Technical Report submitted to EFSA

Transcription

1 Technical Report submitted to EFSA Activities realized within the Service Level Agreement between JRC and EFSA, as a support of the FATE Working Group of EFSA PPR in support of the revision of the guidance document Persistence in Soil Ciro Gardi, Luca Montanarella, Roland Hiederer, Arwyn Jones, Fabio Micale EUR EN European Union, 2010

2 The mission of the JRC-IES is to provide scientific-technical support to the European Union s policies for the protection and sustainable development of the European and global environment. European Commission Joint Research Centre Institute for Environment and Sustainability Contact information Address: Dr. Ciro Gardi European Commission, Joint Research Centre, Institute for Environment & Sustainability (IES), Land Management and Natural Hazard Unit TP 280, Via E.Fermi, 2749, I Ispra (VA) Italy ciro.gardi@jrc.ec.europa.eu Tel.: Fax: Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): (*) Certain mobile telephone operators do not allow access to numbers or these calls may be billed. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server JRC EUR EN ISBN ISSN DOI /87806 Luxembourg: Publications Office of the European Union European Union, 2010 Reproduction is authorised provided the source is acknowledged Printed in Italy

3 TECHNICAL REPORT submitted to EFSA Activities realized within the Service Level Agreement between JRC and EFSA, as a support of the FATE Working Group of EFSA PPR in support of the revision of the guidance document Persistence in Soil 1 Ciro Gardi 1, Luca Montanarella 1, Roland Hiederer 1, Arwyn Jones 1, Fabio Micale 2 1 EC - JRC - IES Land Management and Natural Hazard Unit ISPRA (VA) ITALY 2 EC - JRC - IPSC MARS Unit ISPRA (VA) ITALY Abstract Within the frame of the Collaboration Agreement signed the 10th November 2008 between the Joint Research Centre (JRC) and the European Food Safety Authority (EFSA), a specific Service Level Agreement (SLA/EFSA-JRC/2008/01) supporting the activities of the FATE Working Group of EFSA PPR was established the 16 th of December The aim of this second agreement was to provide soil and climate data to the FATE WG in support of the revision of the guidance document Persistence in Soil. The Land Management and Natural Hazard Unit and the Mars Unit of JRC jointly produced the data set requested for the scenario selection and for the parametrization of the model. This report is a deliverable produced according to the commitments listed in the Service Level Agreement mentioned above. Summary Within the frame of the Collaboration Agreement signed the 10th November 2008 between the Joint Research Centre (JRC) and the European Food Safety Authority (EFSA), a specific Service Level Agreement (SLA/EFSA-JRC/2008/01) supporting the activities of the FATE Working Group of EFSA PPR was established in The aims of this second agreement was to provide soil and climate data to the FATE WG in support of the revision of the guidance document Persistence in Soil. 1 (Question No EFSA-Q ). Accepted for Publication on: to be decided by EFSA 2

4 Data preparation has be tailored according to the needs of the Working Group and has been realized on the base of the two distinct activities of Scenario Selection and Scenario parametrization. For the Scenario selection, aimed to the identification of the realistic worst-case conditions (defined as the 90th spatial percentile of the exposure concentration (maximum in time) in the intended area of use in each of the three regulatory zones described in Annex 1 of the new Regulation concerning the placing of plant protection products on the market), the spatial soil and climate parameters have been provided by the Soil Action and the MARS unit. All these parameters have been produced on a common projection (ETRS 89 LAEA) and using the same spatial frame with a resolution of 1000 m. The file format has been ESRI ARC-Grid raster and data are stored in ASCII. For the Scenario Parameterization, the quality and the resolution of data required was higher. For soil the complete characterization of the profiles, plausible with the selected sites, have been provided. The Soil Profile Database (Spade 8.0) however is still incomplete, and as a consequence of this the process has been developed in an iterative way. For the climatic data, a 30 years, daily observation, data sets have been provided for the scenario parameterization. Also in this case the needs of matching plausible/available soil profile datasets, with the correspondent climatic data sets, led to the adoption of an iterative approach, leading to the production of a high amount of data. Key words: GIS, Inspire, Soil data, Soil profiles, Weather data 3

5 Table of Contents Abstract...2 Summary...2 Table of Contents...4 Background...5 Terms of reference...5 Acknowledgements...8 Introduction and Objectives...9 Materials and Methods...10 Soil and Weather data at JRC Structure and functions of the European Soil Data Centre Accuracy and quality check on soil data Acquisition, checks and processing daily meteorological data within MARS unit Acquisition, checks and processing daily meteorological data within MARS unit Data Provided to EFSA for the activities of the PPR FATE WG Organic Matter in Topsoil Mean Monthly Temperature Mean Monthly Precipitation Mean Monthly Precipitation Corine Land Cover Soil Texture Member State Territories Topsoil ph Topsoil Bulk Density Soil Mapping Units Topsoil Available Water Capacity Agricultural Area commons to 1990 and Spade 8.0 associated areas Soil profile data from Spade Conclusions and Recommendations...36 References...36 Glossary and Abbreviations

