha^-1 year^-1. These findings highlight the need to consider dynamic factors within the RUSLE model and can prove vital for identifying areas of high erosion risk for future targeted investigation and conservation action.

Environmental drivers of dynamic soil erosion change in a Mediterranean fluvial landscape

Background

Rainfall and other climatic agents are the main triggers of soil erosion in the Mediterranean region, where they have the potential to increase discharge and sediment transport and cause long-term changes in the river system. For the Magra River Basin (MRB), located in the upper Tyrrhenian coast of Italy, we estimated changes in net erosion as a function of the geographical characteristics of the basin, the seasonal distribution of precipitation, and the vegetation cover.

Methods and findings

Based on rainfall erosivity and surface flow and transport sub-models, we developed a simplified model to assess basin-wide sediment yields on a monthly basis by upscaling the point rainfall input. Our calibration dataset of monthly data (Mg km^-2 month^-1, available for the years 1961 and 1963–1969) revealed that our model satisfactorily reproduces the net soil erosion in the study area (R² = 0.81). For the period 1950–2020, the reconstruction of an annually aggregated time-series of monthly net erosion data (297 Mg km^-2 yr^-1 on average) indicated a moderate decline in sediment yield after 1999. This is part of a long-term downward trend, which highlights the role played by land-use changes and reforestation of the mountainous areas of the basin.

Conclusion

This study shows the environmental history and dynamics of the basin, and thus the varying sensitivity of hydrological processes and their perturbations. Relying on a few climatic variables as reported from a single representative basin location, it provides an interpretation of empirically determined factors that shape active erosional landscapes. In particular, we showed that the most recent extreme storms associated with sediment yield have been characterised by lower cumulative rainfall, indicating a greater propensity for the basin to produce sediment more discontinuously over time.

A millennium-long climate history of erosive storms across the Tiber River Basin, Italy, from 725 to 2019 CE

Rainfall erosivity drives damaging hydrological events with significant environmental and socio-economic impacts. This study presents the world's hitherto longest time-series of annual rainfall erosivity (725-2019 CE), one from the Tiber River Basin (TRB), a fluvial valley in central Italy in which the city of Rome is located. A historical perspective of erosive floods in the TRB is provided employing a rainfall erosivity model based on documentary data, calibrated against a sample (1923-1964) of actual measurement data. Estimates show a notable rainfall erosivity, and increasing variability, during the Little Ice Age (here, ~ 1250-1849), especially after c. 1495. During the sixteenth century, erosive forcing peaked at > 3500 MJ mm hm^-2 h^-1 yr^-1 in 1590, with values > 2500 MJ mm hm^-2 h^-1 yr^-1 in 1519 and 1566. Rainfall erosivity continued into the Current Warm Period (since ~ 1850), reaching a maximum of ~ 3000 MJ mm hm^-2 h^-1 yr^-1 in the 1940s. More recently, erosive forcing has attenuated, though remains critically high (e.g., 2087 and 2008 MJ mm hm^-2 h^-1 yr^-1 in 1992 and 2005, respectively). Comparison of the results with sediment production (1934-1973) confirms the model's ability to predict geomorphological effects in the TRB, and reflects the role of North Atlantic circulation dynamics in central Italian river basins.

Assessing Soil Loss by Water Erosion in a Typical Mediterranean Ecosystem of Northern Greece under Current and Future Rainfall Erosivity

Soil is a non-renewable resource essential for life existence. During the last decades it has been threatened by accelerating erosion with negative consequences for the environment and the economy. The aim of the current study was to assess soil loss changes in a typical Mediterranean ecosystem of Northern Greece, under climate change. To this end, freely available geospatial data was collected and processed using open-source software package. The widespread RUSLE empirical erosion model was applied to estimate soil loss. Current and future rainfall erosivity were derived from a national scale study considering average weather conditions and RCMs outputs for the medium Representative Concentration Pathway scenario (RCP4.5). Results showed that average rainfall erosivity (R-Factor) was 508.85 MJ mm ha h−1 y−1 while the K-factor ranged from 0.0008 to 0.05 t ha h ha−1 MJ−1 mm−1 and LS-factor reached 60.51. Respectively, C-factor ranged from 0.01 to 0.91 and P-factor ranged from 0.42 to 1. The estimated potential soil loss rates will remain stable for the near future period (2021–2050), while an increase of approximately 9% is expected by the end of the 21th century (2071–2100). The results suggest that appropriate erosion mitigation strategies should be applied to reduce erosion risk. Subsequently, appropriate mitigation measures per Land Use/Land Cover (LULC) categories are proposed. It is worth noting that the proposed methodology has a high degree of transferability as it is based on open-source data.

