Quantifying human impacts on catchment sediment yield: A continental approach

Quantifying human impacts on catchment sediment yield: A continental approach

Global and Planetary Change 130 (2015) 22–36 Contents lists available at ScienceDirect Global and Planetary Change journal homepage: www.elsevier.co...

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Global and Planetary Change 130 (2015) 22–36

Contents lists available at ScienceDirect

Global and Planetary Change journal homepage: www.elsevier.com/locate/gloplacha

Quantifying human impacts on catchment sediment yield: A continental approach Matthias Vanmaercke a,b,⁎, Jean Poesen a, Gerard Govers a, Gert Verstraeten a a b

Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium Research Foundation Flanders (FWO), Brussels, Belgium

a r t i c l e

i n f o

Article history: Received 9 July 2014 Received in revised form 10 April 2015 Accepted 12 April 2015 Available online 23 April 2015 Keywords: Soil erosion Land use Catchment area Mediterranean Seismic activity Topography Lithology Europe

a b s t r a c t Both from a scientific and environmental management perspective, there is a large need to assess the magnitude and controlling factors of human impacts on catchment sediment yield. Quantifying this impact is difficult, since it requires knowing both the actual sediment yield (SYa, [t km−2 y−1]) as well as the corresponding “pristine” value of a catchment (SYp, [t km−2 y−1]; i.e. the sediment yield that can be expected if the catchment was not affected by humans). Here we address this problem by comparing measured SYa values for 165 European catchments that were unaffected by dams or reservoirs with their corresponding SYp, which were predicted using a recently developed regression model. The ratio between these two values is expected to reflect the degree of human impact on catchment sediment yield (HIF). Correlation and partial correlation analyses showed that spatial variability in HIF is mainly explained by differences in land use (i.e. the fraction of arable land) and catchment area. The effect of these two factors was clearly linked in western and central Europe: whereas SYa can be easily 40 times higher than SYp in intensively cultivated small (≤1 km2) catchments, the difference is negligible for large (N1000 km2) catchments with the same land use. While, this concurs with our knowledge that the effects of land use (change) on erosion rates can be buffered at the catchment scale, this study provides a first robust quantification of this effect. Apart from a potential climatic effect (i.e. a correlation between HIF and the average annual air temperature) no other factors could be identified that are significant in explaining observed differences in HIF. This indicates that HIF is mainly controlled by catchment scale and land use, while other factors may be only of secondary importance at an intra-continental scale. Nonetheless, more accurate quantifications of these HIF values and more refined characterizations of the catchments in terms of (historical) land use, soil types/lithology, weather conditions and topography may reveal additional trends. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Numerous studies have shown that human-induced land use changes (e.g. deforestation and agriculture) may have very significant impacts on both erosion rates at the hillslope scale (e.g. Montgomery, 2007; Maetens et al., 2012) and sediment yield at the catchment scale (e.g. Walling, 1999, 2006; Vanacker et al., 2007, 2014). A reliable quantification of this impact is highly relevant for e.g. the correct interpretation of stratigraphic records or the development of catchment management strategies to reduce the negative impacts of high river sediment loads, such as reservoir capacity losses and eutrophication (e.g. Owens et al., 2005; Dearing et al., 2006; Syvitski and Kettner, 2011; Vanmaercke et al., 2011b; Wisser et al., 2013). Several factors can control the degree of human impact on catchment sediment yield (SY, [t km− 2 y−1]). Excluding reservoirs and ⁎ Corresponding author at: Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium. E-mail address: [email protected] (M. Vanmaercke).

http://dx.doi.org/10.1016/j.gloplacha.2015.04.001 0921-8181/© 2015 Elsevier B.V. All rights reserved.

other technical interventions in floodplains, land cover and land management changes are the primary cause of human impact on SY. Several studies, both at the plot scale (e.g. Gyssels et al., 2005; Smets et al., 2008) and at the catchment scale (e.g. Bednarczyk and Madeyski, 1996; Vanacker et al., 2007; Nadal-Romero et al., 2011), have reported a negative exponential relationship between vegetation cover and soil loss rates or SY. However, the impact of vegetation removal on SY may also depend on other factors such as lithology (e.g. Bruijnzeel, 2004), topography (e.g. Dietrich et al., 2003), or the type of land cover before and after the conversion (e.g. Molina et al., 2008; Vanacker et al., 2014). Furthermore, recent studies showed that seismic activity strongly influences erosion rates and SY and indicated that land cover disturbances by humans may amplify this influence (e.g. Cox et al., 2010; Portenga and Bierman, 2011; Vanmaercke et al., 2014a,b). Likewise, the sensitivity of a catchment to vegetation disturbances may also depend on its climate (e.g. Walling and Kleo, 1979). There are, however, few empirical studies supporting this claim. Also legacy effects of land use can be important to understand human impacts on SY. Contemporary sediment yields are not only