6 Background During the review process of the substances of the second list, several concerns were raised regarding the Guidance Document on persistence in soil. A number of Member states have expressed interest in a revision of the current Guidance Document on persistence in soil during the general consultation of Member States on Guidance Documents in answer to the request by the Director of Sciences of EFSA in letter 3 July 2006 sent via the Standing Committee on the Food Chain and Animal Health. Further the EFSA PRAPeR Unit has noted that Guidance Document needs to be brought in line with the FOCUS degradation kinetics report (SANCO/100058/2005, version 2.0, June 2006). FOCUS (1997) developed the first guidance at EU level for exposure assessment in soil. This included a simple approach for estimating PEC SOIL but FOCUS (1997) did not develop first-tier scenarios (in contrast to subsequent FOCUS workgroups that developed such scenarios for surface water and groundwater as development of soil scenarios was a lower priority at that time). FOCUS (2006) developed detailed guidance on estimating degradation rate parameters from laboratory and field studies, but did not develop exposure scenarios. Nevertheless there is need for such scenarios in view of ongoing discussions in PRAPeR experts groups regarding PECSOIL as current approaches at EU level just represent the range of climatic conditions covered by available field dissipation and or accumulation studies and member states would like tools to be able to extrapolate to a wider range of climates present in the EU. The existing Guidance Document on Persistence in Soil (9188/VI/97 rev 8) published in 2000 did not include scenarios. The intention with the new guidance document is to update the existing Guidance Document on Persistence in Soil to include European exposure scenarios for soil and to provide guidance on best practice for using the results of field experiments and soil accumulation studies in the exposure assessment. Terms of reference The intention with this report is to provide details on the methodology used preparing the data requested for the selection of the EU soil scenarios, for estimating exposure of pesticides to soil organisms. The report will provide scientific input to address the terms of references tasked by EFSA to the PPR Panel and approved on the 10 th of December 2007 and extended on the 3 rd of December 2009 by the EFSA Executive Director. The Scientific Panel on Plant Protection Products and their Residues (PPR Panel) of EFSA is asked to prepare a revision of the Guidance Document on persistence in soil (SANCO/9188VI/1997 of 12 July 2000). The technical specifications of the Service Level Agreement (SLA/EFSA-JRC/2008/01)are given below: 5

7 A. Description of the project tasks and deliverables 1. Collecting, integrating and providing relevant soil and weather data in a format to be defined for development of the Analytical Model used for the scenario selection procedure. All data to be provided in the same format, e.g. ASCII or any other format requested by EFSA. (Detailed specification on this topic in section B of this document.) Integrated soil and weather data to be provided by January 2009 together with an explanatory report. 2. Provide grid maps for European scenario selection by January 2009 together with an explanatory report: Organic matter in soil (kg/kg) (Could be derived from OCTOP) Temperatures (deg C) from MARS Soil profile information from SPADE I and SPADE 2 (See also point 5) Land-use from Corine Soil Texture (to derive the soil and water content) (Available in the SGDBE at the level of soil mapping units. To be converted from GIS to grid cells) Climatic zones Member State boundaries ph Dry Bulk Density (kg/m3) Soil polygons (in gridded map) 3. Soil data are required only for the top 30 cm. All data should be provided in a common resolution and with common projections (e.g. INSPIRE, reference grid 10x10 km2 to be used in the SGDBE v2). Data should be provided in resolution of 1x1 km2. 4. Provide for approximately 10 weather stations covering EU-27, daily information on rainfall, temperature, solar radiation, wind speed of time window of 20 years for all parameters. 5. Soil profiles from SPADE 1 and SPADE 2 need to be provided with the highest possible spatial coverage. Only those soil profiles that have arable land-use (including permanent grassland and perrenial crops) to be included. A link between SPADE and the SGDBE, either implicit or explicit, should be established and the confidence level of the link should be described. As many as possible STUs within the SGDBE should be assigned a soil profile. If this is not possible, a procedure should be made available to assign the non-covered STUs a soil profile. For each soil horizon within the soil profiles, information should be provided on organic matter, soil texture (sand, silt and clay) and horizon designations (depth and thickness of horizons). Information on soil bulk-density and ph should be included. The integrated soil profile data from SPADE 1 and SPADE 2 should be delivered by January

8 6. For later stages (January 2009 and onwards), additional weather and soil data as well as timeseries should be delivered on request by EFSA and according to format requested by EFSA. 7. Develop ecoregion-maps for Portugal, Germany and Finland by March 2009, integrating the biogeographical database provided by EFSA (Lumbricidae, Enchytraeidae, Collembola and Isopoda), any other available soil biodiversity data, geographic soil properties data (e.g. organic matter content, soil type, soil texture, ph, bulk density, water holding capacity), land use, and weather data available at JRC. The soil ecoregions map will be used in risk assessment for the soil compartment. 8. Delivery of the data behind the Ecoregion maps (weather, soil, biogeographical data) in the same format as stated under point 1 and detailed on section B of this document. 9. Based on the process used for the ecoregions maps for Portugal, Germany and Finland, and further input of EFSA experts, extrapolate these maps to EU-27 using the same approach as above by December For extrapolation the database should be extended to other countries, focusing on countries which are not represented by the biogeographical information provided by EFSA (Portugal, Germany, and Finland). Regular meetings between EFSA and JRC may need to be organized on demand for organizing work and discussing results and further procedure (possible meeting in December 2008 / January 2009 to discuss / agree on workplan). Progress reports should be provided to EFSA every 3 months. Data and report will be delivered at the termination of the SLA. 10. During collecting, integrating and providing relevant soil biodiversity information for point 7-9, grey literature should also be considered (e.g. national databases regarding monitoring programs in soil like for The Netherlands, Germany, Italy). Quality criteria need to be defined in advance by EFSA in cooperation with JRC for deciding if references are added to the database or not. 11. Ad hoc support for modeling and testing of modeling. If requested report and models to be delivered as agreed. B. Data need for the Analytical Model used for the scenario selection procedure Point B is to provide more details to point A of this document. The Analytical Model needs the following four spatially distributed parameters: 1. Temperature (deg C) 2. Organic matter content (kg/kg) of the top 30 cm 3. Dry bulk density (kg/m3) of the top 30 cm 4. Soil water content (L/kg) of the top 30 cm All data should be available at a common resolution, and with a common projection. I suggest to use the INSPIRE 10x10 km2 reference grid, which is also used in the SGDBE, v2. See for details. Instead of using the 10x10 resolution, however, I suggest to use a resolution of 1x1 km 2. The number of grid cells in the EU 27 then amounts to approximately (including nonagricultural use, excluding water). The preferred format is ASCIIGRID. 7

