Geomorphology 122 (2010) 167–177

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Geomorphology

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Rates and spatial variations of soil erosion in Europe: A study based on erosion plot data

O. Cerdan a,⁎, G. Govers b, Y. Le Bissonnais c, K. Van Oost d, J. Poesen b, N. Saby e, A. Gobin b, A. Vacca f,J. Quinton g, K. Auerswald h, A. Klik i, F.J.P.M. Kwaad j, D. Raclot c, I. Ionita k, J. Rejman l, S. Rousseva m,T. Muxart n, M.J. Roxo o, T. Dostal p

a RNSC, BRGM, Orleans 45060, Franceb Physical and Regional Geography, University of Leuven B-3001, Belgiumc UMR LISAH, INRA, IRD Montpellier 34060, Franced Geography, University of Louvain-la-Neuve B-1348, Belgiume INFOSOL, INRA Orléans 45000, Francef Department of Earth Sciences, University of Cagliary 09127, Italyg Catchment and Aquatic Processes, University of Lancaster LA1 4YQ, UKh Grassland Science University of München D-85350 Freising-Weihenstephan, Germanyi University of Natural Resources and Applied Life Sciences Vienna Department of Water, Atmosphere and Environment Institute of Hydraulics and RuralWaterManagement, A-1190 Vienna, Austriaj Reigerpark 44, 1444 AC Purmerend, The Netherlandsk CRSSEC Central Research Station for Soil Erosion Control CRSSEC, P.O. Box 1, 6400 Barlad, Romanial Institute of Agrophysics PAS, str. Doswiadczalna 4, Lublin, Polandm Soil Erosion Department Institute of Soil Science Sofia 1080, Bulgarian Physical Geography, UMR 8591, 92195 Meudon, Franceo Development Geography at the Geography and Regional Planning Department, Faculty of Social and Human Sciences, University of Lisbon, Portugalp Dept. of Irrigation, Drainage and Landscape Engineering Faculty of Civil Engineering Czech Technical University in Prague, Prague 6, 16629, Czech Republic

⁎ Corresponding author. Tel.: +33 238 64 31 55; fax:E-mail address: o.cerdan@brgm.fr (O. Cerdan).

0169-555X/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.geomorph.2010.06.011

a b s t r a c t

a r t i c l e i n f o

Article history:Received 30 April 2009Received in revised form 9 June 2010Accepted 14 June 2010Available online 23 June 2010

Keywords:Erosion plotsEuropeLand useSlope gradientSoil textureStoniness

An extensive database of short to medium-term erosion rates as measured on erosion plots in Europe undernatural rainfallwas compiled from the literature. Statistical analysis confirmed thedominant influenceof landuseand cover on soil erosion rates. Sheet and rill erosion rates are highest on bare soil; vineyards show the secondhighest soil losses, followed by other arable lands (spring crops, orchards and winter crops). A land with apermanent vegetation cover (shrubs, grassland and forest) is characterised by soil losses which are generallymore than an order of magnitude lower than those on arable land. Disturbance of permanent vegetation by fireleads to momentarily higher erosion rates but rates are still lower than those measured on arable land. We alsonoticed important regional differences in erosion rates. Erosion rates are generally much lower in theMediterranean as compared to other areas in Europe; this is mainly attributed to the high soil stoniness in theMediterranean.Measured erosion rates on arable and bare landwere related to topography (slope steepness andlength) and soil texture, while this was not the case for plots with a permanent land cover.We attribute this to afundamental difference in runoff generation and sediment transfer according to land cover types.On the basis of these results we calculatedmean sheet and rill erosion rates for the European area covered by theCORINE database: estimated rill and interrill erosion rates are ca. 1.2 t ha−1 year−1 for the whole CORINE areaand ca. 3.6 t ha−1 year−1 for arable land. These estimates are much lower than some earlier estimates whichwere based on the erroneous extrapolation of small datasets. High erosion rates occur in areas dominated byvineyards, the hilly loess areas in West and Central Europe and the agricultural areas located in the piedmontareas of the major European mountain ranges.

+33 238 64 35 49.

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© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Landscapes where human activities are expanding commonlywitness a shift from natural to accelerated erosion (Wilkinson, 2005)which threatens the soil resources and the sustainability of natural

ecosystems (CEC, 2006). The negative effects of soil erosion includewater pollution and siltation, crop yield depression, organic matter lossand reduction in water storage capacity (e.g. Pimentel et al., 1995;Bakker et al., 2004; Boardman and Poesen, 2006), which may lead tofundamental social challenges such as land abandonment and thedecline of rural communities (Bakker et al., 2005). The protection of soilresources has therefore been recognised as an important objective ofenvironmental policy (CEC, 2006): this requires a correct assessment oferosion rates and their geographical distribution. Such an assessment is

168 O. Cerdan et al. / Geomorphology 122 (2010) 167–177

also important from a purely scientific point of view: soil erosion affectsglobal biogeochemical cycles (VanOost et al., 2007, Quinton et al., 2010)and understanding the human impact on river sediment fluxes requiresthat sediment mobilisation by soil erosion is correctly quantified(Hooke, 2000; Wilkinson, 2005).

The United States is probably the only country where soil erosiondata have been collected over a long period of time using standardizedmethodology in relation to the development of the (Revised)UniversalSoil Loss Equation (Wischmeier and Smith, 1978; Nearing et al., 2000).For other areas including European, national or continental erosionestimates have mostly been based on sporadic evidence. For example,Pimentel et al. (1995) extrapolated erosion rates measured on a fewplots on a single location in Belgium to estimate average erosion ratesfor the whole continent, a procedure which was later criticized byBoardman (1998).