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controlled by the present-day land use, but can also be influenced by the land use history and geomorphology of the catchment. Prolonged hillslope erosion may lead to soil depletion and, hence, a reduction in hillslope erosion rates (e.g. Poesen and Lavee, 1994; Clapp et al., 2000; Montgomery, 2007; Seeger and Ries, 2008; Dusar et al., 2011). On the other hand, increases in anthropogenic soil erosion rates may initially lead to the storage of large volumes of sediment within the catchment (e.g. Trimble, 1999; Notebaert et al., 2011a). These stores may become a source of sediment for the river in a later phase (e.g. Trimble, 1999; Nyssen et al., 2008). These legacy effects relate to another prominent issue, namely the scale-dependency of human impacts on SY. A multitude of studies at the plot and hillslope scale have shown that soil erosion rates may increase by several orders of magnitude due to deforestation, agriculture and stock breeding (e.g. Montgomery, 2007; Maetens et al., 2012). Van Oost et al. (2007) estimated that agriculture and livestock breeding increased worldwide soil erosion by water with ca. 28 Gt of soil per year. Most of this sediment is mobilised on land that, under natural conditions (forest or grassland) would be subjected to soil losses that are, on average, ca. 2 orders of magnitude lower (Montgomery, 2007). On the other hand, human-induced land cover changes would have increased the global sediment flux to seas and oceans by only ca. 3.8 Gt y−1, a relatively limited increase compared to the estimated pre-Anthropocene sediment flux of ca. 14 Gt y−1 (in reality, the actual sediment flux to seas and oceans is even lower than the pre-Anthropocene flux due to the impact of reservoirs; Syvitski et al., 2005). This discrepancy between the hillslope and river basin scale is explained by the fact that with increasing spatial scale, there is an increased probability that eroded sediments are redeposited at foot slopes or in alluvial plains (e.g. Wilkinson and McElroy, 2007). This ‘storage potential’ can buffer large changes in erosion rates at the hillslope scale, leading to no or little change in catchment SY (e.g. Trimble, 1999; Dearing and Jones, 2003). All factors discussed above are expected to affect the degree of human impact on contemporary SY. However, their significance, relative importance and potential interactions remain largely unclear. As a result, also regional patterns of human impacts on SY are currently poorly understood. For example, whereas some studies clearly indicate that the long and intense history of human occupation have a currently lasting impact on sediment fluxes in Mediterranean catchments (e.g. Clapp et al., 2000; Bintliff, 2002; Dusar et al., 2011), other studies point out that SY in Mediterranean catchments are also strongly controlled by climatic, geomorphic and tectonic factors (e.g. Woodward, 1995; Vanmaercke et al., 2014a). The relative importance of anthropogenic (i.e. past and current land use) and non-anthropogenic (i.e. climate, geomorphology, lithology and seismicity) factors in explaining the overall high SY values of Mediterranean catchments and the contrast with other regions still remain important topics of debate (e.g. Dedkov and Moszherin, 1992; Woodward, 1995; Clapp et al., 2000; Bintliff, 2002; de Vente and Poesen, 2005; García-Ruiz, 2010; Dusar et al., 2011; Vanmaercke et al., 2011a,b, 2014a). Assessing the magnitude and controlling factors of human impact on SY is impeded by the fact that SY is controlled by both anthropogenic and non-anthropogenic factors (e.g. Syvitski and Milliman, 2007). Disentangling these two types of factors is in principle possible if one knows both the contemporary SY and the ‘baseline’ or ‘pristine’ SY (i.e. the SY that could be expected if the catchment was not significantly disturbed by humans) of the catchment (e.g. Dearing and Jones, 2003; Vanacker et al., 2007). Whereas contemporary SY measurements are available for thousands of catchments worldwide, the corresponding baseline SY values of these catchments are generally unknown. Tracer studies (e.g. using cosmogenic nuclides), detailed sediment budgets or the analyses and dating of sedimentation rates in lakes may provide estimates of this baseline SY (e.g. Trimble, 1999; Dearing and Jones, 2003; Vanacker et al., 2007; Notebaert et al., 2011a). However, such studies are costly, labour-intensive and not always feasible. Hence, direct estimates of the degree of human impact on SY are only available for a

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limited number of catchments (Dearing et al., 2006; Vanacker et al., 2014). The available data do currently not allow to fully explore the relative importance of the various factors potentially controlling human impacts on SY (e.g. de Vente et al., 2013; Vanacker et al., 2014). As a result, also our ability to predict the impact of land use changes on SY remains very limited. Nonetheless, this is a highly relevant issue, e.g. for assessing the sustainability of constructed and planned water reservoirs (e.g. Wisser et al., 2013). Here we propose a different approach to this problem. A recently developed regression model allows estimating the baseline SY for European catchments with an acceptable accuracy (Vanmaercke et al., 2014a). Comparing these simulated baseline estimates with contemporary SY measurements allows estimating the magnitude of human impact on SY for catchments with no baseline SY observations available. By doing this for a large number of catchments across Europe, we explore to what extent these impacts are controlled by spatial scale, land use, lithology, topography, seismicity, climate or land use history. We focus on human impacts on SY by land use changes. Various studies have shown that also the construction of reservoirs and dams can significantly affect SY (e.g. Syvitski et al., 2005; Walling, 2006; Syvitski and Kettner, 2011). However such impacts on SY are not the scope of this study. 2. Methodology 2.1. Assessing the human impact on catchment sediment yield We define the degree of human impact on catchment sediment yield as: HI F ¼

