Quality check of European Datasets contributing to RECARE project

The aims of this document are the description of the procedures adopted to assess the quality of the data hosted on the JRC data management system and the quality of the data itself. The main aim of the data management platform is to provide data to the users in the project, in particular to case studies partners. Other aims are to provide a long term storage and web hosting to the data provided by partners and legacy data. Finally the data collected during the project will be made available to external users after the end of the project itself. Thus ensuring that the data hosted meets the highest quality standards is critical for the data management system. This document is divided into chapters that can be briefly described as follows: • Chapter 1: Description of the WP10 and the tasks that should be fulfilled • Chapter 2: Description of the procedures adopted to assess data quality • Chapter 3: Description Results of the data quality check for the hosted data • Chapter 4: Technical description of the metadata and data consistencyJRC.D.3-Land Resource

A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water

The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale.JRC.H.5-Land Resources Managemen

CASCADE Database and Web Services

This document presents a ‘Cookbook’ description for installing the software tools necessary to develop and configure the CASCADE Project database and Web Services. The portal developed may provide the basis for project, the data collected, processed and provided by the project partners. The contents below describe the process of preparing the data and the portal to host the project outputs and associated components. The work presented here is a direct output and deliverable of the EU Framework 7 project ‘CASCADE - Catastrophic Shifts in Drylands’, Grant Agreement Number 283068. CASCADE Project investigates and analyses a range of dryland ecosystems in southern Europe to obtain a better understanding of sudden shifts in drylands that may lead to major losses in biodiversity and concomitant ecosystem services. Based on these analysis, CASCADE develops ways to predict the proximity of the CASCADE’s dryland ecosystems to thresholds in such a way that these predictions can be used by policymakers and land users for more sustainable management of drylands worldwide. The work described here was conducted by the Land Resources Unit of the Joint Research Centre of the European Commission. The Land Resources Unit provides information for European and International policies aiming to balance competing land-use demands whilst securing access to natural resources and maintaining ecosystem services. Land and soil should be considered as finite resources – we must optimise food, fibre and fuel production whilst maintaining and enhancing the land’s role as a carbon sink and a hydrologic reservoir that underpins biological diversity; our research documents trends in the condition of land, the efficiency of its use and management choices, along with how these respond to changing environmental, societal and economic conditions.  JRC.D.3-Land Resource

New Insights into the Geography and Modelling of Wind Erosion in the European Agricultural Land. Application of a Spatially Explicit Indicator of Land Susceptibility to Wind Erosion

The current state of the art in erosion research does not provide answers about the ‘where’ and ‘when’ of wind erosion in European agricultural lands. Questions about the implications for the agricultural productivity remain unanswered. Tackling this research gap, the study provides a more comprehensive understanding of the spatial patterns of land susceptibility to wind erosion in European agricultural lands. The Index of Land Susceptibility to Wind Erosion (ILSWE) was introduced in a GIS environment. A harmonised input dataset ranked following a fuzzy logic technique was employed. Within the 36 European countries under investigation, moderate (17.3 million ha) and high levels (8.8 million ha) of land susceptibility to wind erosion were predicted. This corresponds to 8.0% and 4.1 % of total agricultural land, respectively.JRC.H.5-Land Resources Managemen

A New Spatiotemporal Risk Index for Heavy Metals: Application in Cyprus

The main aim of this research was to improve risk mapping of heavy metals by accounting for erosion effects. A new spatiotemporal index, namely G2met, is introduced, incorporating the Hakanson index, the G2 model and the Gavrilovic model. The G2met index is expressed in terms of monthly time series of risk maps for each heavy metal and totally. The rich in heavy metals and vulnerable to erosion, island of Cyprus, was selected as a study area, which though was limited to the southern part of the island, where all required data were available. Concentration of major heavy metals was extracted by sampling soil from a grid of 5350 sites. Rainfall, vegetation, soil, land use, topographic, and hydrologic data were collected or calculated from existing European or global databases (WorldClim, BioBar, ESDAC, CORINE, ASTER GDEM, and USGS). A large number of regional-scale risk maps were produced (500-m ell size), i.e. one for each heavy metal and totally per month and annually. Also, choropleth maps per heavy metal are potentially available, in terms of statistics per river basin. The G2met maps provide different patterns in comparison to those depicted by the Hakanson index alone.JRC.H.5-Land Resources Managemen

Soil Protection Activities and Soil Quality Monitoring in South Eastern Europe

The conference Soil Protection Activities and Soil Quality Monitoring in South Eastern Europe was organized in Sarajevo as a joint action by the Soil Science Society of Bosnia and Herzegovina and the Soil Science Society of Slovenia. The main objectives of conference were to review the soil protection and soil quality monitoring activities in SEE including research activities, project reports, good practice guides and various methodologies and monitoring strategies. The special emphasis was laid on the ecological and technical soil functions, remediation and re-cultivation measures, data collection and processing, soil protection policy, soil quality and soil resources management issues on the regional level. The conference was an opportunity to key regional soil science research institutions to present activities and achievements with further prospects of cross - border scientific collaboration. This publication presents a selection of 20 conference papers prepared by the authors from SEE countries (Croatia, Bosnia and Herzegovina, Serbia, Former Yugoslav Republic of Macedonia) and guest contributions from Austria, Slovenia and Syria.JRC.DDG.H.7-Land management and natural hazard

The implications of fire management in the Andean paramo: A preliminary assessment using satellite remote sensing