Impacts of grazing on ground cover, soil physical properties and soil loss via surface erosion: A novel geospatial modelling approach

Agricultural expansion and overgrazing are globally recognized as key contributors to accelerated soil degradation and surface erosion, with direct consequences for land productivity, and environmental health. Measured impacts of livestock grazing on soil physical properties and ground cover are absent in soil loss models (e.g., Revised Universal Soil Loss Equation, RUSLE) despite significant impacts to surface erosion. We developed a novel model that captures changes to ground cover and soil properties (permeability and structure) as a function of grazing intensity (density, duration, history, and stock type), as well as soil clay and water contents. The model outputs were integrated within RUSLE to calculate a treaded soil erodibility (K_tr) and grazed cover factors (C_gr) at seasonal timescales (3-month windows) to account for variability in soil moisture content, grazing practices, vegetation growth and senescence, and rainfall. Grazed pastures and winter-forage paddocks exhibit distinct changes in soil erodibility and soil losses, which are most pronounced for wet soils when plant cover is reduced/minimal. On average, typical pasture grazing pressures increase soil erodibility by 6% (range = 1-90%), compared to 60% (18-310%) for intensive winter forage paddocks. Further, negligible ground cover following forage crop grazing increases surface erosion by a factor of 10 (±₁³) relative to grazed pastures, which exhibit soil losses (μ = 83 t km^-2 yr^-1; range = 11.6 to 246) comparable to natural uncropped catchments (100-200 t km^-2 yr^-1). Exacerbated soil losses from winter forage-crop paddocks (μ = 1,100 t km^-2 yr^-1) arose from significant degradation of soil physical properties and exposing soils directly to rainfall and runoff. We conclude that proactive decisions to reduce treading damage and avoid high-density grazing will far exceed reactive practices seeking to trap sediments lost from grazed lands.

Fingerprint of climate change in precipitation aggressiveness across the central Mediterranean (Italian) area

Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways.

Simulating Erosion-Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model

Global water erosion strongly affects the terrestrial carbon balance. However, this process is currently ignored by most global land surface models (LSMs) that are used to project the responses of terrestrial carbon storage to climate and land use changes. One of the main obstacles to implement erosion processes in LSMs is the high spatial resolution needed to accurately represent the effect of topography on soil erosion and sediment delivery to rivers. In this study, we present an upscaling scheme for including erosion-induced lateral soil organic carbon (SOC) movements into the ORCHIDEE LSM. This upscaling scheme integrates information from high-resolution (3″) topographic and soil erodibility data into a LSM forcing file at 0.5° spatial resolution. Evaluation of our model for the Rhine catchment indicates that it reproduces well the observed spatial and temporal (both seasonal and interannual) variations in river runoff and the sediment delivery from uplands to the river network. Although the average annual lateral SOC flux from uplands to the Rhine River network only amounts to 0.5% of the annual net primary production and 0.01% of the total SOC stock in the whole catchment, SOC loss caused by soil erosion over a long period (e.g., thousands of years) has the potential to cause a 12% reduction in the simulated equilibrium SOC stocks. Overall, this study presents a promising approach for including the erosion-induced lateral carbon flux from the land to aquatic systems into LSMs and highlights the important role of erosion processes in the terrestrial carbon balance.

Influence of DEM Elaboration Methods on the USLE Model Topographical Factor Parameter on Steep Slopes