SY a SY p

ð1Þ

With HIF the human impact factor (on catchment sediment yield), SYa the measured actual (contemporary) sediment yield of a catchment and SYp the corresponding sediment yield if the catchment would be in pristine conditions, i.e. unaffected by human activities. SYa values were selected from an earlier constructed database of SY measurements in European catchments (Vanmaercke et al., 2011a). Only catchments covered by the CORINE land cover dataset (EEA, 2010) were considered to allow consistent and detailed assessment of the contemporary land use in each catchment. Furthermore, catchments in which reservoirs or lakes could be detected that potentially affected N 10% of the total catchment area were not considered. As no complete lake and reservoir inventories exist for Europe, the absence of lakes and reservoirs in each catchment was evaluated visually in Google TM Earth in combination with available datasets on reservoirs (e.g. Lehner et al., 2008; EEA, 2010). This method does not fully guarantee the absence of lakes or reservoirs in these catchments. Nonetheless, we are confident that the impact of potential lakes or reservoirs on the SY of these catchments is generally negligible. In total 165 catchments were selected for further analysis (Fig. 1). Details on the SY measurements for these catchments are given in Table 1. For each of these 165 catchments, the corresponding SYp value was predicted using an empirical regression model. A full description and discussion of this model is given in Vanmaercke et al. (2014a). Here, we give a brief summary on the model and its construction. The SYp model was calibrated and validated based on SY-measurements from 146 small to medium sized (0.33–3940 km2) undisturbed catchments in Europe (Fig. 1). We considered these catchments to be undisturbed because (i) they were not affected by canals, extensive drainage or mining; (ii) had no significant natural glaciers, lakes or man-made reservoirs; and (iii) the areal fraction of disturbed land (i.e. the sum of arable land, permanent crops and built-up area) was less than 20% during the SY measuring period. Sixty of the considered catchments had a forest cover of at least 80% during the monitoring period. For the other

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M. Vanmaercke et al. / Global and Planetary Change 130 (2015) 22–36

Fig. 1. Outlet locations of the 165 catchments considered in this study for which contemporary sediment yield data were available (Table 1) and of the 146 catchments used for the calibration of the pristine sediment yield model (Eq. (2); Vanmaercke et al., 2014a).

catchments, (part of) the undisturbed area corresponded with other types of (semi-) natural vegetation, such as (alpine) pasture, shrubland, or heathland. Evidently, forests, pastures and heathland can also be affected by human impact. However, as a high vegetation cover is maintained, the effect of such disturbances on sediment production and yield can be expected to be very limited (e.g. Ward et al., 2009; Cerdan et al., 2010; Maetens et al., 2012). Due to a lack of reliable historical land use data, we cannot exclude the possibility that some of these catchments were subjected to significant land use changes before the period corresponding to the SY-measurement. However, comparison of our catchments with spatially and temporally explicit modelled deforestation rates in Europe (Kaplan et al., 2009) indicated that the large majority of these catchments were also undisturbed in the period 3000–0 BP. For each of these catchments, we derived a large number of parameters describing the size, topography, lithology, degree of seismic activity, land use, glacial history and climate (Vanmaercke et al., 2014a). Correlation and partial correlation analyses showed that, of all these considered parameters, differences in observed SY are best explained by the degree of seismic activity, the average catchment slope and the lithology of each catchment, resulting in the following regression model (Vanmaercke et al., 2014a): SY p ¼ 5:93  S1:01  L1:01  PGA0:55

calculated based on a methodology proposed by Syvitski and Milliman (2007). Using a global lithology map by Dürr et al. (2005), a score was assigned to each lithology class, depending on its erodibility. Scores ranged between 0.5 for erosion-resistant rock types (e.g. acidic plutonic or metamorphic rocks) and 3 for very erodible lithologies (e.g. loess). An area-weighted average score was then calculated for each catchment. Average PGA-values were derived from the global GSHAP dataset (Giardini et al., 1999; Shedlock et al., 2000). This regression model explained 56% of the spatial variation in observed SY for pristine catchments. Circa 97% of the simulated SYpvalues deviated less than one order of magnitude from their corresponding measured value, while 85% of the data deviated less than a factor 5 (Fig. 2). This is a level of performance that is very comparable to other currently used SY models (e.g. Syvitski and Milliman, 2007; de Vente et al., 2013). Thorough validation showed that the model is stable and that the significance of the included factors in explaining the observed variation in SY cannot be attributed to inter-correlations or specific observations in the calibration dataset. The model could not be further improved by incorporating factors related to climate, catchment size or other catchment characteristics (Vanmaercke et al., 2014a). 2.2. Assessing the uncertainty on the human impact factors

ð2Þ

Where SYp ([t km−2 y−1]) is the predicted pristine sediment yield, S ([°]) is the average catchment slope, L is dimensionless factor describing the catchment lithology and PGA ([m s−2]) is the average expected Peak Ground Acceleration due to earthquakes in the catchment with an exceedance probality of 10% in 50 years. Values of S were derived from digital elevation models with a horizontal resolution of 100 m. L was

Clearly, the calculated HIF values (Eq. (1)) are subject to uncertainties, which are attributable to both model errors on the predicted SYp values and measurement errors on the SYa values. We assessed these uncertainties by means of Monte Carlo simulations. Uncertainties on the simulated SYp-values were estimated based on the residues of the SYp-model (Eq. (2); Fig. 2). These residues could be well described by a log-normal distribution with a mean of zero and a