Various models of erosion assessment for large areas in Europehave been developed (e.g. Gobin et al., 2004; Kirkby et al., 2008) andsuch models provide valuable information on the spatial distributionof soil erosion and how it may be affected by land use and/or climatechange. However, model evaluation has hitherto been limited (e.g.Tsara et al., 2005; Licciardello et al., 2009). As demonstrated by Favis-Mortlock (1998), results of erosion models are often poor when theyare applied to other areas than where they were tested. Such modelsmay also require input data that are not always available for largeareas with the required accuracy (Jones et al., 2003).

In Europe as a whole, existing field data have hitherto been used toa limited extent only to assess national or continental soil erosion ratesbecause a transnational programmeof soil erosionmeasurement usinga standardized procedure has never been conducted. Nevertheless,erosion data have been collected in Europe through a number ofresearch projects based on various technologies including measure-ments of changes in surface height, rainfall simulation experimentsand tracer analyses. The majority of data have been collected onbounded field plots under natural rainfall, more or less comparable tothose used by the USDA for the calibration and validation of the (R)USLE. Although plot size, methodology, and the length of themeasurement period vary considerably, these erosion plot datapresent a wealth of information about actual erosion rates in Europe.

We compiled a large database of erosion rates measured onmedium-sized plots (N3 mand b200 m in length) under natural rainfallconditions on 81 sites in Europe. We used this database to assess theoverall intensity and spatial distribution of sheet and rill erosion undervarious landuse types. Specific objectives of this paper are: 1) todevelopa documented database on sheet and rill erosion rates under variouslanduse types; 2) to identify and, if possible, quantify theeffect of factorscontrolling erosion rates; 3) to estimate average sheet and rill erosionrates under various landuse types, and4) tomap the spatial distributionof erosion based on the measured erosion rates, topography, land useand soils.

2. Methods

2.1. The plot database

The database was compiled based on published literature andpersonal communication. Only data from direct field measurements(e.g. using collecting tanks or tipping buckets with flow proportionalsamplers) with information about plot size, slope gradient and lengthand landusewere retained. Furthermore,wedid not includedata if themeasurement period was b12 months or the plot length was b3 mbecause these were considered not to be representative. Data fromplots where a large temporal change in erosion rates occurred due tothe nature of experiments (e.g. Francia et al., 2002)were also excluded.As we did want to quantify erosion as it occurs under standardagricultural conditions, we did not include experiments whereconservation measures were implemented.

The data that were retained were collected on 81 experimentalsites in 19 countries, covering 2741 plot-years in total. In a first step,the data were grouped into entries. Each entry corresponds to aunique combination of land use (crop type), slope, soil type and tillagesystem for a single experimental site. Thus, a single entry maycombine the measurements of several replicate plots or, what is lessfrequent, the average erosion rate for a given crop/land cover asmeasured on different plots on the same site having the same slope,size etc. under a different or time-shifted crop rotation. If measure-ments were carried out during one year on two replicates, themeasurement time was taken as 24 plot-months or 2 plot-years. Foreach entry, the total number of plot-months and the averagemeasured erosion rate were entered and, where possible, an averageprecipitation amount and runoff rate were calculated. In total, 259database entries were constructed. The following land use types weredistinguished: bare land (no vegetation), arable land, forest, grass-land, shrubs, orchards, vineyards and post-fire (burnt down grasslandor forest). For about 50% of the data some information on topsoilcomposition was also available: this information varied from adetailed description of a soil profile including grain size distributionto simple qualitative information on soil texture. We used theinformation to classify soils into five textural classes: very fine (VF),fine (F), medium fine (MF), medium (M) and coarse (C) following thescheme of the 1:1,000,000 scale soil database and map of Europe(European Commission, 2004). In order to investigate possibleregional differences we also identified the biogeographical zone ofeach site using the biogeographical map of Europe as compiled by theEuropean Environmental Agency (www.eea.europa.eu).

Table 1 gives an overwiew of the literature sources used for thedatabase. The average length of monitoring for a plot was 122 months(∼10 years) with a median value of 72 months. The most extensivemeasurements were carried out on cereal plots in Portugal (96 plot-years; Lopes et al., 2002) and on bare plots in Germany (60 plot-years;Martin, 1988; Auerswald, 1993). The mean annual rainfall on a plotranged from 196 mm (Is Olias, Sardinia) to 1304 mm (Hohenpeissen-berg, Germany) with a median and mean of 595 and 621 mm,respectively. The average plot size is close to that of the standardWischmeier plot with an average length of 23.7 m, an average surfacearea of 378 m² and an average slope gradient of 15.2%.

2.2. Statistical analysis

Given the large variability in measurement periods and protocolsand the varying level of detail that was available for different sites werestricted our statistical analysis to the identification and descriptionof the main trends with respect to the effects of land use, soil type andtopography on erosion rates using simple linear models. Climate wasnot included in the analysis because insufficient data were available.

Because erosion rates are highly variable in time we gave a greaterweight to values obtained after a longer measurement period.According to the central limit theorem the standard error of theestimated average is inversely proportional to the square root of thenumber of observations: therefore the square root of the number ofplot-months was used as the weighing factor. As the number ofobservations for some land use categories was quite limited, weaggregated forest, grassland and shrub sites into the category‘permanent natural vegetation’ (PV) and orchards and vineyards intoa category ‘permanent cultures’ (PC). All statistical analysis wascarried out using SAS software (SAS, 2006).