9 Apart from the Analytical Model input, we also need land-use to filter out non-agricultural land-use. This could be derived from CORINE. Temperature The current version of the Analytical Model uses the mean annual temperature. We agreed that the mean monthly temperature should be used instead. Therefore, we need the mean monthly temperature (deg C) as a grid map. 12 maps of mean monthly temperature, to be derived from MARS. Resolution 1x1 km2. Organic matter Organic matter content of the top 30 cm (kg/kg). Organic matter must be derived from OCTOP. Data must first be transformed from organic carbon to organic matter (OM=1.7*OC). To retain the original distribution in the database, the data must be transformed to the 1x1 km 2 grid cells, using central values. 1 map of organic matter in the top 30 cm, resolution 1x1 km 2 Dry bulk density Dry bulk density is derived from organic matter using a pedotransfer function. The dry bulk density can be calculated within the Analytical Model, so a separate input file is not necessary. Water content Soil water content is approximated by the water content at field capacity. Water content at field capacity is calculated using the HYPRES pedotransfer rules. These transfer rules are based on soil texture, which is available in the SGDBE at the level of soil mapping units. This information must be converted from a vector-based GIS to grid cells. 1 map of soil textural class (resolution 1x1 km2). Land use 1 grid map with the land-use class according to CORINE (central value in 1x1 km2 grid cells) Soil polygons To test aggregation procedures, a gridded map of the soil polygons can be helpful (showing the ID s of the soil polygons). (1x1 km 2 grid cells). Acknowledgements This contract/grant was awarded by EFSA to: JRC/IES Contractor/Beneficiary JRC/IES Service Level Agreement title: Provide data and scientific support to EFSA PPR Working Groups Service Level Agreement number: EFSA-JRC/2008/01 8

10 Introduction and Objectives INTRODUCTION The revision of the Guidance Document on Persistence in Soil (9188/VI/97 rev 8) will provide notifiers and Member States with guidance in the area of environmental fate and behavior of pesticides in soil in the context of the review of active substances notified for inclusion in Annex I of Directive 91/414/EEC and the Regulation 1107/2009 (EC) as well as for the review of plant protection products for national registrations in Member States. OBJECTIVES The aim of this revision is to develop a tiered approach for exposure assessment in soil at EU level including: (i) the development of a range of scenarios representing realistic worst-case conditions including ecological and climatic considerations, (ii) the appropriate definition of the role of results of field persistence and soil accumulation experiments in the tiered assessment. The selection of realistic worst-case scenario is a combination of soil and climate properties within a certain region for which predicted concentrations are equal to a certain percentile of the distribution of concentrations for all climate and soil property combinations within the region. Most often, the 90th percentile is chosen. The application of this definition to determine scenarios is, however, complicated by a several factors. For the application of the scenario-selection activities, carried out by the PPR FATE WG of the PPR Panel, sets of soil and climatic data, at the European level, are required. In the context of these activities, on the basis of the Service Licence Agreement signed between EFSA and JRC (SLA/EFSA- JRC/2008/01) under the umbrella of the JRC and EFSA collaboration agreement (Code of the agreement), the Land Management and Natural Hazard Unit and the Mars Unit of JRC has produced and delivered the following data sets: 1. Grid maps for the following parameters: a. Organic matter in soil (kg/kg) b. Temperatures (deg C) from MARS c. Soil profile information from SPADE I and SPADE 2 d. Land-use from Corine e. Soil Texture f. Climatic zones 9

11 g. Member State boundaries h. ph i. Dry Bulk Density (kg/m 3 ) j. Soil polygons All data have been provided at a common resolution and with common projections (e.g. INSPIRE, reference grid 10x10 km 2 to be used in the SGDBE v2), with a resolution of 1x1 km Daily data on rainfall, temperature, solar radiation, wind speed of time window of 20 years, for 10 weather stations covering EU-27,.Estimate of ET0, based on modified Penman equation. 3. Soil profiles from SPADE 1 and SPADE 2 need to be provided with the highest possible spatial coverage. Only those soil profiles that have arable land-use (including permanent grassland and perennial crops) to be included. A link between SPADE and the SGDBE, either implicit or explicit, should be established and the confidence level of the link should be described. As many STUs within the SGDBE should be assigned a soil profile. If this is not possible, a procedure should be made available to assign a soil profile to the non-covered STUs. For each soil horizon within the soil profiles, information should be provided on organic matter, soil texture (sand, silt and clay) and horizon designations (depth and thickness of horizons). Information on soil bulk-density and ph should be included. The integrated soil profile data from SPADE 1 and SPADE 2, has been delivered in January Materials and Methods Soil and Weather data at JRC 1. Structure and functions of the European Soil Data Centre The European Soil Data Centre (ESDAC) is the thematic centre for soil related data in Europe has been established according to a decision taken at the end of 2005 by the European Commission's DG ENV, JRC, ESTAT and the European Environment Agency (the so-called "group of four" or G04) to establish ten data centres in Europe. Each such data centre acts as the primary data contact point for DG ENV in order to fulfil its information needs. It has the task of ensuring that the collected data fit DG ENV's requirements, that data collection is organized in an efficient way, that the necessary quality assurance is performed and that all relevant existing data are accessible to other Go4 parties. It thus has the primary responsibility for organizing the availability and quality of the data required for policy. 10