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Table 1 Details and data sources of the contemporary sediment yield (SYa) measurements for the 165 catchments selected for this study (Fig. 1). ‘# catchments’ indicates the number of catchments derived from the indicated source, while ‘A’ shows their (range of) drainage areas. Method indicates how the SY-value was measured: ‘R’ means that SYa was derived from reservoir sedimentation rates. ‘GS’ indicates that SYa was derived from suspended sediment measurements at a gauging station. ‘(BL)’ indicates that also bedload transport was measured at the gauging station. ‘MP indicates the (range of) measuring periods of the SYa values. Country

Reference

# catchments

A (km2)

Method

MP (y)

Austria

Habersack (1996) Tschada and Hofer (1990) Schröder and Theune (1984) Lemin et al. (1987) Steegen (2001) Verstraeten and Poesen (2001) VMM (Vlaamse Milieu Maatschappij) (2008) Voet (1997) Dedkov and Mozzherin (1984) Gergov (1996) Jaoshvili (2002) Becvar (2007) Kadlec et al. (2007) Krasa et al. (2005) Bayer. LfU and Environment Agency (2002) Schröder and Theune (1984) Schröder and Theune (1984) Hasholt (1983) Batuca and Jordaan (2000) Cravero and Guichon (1989) Barlow and Thompson (2000) Bronsdon and Naden (2000) Butcher et al. (1993) Foster and Lees (1999) Harlow et al. (2006) Holliday et al. (2003) Labadz et al. (1991) Small et al. (2003) Bogardi et al. (1983) Van Rompaey et al. (2005) Bednarczyk and Madeyski (1998) Branski and Banasik (1996) Lajczak (1996); Lajczak (2003) Rodzik et al. (2007) White (2001) Diaconu (1969) INHGA (Romanian National Institute of Hydrology (2010)) Janský (1992); Haigh et al. (2004) de Vente et al. (2005)

1 1 1 1 2 21 11 3 1 6 2 1 1 2 3 2 3 1 1 1 5 1 4 3 2 1 1 6 3 19 1 2 3 1 1 3 11 19 14

160 60.6 156 190 1.17–2.50 0.07–48.7 1.71–107 1.76–11.7 44.2 220–330 224–338 316 371 32–339 406–2125 14–17.4 22.3–31.5 42 254 3600 0.84–8.95 1500 2.16–21.1 2.56–4.04 226–262 17.8 12 2.85–9.31 24–52 14.3–352 218 2092–3516 208–1124 8.6 148 131–1164 44–666 2–28 31–469

R GS (BL) GS GS GS R GS GS GS GS GS GS GS R GS R GS GS R R R GS R R GS R R R R R GS GS R GS R GS GS R R

17 25 5 1 2–3 2–66 1–5 4–4 11 3–38 unknown (N1) 10 10 4–13 13–22 6–10 1–10 2 30 10 73–146 2 36–85 75–205 10–10 64 17 unknown (N20) 10–17 unknown (N30) 22 34–34 unknown (N1) 6 15 4–8 14–53 unknown (N1) 7–98

Belgium

Bulgaria

Czech Republic

Germany

Denmark France Great Britain

Hungary Italy Poland

Romania Slovakia Spain

standard deviation of 0.46. Based on this information, other potentially true SYp values were calculated as: SY p;sim ¼ 10RNð0;0:46Þ SY p

ð3Þ

where SYp,sim is the simulated alternative pristine SY, SYp is the originally predicted pristine SY-value using Eq. (2), and RN(0, 0.46) is a random number, picked from a normal distribution with a mean of zero and a standard deviation equal to 0.46. Overall, the original sources of data provided no estimates of the uncertainty associated with SYa (Table 1). Therefore, uncertainties on SYa values were simulated by considering the most important sources of measuring errors. More specifically, other potential SYa values were simulated using the following equation: SY a 1=U BL −1  U MP

SY a  U ME þ SY a;sim ¼

Fig. 2. Calibration results of the pristine catchment sediment yield model (Eq. (2)), showing the predicted versus the observed pristine sediment yield (SYp) for the 146 selected undisturbed catchments indicated on Fig. 1. Symbol colors indicate the area of each catchment.

US F

ð4Þ

where SYa,sim is another potentially true value of SYa, after taking into account the various sources of uncertainty. UME reflects the uncertainties associated with measuring errors, UBL those associated with the lack of bedload measurements, USF the uncertainties associated with low sampling frequencies and UMP those associated with the duration of the measuring period. These various sources of uncertainties were estimated, based on information provided in literature.