Runoff erosion is an important theme in hydrological investigations. Models assessing soil erosion are based on various algorithms that determine the relief coefficient using rasterized digital elevation models (DEMs). For evaluation of soil loss, the most-used model worldwide is the USLE (Universal Soil Loss Equation), where the most essential part is the LS parameter, which is, in turn, generated from two parameters: L (slope length coefficient) and S (slope inclination). The most significant limitation of LS is the difficulty in obtaining the data needed to generate detailed DEMs. We investigated three popular data generation methods: aerial photographs (AP), aerial laser scanning (ALS), and terrestrial laser scanning (TLS) by assessing the quality and effect of DEMs generated from each method over an area of 40 m × 200 m in Silesia, Poland. Additionally, the relationship between particular LSUSLE  parameter components was carried out based on its final distribution. Our results show that resolution strongly influences DEMs and the LSUSLE  parameters. We found a strong relationship between the degree of height data resolution and the accuracy level of the calculated parameters. Based on our investigations we confirmed the highest influence on the LSUSLE  came from the S parameter. Additionally, we concluded that in examinations over large areas, terrestrial laser scanners are not ideal; the benefits of their additional accuracy are outweighed by the additional time and labor consumption; in addition, terrestrial-based scans are sometimes not possible due to ground obstacles the limited scope of most lasers. Aerial photographs or point clouds generated by aerial laser scanners are sufficient for most purposes connected with surface flow, and further developments can be based on the use of these techniques for obtaining ground information for modeling erosion processes.

Geospatial Assessment of Soil Erosion Intensity and Sediment Yield Using the Revised Universal Soil Loss Equation (RUSLE) Model

Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is helpful to researchers and planners for better planning to control the loss of soil in the high severity zones. Plantation of trees and structures should be built like check dams, which effectively control the soil erosion process.

Assessment of rainfall erosivity (R-factor) during 1986-2015 across Nepal: a step towards soil loss estimation

Rainfall is a main cause of soil erosion which varies spatially and temporarily. R-factor is an erosive power of the rainfall that is responsible for soil detachment and subsequent displacement. Mathematically, it is expressed as a sum of the product of kinetic energy and maximum 30-min rain intensity. A precise assessment of R-factor needs higher temporal resolution rainfall data (sub-hourly) for a period of several years, which is rarely available. Many empirical approaches are used to predict R-factor as a function of mean monthly and annual rainfall amount. In this study, we used Loureiro and Countinho (Journal of Hydrology 250:12-18, 2001) approximation approach to estimate R-factor and explore its intra-annual variability using 30 years (1986-2015) of daily rainfall data from 280 stations distributed across Nepal. This study employs different intra-annual variability indices and calculates erosivity density (ED) and weighted erosivity density (WED). The country average mean annual R-factor (MAR), annual ED, and WED are found to be 9434.8 MJ mm ha^-1 h^-1 year^-1, 4.39 MJ ha^-1 h^-1,and 1.61 MJ ha^-1 h^-1, respectively. On a monthly scale, July is the highest erosive month followed by August (> 2000 MJ mm ha^-1 h^-1 month^-1). Likewise, November is the lowest erosive month followed by December (~ 50 MJ mm ha^-1 h^-1 month^-1). Spatial distributions of these indices show clear delineations of areas with different erosivity patterns at different time of the year. In addition, this study explores inter-annual variation, temporal evolution, and trend estimation of R-factors over the country (for the first time). Significant rising trends are observed in the western region of the country. We found that the mean soil erosion for Nepal is estimated at 21.01 ton ha^-1 year^-1. The smallest R-factors are observed in the north-western region of the country and the maximum values are observed at mid hills and southern plains of the country. Our study could be an initial but important step for effective soil conservation, land use planning, and agricultural production.

Variation characteristics of rainfall erosivity in Guizhou Province and the correlation with the El Niño Southern Oscillation

Rainfall erosivity is an important indicator that can be used to measure the ability of rain to cause erosion and is connected with the El Niño Southern Oscillation (ENSO) through the transmission of rainfall. This work aimed to explore the characteristics of rainfall erosivity in Guizhou Province and determine its correlation with ENSO. Rainfall erosivity was calculated from daily rainfall data from January 1960 to December 2017. The analyses were conducted using a daily rainfall erosivity model, inverse distance weighted (IDW) interpolation, linear regression analysis, Mann-Kendall test and correlation analysis. The long-term (1960-2017) average rainfall erosivity was 5825.60 MJ·mm·ha^-1·h^-1 in the study area and showed a high temporal variability with the estimates from the linear trend line ranging from -449.5 MJ·mm·ha^-1·h^-1/10a to 496.8 MJ·mm·ha^-1·h^-1/10a. According to rainfall and erosive rainfall, an uneven spatial distribution of rainfall erosivity was observed with an increasing trend from south to north. Temporal distribution of monthly rainfall erosivity was consistent with that of seasonal rainfall erosivity, and concentrated in the summer months (June to August). As the representation indices of ENSO phenomena, the Oceanic Niño Index (ONI), Southern Oscillation Index (SOI) and multivariate ENSO Index (MEI) were selected for correlation analysis with rainfall erosivity. During El Niño events, the ONI, SOI and MEI showed significant correlations (>95% confidence level) with rainfall erosivity, while during La Niña events, only the ONI and MEI were significantly correlated with rainfall erosivity, but no significant correlation was detected during the neutral period or for the entire study period. The degree of rainfall erosion is proportional to the ENSO duration; the longer the ENSO duration, the greater the rainfall erosivity. These findings could help predict soil erosion and be used to develop further adaptation measures to prevent water and soil loss.

