Soil Erosion At European Level: A step forward data harmonization and collection with the contribution of a European Network

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1 Soil Erosion At European Level: A step forward data harmonization and collection with the contribution of a European Network Panagos Panos, Katrin Meusburger, Cristiano Ballabio, Pasquale Borrelli and Luca Montanarella

2 Outline Soil Erosion and recent policy developments Soil Erosion Collection Why we have done it? Country participation Overall Map Comparison per Country: PESERA model & EIONET data New developments in soil erosion in Europe ( ): o Soil Erodibility o Rainfall Erosivity o Wind Erosion

3 Policy & Soil Erosion (1/3) EU Thematic Strategy for Soil Protection adopted by the European Commission on the 22 nd of September 2006 COMMUNICATION COM(2006) 231 on the Thematic Strategy for Soil Protection DIRECTIVE COM(2006) 232 establishing a framework for the protection of soil and amending Directive 2004/35/EC IMPACT ASSESSMENT SEC(2006) 620 of the Thematic Strategy for Soil Protection

4 Policy & Soil Erosion (2/3) Sealing Soil Biodiversity loss Erosion Decline of Soil Organic Matter Soil Threats Salinization Compaction Landslides Contamination 4/22

5 Policy & Soil Erosion (3/3) Common Agricultural Policy (CAP) Requirement to keep land in Good Agricultural and Environmental Condition (GAEC), targeting soil erosion,. Resource Efficiency By 2020, the area of land in the EU that is subject to soil erosion of more than 10 tonnes per hectare per year should be reduced by at least 25%. Rio +20 Conference Agreement of a Sustainable Development Goal (SDG) on Land and Soil: Zero Net Land Degradation. Danube Strategy

6 European Soil Data Centre European Commission (EU funded soil related projects) Data from specific in-house JRC actions (e.g. ESDB, SOTER) Member States EIONET, EEA, etc European Soil Data Centre (ESDAC) Data from related JRC and EC actions (e.g. LUCAS, BIOSOIL) Network of soil centres (e.g. ESBN) Collaborative research (e.g. EuroGeoSurveys, FAO, ISRIC)

7 PESERA model Policy Makers: According to the obtained results it is possible to define the soil erosion risk areas at European level Input Data: Climate: Rainfall, Temperature, etc Soil: European Soil Database ver 2.0 Land cover: Corine Land Cover 1990 Topographic data: SRTM Data are freely available in ESDAC (450 data licenses have distributed since 2005) Limitations: Reproducibility: Difficult to run the model Technical: Fortran code Demanding: Too many input layers. Kirkby et al., (2008)

8 Data collection Specifications 2 Indicators: Soil Organic Carbon (SOC), Soil Erosion Soil Erosion (t/ha/y): Estimation of soil erosion loss expressed in tones per hectare per year (t/ha/y) ESDAC adopted a light data collection protocol Approach: INSPIRE Grid format (Grid Cells of 1km x 1km) were delivered to Member States Metadata: The Model used for estimation of soil erosion, the land use types covered. Time:

9 Erosion data collection: Participation 8 countries provided Complete datasets: Austria, Belgium, Bulgaria, Germany, Italy, Netherlands, Poland, Slovakia 6 countries provided data that could not serve in a nationwide comparison: France and Denmark provided only classes of soil erosion risk; the former Yugoslav Republic of Macedonia (FYROM) provided a complete dataset in non-comparable measuring units; Ireland provided PESERA data itself; Norway and Estonia provided data for a limited spatial coverage. 8 countries have replied on the request: Finland, Greece, Hungary, Iceland, Turkey, Lithuania, Luxembourg, Sweden. 16 Countries didn t reply at all

10 Erosion: A Snapshot of EIONET data Country Number of 1km Cells Area Coverage with erosion Value Average soil erosion No. % t/ha/y Method and land use covered Austria 83, Method: Combination of USLE and RUSLE Coverage: all land-cover types Belgium 17, Method: RUSLE Coverage: all land-cover types (except for urban areas) Bulgaria 102, Method: USLE Coverage: all land-cover types Germany 168, Method: USLE Coverage: agricultural land Italy 151, Method: RUSLE Coverage: 9 regions provided data using different rainfall erosivity estimation Netherlands 36, Method: RUSLE Coverage: all land-cover types (except for urban areas) Poland 220, Method: USLE Coverage: agricultural land Slovakia 49, Method: USLE Coverage: all land-cover types

11 Soil Erosion EIONET Map Panagos et al. (2014), Soil Science & Plant Nutrition Vol. 60(1), pp.15-29

12 Comparison for EIONET countries EIONET Model: use of (R)USLE

13 Comparison for EIONET countries.

14 Deviation between EIONET & PESERA Green colour represents good correspondence. Austria: Slight under-estimation of PESERA Italy: Different patterns (Mountains & Plains) Wallonie (Belgium), Southern Poland: High underestimation of PESERA

15 Summary of the Comparison The EIONET data collection were compared to PESERA as this is the only peer reviewed harmonized pan-european Dataset. Certain under-estimation of erosion rates on behalf of PESERA The comparison using Topographic criteria: The mean EIONET-SOIL erosion values are higher than mean PESERA values in all slope classes between 2 and 10 degrees In flat areas (0-1 degrees of slope) the difference (EIONET-SOIL minus PESERA) is lower than the overall difference in the country. The comparison using Land Cover criteria: The mean EIONET-SOIL erosion values are larger than the mean PESERA estimates for all types of land cover except for arable lands.