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For SYa values derived from sedimentation rates in reservoirs (Table 1), UME is mainly attributable to errors on the measured sediment volume deposited in the reservoir, the dry bulk density of these sediments and the trapping efficiency of the reservoir. Verstraeten and Poesen (2002) report that the integrated effect of these errors leads to uncertainties of 20 to 25%. For SY values derived from measurements at gauging stations (Table 1), UME reflects the integrated effect of errors on individual runoff discharge measurements, suspended sediment concentration measurements and uncertainties due to intra-daily variation in runoff and sediment concentrations that are not captured by the measurements. Previous studies reported that also these errors are commonly 20-30% (e.g. Steegen and Govers, 2001; Harmel et al., 2006; Vanmaercke et al., 2010). We therefore expected that 30% provides a realistic and fairly conservative estimate of the uncertainty on SY-values associated with measuring errors. Hence, UME was simulated as a random number from a normal distribution with mean 1 and standard deviation 0.30. However, SY values derived from infrequent measurements at gauging stations are subject to additional uncertainties and may underestimate the true SY (e.g. Webb et al., 1997; Phillips et al., 1999; Moatar et al., 2006). USF takes these uncertainties into account and reflects the ratio between the measured SY value and the SY value that could be expected if runoff discharge and sediment concentration was measured continuously. For SYa-values derived from reservoir sedimentation rates or from gauging station measurements where runoff discharge and sediment concentrations were measured at least daily this ratio was considered to be one. For SY-values based on less frequent measurements we simulated realistic values of this ratio based on the catchment area and sampling frequency of the considered SY value, using equations proposed by Moatar et al. (2006). For SY-observation with an unknown sampling interval, we assumed a sampling frequency of 7 days, which is a commonly applied sampling interval (Vanmaercke et al., 2011a). UBL in Eq. (4) represents the uncertainty associated with the unmeasured bedload fraction. Based on a global compilation of river bedload measurements by Turowski et al. (2010), we found that the long-term (N1 year) fraction of SY transported as bedload (fBL) is negatively correlated to catchment area (A, [km2]) for catchments between 0.1 and 10,000 km2 (r2 = 0.22, n = 78): f BL ¼ 0:45−0:04  ln ðAÞ

ð5Þ

Hence, UBL was simulated as a random number from a normal distribution with a mean value determined by Eq. (5) and a standard deviation equal to that of the residuals of Eq. (5), i.e. 0.19. Evidently, UBL values were restricted to values between 0 and 1, while UBL was considered to be zero for SYa values derived from reservoir sedimentation rates or gauging station measurements that already included bedload (Table 1). UMP in Eq. (4) accounts for the fact that average SY-values are also subjected to uncertainties related to year-to-year variations in sediment export. Based on long term (N30 years) SY time series for 202 catchments worldwide, Vanmaercke et al. (2012b) found that year-to-year variations in SY can be well described by Weibull distributions (with as median parameters: λ = 172.7 and k = 1.22) and that this variability does not clearly depend on specific catchment characteristics (e.g. land use, catchment scale, topography, etc.). Therefore, UMP (see Eq. (4)) was simulated by randomly selecting n values from this median Weibull distribution (with ‘n’ the number of years in the SYa measuring period), calculating the average of the randomly selected values and by then dividing this average by the actual mean of the Weibull distribution. In case that the length of the measuring period was not exactly known, the available minimum estimate was used (Table 1). Eqs. (3) and (4) were used to simulate respectively 1000 alternative SYp and SYa values for each catchment. Dividing these simulated SYa and SYp values also yielded alternative HIF values (Eq. (1)). From these

values we calculated 95% confidence intervals on the SYp, SYa and HIF of each catchment (i.e. the difference between the 97.5% and 2.5% quantile of the 1000 simulated values). 2.3. Identifying the factors controlling the human impact on catchment sediment yield To explore which factors potentially explain the spatial variation in HIF, a set of variables was extracted for each of the 165 considered catchments (Fig. 1; Table 1). These variables describe the size, topography, lithology, degree of seismic activity, climatic conditions, contemporary land use conditions, estimated degree of sheet and rill erosion, and estimated historic land use of each catchment. An overview of these variables and their sources is given in Table 2. The potential importance of these variables in explaining the observed spatial variability in HIF was then explored by means of nonparametric Spearman's rank correlation coefficients. To account for the fact that many of these variables are inter-correlated, also partial Spearman's rank correlation coefficients were calculated. Partial correlation measures the degree of association between two considered variables, with the effect of other controlling variables removed (Fisher, 1924; Steel and Torrie, 1960). This is done by conducting regressions between the control variables and both of the considered variables and by then calculating the correlation between the residues of these two regressions. We not only calculated these (partial) correlation coefficients for the original 165 HIF values, but also for the 1000 simulated sets of alternative HIF values (see Section 2.2). Evidently, this list of factors considered (Table 2) is not complete and other variables may reveal different trends with HIF. For example, due to a lack of reliable data we did not consider the potential extent of glaciers in our 165 non-pristine catchments. Since our pristine SY model only was calibrated for non-glaciated catchment, the presence of glaciers may result in a high HIF value that is actually not attributable to human impacts. Nevertheless, the potential impact of glaciers on our results can be expected to be very limited, because the large majority of our considered catchment is with certainty not glaciated (Fig. 1). In addition, many of the variables are subject to important uncertainties, which could obscure a potential relation with HIF. This is especially the case for variables describing the lithology and historic land use in our catchments, but also for other factors (Table 2). These sources of error and uncertainties are not unique for this study but affect most attempts to study SY at regional or continental scales. The variables considered here represent a wide selection of parameters that were also considered in other SY studies, using data of similar quality (e.g. Aalto et al., 2006; Syvitski and Milliman, 2007; de Vente et al., 2013). Also the uncertainties on our simulated pristine SY values (used to quantify HIF) are of a similar order of magnitude as for values obtained with other commonly SY models (e.g. de Vente et al., 2013). 3. Results SYa values for the considered disturbed catchments are on average about 13 times higher than their corresponding estimated SYp (Fig. 3). However, uncertainties are generally large: the 95% confidence intervals on SYp and SYa both range over up to two orders of magnitude (Fig. 3). Although SYa and SYp values are significantly correlated, this correlation explains relatively little of the observed variability (Fig. 3). Of all considered variables (Table 2), the originally calculated HIF values showed the most significant correlation with the fraction of arable land (AL), closely followed by catchment area (A) and average catchment slope (S; see Table 3). Also the average annual air temperature (T) and variables describing the estimated historical land use and rate of sheet and rill erosion showed a highly significant correlation with HIF (Table 3). Similar results were obtained for the 1000 generated sets of alternative of HIF-values (Section 2.2), which confirms that these correlations are not attributable to model or measuring errors (Fig. 4a).