2.3. Regional extrapolation

Plot erosion rates cannot be directly extrapolated to larger areas.First of all, the sample may be biased: it may be expected that most ofthe erosion studies are carried out in areas where a significant erosionproblem is expected. It is therefore no surprise that the average slope

Table 1Published sources used for the compilation of the soil erosion plot database.

Country References Number of entries*

Austria Klik et al., 2001;Klik, 2003 8Belgium Bolline, 1982 3Bulgaria Biolchev, 1975; Krumov, 1995; Krumov and Malinov, 1989; Marinov, 1995, Malinov, 1999;

Mihailova, 2000; Rousseva, 1987, 2002a,b; Rousseva et al., 200335

Czech Republic Dostal et al., 2006. 1Denmark Veihe and Hassolt, 2006 5France Clauzon and Vaudour, 1971; Messer, 1980; Martin et al., 1997; Muxart et al., 1990; Viguier,

1993; Cerdan et al., 2002; Le Bissonnais et al., 2003, 200411

Germany Voss, 1978; Jung and Brechtel, 1980; Dikau, 1986; Goeck, 1989; Martin, 1988; Goeck andGeisler, 1989; Emde, 1992; Auerswald, 1993

29

Greece Diamantopoulos et al., 1996; Kosmas et al., 1996; Romero-Diaz et al., 1999 8Hungary Kertesz and Centeri, 2006 7Italy Zanchi, 1983; Tropeano, 1983; Zanchi, 1988; Rivoira et al., 1989; Porqueddu and Roggero, 1994;

Caredda et al., 1997; Vacca et al., 2000; Basso et al., 200232

Lithuania Jankauskas and Jankauskiene, 2003 11The Netherlands Kwaad, 1991, 1994; Kwaad et al., 1998 2Poland Gil 1986; Rejman 1997; Rejman et al. 1998; Szpikowski, 1998 8Portugal Roxo et al. 1996; Figueiredo et al., 1998; Lopes et al., 2002; Lopez-Bermudez et al., 1998 16Roumania Ionita et al., 2006 17Spain La Roca, 1984 cited by Cerdà 2001; Lopez-Bermudez et al., 1991; Andreu et al., 1994 cited by

Cerdà, 2001; Bautista et al., 1996; Puigdefabregas et al., 1996; Sirvent et al., 1997; Castillo et al.,1997, Lopez-Bermudez et al., 1998;Padron et al., 1998; Andreu et al., 1998a,b; Bautista, 1999 cited by Cerdà, 2001; Romero-Diaz et al.,1999; Andreu et al., 2001; Bautista et al., 1996; Canton et al., 2001; Nicolau et al., 2002

39

Switzerland Schmidt, 1979 2United Kingdom Fullen and Reed, 1986; Fullen, 1991, 1992; Quinton, 1994 18

*One entry corresponds to the combination of one land use, slope, etc. for one experimental site.

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of arable land erosion plots is much higher than the average slope ofarable land in Europe (Cerdan et al., 2006). Second, erosion plots donot capture entirely the spatial variability of erosion processes andrates as they occur at the landscape scale. The direct extrapolation ofplot measurements to estimate regional erosion rates for a given landuse may therefore result in an over- or underestimation of true sheetand rill erosion rates, depending on the level of bias and on the wayerosion processes operate at different spatial scales.

2.3.1. Calculation of a slope mapSlope gradient estimates for Europe were derived from SRTM

topography (ca. 90 m grid resolution, vertical error N10 m; http://www2.jpl.nasa.gov/srtm/; CIAT, 2004). Previous studies have shownthat slope estimates are scale-dependent where coarser grid resolu-tions result in lower slope gradient estimates (Van Rompaey et al.,1999). In order to obtain slope estimates that are compatible with thespatial scales of the empirical soil erosion data, i.e. the plot scale (10–50 m), we removed bias from the SRTM slope estimates using highspatial resolution and high vertical accuracy topographical data usingthe procedure proposed by Van Rompaey et al. (1999). In a first step,slope gradients for England and Wales were derived from SRTMtopography. For this purpose, SRTM data were first resampled at an80 m resolution in a UTM projection using a bilinear interpolationmethod in order to obtain equal grid cell sizes. SRTM slope estimateswere then compared with slope gradient values derived from a highresolution Ordnance Survey (OS) DEM (OS, 2006, 10 m grid resolutionand vertical accuracy b2.5 m) for a total area of ∼40,000 km2 coveringthe whole geomorphological spectrum from flat coastal plains overundulating topography to mountainous areas (Ordnance grids SO, SU,NN and TL). The OS slope gradient estimates were averaged on an80 m grid and were aligned to the SRTM grid. A random subsample of60,000 slope gradient estimates was extracted from both SRTM andOS Profile databases and used in the analysis. Regression analysisshowed that SRTM derived estimates of slope gradient had to bescaled with a factor of 1.35 for slopes below 8% and 1.17 for slopesabove 8% in order to remove systematic bias. The error in thecorrected SRTM grid cell predictions is relatively high with a relativeroot mean square error (RRMSE) of 75%. However, a large fraction of

this error is random noise as evidenced by a much lower RRMSE of 9%after spatial aggregation of the data to a resolution of only 2500 m. Atthis larger spatial resolution, slope gradient values could be reliablyestimated, with a model efficiency (MEF) of 98%. As SRTM coverage isonly available up to 60°N, slope estimates for above this latitude werederived from the GTOPO30 database (ca. 1 km grid resolution, verticalerror b30 m, http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html). As described above, slope gradient values derivedfrom the GTOPO30 data were calibrated against corrected SRTMestimates for Europe (the area covered by CORINE land use).GTOPO30 slope estimates (Sg) were scaled using a power function[0.8+Sg+2.95Sg0.73] in order to obtain an unbiased slope gradientestimate. The RRMSE for individual GTOPO30 grid cell predictions isca. 90% and 41% for a spatial resolution of 1 and 10 km, respectively. Atthe latter spatial resolution, an MEF of 83% was obtained.