12 The requirements of DG ENV in relation to the ESDAC are on the one hand to receive scientific and technical support for issues in relation to the proposed Soil Framework Directive and for the development of European datasets, and on the other hand the availability of a suitable IT facility that allows management of and access to the soil data and information collected during the course of providing the scientific and technical support. This includes the specification of guidelines for the identification of risk areas and of associated guidelines on data issues (quality, data-exchange formats) and the production of maps of risk for the different soil threats in the EU. The sources for the soil information that reside at the ESDAC are: JRC in-house and commissioned soil research activities; results from activities within the European Soil Bureau Network; results from EUfunded soil-related projects; results from collaborations with other organizations (e.g. EuroGeoSurveys, ISRIC, FAO); data on contaminated sites through EIONET; data from Member States (e.g. reporting data in the context of the future Soil Framework Directive); The ESDAC facility hosts soil-related products: datasets, documents, services and other types of information such as maps and graphs and provides web-based tools for the access to and the update of its contents. The ESDAC facility currently operates in a centralized way, meaning that it presents only information that is currently available at ESDAC. In order to be interoperable with other emerging services (e.g. in other environmental data centres), metadata will be created according to agreed metadata standards; in a second phase, metadata services will be set-up so that ESDAC can search in distributed catalogues and that the ESDAC catalogue can be consulted by remote ESDAC-external services. Similarly, online applications (e.g. web map services) will be developed according to standards so that they are as interoperable as possible with other similar services. One particular aspect that ESDAC will promote is the use of grids (or rasters) as a possible way to exchange data between organizations. The basis for such work is the availability of standard (or reference) grids in a common co-ordinate system with fixed cell sizes and common cell coding. A proposal for such grids has been adopted during the European Reference Grids Workshop, held at Ispra (October 2003). 1.1 Accuracy and quality check on soil data Soil data within the SGDBE are characterized by the following degree of accuracy: Overall positional accuracy: Estimated: m (0.5-5 mm at scale 1:1,000,000). Dataset internal tolerance set to 100 m (0.1 mm at scale 1:1,000,000). Minimum polygon area set to 9 ha (0.3 x 0.3 mm at scale 1:1,000,000). 11

13 Overall thematic accuracy: Each Soil Typological Unit in the database is characterised by a overall confidence level (high, medium, low; see further "data definition and classification"). All polygons representing areas above 25 km 2 have been checked/corrected against original soil map. The quality and reliability of the information held in the European Soil Database is based on the Contributor organisations (Member States) and are validated by the Scientific Committee of the European Soil Bureau. Some of the soil parameters raster maps provided to the FATE WG has been derived from the SGDBE, while others has been derived from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2008). The Land Use map has been produced from Corine Land Cover database (Nunes De Lima, 2005), and for the accuracy and quality check of these data it is necessary to refer to the reference mentioned in the Metadata section of Corine Land Cover

14 2. Acquisition, checks and processing daily meteorological data within MARS unit The meteorological station data acquisition and check consists of: Station information. Raw daily meteorological data. Processed daily meteorological data. The stations are limited to those for which data not only are regularly collected but which can also be received and processed in semi-real time (Burrill and Vossen, 1992). Relevant information of stations includes WMO station number, station name, latitude, longitude and altitude. This data is available in the tables STATIONS and WEATHER_STATION. Fig. 2.1 gives an overview of all stations for which daily data are available for (part of) the period from 1975 to the present. The total number balances around Only around 2000 have a sufficient temporal coverage (enough observations within one year) to be used in the spatial interpolation procedure. Fig. 2.1: The meteorological stations for which data are available for (part of) the period from1975 until the current day. 13