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Table 2 Overview and data sources of the catchment characteristics considered in the (partial) correlation analyses. Variable

Description

Range

Units

Source

A S PGA L

Drainage area of the catchment Average catchment slope Peak Ground Acceleration with an exceedance probability of 10% in 50 years. Catchment lithology erodibility factor as defined by Syvitski and Milliman (2007). Based on a global lithology map (Dürr et al., 2005), a score was assigned to each lithology, depending on their erodibility. Scores ranged between 0.5 for erosion-resistant rock types (e.g. acidic plutonic or metamorphic rocks) and 3 for very erodible lithologies (e.g. loess). An area-weighted average score was then calculated for each catchment Average (1961–1990) annual rainfall Modified Fournier Index, as defined by Arnoldus (1980) Estimated maximum daily rainfall depth occuring in the period 1983-2012 Estimated annual runoff depth, based on observed river discharges and simulated water balances Standard deviation of estimated monthly runoff depths, based on observed river discharges and simulated water balances Average (1961–1990) annual air temperature Latitude of the catchment outlet Boolean variable to indicate if catchment is located in the Mediterranean climatic zone (1) or not (0) Fraction of arable land (i.e. ‘arable land’ + ‘permanent crops’ + ‘heterogenous agricultural area’) in the catchment, according to the CORINE 1990 land cover dataset Estimated average deforested fraction for the period −1050 BC–2000 AD Estimated average deforested fraction for the period 0 AD–2000 AD Estimated average deforested fraction for the period 1000 AD–2000 AD Area-weighted estimated rate of sheet and rill erosion. Estimates are based on an extrapolation of field measurements, taking into account land use, topography and soil properties.

0.03–3600 0.9–29.2 0.16–4.06 0.5–3

km2 ° m s−2 -

Original source of the sediment yield data CGIAR (2008) Giardini et al. (1999); Shedlock et al. (2000) Dürr et al. (2005)

447–1643 45–139 37–148 0.62–1458

mm mm mm mm

New et al. (2002) New et al. (2002) Haylock et al. (2008); ECA&D (2014) Fekete et al. (1999)

0–83

mm

Fekete et al. (1999)

−2–15 36.86–56.23 0–1

°C °N -

New et al. (2002) Original source of the sediment yield data Mücher et al. (2010)

0–1

-

EEA (2010)

0.02–0.87 0.02–0.92 0.04–0.92 2.2–1531

t km−2 y−1

Kaplan et al. (2009; 2012) Kaplan et al. (2009; 2012) Kaplan et al. (2009; 2012) Cerdan et al. (2010)

Pa MFI Pdmax ROa ROmstd T LAT MED AL

HAD HAD0 HAD1000 SRE

After controlling for AL (i.e. the variable showing the strongest partial correlation with HIF), HIF showed a much weaker correlation with average catchment slope, with variables describing the historical land use or with the estimated rate of sheet and rill erosion (Fig. 4b). This is to be expected, given that these variables were also strongly correlated to AL (Table 3). After controlling for AL, catchment area (A) shows the strongest partial correlation with HIF (Fig. 4b), while average annual air temperature (T) shows the strongest partial correlation after controlling for both AL and A (Fig. 4c). None of the remaining considered variables are clearly partially correlated with HIF after controlling for AL, A and T (Fig. 4d). Thus, these stepwise partial correlation analyses

shows that differences in HIF are best explained by the fraction of arable land, the drainage area and the average annual air temperature of the considered catchments. Altering the order of control variables (e.g. first correcting for A and T) did not change the significance of these variables. It is noteworthy that correlations between the considered variables and HIF strongly differ from those with the observed SYa values (Table 3). In contrast to HIF, SYa shows no significant correlation with AL or A (Fig. 5). Also the correlation between T and SYa is weaker than between T and HIF (Fig. 5).

4. Discussion 4.1. The role of land use versus geomorphic and seismic controls

Fig. 3. Observed contemporary sediment yields (SYa) versus their corresponding estimated pristine sediment yield (SYp) for the 165 selected catchments (Fig. 1). Error bars indicate the 95% confidence interval on both the SYa and SYp values, estimated according to the procedure described in Section 2.2.