Spatial and statistical trend characteristics of rainfall erosivity (R) in upper catchment of Baram River, Borneo

The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha^-1 h^-1 year^-1, respectively_. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha^-1 h^-1 year^-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha^-1h^-1year^-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha^-1 h^-1 year^-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha^-1 h^-1 year^-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha^-1 h^-1 year^-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha^-1 h^-1 year^-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha^-1 h^-1 year^-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.

Time Scale Effects and Interactions of Rainfall Erosivity and Cover Management Factors on Vineyard Soil Loss Erosion in the Semi-Arid Area of Southern Sicily

Several authors describe the effectiveness of cover crop management practice as an important tool to prevent soil erosion, but at the same time, they stress on the high soil loss variability due to the interaction of several factors characterized by large uncertainty. In this paper the Revised Universal Soil Loss Equation (RUSLE) model is applied to two Sicilian vineyards that are characterized by different topographic factors; one is subjected to Conventional Practice (CP) and the other to Best Management Practice (BMP). By using climatic input data at a high temporal scale resolution for the rainfall erosivity (R) factor, and remotely sensed imagery for the cover and management (C) factor, the importance of an appropriate R and C factor assessment and their inter and intra-annual interactions in determining soil erosion variability are showed. Different temporal analysis at ten-year, seasonal, monthly and event scales showed that results at events scales allow evidencing the interacting factors that determine erosion risk features which at other temporal scales of resolution can be hidden. The impact of BMP in preventing soil erosion is described in terms of average saved soil loss over the 10-year period of observation. The evaluation of soil erosion at a different temporal scale and its implications can help stakeholders and scientists formulate better soil conservation practices and agricultural management, and also consider that erosivity rates are expected to raise for the increase of rainfall intensity linked to climate change.

Soil erosion modelling: The new challenges as the result of policy developments in Europe

New challenges and policy developments after 2015 (among others, the Common Agricultural Policy (CAP), Sustainable Development Goals (SDGs)) are opportunities for soil scientists and soil erosion modellers to respond with more accurate assessments and solutions as to how to reduce soil erosion and furthermore, how to reach Zero Net Land Degradation targets by 2030. This special issue includes papers concerning the use of fallout for estimating soil erosion, new wind erosion modelling techniques, the importance of extreme events (forest fires, intense rainfall) in accelerating soil erosion, management practices to reduce soil erosion in vineyards, the impact of wildfires in erosion, updated methods for estimating soil erodibility, comparisons between sediment distribution models, the application of the WaTEM/SEDEM model in Europe, a review of the G2 model and a proposal for a land degradation modelling approach. New data produced from field surveys such as LUCAS topsoil and the increasing availability of remote sensing data may facilitate the work of erosion modellers. Finally, better integration with other soil related disciplines (soil carbon, biodiversity, compaction and contamination) and Earth Systems modelling is the way forward for a new generation of erosion process models.

Rainfall erosivity in Slovenia: Sensitivity estimation and trend detection

Slovenia is one of the EU countries with the largest values and largest amounts of variability in rainfall erosivity, with maximum annual values exceeding 10,000 MJ mm ha^-1 h^-1 yr^-1. Five-minute rainfall data was analysed from 10 Slovenian rainfall stations with data-length availability longer than 25 years with a maximum data length of 69 years and a total data-station length equal to 443 years. Trends in the rainfall erosivity R-factor were detected for four different sub-samples using monthly, half-year, and annual rainfall erosivity values. The results indicate that rainfall erosivity trends for the selected Slovenian stations are mostly statistically insignificant, with the selected significance level of 0.05. However, a larger share of identified trends are positive than negative. The maximum annual rainfall erosivity values were obtained for one specific mountain station. Furthermore, a sensitivity analysis regarding the rainfall erosivity factor R calculation showed that the rainfall threshold parameter (12.7 mm) that is used to remove the small-magnitude rainfall events in order to reduce the computational burden can attribute up to 10% of the average annual R-values in cases where this threshold is not used. Other parameters have, on average, a smaller impact on the calculated rainfall erosivity. Furthermore, the application of local kinetic energy equations resulted in, on average, about 20% higher annual rainfall erosivity values compared to the equation that is proposed by the Revised Universal Soil Loss Equation (RUSLE) manual and was not developed specifically for this region.