15 Some of the historical meteorological data are purchased directly from various National Meteorological Services, and others are acquired via the Global Telecommunication System (GTS). As the data are obtained from a variety of different sources, considerable reprocessing is necessary to convert them to a standard format. Two procedures are applied to distinct subsets of the data set. The historical data came directly from National Meteorological Services. Around 1992 they represented approximately 380 stations in the EU, Switzerland, Poland and Slovenia with data from 1949 to 1991 (Burrill and Vossen, 1992). Later the historic sets have been extended with stations in Eastern Europe, western Russia, Maghreb and Turkey. The historical data were converted into consistent units and were checked as to realistic values. The database was also scanned for inconsistencies, such as successive days with the same value for a variable, or minimum temperatures higher than maximum temperatures (Burrill and Vossen, 1992). From 1991 to the present, meteorological data are received in near real time from the GTS network for different hours within one day. The data are pre-processed and quality checked using the AMDAC software package (MeteoConsult, 1991) which extracts, decodes and processes the GTS data. After decoding, the following data are checked for consistency and errors: air temperature, dew-point temperature (humidity), pressure at sea level, wind speed, amounts of precipitation, clouds, and sunshine duration. This error checking compares each observation with the corresponding values of the surrounding stations and compares that particular observation with observations at other times in the same day at the same station. Obvious errors in the observations are corrected automatically and a message is written to a log file; other errors are flagged for possible correction by an operator (Burrill and Vossen, 1992). Finally, the data are converted into daily values. This comprises the selection of minimum and maximum temperature, the aggregation of the rainfall, cloud cover and sunshine duration, the calculation of mean vapour pressure etc. The processed daily meteorological data consist of 29 meteorological parameters including various cloud cover indicators, air temperature, vapour pressure, wind speed and rainfall. Because European stations follow different measurement schemes many records contain blank fields for parameters which are never registered. Stations often also include blank fields for parameters which were not available for limited periods. However, the stations selected for inclusion in the database are those which normally report at least the minimum and maximum daily air temperature, rainfall, wind speed, vapour pressure (or humidity) as well as either global radiation, sunshine hours or cloud cover (Burrill and Vossen, 1992). Each day, the processed daily meteorological data are inserted into the CGMS database by the program MeteoInsert (IPSC-JRC, 2002) (see Fig. 1 in Annex 4). In this way, an up-todate database (the table METDATA) of harmonized, quality checked daily data from a network of stations across western and Eastern Europe, western Russia, the Maghreb and Turkey was established. The longest time series go back to Over the years the number of stations has increased. Good coverage over western Europe has been obtained since The extension to the other region has taken place during the nineties. Evapotranspiration and global radiation are necessary for the agrometeorological model in the MARS Crop Yield Forecasting System (MCYFS), but not all the stations provide these data and they are derived from the other available data and added to the database (Table. 2.1). 14

16 Table 2.1: Available number of meteorological stations by country 15

17 The daily meteorological data are interpolated towards the centres of a regular climatic gridthat measures 50 by 50 kilometres and amounts to 5625 cells. The data of the climatic grid are stored in table GRID_WEATHER and are related to the following parameters: All the input and output data of the CGMS, such as the climatic grid presented in Fig. 2.2, are given in a projected co-ordinate system. This is the Lambert-Azimuthal projection with metres as units and the parameters: Radius of sphere of reference: (m). Longitude of centre of projection: 9,00º. Latitude of centre of projection: 48,00º. The grid cell size is based on the assumption that within a region of 50 by 50 kilometers the meteorological data are homogeneous. It is expected that temperature, sunshine, humidity, and wind speed gradually change over distances of 50 to 150 kilometres. Local weather variations may be larger than changes over such distances, most obviously for solar radiation caused by differences in slope aspect, or presence of persistent clouds. Temperature conditions near the surface follow variations in solar radiation but there are also temperature gradients due to cold air drainage, or differences in surface cover and in topography. Sometimes large differences occur over a short distance (10-40 km) when there is sharp transition between two different air masses, e.g. when a front passes or when a region under a persistent cloud cover exists adjacent to a region under a cloudless sky (van Diepen, 1998). Beek (1991a) reports that if daily means are used, most meteorological variables do not differ too much over distances in the range of km. More complicated is the spatial variation in precipitation, usually in the form of rainfall. Rain may fall from a local cumulonimbus in showers with high density (convective rains), or in a front passage with low density over large areas (depression rains). Geographic patterns of rainfall are influenced by the geometry of land and sea surfaces, and by 16

18 general circulation patterns. Western-facing slopes of hill and mountain ranges receive more than average rainfall, east-facing slopes less. The spatial distribution patterns of rainfall are thus irregular (Beek, 1991a; van Diepen, 1998). The methodology for the spatial interpolation of the data of the existing network ofmeteorological stations towards the climatic grid cells centre is based on the studies of Beek et al. (1991a) and van der Voet et al. (1994). It is described by van der Goot (1998a). This method was chosen because its simple approach made it easy to automate while the accuracy was sufficient to serve as input to the crop growth model. The interpolation is executed in two steps: first the selection of suitable meteorological stations to determine representative meteorological conditions for a specific climatic grid cell. Second, a simple average is calculated for most of the meteorological parameters, with a correction for the altitude difference between the station and grid cell centre in case of temperature and vapour pressure. As an exception rainfall data are taken directly from the most suitable station. Fig. 2.2: Meteorological grid used in MCYFS 17

19 3. Data Provided to EFSA for the activities of the PPR FATE WG All data are provided with a common projection (ETRS 89 LAEA) and use the same spatial frame with a resolution of 1000 m (Fig.3.1). The file format is ESRI ARC-Grid raster and data are stored in ASCII. Common Properties file format : ESRI ARCRASTER ASCII columns : 3500 rows : 4100 ref. system : ETRS 89 LAEA 2 unit dist. : min. X 3 : max. X : min. Y : max. Y : resolution : 1000 unit : m 2 Projection: Lambert Azimuthal Equal Area, Datum: European Terrestrial Reference System These value are the metric coordinates 18