Our results indicate that land use (expressed as AL, i.e. the fraction of arable land in a catchment) has a strong impact on HIF(Table 3, Figs. 4a and 5). This finding is in line with other studies at the plot scale (e.g. Gyssels et al., 2005; Smets et al., 2008; Nadal-Romero et al., 2011; Maetens et al., 2012) and at the catchment scale (e.g. Bednarczyk and Madeyski, 1996; Vanacker et al., 2007) and can be attributed to several mechanisms. These include a better resistance against sediment detachment by raindrops and overland flow, increased soil structure and soil cohesion and an increased probability of sediment deposition (Abrahams et al., 1995; Gyssels et al., 2005; De Baets et al., 2007). One may argue that not only arable land, but also other anthropogenic land use types (e.g. pasture) can affect the human impact on SY. We tested this by exploring if also considering other land use classes (e.g. pasture, shrubland) yielded stronger correlations with HIF. However, this was not the case which suggests that land use impacts on catchment SY are mainly caused by agriculture (i.e. arable land and permanent crops). Also soil loss measurements on runoff plots indicate that sheet and rill erosion rates in rangeland, grassland, shruband and forest are generally very similar and one to two orders of magnitude lower than under permanent crops, vineyards and croplands (Montgomery, 2007; Cerdan et al., 2010; Maetens et al., 2012).

1 1 −0.41 1 0.36 0.66 1 0.08 −0.37 0.32 1 0.48 0.01 −0.44 0.36 1 0.98 0.50 0.00 −0.45 0.37 1 0.98 0.96 0.50 0.00 −0.48 0.38 1 0.60 0.58 0.58 0.76 0.08 −0.52 0.45 1 −0.09 0.11 0.13 0.16 −0.29 0.42 0.42 0.10 1 0.50 −0.18 0.02 0.18 0.27 0.27 0.23 −0.21 −0.64 0.43 −0.71 −0.51 −0.48 −0.48 −0.54 0.23 0.89 −0.45

1 0.07 −0.25 −0.15 0.05 −0.13 −0.14 0.11 −0.68 0.28 −0.09 −0.19 −0.20 −0.20 −0.03 0.44 0.73 −0.14

1 −0.26 −0.26 0.13 −0.19 −0.19 0.23 0.00 0.09 0.21 0.01 0.01 0.05 0.13 0.19 0.17 0.09

1 0.95 0.17 0.65 0.58 −0.40 0.49 −0.24 −0.32 −0.10 −0.15 −0.18 −0.25 −0.20 −0.17 −0.04

1 0.17 0.68 0.62 −0.45 0.34 −0.15 −0.41 −0.19 −0.23 −0.25 −0.29 −0.14 0.00 −0.12

1 0.18 0.11 0.17 −0.19 0.42 −0.18 −0.09 −0.08 −0.08 −0.29 0.34 0.25 0.11

1 0.94 −0.41 0.25 −0.07 −0.50 −0.31 −0.32 −0.36 −0.37 −0.12 0.10 −0.15

1 −0.35 0.24 −0.04 −0.45 −0.29 −0.29 −0.31 −0.30 −0.09 0.08 −0.10

1 −0.33 0.59 0.41 0.49 0.47 0.50 0.11 0.33 −0.10 0.44

1 −0.66 0.12 0.18 0.14 0.12 0.13 −0.45 −0.72 0.11

AL T ROmstd ROa Pdmax MFI Pa L PGA S A

1 0.53 0.32 0.04 −0.27 −0.13 0.12 −0.05 −0.10 −0.10 −0.58 0.32 −0.24 −0.41 −0.37 −0.37 −0.20 0.00 0.55 −0.45 A S A L Pa MFI Pdmax ROa ROmstd T LAT MED AL HAD HAD0 HAD1000 SRE SYa SYp HIF

LAT

MED

HAD

HAD0

HAD1000

SRE

SYa

SYp

HIF

M. Vanmaercke et al. / Global and Planetary Change 130 (2015) 22–36 Table 3 Correlation matrix of the considered catchment characteristics (see Table 2), observed contemporary sediment yields (SYa), simulated pristine sediment yields (SYp) and estimated degrees of human impact (HIF, Eq. (1)). Values indicate the full Spearman's rank correlation coefficients. Values in bold are significant (p b 0.05), while bold and underlined values are highly significant (p b 0.0001). Values in italic are insignificant (p ≥ 0.05).