2.3.2. Topographic correctionThe European slope gradient map obtained was then used to

calculate the mean average erosion rates per grid cell. We assumedthat only a single land use was present in each grid cell (the dominantland use was selected). A slope factor for each grid cell (SN) wascalculated using the equation proposed by Nearing (1997):

SN = −1:5 +17

1 + e2:3−6:1sinS ð1Þ

where S=the slope gradient (rad). As information about thedistribution of field/slope lengths throughout Europe is at presentnot available, we assumed for arable land a constant field length of100 m and assumed that sheet and rill erosion is proportional to thesquare root of slope length (i.e. the standard length factor in theoriginal USLE; Wischmeier and Smith, 1978).

Sheet and rill erosion rates for arable land and permanent culturesas reported in the plot database were then corrected for topographicaleffects as follows:

Ecorr;plot =ESN

22x

� �0:5ð2Þ

Fig. 1. Spatial distribution of soils with a high rock fragment content in Europe. Areaswith soils having a rock fragment cover N30% are shown in dark brown. Data derivedfrom the Soil Geographical Database of Europe (European Commission, 2004).

Table 3Overview of the methodology used in the extrapolation of plot erosion data to theEuropean scale.

Factors Data source Methodology

Land use CORINE Land Cover Database This studyTopography SRTM topography (CIAT, 2004);

Ordnance Survey DEM(OS, 2006)

This study; Nearing, 1997;Van Rompaey et al., 1999

Soil properties(stoniness, soilcrusting anderodibility)

Geographical Soil Databaseof Europe (EC., 2004; http://eusoils.jrc.it/ESDB_Archive/ESDB/index.htm)

This study; Poesen andLavee, 1994; Poesen et al.,1994; Le Bissonnais et al.,2005

170 O. Cerdan et al. / Geomorphology 122 (2010) 167–177

where, Ecor,plor=the topographically corrected erosion rate (t ha−1

year−1), SN,plot=the Nearing dimensionless slope factor value forthe plot and x=the plot length (m).

Finally, the erosion rate for each grid cell was calculated as:

Ecorr;g =�Ecorr;pSN;g

10022

� �0:5ð3Þ

where, E_corr,p=the (weighted) average corrected plot erosion rate for a

given land use (t ha−1 year−1), Ecorr,g=the topographically correctederosion rate for the grid cell (t ha−1 year−1) and SN,g=the Nearingdimensionless slope factor value for the grid cell.

As erosion rates under permanent vegetation are not significantlyrelated to slope length (see below) we did not correct these erosionrates for slope length. Given the fact that plots are often much shorterthan natural slopes, the application of a length correction factor wouldlead to a dramatic overestimation of erosion on surfaces with natural

Table 2Decision rules combining the erodibility and crusting factor of the Soil GeographicalDatabase of Europe (Le Bissonnais et al., 2005) to elaborate a correction factor for theeffect of topsoil texture on erosion rates.

Erodibility factor (SGBDE V2)

1 2 3 4 5

Crusting factor (SGBDE V2) 1 0.3 0.1 0.1 0.3 12 0.3 0.1 0.3 1 23 0.3 0.1 0.3 1 24 0.3 0.3 1 2 35 0.3 1 2 3 3

vegetation. Erosion rates measured under permanent vegetationwerealso not related to slope gradient. Nevertheless, we did correct erosionrates measured under natural vegetation for slope gradient, accordingto the procedure described above as not doing so would result in asignificant overestimation of erosion rates for forested areas on lowslopes. We recognise that this procedure may lead to bias: the use ofplot erosion values that are not corrected for length effects may leadto an overestimation as average sheet and rill erosion rates for largerareas may well be lower than the values reported for plots due to thefact that sediment fluxes are transport-limited (see below).

2.3.3. Soil propertiesSoil stoniness is known to have a large influence on erosion rates

(Poesen et al., 1994). Detailed information on soil stoniness does notexist for Europe. However, the Geographical Soil Database of Europe(EC., 2004 http://eusoils.jrc.it/ESDB_Archive/ESDB/index.htm) doesprovide information about the spatial distribution of soils with animportant rock fragment content (e.g. Regosols, Lithosols etc., Fig. 1).We therefore assumed that the presence of an important rockfragment fraction reduced erosion by 30% as compared to a non-stony soil in the same geographical position (Poesen and Lavee, 1994;Poesen et al., 1994); thus, sheet and rill erosion on stony soils was 30%of the reference value.

Concerning soil texture, a correction factor was derived from theSoil Geographical Database of Europe (EC, 2004). This factor wascalculated from the erodibility and the crusting factors which werecalculated for the whole of Europe by Le Bissonnais et al. (2005) andwhich were combined in a decision rule (Table 2). An overview of themethodologies used to derive regional sheet and rill erosion rates isgiven in Table 3.

3. Results

3.1. Land use

The mean weighted erosion rates as calculated from the erosionplot database for each land use class where no conservation measureswere applied are given in Table 4. Erosion rates on bare land were

Table 4Plot erosion rates (weighted mean and standard deviation) for different land uses (alldata combined).

Land use Numberof entries*

Plot-months Mean(t ha−1 year−1)

Std. dev.