20 Figure 3.1: Spatial extent of the provided raster maps. 19

21 1.1. Organic Matter in Topsoil The Organic Matter Content has been estimated applying the conversion factor of to the Topsoil Organic Carbon Map of Europe (Fig. 3.2), (Jones et al., 2005). Topsoil Organic Carbon (OC) was calculated applying a pedotransfer rule (PTR), based on five parameters and comprising 140 conditions: 1. first character in FAO code 2. second character in FAO coe 3. third character in FAO code 4. dominant surface textural class 5. Land-use class This PTR derives from a modification of Van Rast PTR (Van Rast et al., 1995). The application of the OC PTR, on the basis of the Soil Geographical Database of Eurasia and of Corine Land Cover map, allowed the estimation five classes of organic carbon content (Low, Medium, High, Very High and Extremely High). For each combination between Soil Typological Unit and Land Use Class, a OC class was assigned. The calculation of the weighted average for each grid (on the basis of the Soil Typological Units present in each Soil Mapping Unit), and the correction on the base of temperature 5, allowed the calculation of continuous values for each grid cell. Metadata File name : OM_TOP_EFSA Layers : 1 file type : real data type : real ref. units : % value units : % flag value : 0 flag def'n : background Source : JRC OC_TOP Processing : R. Hiederer, JRC Reference : Jones et al., This conversion factor, occasionally used in some part of Europe, was adopted in order to to not exceed the value of 100 % Organic Matter (max OC = 63.3%), even if the most common factor is cos( AAAT 1.1) 0. 7 TEMPcor 20

22 1.2. Mean Monthly Temperature Figure 3.2: Map of Top Soil Organic Carbon The mean monthly temperature for EU 27 (Fig. 3.3) has been extracted from the Worldclim dataset ( of interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 1x1 km. Input data were gathered from a variety of sources and, where possible, were restricted to records from the period period. To compute the data a 21

23 thin-plate smoothing spline algorithm implemented in the ANUSPLIN package, have been applied for interpolation, using latitude, longitude, and elevation as independent variables was used. Uncertainty is highest in mountainous and in poorly sampled areas. For further details on the procedures applied refer to the reference reported below. Metadata File name : TMEANn_EFSA Layers : 12 file type : real data type : real ref. units : deg C value units : average monthly mean temperature flag value : flag def'n : background Source : Processing : R. Hiederer, JRC Reference : Hijmans et al.,

24 Figure 3.3: Map of Mean Temperature 23

25 1.3. Mean Monthly Precipitation The mean monthly precipitation for EU 27 has been extracted from the Worldclim dataset ( ) of interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 1-km. Input data were gathered from a variety of sources and, where possible, were restricted to records from the period period. To compute the data a thin-plate smoothing spline algorithm implemented in the ANUSPLIN package, have been applied for interpolation, using latitude, longitude, and elevation as independent variables was used. Uncertainty is highest in mountainous and in poorly sampled areas. For further details on the procedures applied refer to the reference reported below. For further details on the procedures applied refer to the reference reported below. In Fig the map of total precipitation, calculated as the sum of the mean monthly precipitation, is shown. Metadata File name : PRECn_EFSA Layers : 12 file type : real data type : real ref. units : mm value units : average monthly precipitation flag value : flag def'n : background Source : Processing : R. Hiederer, JRC Reference : Hijmans et al.,

26 1.4. Mean Monthly Precipitation Figure 3.4: Map of Total Precipitation The mean monthly precipitation ( ) has been derived from the MARS datatbase (described at point 2 of the present report). The original data, attributed to grid cells of 50 x 50 km, have been converted in a raster with 1 km resolution. 25

27 Metadata File name : MARS_PRECn Layers : 12 file type : real data type : real ref. units : mm value units : average monthly precipitation flag value : flag def'n : background Source : Processing : C. Gardi, JRC Reference : MARS Precipitation data 1.5. Corine Land Cover 2000 Corine Land Cover 2000 (CLC2000) is an update for the reference year 2000 of the first Corine Land Cover database which was finalised in the early 1990s as part of the European Commission programme to COoRdinate INformation on the Environment (Corine). It provides consistent information on land cover and land cover changes during the past decade across Europe. CLC2000 is based on the photointerpretation of satellite images by the national teams of the participating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. The resulting European database is based on standard methodology and nomenclature. CLC2000 (Fig. 3.5) shows the land cover changes in ecosystems such as forests, lakes, pastures etc. and the impact of human activities (such as housing, food production, transport etc.) on land use. Forty-four land cover classes are used to map changes over time, all of which tell their own story of how decisions made across Europe have led to alterations in the landscape. Metadata File name : CLC_2000_EFSA Layers : 1 file type : integer data type : binary ref. units : none value units : land cover class flag value : 0 flag def'n : background 26

28 Source : Corine land cover 2000 (CLC2000) 250 m - version 8/ Processing : R. Hiederer, JRC Reference : Nunes De Lima M.V., 2005 IMAGE 2000 and CLC2000. Products and Methods. JRC-IES, European Communities, Italy.. EUR EN, ISBN Legend : GRID CLC LABEL Continuous urban fabric Discontinuous urban fabric Industrial or commercial units Road and rail networks and associated land Port areas Airports Mineral extraction sites Dump sites Construction sites Green urban areas Sport and leisure facilities Non-irrigated arable land Permanently irrigated land Rice fields Vineyards Fruit trees and berry plantations Olive groves Pastures Annual crops associated with permanent crops Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural vegetation Agro-forestry areas Broad-leaved forest Coniferous forest Mixed forest Natural grasslands Moors and heathland Sclerophyllous vegetation Transitional woodland-shrub Beaches, dunes, sands Bare rocks Sparsely vegetated areas Burnt areas Glaciers and perpetual snow Inland marshes Peat bogs Salt marshes Salines Intertidal flats Water courses Water bodies 27