16 Possible reasons for different results between PESERA and EIONET-SOIL 1. Difference in mapping procedures: The EIONET data have a smoother distribution compared to the PESERA data for almost all countries. PESERA data has certain peak values mainly driven by the delineation of Soil Mapping Units in European Soil Database 2. The Sediment module in PESERA results in higher values in plains (Po plain in Italy and plain in Slovakia). 3. Different input datasets and Scale: The finer resolution DEMs (20m, 40m) used in EIONET result in sharp changes in slopes compared to GTOPO30 used in PESERA. EIONET countries used CORINE Land Cover 2006 which is much improved to the CORINE 1990 (used in PESERA) 4. Climatic Data are of crucial importance: Lack of pan- European rainfall erosivity dataset.

17 EIONET vs. dataset of soil erosion rates based on plot measurements EIONET values were closer to Plot measurements data than those of PESERA EIONET mean values are much lower in Slovakia and Austria and slightly lower in Netherlands and Germany compared to the mean erosion based on plots. There is a perfect correspondence of EIONET mean erosion values with mean erosion rate based on plots in Bulgaria and Poland. EIONET mean values are much higher in Belgium and Italy compared to the mean erosion rates based on plots measurements A future study can identify the reasons of difference. Cerdan et al., 2010

18 EIONET Data collection (1) The EIONET data collection was the first attempt to perform such an exercise at European level. Envisioning the application of a future Soil Framework Directive: Data coming from countries are collected, checked, harmonized, stored and made available in European Soil Data Centre. The main reason for the limited participation was that national institutes were not legally obliged to provide the requested data. The current findings could have greater value with the contribution of: large countries (Romania, Sweden, Finland, United Kingdom) countries with potentially large soil loss (Spain, Greece, Portugal).

19 EIONET Data collection (2) The Metadata (model used, land uses, period, input factors) accompanied the data proved of extreme importance for the correct interpretation of the data. The input layers (climate, land cover, soil, topography) are nationwide and as such have better resolution than the European Input layers (used in PESERA)..but they are not harmonized! EIONET-SOIL represents the best, albeit still fragmented, picture at European level which can potentially be improved!

20 Contribution to modelling validation The exercise of the EIONET data collection will validate the soil erosion estimates of a pan-european USLE (developed by JRC)

21 Concluding remarks The comparison identifies regions where there is still high uncertainty related to soil erosion rates. Modelled data need a cross validation with national datasets Better understanding of the national/regional situation Based on more detailed input datasets A participatory approach is strongly recommended.

22 What s Soil Erodibility(K-factor)? The K-factor is a lumped parameter that represents an integrated annual value of the soil profile reaction to the process of soil detachment and transport by raindrops and surface flow. Methodology applied: (R)USLE K-factor (Renard et al., 1997; Wischmeier and Smith, 1978): K = [(2.1 x 10-4 M 1.14 (12 OM) (s -2) (p - 3) ) / 100 ] * M : the textural factor with M = (m silt + m vfs ) (100 - m c ); m c [%]: clay fraction content (<0.002mm); m silt [%]: silt fraction content ( mm); m vfs [%]: very fine sand fraction content ( mm); OM [%]: the organic matter content; S: the soil structure class (s=1: very fine granular, s=2: fine granular, s=3, medium or coarse granular, s=4: blocky, platy or massive); P: the permeability class (p=1: very rapid,, p=6: very slow). Input data: c.a 20,000 LUCAS Topsoil soil samples, European Soil Database

23 Soil Erodiblity (K-factor) Panagos et al (2014)

24 Soil Erodibility (K-Factor) incorporating Stone cover Stone cover effect: 15% 24/22

25 Improvements compared to the past European Union 25 countries. High spatial resolution (500m) and application of Cubist Regression Interpolation compared to simple regression Soil structure was included in the K-factor estimation. Coarse fragments were taken into account for the better estimation of soil permeability. Surface stone content acting as protection against soil erosion. This correction is of great interest for the Mediterranean countries. The estimated soil erodibility dataset was verified against 21 local, regional and national datasets from 13 countries (literature) Data available in the European Soil Data Centre(ESDAC) Panagos et al., (2014)Soil erodibility in Europe: A high-resolution dataset based on LUCAS, Science of Total Environment, (2014) pp

26 What s rainfall erosivity (R-factor)? Rainfall erosivity is the kinetic energy of rainfall (MJ mm ha -1 h -1 y -1 ) and one of the main drivers of soil erosion by water caes.uga.edu (R)USLE R-factor (Renard et al., 1997; Brown and Foster, 1987; Wischmeier and Smith, 1978): R 1 n n m j j 1 k 1 ( EI 30 ) k n is the number of years of records mj is the number of erosive events of a given year j E unit rainfall energy (MJ ha 1 mm 1 ) I 30 is the maximum rainfall intensity during a period of 30 min in the event k (mm h 1 )

27 Rainfall intensity data 1,541 Precipitation stations with detailed rainfall intensity Resolution: 30-Minutes Time series: Years Participatory approach with scientists from EU countries Meteorological services Individual scientists Literature

28 R-factor in Europe Using the 1561 stations and applying geostatistics Based on real data Previous works use either daily rainfall or local functions To be submitted soon in a peer review journal data available in Autumn 2014

29 Soil erosion by wind Wind Erosion Susceptibility of European Soils As a erodible fraction (%) 500m x 500m Grid cell EU member states and neighboring countries Based on Soil texture (sand, silt, clay), Organic Matter and Calcium Carbonate contents Data available soon in ESDAC Borrelli et al. Wind Erosion Susceptibility of European Soils. Geoderma (2014)

30 Thank you for your attention Panos Panagos European Commission, JRC - Institute for Environment and Sustainability - Land Resource Management ESDAC - European SOIL PORTAL Panos.panagos@jrc.ec.europa.eu 16 June /22