Bare 82 10,827 15.1 31.89Arable 103 11,998 4.40 (SC=12.3; WC=1.6) 12.15Forest 6 612 0.14 0.19Grass 18 3235 0.30 1.08Shrub 31 3283 0.51 1.65Vineyard 10 1350 12.22 27.78Orchards 4 728 11.75 24.31

*One entry is the combination of one land use, slope, etc. for one experimental site. SC:Spring crops. WC: Winter crops.

Table 5Comparison of plot erosion rates (weighted mean and standard deviation) for different land uses between the Mediterranean zone and the rest of Europe.

Other regions Mediterranean

Land use Database entries* Plot-months Mean (t ha−1 year−1) Std. Dev. Entries* Plot-months Mean (t ha−1 year−1) Std. Dev.

Bare 62 7599 17.12 30.23 20 2868 9.05 35.23Arable 73 6635 6.33 13.46 30 5363 0.84 1.66Forest 2 60 0.003 0.0018 4 552 0.18 0.18Grassland 7 1535 0.29 1.15 11 1700 0.32 1.09Shrub 3 90 0.13 0.19 28 3193 0.54 1.74Vineyard 4 144 23.64 26.0 6 1210 8.62 27.4Orchard 2 408 20.6 19.4 2 321 1.67 5.21

*One entry is the combination of one land use, slope, etc. for one experimental site.

171O. Cerdan et al. / Geomorphology 122 (2010) 167–177

clearly the highest (ca. 15 t ha−1 year−1), followed by those inorchards, vineyards and arable land. Shrub land, grassland and foresthave the lowest erosion rates, i.e. b1 t ha−1 year−1. Erosion for alldifferent land use classes differ significantly (pb0.0001; p values areall from the non-parametric Kruskal–Wallis test). Disturbance ofpermanent vegetation by fire leads to a measurable increase oferosion rates, but rates are still lower than those measured on arableland or in vineyards. There are also important regional differences. Inthe Mediterranean zone (MZ), rates are much lower for bare soils,arable lands and vineyards as compared to the rest of Europe(Table 5). Sheet and rill erosion rates are ca. 40% lower for baresoils, ca. 85% lower for arable land and ca. 90% lower for orchards(pb0.001). Conversely, erosion rates for land uses with a permanentvegetation cover such as grasslands, forests or shrubs are somewhathigher in the MZ than in the rest of Europe but absolute values arelow.

3.2. Topsoil texture and topography

Based on previous work on the relationship between soil textureand soil erodibility (e.g. Torri et al., 1997) medium and fine soiltextures were classified as highly erodible (HE) and very fine andcoarse soil textures as less erodible (LE). The difference in the averageerosion rate between the two categories is only statistically significantfor arable land (4.87 and 0.94 t ha−1 year−1 respectively, pb0.01).Linear regression analysis also revealed weak but statisticallysignificant positive relationships between reported erosion ratesand slope length for arable land, bare land and permanent cultures(Table 6) whereas no statistically significant relationships was foundfor permanent vegetation (Table 6, Fig. 2).

A statistically significant relationship with slope gradient was onlypresent for bare land (Table 6). A separate analysis for non-Mediterranean arable land revealed a significant slope effect onarable land for measurements in non-Mediterranean areas but not forthe MZ (Table 7, Fig. 3). The effect of slope gradient may also interactwith that of soil texture: if results for plots with a high and low soilerodibility are analysed separately, correlations for bare plots becomestronger and more significant (Table 8).

Table 6Correlations between topography and plot erosion rates for different land uses (all datacombined, PC = permanent cultures, PV = permanent vegetation).

Arable Bare PC PV

Slope 0.12*** 0.05* 0.42* n.sLength (n=103) (n=82) (n=14) (n=55)Slope n.s 0.12** ns nsGradient (n=101) (n=82) (n=14) (n=48)TF 0.09** 0.16*** ns ns

(n=101) (n=82) (n=14) (n=47)

* 0.05, ** 0.01, *** 0.001.

Thus, on bare land as well as on arable land outside the MZ bothslope gradient and length are related to plot erosion rates. For bareplots with highly erodible soils, the erosion rate is relatively stronglyrelated (r²=0.30, n=38) to a topographical factor (TF) defined as:

TF = S′L0:5 ð4Þ

Where S′=the slope gradient (%) and L=the slope length (m). TFis also significantly related to erosion rates on arable land but thecorrelation is somewhat lower than when only slope gradient isconsidered. Correlation does not improve here by making adistinction between highly erodible and other soils (Table 8).However, the correlation between the plot erosion rate and TF doesimprove when a distinction is made between the Mediterranean andthe rest of Europe (Table 7).

3.3. Regional estimates

The resulting map with a 100 m resolution (corresponding to thechosen average slope length) is shown in Fig. 4. Erosion rates arerelatively high (2–10 t ha−1 year−1) in the hilly loess areas ofWestern and Central Europe. For the MZ the results show a strongspatial variation, with high estimated erosion rates for many areas inItaly (Apennine slopes and Sicily) and some areas in Spain (southernpart of the Guadalquivir basin and the area around Zaragoza). Erosionrates are much lower in Greece, mainly due to the high stone contentof the soils. In France, some of the highest erosion rates are expectedin wine-growing areas located on steep slopes such as the Alsace. ThePyrenean footslopes also suffer from severe erosion. Large parts of theCzech republic, located within the basin bordered by the Erzgebirge,Böhmerwald and Sudeten show high erosion rates. In Germany, theintensively cropped hilly areas towards the north of the Erzgebirge

Fig. 2. Plot erosion rate vs. slope length for plots under permanent vegetation (grassland,forest).