The upper ranges of the northern Andes are characterized by unique Neotropical, high altitude ecosystems known as paramos. These tundra-like grasslands are widely recognized by the scientific community for their biodiversity and their important ecosystem services for the local human population. Despite their remoteness, limited accessibility for humans and waterlogged soils, paramos are highly flammable ecosystems. They are constantly under the influence of seasonal biomass burning mostly caused by humans. Nevertheless, little is known about the spatial extent of these fires, their regime and the resulting ecological impacts. This paper presents a thorough mapping and analysis of the fires in one of the world’s largest paramo, namely, the ‘Complejo de Páramos’ of Cruz Verde – Sumapaz in the Eastern mountain range of the Andes (Colombia). Landsat TM/ETM+ and MODIS imagery from 2001 to 2013 were used to map and analyse the spatial distribution of fires and their intra- and inter-annual variability. Moreover, a logistic regression model analysis was undertaken to test the hypothesis that the dynamics of the paramo fires can be related to human pressures. The resulting map shows that the burned paramo areas account for 57,179.8 hectares of which 50% (28,604.3 hectares) are located within the Sumapaz National Park. The findings show that the fire season mainly occurs from January to March. The accuracy assessment carried out using a confusion matrix based on 20 reference burned areas shows values of 90.1% (producer accuracy) for the mapped burned areas with a Kappa Index of Agreement (KIA) of 0.746. The results of the logistic regression model suggest a significant predictive relevance of the variables road distance (0.55 ROC (Receiver Operating Characteristic)) and slope gradient (0.53 ROC), indicating that the higher the probability of fire occurrence the smaller the distance to the road and the higher the probability of more gentle slopes. The paper sheds light on fires in the Colombian paramos and provides a solid basis for further investigation of the impacts on the natural ecosystem functions and biodiversity.JRC.H.5-Land Resources Managemen

Soil Erosion map of Europe based on high resolution input datasets

Modelling soil erosion in European Union is of major importance for agro-environmental policies. Soil erosion estimates are important inputs for the Common Agricultural Policy (CAP) and the implementation of the Soil Thematic Strategy. Using the findings of a recent pan-European data collection through the EIONET network, it was concluded that most Member States are applying the empirical Revised Universal Soil Loss Equation (RUSLE) for the modelling soil erosion at National level. This model was chosen for the pan-European soil erosion risk assessment and it is based on 6 input factors.JRC.H.5-Land Resources Managemen

LUCAS 2018 - SOIL COMPONENT: Sampling Instructions for Surveyors

The European Commission launched a soil assessment component to the periodic LUCAS Land Use/Land Cover Area Frame Survey in 2009. Composite soil samples from 0-20-cm depth were taken, air-dried and sieved to 2 mm in order to analyse physical and chemical parameters of topsoil in 25 Member States (EU-27 except Bulgaria, Romania, Malta and Cyprus). The aim of the LUCAS Soil Component was to create a harmonised and comparable dataset of main properties of topsoil at the EU. The LUCAS Soil Component was extended to Bulgaria and Romania in 2012. Overall, ca. 22,000 soil samples were collected and analysed. All samples were analysed for percentage of coarse fragments, particle-size distribution, pH, organic carbon, carbonates, phosphorous, total nitrogen, extractable potassium, cation exchange capacity, multispectral properties and heavy metals. In 2015, the soil sampling was repeated in the same set of points of LUCAS 2009/2012 to monitor changes in topsoil physical and chemical parameters across the EU. The soil component was extended to points above elevations of 1000 m, which were not sampled in LUCAS 2009/2012. Furthermore, soil samples were taken in Albania, Bosnia-Herzegovina, Croatia, Macedonia, Montenegro, Serbia and Switzerland. The soil sampling was carried out following the instructions already used in LUCAS 2009/2012. Approximately 27,000 samples were collected and will be analysed during 2016 and 2017. In 2018, a new soil sampling campaign will be carried out within the LUCAS framework. Soil samples will be taken in repeated points of LUCAS 2009/2012 and LUCAS 2015. The novelty of the survey is that new physical, chemical and biological parameters will be analysed. Key parameters for evaluating soil quality, such as bulk density and soil biodiversity, will be analysed. These analyses require specific methods of soil sampling, preparation and storage of samples. Furthermore, field measurements such as the thickness of organic layer in peat soils, and visual assessment of signs of soil erosion will be carried out in 2018. This technical report compiles the instructions for collecting the various soil samples and for performing field measurements in the soil survey of 2018. These instructions will be used for all LUCAS surveyors, to create a comparable database of soil characteristics all over Europe.JRC.D.3-Land Resource

Soils of the European Union

This report make a detailed summary of the soil resources of the EU. Contents: Acknowledgements 2 1. Introduction 3 2. Materials and methods 4 2.1 Soil Geographical Database of Eurasia at scale 1:1,000,000 (SGDBE) 4 2.2 Nomenclature of soil types 6 2.3 Map legend and representation 6 3. Soils of the European Union: an overview 8 4. Spatial distribution of the major soils in the European Union 11 4.1 Acrisols 11 4.2 Albeluvisols 13 4.3 Andosols 15 4.4 Anthrosols 17 4.5 Arenosols 19 4.6 Calcisols 21 4.7 Cambisols 23 4.8 Chernozems 25 4.9 Fluvisols 27 4.10 Gleysols 29 4.11 Gypsisols 31 4.12 Histosols 33 4.13 Kastanozems 35 4.14 Leptosols 37 4.15 Luvisols 39 4.16 Phaeozems 41 4.17 Planosol 43 4.18 Podzols 45 4.19 Regosols 47 4.20 Solonchaks 49 4.21 Solonetz 51 4.22 Umbrisols 53 4.23 Vertisols 55 5. Concluding remarks 57 References 58 Appendix 1. 59 Appendix 2. 62JRC.H.7-Land management and natural hazard