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

Outline

• Soil Erosion and recent policy developments

• Soil Erosion Collection – Why we have done it?

• Country participation – Overall Map

• Comparison per Country: PESERA model & EIONETdata

• New developments in soil erosion in Europe (2013-2014):

o Soil Erodibility

o Rainfall Erosivity

o Wind Erosion

EU Thematic Strategy for Soil Protection adopted by the European Commission on the 22nd 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

http://eusoils.jrc.ec.europa.eu/

Policy & Soil Erosion (1/3)

Soil Biodiversity loss

Sealing

Erosion

Decline of Soil Organic Matter

Salinization Compaction

Landslides

Contamination

Soil Threats

4/22

Policy & Soil Erosion (2/3)

• 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…

Policy & Soil Erosion (3/3)

European Commission(EU funded soil related projects)

Data from specific in-house JRCactions (e.g. ESDB, SOTER)

Member States

EIONET, EEA, etc

Collaborative research(e.g. EuroGeoSurveys, FAO, ISRIC)

Data from related JRCand EC actions

(e.g. LUCAS, BIOSOIL)

Network of soil centres(e.g. ESBN)

European Soil Data Centre(ESDAC)

European Soil Data Centre

Policy Makers: According to the obtained results it is possible to define the soil erosion risk areas at European level

Limitations:Reproducibility: Difficult to run the modelTechnical: Fortran codeDemanding: Too many input layers….

Data are freely available in ESDAC(450 data licenses have distributed

since 2005)

PESERA model

Input Data:Climate: Rainfall, Temperature, etc Soil: European Soil Database ver 2.0 Land cover: Corine Land Cover 1990Topographic data: SRTM

Kirkby et al., (2008)

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: 2009 - 2010

Data collection Specifications

8 countries provided Complete datasets: Austria, Belgium, Bulgaria, Germany, Italy, Netherlands, Poland, Slovakia

6 countries provided data that could not serve in a nation-wide 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

Erosion data collection: Participation

Erosion: A Snapshot of EIONET data

Country Number of 1km Cells

Area Coverage with erosion

Value

Average soil

erosion

Method and land use covered

No. % t/ha/y

Austria 83,338 97.6 0.66 Method: Combination of USLE and RUSLE Coverage: all land-cover types

Belgium 17,534 87.3 3.65 Method: RUSLE Coverage: all land-cover types (except for urban areas)

Bulgaria 102,443 91.2 1.88 Method: USLE Coverage: all land-cover types

Germany 168,359 46.6 1.39 Method: USLE Coverage: agricultural land

Italy 151,008 49.2 6.6 Method: RUSLECoverage: 9 regions provided data using different rainfall erosivity estimation

Netherlands 36,560 94.7 0.25 Method: RUSLE Coverage: all land-cover types (except for urban areas)

Poland 220,090 70.1 1.47 Method: USLECoverage: agricultural land

Slovakia 49,705 99.5 1.04 Method: USLECoverage: all land-cover types

Soil Erosion EIONET Map

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

Comparison for EIONET countries

EIONET Model: use of (R)USLE

Comparison for EIONET countries

.

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

• 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.

Summary of the Comparison

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.

Possible reasons for different results between PESERA and EIONET-SOIL

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

• 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).

EIONET Data collection (1)

• 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!

EIONET Data collection (2)

The exercise of the EIONET data collection will validate the soil erosion estimates of a pan-European USLE (developed by JRC)

Contribution to modelling validation

• 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.

Concluding remarks

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 M1.14 (12 – OM) +3.25 (s -2) + 2.5 (p - 3) ) / 100 ] * 0.1317

M : the textural factor with M = (msilt + mvfs ) (100 - mc );mc [%]: clay fraction content (<0.002mm);msilt [%]: silt fraction content (0.002 – 0.5mm);mvfs [%]: very fine sand fraction content (0.05 – 0.1mm); 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

Panagos et al (2014)

Soil Erodiblity (K-factor)

24/22

Soil Erodibility (K-Factor) incorporating Stone cover

Stone cover effect: 15%

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, 479–480 (2014) pp. 189–200.

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

(R)USLE R-factor (Renard et al., 1997; Brown

and Foster, 1987; Wischmeier and Smith,

1978):

caes.uga.edu

k

n

j

m

k

IEn

Rj

)(1

30

1 1

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)

I30 is the maximum rainfall intensity during a period of 30 min in the event k (mm h−1)

Rainfall intensity data

• 1,541 Precipitation stations with detailed rainfall intensity

• Resolution: 30-Minutes

• Time series: 10 – 25 Years

• Participatory approach with scientists from EU countries

• Meteorological services

• Individual scientists

• Literature

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

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)

Panos PanagosEuropean Commission, JRC - Institute for Environment and Sustainability - Land Resource Management

ESDAC - European SOIL PORTAL http://eusoils.jrc.ec.europa.eu/

Panos.panagos@jrc.ec.europa.eu

16 June 2014 30/22

Thank you for your attention