Table 7Correlations between topography and plot erosion rates on bare and arable land for theMediterranean zone (M) and the rest of Europe (O).

Arable (M) Arable (O) Bare (M) Bare(O)

Slope length 0.30** 0.14(***) ns 0.12**(n=30) (n=73) (n=62)

Slope gradient ns 0.11** 0.27* 0.15**(n=71) (n=20) (n=62)

TF 0.22** 0.22*** ns 0.22***(n=30) (n=71) (n=62)

* 0.05, ** 0.01, *** 0.001.

Fig. 3. Plot erosion rate vs. slope length for plots on arable land.

Fig. 4. Estimated sheet and rill erosion rates (t ha−1 year−1) calculated for the areas ofEurope covered by the CORINE database.

172 O. Cerdan et al. / Geomorphology 122 (2010) 167–177

and Sudetenwald and between the Böhmerwald and the German Alpsare strongly affected by erosion. Intensive erosion is also expected onthe hilly footslopes of the Carpathians in Slovakia and Romania and toa lesser extent in Hungary. Finally, significant erosion occurs in thehilly moraine landscapes of Denmark and limited areas in eastEngland and east Scotland.

We aggregated the results per country (Table 9), per biogeo-graphical zone (Table 10) and per land use class (Table 11). Totalsheet and rill erosion in Europe as covered by the CORINE database isestimated to be ca. 5.5×108 t over a total surface area ofca. 4.46×108 ha. Thus the average sheet and rill erosion rate forEurope is estimated to be slightly over 1 t ha−1 year−1. This averagerate varies between 0.2 and 3.2 t ha−1 year−1 on a per country basis,the highest value being calculated for Slovakia. Mean rates per landuse class show very important variations: predicted erosion rates arehighest for vineyards (ca. 17 t ha−1 year−1). The mean value weobtained for arable land is 3.6 t ha−1 year−1, while the value fororchards is somewhat lower (3.1 t ha−1 year−1). For all other landuses, mean values are well below 1 t ha−1 year−1.

Table 8Correlations between topography and plot erosion rates on bare and arable land for soils witherodibility = very fine and coarse textures).

Arable Arable (HE) Arable (LE) B

Slope length 0.12*** 0.08 ns 0(n=103) (n=54) 0

Slope gradient ns ns 0.15* 0(n=101) (n=29) (

TF 0.09** ns 0.08* 0(n=101) (n=28) (

* 0.05, ** 0.01, *** 0.001.

4. Discussion

4.1. Factors influencing sheet and rill erosion ratesmeasured onerosion plots

Land use is clearly the most important control on erosion rates.Land uses causing a significant percentage of the soil to be bare overlonger time periods, due to either a spatially incomplete cover (wideinterrow length and low leaf cover, e.g. vineyards) or the presence oflong periods without significant cover in the cropping system (e.g.maize or more generally, spring crops), clearly have the highesterosion rates.

More surprisingly, we observed that soil erosion rates on arableland and vineyards are much lower in the MZ as compared to the restof Europe. Erosion rates on arable land in the MZ are, on average, only13% of those measured in the rest of Europe. Also for vineyards andbare land there is a significant, albeit smaller difference (ca. 50%).These differences cannot be attributed to a lack of data for the MZ: thedatabase contains data for 1267 plot-years and 101 entries for the MZand 1372 plot-years and 152 entries for the rest of Europe (Table 5).

a different erodibility (HE= high erodibility = fine andmedium soil texture; LE= low

are Bare (HE) Bare (LE) PC PV

.05* ns ns 0.42* ns

.42* (n=14) (n=55)

.12** 0.21** 0.11* ns nsn=82) (n=38) (n=36) (n=14) (n=48).16*** 0.30*** 0.17* ns nsn=82) (n=38) (n=36) (n=14) (n=47)

Table 9Estimated mean and total regional erosion rates aggregated per country.

Country Mean erosion(t ha−1 year−1)

Std.Dev.

Area(103 km²)

Total erosion(105 t yr−1)

% totalEuropeanerosion

San Marino 5.0 13 0.1 0 0.0Slovakia 3.2 9 49.0 156 2.8Denmark 2.6 3 42.1 109 2.0Czech Republic 2.6 6 78.9 202 3.7Italy 2.3 9 299.5 691 12.5Bulgaria 1.9 6 110.8 211 3.8Germany 1.9 7 356.7 674 12.2Liechtenstein 1.8 2 0.1 0 0.0Romania 1.8 7 237.8 423 7.6Austria 1.6 6 84.0 135 2.4Poland 1.5 4 311.7 480 8.7France 1.5 5 547.4 805 14.5Belgium 1.4 4 30.6 42 0.8Luxembourg 1.3 6 2.6 3 0.1Portugal 1.2 7 88.4 109 2.0Switzerland 1.2 6 2.8 3 0.1Slovenia 1.2 11 20.3 23 0.4Hungary 1.0 4 93.1 96 1.7Spain 1.0 4 497.2 503 9.1Lithuania 1.0 2 64.9 62 1.1United Kingdom 0.9 3 241.7 222 4.0Macedonia 0.9 4 25.3 23 0.4Ukraine 0.9 5 1.4 1 0.0Belarus 0.8 2 2.0 2 0.0Greece 0.8 3 129.5 98 1.8Vatican 0.6 3 2.6 2 0.0Ireland 0.5 2 68.7 37 0.7Latvia 0.5 2 64.5 35 0.6Sweden 0.5 2 446.3 225 4.1Estonia 0.4 1 44.5 20 0.4Croatia 0.4 4 55.7 24 0.4Albania 0.4 3 28.5 12 0.2Netherlands 0.4 1 34.5 12 0.2Turkey 0.3 1 0.1 0 0.0RussianFederation

0.2 1 2.7 1 0.0

Bosnia andHerzegovina

0.2 1 51.2 13 0.2

Finland 0.2 1 334.8 82 1.5Moldova 0.2 1 0.2 0 0.0Norway 0.2 1 2.5 1 0.0Andorra 0.2 0 0.2 0 0.0Monaco 0.1 0 0.0 0 0.0Gibraltar 0.1 0 0.0 0 0.0

Table 10Estimated mean and total regional erosion rates aggregated per biogeographical zone.