29 Coastal lagoons Estuaries Sea and ocean Nodata Unclassified Land Surface Unclassified Water Bodies Figure 3.5: Corine Land Cover

30 1.6. Soil Texture Soil texture data are derived from the Soil Geographical Database of Eurasia (SGDBE) at scale 1:1,000,000. The database contains a list of Soil Typological Units (STU). Besides the soil names they represent, these units are described by variables (attributes) specifying the nature and properties of the soils: for example the texture, the water regime, the stoniness, etc. The geographical representation was chosen at a scale corresponding to the 1:1,000,000. At this scale, it is not feasible to delineate the STUs. Therefore they are grouped into Soil Mapping Units (SMU) to form soil associations and to illustrate the functioning of pedological systems within the landscapes. Each SMU corresponds to a part of the mapped territory and as such is represented by one or more polygons in a geometrical dataset. The SGDBE consists of both a geometrical dataset and a semantic dataset (set of attribute files) which links attribute values to the polygons of the geometrical dataset. Soil texture is expressed as dominant surface textural class of the STU (Fig. 3.6). The textural classes are reported below in the legend. Metadata File name : Text_DOM_EFSA Layers : 1 file type : integer data type : binary ref. units : none value units : soil texture class flag value : 0 flag def'n : background Source : Processing : R. Hiederer, JRC Reference : Soil Geographical Database of Eurasia At Scale 1:1,000,000, Version 4 beta, 25/09/2001 Legend : 1 Coarse (18% < clay and > 65% sand) 2 Medium (18% < clay < 35% and >= 15% sand, or 18% <clay and 15% < sand < 65%) 3 Medium fine (< 35% clay and < 15% sand) 4 Fine (35% < clay < 60%) 5 Very fine (clay > 60 %) 9 No mineral texture (Peat soils) 29

31 Figure 3.6: Map of Soil Textural Classes 1.7. Member State Territories The map of member state territories has been produced from the boundaries of NUTS 0 level. The Nomenclature of Territorial Units for Statistics (NUTS) was established by Eurostat more than 30 30

32 years ago in order to provide a single uniform breakdown of territorial units for the production of regional statistics for the European Union. Metadata File name : NUTS_L0_EFSA Layers : 1 file type : integer data type : binary ref. units : none value units : NUTS L0 class flag value : 0 flag def'n : background Source : Eurostat GISCO V9 Processing : R. Hiederer, JRC Reference : Topsoil ph This map is derived from the Harmonized World Soil Database, and shows the Topsoil ph, measured in water solution. Metadata File name : PH_TOP_EFSA Layers : 1 file type : real data type : real ref. units : none value units : ph flag value : 0 flag def'n : background Source : Processing : R. Hiederer, JRC Reference : FAO/IIASA/ISRIC/ISS-CAS/JRC, Harmonized World Soil Database (version 1.0). FAO, Rome, Italy and IIASA, Laxenburg, Austria. 37pp Topsoil Bulk Density The Topsoil Bulk Density has been estimated using two different approaches: 1. Soil types characterized by BD lower than 1.2 g cm Soil types characterized by BD higher than 1.2 g cm -3 31

33 1. The estimate of BD is based on the application of a PTR that relate the BD value to the organic carbon content (publication pending) 2. The estimate of BD is based on the application of PTR for the estimate of Packing Density (PD), and one equation that enable to estimate the BD using PD and clay content (% in weight): Output attributes Input attributes Output classes Topsoil Packing Density (PD_TOP) BD = PD C Where C = clay % in weigth STR_TOP - Topsoil structure class TEXT - Topsoil textural class USE - Regrouped land use class L(ow): < 1.4 g/cm 3 M(edium): g/cm 3 Metadata File name : BD_TOP_EFSA Layers : 1 file type : real data type : real ref. units : none value units : g cm-3 flag value : 0 flag def'n : background Source : JRC OC_TOP Processing : R. Hiederer, JRC Reference :Pedotransfer Rules Database V.2.0 for Environmental Interpretations, Joël DAROUSSIN - Dominique KING, Institut National de la Recherche Agronomique ARDON France. The use of pedotransfer in soil hydrology research in Europa workshop proceedings Orléans, France, October R.J.A. Jones, G. Spoor and A.J Thomasson, Vulnerability of subsoils in Europe to compaction: A preliminary analysis.. Soil and Tillage Research, 73, Soil Mapping Units The map of soil mapping units has been produced converting the vector data of the SGDBE to the standard raster format of the other thematic data layers. Metadata 32

34 File name : SGDBE_SMU_EFSA Layers : 1 file type : real data type : real ref. units : none value units : SMU code flag value : 0 flag def'n : background Source : Processing : R. Hiederer, JRC Reference : Soil Geographical Database of Eurasia At Scale 1:1,000,000, Version 4 beta, 25/09/ Topsoil Available Water Capacity Topsoil Available Water Capacity has be defined from Topsoil textural class and Topsoil packing density, as input attributes derived by SGDBE, using using a pedotransfer rule of the PTR database of the SGDBE. Metadata File name : AWC_TOP_EFSA Layers : 1 file type : integer data type : binary ref. units : none value units : mm/m flag value : 0 flag def'n : background Source : Processing : R. Hiederer, JRC Reference : SOIL GEOGRAPHICAL DATABASE OF EURASIA AT SCALE 1:1,000,000, VERSION 4 beta, 25/09/

