Cristiano Ballabio

Soil erodibility in Europe: A high-resolution dataset based on LUCAS

The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 thahha(-1)MJ(-1)mm(-1) with a standard deviation of 0.009 thahha(-1)MJ(-1)mm(-1). The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.

An assessment of the global impact of 21st century land use change on soil erosion

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.Human activity and related land use change are the primary cause of soil erosion. Here, the authors show the impacts of 21st century global land use change on soil erosion based on an unprecedentedly high resolution global model that provides insights into the mitigating effects of conservation agriculture.

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.

Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection

Under the European Union Thematic Strategy for Soil Protection, the European Commission Directorate-General for the Environment and the European Environmental Agency (EEA) identified a decline in soil organic carbon and soil losses by erosion as priorities for the collection of policy relevant soil data at European scale. Moreover, the estimation of soil organic carbon content is of crucial importance for soil protection and for climate change mitigation strategies. Soil organic carbon is one of the attributes of the recently developed LUCAS soil database. The request for data on soil organic carbon and other soil attributes arose from an on-going debate about efforts to establish harmonized datasets for all EU countries with data on soil threats in order to support modeling activities and display variations in these soil conditions across Europe. In 2009, the European Commissions Joint Research Centre conducted the LUCAS soil survey, sampling ca. 20,000 points across 23 EU member states. This article describes the results obtained from analyzing the soil organic carbon data in the LUCAS soil database. The collected data were compared with the modeled European topsoil organic carbon content data developed at the JRC. The best fitted comparison was performed at NUTS2 level and showed underestimation of modeled data in southern Europe and overestimation in the new central eastern member states. There is a good correlation in certain regions for countries such as the United Kingdom, Slovenia, Italy, Ireland, and France. Here we assess the feasibility of producing comparable estimates of the soil organic carbon content at NUTS2 regional level for the European Union (EU27) and draw a comparison with existing modeled data. In addition to the data analysis, we suggest how the modeled data can be improved in future updates with better calibration of the model.

Preferential retention of POPs on the northern aspect of mountains

Soils are the main reservoirs of POPs in mountain ecosystems, but the great variability of the concentrations, also at small scale, leaves some uncertainties in the evaluation of environmental burdens and exposure. The role of the aspect of the mountain side and the seasonal variation in the contamination levels was analysed by means of several soil samples taken from central Italian Alps. A greater contamination content was present in northern soils with a mean ratio between the north vs. south normalised concentration of around a factor of 2 (North-South Enrichment Factor). Experimental factors agreed with theoretical calculations based on temperature-specific calculated K(sa) values. From May to November consistent differences in normalised concentrations up to 5-fold were observed. A dynamic picture of the POP contamination in high altitudinal soils is derived from the data in this work, with spring-summer half-lives between 60 and 120 days for most of the compounds.

Reliability of groundwater vulnerability maps obtained through statistical methods

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices.

Influence of threshold value in the use of statistical methods for groundwater vulnerability assessment

Statistical techniques can be used in groundwater pollution problems to determine the relationships among observed contamination (impacted wells representing an occurrence of what has to be predicted), environmental factors that may influence it and the potential contamination sources. Determination of a threshold concentration to discriminate between impacted or non impacted wells represents a key issue in the application of these techniques. In this work the effects on groundwater vulnerability assessment by statistical methods due to the use of different threshold values have been evaluated. The study area (Province of Milan, northern Italy) is about 2000 km(2) and groundwater nitrate concentration is constantly monitored by a net of about 300 wells. Along with different predictor factors three different threshold values of nitrate concentration have been considered to perform the vulnerability assessment of the shallow unconfined aquifer. The likelihood ratio model has been chosen to analyze the spatial distribution of the vulnerable areas. The reliability of the three final vulnerability maps has been tested showing that all maps identify a general positive trend relating mean nitrate concentration in the wells and vulnerability classes the same wells belong to. Then using the kappa coefficient the influence of the different threshold values has been evaluated comparing the spatial distribution of the resulting vulnerability classes in each map. The use of different threshold does not determine different vulnerability assessment if results are analyzed on a broad scale, even if the smaller threshold value gives the poorest performance in terms of reliability. On the contrary, the spatial distribution of a detailed vulnerability assessment is strongly influenced by the selected threshold used to identify the occurrences, suggesting that there is a strong relationship among the number of identified occurrences, the scale of the maps representing the predictor factors and the model efficiency in discriminating different vulnerable areas.

Seasonal changes and temperature-dependent accumulation of polycyclic aromatic hydrocarbons in high-altitude soils.

Polycyclic Aromatic Hydrocarbons (PAHs) are a major group of pollutants whose occurrence in the environment is mainly of anthropogenic origin. In this paper, we examine the effect of topographical slope exposure on PAH contamination and seasonal change in PAH concentrations in soils. We collected soil samples on three dates in 2007 (early May, end of July and beginning of November) from south- and north-facing aspects at 1900 m a.s.l. in the central Italian Alps. We found greater PAH contamination in soils from a north-facing slope than in those from a south-facing slope at all seasons. We calculated North-South Enrichment Factors as the ratio between the concentrations measured in soils from northern and southern aspects. These ratios ranged from 1.4 to 1.9 for lighter PAHs (from 2 to 4 rings). These values are consistent with theoretical calculations based on temperature-specific octanol-air partition coefficients (predicted North-South Enrichment Factors range from 1.6 to 2.0). For heavier PAHs (from 5 to 6 rings), smaller differences were observed between soils from northern and southern aspects, due to the gas/particle distribution of these compounds. We also found consistent differences in normalised PAH concentrations across the three sampling periods. The majority of compounds showed a significant decreasing trend from the beginning of May to the end of July, due to the annual cycles of physical processes (deposition vs. volatilisation) and biological processes (uptake and/or biotransformation). Only a few compounds showed different trends, presumably due to season-specific local emission sources.

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

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

Graphical abstract