Mean erosion rate(t ha−1 year−1)

Std.Dev.

Area(ha)

Totalerosion(t year−1)

% of totalEuropeanerosion

Continental 1.8 6.8 131,587,000 240,443,662 43.5Steppic 1.6 4.5 3,753,200 6,127,549 1.1Pannonian 1.3 5.1 13,418,200 17,924,702 3.2Mediterranean 1.3 5.1 91,142,700 118,726,127 21.5Atlantic 1.2 3.6 77,065,900 93,502,515 16.9Black sea 1.0 4.5 1,092,610 1,097,560 0.2Alpine 0.9 4.9 43,677,800 37,788,329 6.8Boreal 0.5 1.8 82,634,500 37,457,888 6.8None 0.1 0.1 849 109 0.0

Table 11Estimated regional erosion rates (mean and standard deviation) aggregated per landuse class.

Mean erosion rate(t ha-1 year−1)

Std.Dev.

Area Total Erosion(t yr−1)

% erosion

Vineyard 17.4 34.8 3,904,570 67,941,082 12.3Arable land 3.6 6.2 109,999,004 400,751,678 72.4Orchards 3.1 5.8 6,450,110 20,066,744 3.6Burnt areas 0.8 1.3 136,965 114,410 0.0Grassland 0.4 0.8 49,098,401 21,866,267 3.9Forest 0.2 0.3 138,818,997 28,856,723 5.2Shrubs 0.2 0.3 42,320,301 8,225,120 1.5Complexcultivationpatterns

0.1 0.2 50,423,398 6,038,353 1.1

Urban area 0.0 0.0 35,689,500 0 0.0Rice 0.0 0.0 564,790 0 0.0Wetland 0.0 0.0 831,6000 0 0.0

173O. Cerdan et al. / Geomorphology 122 (2010) 167–177

The low rates measured in the MZ can neither be explained bydifferences in topography or climate: plot slopes were, on average,higher in the Mediterranean (e.g. 14.6% vs. 11.5% for arable land in therest of Europe) and rainfall erosivity in Mediterranean climates isgenerally at least as high as inWestern or Central Europe (e.g. Diodato,2004). The most likely explanation for the low erosion Mediterraneanerosion rates is the high rock fragment content ofMediterranean soils:the presence of a rock fragment cover is known to reduce sheet andrill erosion rates (e.g. Poesen and Lavee, 1994; Poesen et al., 1994;Puigdefabregas et al., 1996). As there is a negative exponentialrelationship between (rock fragment) cover and erosion rates, a rockfragment cover of only 30% will, on average, reduce sheet and rillerosion rates by 70% while a 50% cover can be expected to reduceerosion rates by 87% (Poesen et al., 1994). These reductions are of thesame order of magnitude than the observed differences in erosionrates between arable land in the MZ and arable land in the rest ofEurope. Other factors may also be involved: much of the arable land inWestern and Central Europe is located on loess soils, which are knownfor their extremely high erodibility (e.g. Auerswald, 1993). Highlyerodible loess soils are nearly absent in the MZ.

Erosion rates measured under permanent vegetation are some-what higher in the MZ than in the rest of Europe. This is probably

related to differences in vegetation density. Permanent vegetationwill generally be less well developed in the MZ leading to anincomplete soil cover by vegetation and hence to higher erosion rates.

The positive relationship between erosion rates and slope lengthon bare and arable land is as expected. In such conditions, runoff ratesare relatively high and runoff will most often be spatially continuousso that total runoff amounts increase in the downslope direction.Sediment is dominantly detached by overland flow (Govers andPoesen, 1988; Whiting et al., 2001) and as both detachment capacityand transporting capacity increase in the downlope direction erosionrates increase with slope length (Meyer et al., 1975; Govers, 1991).

The erosion system on plots with permanent vegetation isdifferent. Such surfaces are characterised by an extremely high spatialvariation in infiltration capacity (e.g. Bonell and Williams, 1986;Cammeraat, 2002): consequently, runoff is often discontinuous and inmany cases no net increase of total runoff amounts with slope lengthoccurs (Cammeraat, 2002; Cerdan et al., 2004). Under such conditions,the capacity of the overland flow to transport the sediment that isprimarily detached by drop impact does not increase in the downslopedirection and average erosion rates may remain constant or evendecrease with slope length (Rejman and Usowicz, 2002; Parsons et al.,2004).

We found a positive relationship between slope gradient anderosion rates on bare land and on arable land in zones other than theMZ while no such relationship was found for arable land in the MZ.We hypothesise that the latter is due to an interaction between soilproperties and topography. The soil cover by rock fragments on thestony soils of the MZ is known to be positively related to slopegradient, thereby reducing erosion rates and leading to an equilibriumsituation of relatively low, spatially homogeneous erosion rates(Govers et al., 2006). Erosion rates on arable land in the MZ areindeed consistently low, also on steep slopes (Fig. 3). The high erosionrates that were measured on bare, steep land in the MZ result mainly

Fig. 5.Histograms and cumulative frequency distributions of erosion rates derived from (A) the erosion plot database and (B) the erosionmap of Europe for (1) arable land and (2) allland use categories.