35 1.12. Agricultural Area commons to 1990 and 2000 This map show the areas that on the basis of Corine Land Cover were classified as Agricultural for both periods, 1990 and In areas without Corine data the USGS Global Land Cover dataset was used. Metadata File name : LC_AGRI_1990_2000_MSQ Layers : 1 file type : integer data type : binary ref. units : none value units : unspecified flag value : 0 flag def'n : background Source : CLC90, CLC2000, USGS GLCC Processing : R. Hiederer, JRC Reference : Tailored application based on Corine Land Cover data Legend: Common Agricultural Land Spade 8.0 associated areas This map as been produced joining the Spade 8 profile database and the SGDBE, using the Soil Mapping Unit as common attribute (Fig. 3.7). Metadata File name : SMU_with_Spade_8_profile.img Layers : 1 file type : integer data type : binary ref. units : none value units : unspecified flag value : 0 flag def'n : background 34

36 Source Processing Reference Legend : SGDBE : C.Gardi, JRC : Tailored application based on SGDBE and Spade 8 databases : 1 - SMU associated with Spade 8 profiles Figure 3.7: Map of Soil Mapping Units that can be associated to a Soil Profile of Spade 8 35

37 1.14. Soil profile data from Spade 8.0 For the parametrization of the scenario selected by the FATE WG, the characteristics of 157 soil profiles and the chemical-physical parameters of the associated soil horizons have been provided. The selection of the plausible soil profile has been realized on the basis of the following attributes: Geographic location Top soil organic carbon content Land use class Conclusions and Recommendations All the data requested within the SLA have been provided, in the correct format, enabling the FATE WG to perform the activities requested for the scenario selection and for the parametrization of the model. During the validation of data, there have been a request of verification of high Organic Matter values, in correspondence of Arable Land of CLC. These values are associated to some agricultural area, that in norther countries, can be performed also on drained peatlands. The parametrization proedures required, for the selected cells, the availability of the chemical and physical parameters for the entire soil profile and the weather data, with the temporal resolution of 1 day. Some difficulties raised from the actual incompleteness of the SPADE database. During the 2010 further activities on the SPADE database will be realised, increasing the percentage of the SMU associated to a soil profile within the database. References Beek, E.G., Spatial interpolation of daily meteorological data. Theoretical evaluation of available techniques. Report 53.1, DLO Winand Staring Centre, Wageningen, The Netherlands, pp 43. Burrill, A., Vossen, P., Development of an operational agro-meteorological monitoring system. In: F. Toselli, J. Meyer-Roux (eds). Proceedings of conference on the applicationof remote sensing to agricultural statistics, November 1991, Belgirate, Italy. EUR EN, Office for Official Publications of the EU, Luxembourg, p Diepen, C.A. van, Application of simple interpolation methods in agrometeorology. In: B. Gozzini, M. Hims (eds). Proceedings of workshop on dealing on spatialisation, 24-25September 1996, Toulouse. EUR EN, Office for Official Publications of the EU, Luxembourg, p FAO/IIASA/ISRIC/ISS-CAS/JRC, Harmonized World Soil Database (version 1.0). FAO, Rome, Italy and IIASA, Laxenburg, Austria. 37pp. Goot, E. van der, Spatial interpolation of daily meteorological data for the Crop Growth Monitoring System (CGMS). In: M. Bindi, B. Gozzini (eds). Proceedings of seminar on dataspatial 36

38 distribution in meteorology and climatology, 28 September - 3 October 1997, Volterra, Italy. EUR EN, Office for Official Publications of the EU, Luxembourg, p Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: IPSC-JRC, Meteorological data consistency checks specifications. Institute for Protection and Safety of the Citizen, Joint Research Centre of the European Commission, Ispra, Italy, pp 6. Jones, R.J.A, R. Hiederer, E. Rusco, P.J. Loveland and L. Montanarella Estimating organic carbon in the soils of Europe for policy support. European Journal of Soil Science, October 2005, 56, p Nunes De Lima M.V., 2005 IMAGE 2000 and CLC2000. Products and Methods. JRC-IES, European Communities, Italy.. EUR EN, ISBN Soil Geographical Database of Eurasia at scale 1:1,000,000 (SGDBE), version 4 beta 37

39 Glossary and Abbreviations CORINE ETRS 89 LAEA FOCUS INSPIRE MARS PEC PPR PRAPeR SPADE SANCO Coordinate Information on the Environment Lambert Azimuthal Equal Area coordinate reference system Forum for the Co-ordination of Pesticide Fate Models and their Use Infrastructure for Spatial Information in the Community. Sets standards for European datasets. Monitoring Agricultural ResourceS. Predicted Environmental Concentration EFSA Scientific Panel on Plant Protection Products and their Residues Pesticide Risk Assessment Peer Review. A unit within EFSA responsible for the EU peer review of active substances used in plant protection products Soil Profile Analytical Database EU Directorate General for Health and Consumers 38

40 European Commission EUR EN Joint Research Centre Institute for Environment and Sustainability Title: Activities realized within the Service Level Agreement between JRC and EFSA, as a support of the FATE Author(s): Ciro Gardi, Luca Montanarella, Roland Hiederer, Arwyn Jones, Fabio Micale Luxembourg: Publications Office of the European Union 2010 pp. 39 EUR Scientific and Technical Research series ISSN ISBN DOI /

41 How to obtain EU publications Our priced publications are available from EU Bookshop ( where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352)

42 The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national. LB-NA EN-C