Computation of rainfall erosivity from daily precipitation amounts

Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.

Copper distribution in European topsoils: An assessment based on LUCAS soil survey

Copper (Cu) distribution in soil is influenced by climatic, geological and pedological factors. Apart from geological sources and industrial pollution, other anthropogenic sources, related to the agricultural activity, may increase copper levels in soils, especially in permanent crops such as olive groves and vineyards. This study uses 21,682 soil samples from the LUCAS topsoil survey to investigate copper distribution in the soils of 25 European Union (EU) Member States. Generalized Linear Models (GLM) were used to investigate the factors driving copper distribution in EU soils. Regression analysis shows the importance of topsoil properties, land cover and climate in estimating Cu concentration. Meanwhile, a copper regression model confirms our hypothesis that different agricultural management practices have a relevant influence on Cu concentration. Besides the traditional use of copper as a fungicide for treatments in several permanent crops, the combined effect of soil properties such as high pH, soil organic carbon and clay, with humid and wet climatic conditions favours copper accumulation in soils of vineyards and tree crops. Compared to the overall average Cu concentration of 16.85 mg kg^-1, vineyards have the highest mean soil Cu concentration (49.26 mg kg^-1) of all land use categories, followed by olive groves and orchards. Gaussian Process Regression (GPR) combined with kriging were used to map copper concentration in topsoils and to evidence the presence of outliers. GPR proved to be performant in predicting Cu concentration, especially in combination with kriging, accounting for 66% of Cu deviance. The derived maps are novel as they include information about the importance of topsoil properties in the copper mapping process, thus improving its accuracy. Both models highlight the influence of land management practices in copper concentration and the strong correlation between topsoil copper and vineyards.

Spatial assessment of land degradation through key ecosystem services: The role of globally available data

Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making.

Enhancing the WorldClim data set for national and regional applications

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m. The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim. Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible. The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.

Modelling spatial and temporal variations of annual suspended sediment yields from small agricultural catchments

Estimates of sediment yield are important for ecological and geomorphological assessment of fluvial systems and for assessment of soil erosion within a catchment. Many regulatory frameworks, such as the Convention for the Protection of the Marine Environment of the North-East Atlantic, derived from the Oslo and Paris Commissions (OSPAR) require reporting of annual sediment fluxes. While they may be measured in large rivers, sediment flux is rarely measured in smaller rivers. Measurements of sediment transport at a national scale can be also challenging and therefore, sediment yield models are often utilised by water resource managers for the predictions of sediment yields in the ungauged catchments. Regression based models, calibrated to field measurements, can offer an advantage over complex and computational models due to their simplicity, easy access to input data and due to the additional insights into factors controlling sediment export in the study sites. While traditionally calibrated to long-term average values of sediment yields such predictions cannot represent temporal variations. This study addresses this issue in a novel way by taking account of the variation from year to year in hydrological variables in the developed models (using annual mean runoff, annual mean flow, flows exceeded in five percentage of the time (Q5) and seasonal rainfall estimated separately for each year of observations). Other parameters included in the models represent spatial differences influenced by factors such as soil properties (% poorly drained soils and % peaty soils), land-use (% pasture or % arable lands), channel slope (S1085) and drainage network properties (drainage density). Catchment descriptors together with year-specific hydrological variables can explain both spatial differences and inter-annual variability of suspended sediment yields. The methodology is demonstrated by deriving equations from Irish data-sets (compiled in this study) with the best model efficiency of 0.84 and best model fit of adjusted R² of 0.82. Presented approach shows the potential for regression based models to model contemporary suspended sediment yields in small river systems.

The G2 erosion model: An algorithm for month-time step assessments

A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month-time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month-time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision-making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha^-1 h^-1 yr^-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets

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.