174 O. Cerdan et al. / Geomorphology 122 (2010) 167–177

from measurements on marl substrates in badlands, where the rockfragment cover is very small so that the soil cover-erosion interactionis absent.

We could only detect a significant effect of soil erodibility on ploterosion rates for arable land. We do not have a firm explanation as towhy such an effect is absent for other land uses: most likely, the effectof inherent soil properties on erosion rates is obscured by theinteraction with other controlling factors (mainly vegetation) on non-arable land. Also, the effect of inherent soil properties on erosion ratesmay become less important over time when the soil is not disturbedby tillage due to erosion-soil cover interactions (Govers et al., 2006).

Finally, it is worthwhile to note that only a relatively small part ofthe variance in the dataset can be explained by the controlling factorsdiscussed above. The heterogeneity of the dataset is one reason forthis. Another key factor contributing to unexplained variability is thatsome of the factors that are known to dramatically affect erosion ratessuch as rainfall intensity could not be included in the analysis as thesedata were not reported in most studies. Finally, several studies haveshown that erosion processes are characterised by inherent variabilityat the plot level, so that a full explanation of the variance observed isnot to be expected, even if standardized plot designs would have beenused and/or erosion events and plot evolution would have beendocumented in more detail (e.g. Nearing et al., 1999; Boix-Fayos et al.,2006).

4.2. Average regional soil erosion rates in Europe

Thedatabase thatwas collected for this study can be used tomake aninformed estimate of average sheet and rill erosion rates in Europe aswell as their spatial distribution. The results clearly show that themeansoil erosion rate for Europe is close to 1 t ha−1 year−1. Even if we takeinto account the uncertainty associated with this estimate, this value ismuch lower than values previously proposed based on the direct

extrapolation of plot data (e.g. Pimentel et al., 1995; 17 t ha−1 year−1).The main reason for this is that plot erosion rates cannot directly beextrapolated as plots are most often located in areas where there is anoticeable erosion problem (Cerdan et al., 2006). Average erosion ratesmeasured on erosion plots are therefore higher than those occurring onaverage on all arable land, resulting in clearly different frequencydistributions (Fig. 5). More recent estimates of average global erosionrates on arable land (7–8 t ha−1 year−1) obtained by Wilkinson andMcElroy (2007) by direct extrapolation of plot erosion rates aretherefore in all probability also overestimating true global erosionrates (Quinton et al., 2010).

Erosion is a spatially varying process. While the overall averagevalue is relatively low, it should be kept in mind that ca. 70% of theerosion occurs over only 15% of the total area of Europe. In these areas,soil erosion and its consequences represent a major environmentalissue. Our spatially distributed assessment of the soil erosion hazardhelps to identify the areas where efforts to prevent soil degradationshould be concentrated. However, the decision on where to allocatesoil protection resources should also account for the thickness of soilthat is (still) present as the latter dramatically affects crop yields(Bakker et al., 2007). Furthermore, the present estimate does notinclude all erosion processes. Gross tillage erosion rates on arable landin Europe are of a similar magnitude than water erosion rates with anestimated average value of 3.3 t ha−1 year−1 (Van Oost et al., 2009).Gully erosion, which may induce very high local losses (e.g. Mathys etal., 2003; Collinet and Zante, 2005) is also not accounted for in ourestimates.

5. Conclusions

The compilation of a large database of sheet and rill erosion ratesmeasured in various European environments allowed us to identifysome important controls on sheet and rill erosions. Land use has an

175O. Cerdan et al. / Geomorphology 122 (2010) 167–177

overwhelming effect on erosion rates: soil losses on conventionallytilled arable land are often more than an order of magnitude higherthan those on surfaces with permanent vegetation. Plot erosion ratesshowed clear regional differences, with much lower values in theMediterranean than in the rest of Europe. This somewhat counterin-tuitive result is explained by the stony nature of many soils in theMediterranean, characterised by low erosion rates due to theprotective effect of the rock fragments there. The lower erosion ratesin the Mediterranean do not necessarily mean that erosion in theMediterranean is a lesser threat for the soil resource: as soils are oftenalready very thin, any additional soil loss may be considered to bedetrimental. Apart from stoniness and land use, sheet and rill erosionrates on arable and bare land are controlled by topography: on arableland, erosion rates generally increase with slope gradient and length,as expected.No topographical effects could be identified for landunderpermanent vegetation cover: this is probably due to the fact thathydrological and erosion processes operate differently in theseenvironments, which are often characterised by discontinuous runoff.

We used our data to derive spatially distributed estimates of sheetand rill erosion rates for Europe.We estimate the average sheet and rillerosion rate for the part of Europe covered by the CORINE database asca. 1.24 and 3.6 t ha−1 year−1 for arable land, respectively. Thesevalues are much lower than some values that were hitherto reported:such overestimations aremostly due to an inadequate extrapolation oflocal measurements. Evidently, these average values mask a largevariation in space: in erosion-prone areas, erosion ratesmay be severaltimes higher.

Acknowledgements

The authors appreciate the help of Jerzy Rejman, Andreas Lang,Anne-Véronique Auzet and Chantal Gascuel in order to locate andaccess data sources. Financial support by the ANR VMC projectMESOEROS and the PESERA project of the EuropeanUnion is gratefullyacknowledged.

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