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It is noteworthy that, while AL significantly correlated with HIF, it does not with SYa (Fig. 5). This indicates that SYa is also strongly controlled by other factors which may obscure the impact of vegetation cover on SY. Also earlier studies showed that spatial variations in SY at the regional or continental scale are often more strongly correlated with geomorphic and tectonic factors than with anthropogenic factors (e.g. Syvitski et al., 2005; Syvitski and Milliman, 2007; Vanmaercke et al., 2014a). In this case, the lack of correlation between land use and contemporary sediment yield is likely explained by the significant correlations between SYa and S, L and especially PGA (Table 3). Furthermore, S shows a strong negative correlation with AL (Table 3). It is only after controlling for these factors (as our pristine SY model does, Eq. (1)), that the influence of land use becomes apparent. This highlights the importance of also considering natural controls when assessing human impacts on SY at regional scales. Especially seismic activity can have a strong but often neglected and poorly understood influence on the spatial patterns of erosion and SY (Cox et al., 2010; Portenga and Bierman, 2011; Vanmaercke et al., 2014a,b). As discussed in the introduction, the role of human impacts in explaining the often high sediment yields of Mediterranean catchments is an important topic for debate (e.g. Dedkov and Moszherin, 1992; Woodward, 1995; Clapp et al., 2000; García-Ruiz, 2010; Dusar et al., 2011). Our results indicate that this human influence may be relatively limited, since the overall higher SYa values of the considered Mediterranean catchments are rather attributable to differences in topography and seismicity than to land use (Table 3). 4.2. Scale-dependency of human impacts 4.2.1. Non-Mediterranean catchments Apart from land use, HIF mainly correlated with catchment area (Figs. 4 and 5). Further exploration of this scale dependency indicated important differences, depending on the region and overall degree of disturbance. In Non-Mediterranean catchments, the negative correlation between A and HIF seems strongly influenced by land use: whereas the HIF-values of catchments with a relatively low degree of agricultural activity (AL b50%) show only a weak correlation with A, catchment area explains 64% of the variation in HIF values of intensively cultivated (AL ≥ 50%) catchments (Fig. 6). The resulting relationship indicates that SYa values of small (b1 km2) and intensively cultivated catchments in Western and Central Europe can easily be 40 times higher than their corresponding SY under pristine conditions, while for intensively cultivated catchments larger than 1,000 km2, the SYa value not necessarily differs from its corresponding SYp (Fig. 6). This land-use dependent scale dependency (Fig. 6) concurs with findings of earlier studies, showing that a significant fraction of the eroded sediments can be stored as colluvium or alluvium within the catchment and therefore have no immediate impact on SY (Trimble, 1999; Van Rompaey et al., 2001; Wilkinson and McElroy, 2007; Verstraeten et al., 2009; Notebaert et al., 2011a). As opportunities for such sediment storage generally increase with catchment size (e.g. Hoffmann et al., 2013), the sensitivity of a catchment to human impacts (and other disturbances) in terms of SY can be expected to decrease with increasing catchment area (e.g. Walling, 1983; Van Rompaey et al., 2001; Dearing and Jones, 2003; Phillips, 2003). Apart from sediment deposition, also scale dependencies in sediment sources can play a role. Hillslope erosion processes (e.g. sheet, rill, gully erosion, landsliding) are often major sediment sources in small catchments and highly sensitive to land use impacts (e.g. Glade, 2003; Poesen et al., 2003; de Vente et al., 2007; Cerdan et al., 2010; Maetens et al., 2012). However, with increasing catchment area, the contribution of reworked alluvial stored sediments to SY often becomes more important (e.g. de Vente and Poesen, 2005; Vanmaercke et al., 2012a). Whereas such sediment reworking can be intensified by human impact, it also occurs in alluvial systems that are little or not affected by human impacts (e.g. Church and Slaymaker, 1989; Birkinshaw and Bathurst, 2006).

M. Vanmaercke et al. / Global and Planetary Change 130 (2015) 22–36

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Fig. 4. Spearman's rank correlation coefficients (Spearman r) between the estimated HIF values (Eq. (1)) and the considered potential controlling factors (see Table 2). Each boxplot shows the distribution of 1000 Spearman r-values, where each value was obtained by randomly simulating a set of alternative HIF-values and calculating the correlation between these values and the indicated variable (see Section 2.3). Whiskers of each boxplot represent 1.5 times the difference between the 75% and 25% quantile. Different grey tones indicate the significance (p-value) of the r-values. The boxplot in dark grey indicates the variable with the most significant median correlation. (a) shows the full Spearman's rank correlation coefficients. (b) Shows the partial Spearman's rank correlation coefficients after controlling for the fraction of arable land (AL). (c) Shows the partial Spearman's rank correlation coefficients after controlling for AL and drainage area (A). (d) Shows the partial Spearman's rank correlation coefficients after controlling for AL, A and the average air temperature (T).

These scale-dependencies of sediment sinks and sources are also often reflected in the actual sediment yield values: catchments in strongly cultivated areas frequently show a negative A-SY relationship, while catchments with an undisturbed land use often show a positive or non-significant A-SY relationship (e.g. Walling, 1983; Church and Slaymaker, 1989; Dedkov, 2004; Birkinshaw and Bathurst, 2006; de Vente et al., 2007). Also in this study, SYa shows a clear negative correlation with A for the intensively cultivated Non-Mediterranean catchments (Fig. 6), while their corresponding estimated pristine values show a weakly positive correlation with A (SYp = 9.37 × A0.12, R2 = 0.13, n = 63). As a result, the HIF values of these catchments (i.e. the ratio between SYa and SYp, Eq. (1)) show an even stronger negative correlation with catchment area (Fig. 6). Likewise the SYa from NonMediterranean catchments with a low (b 0.50) fraction of cultivated land show a weakly positive correlation with A, while the corresponding HIF values show a weakly negative correlation with A (Fig. 6), due to the stronger positive correlation between A and SYp. It is important to point out that, whereas positive correlations between observed SY and catchment area in pristine areas can in some cases be attributed to the reactivation of alluvial sediment stores (e.g.

Church and Slaymaker, 1989; Birkinshaw and Bathurst, 2006), the positive correlations between A and SYp are in this case merely attributed to positive correlations between A and S, PGA or L for the considered catchments. Also for the original 146 pristine catchments used in the calibration of the pristine SY model (Fig. 1), observed SY-values showed a weak positive correlation with A, which became insignificant after controlling for inter-correlations with other factors (Vanmaercke et al., 2014a). These findings are fully consistent with the idea that catchments with an undisturbed land use should have a minimal sediment storage (e.g. Parsons et al., 2006; Notebaert et al., 2011b). Hence, our results (Fig. 6) show that the observed negative correlations between A and SY in intensively cultivated areas of Central and Western Europe (e.g. Verstraeten and Poesen, 2001; Vanmaercke et al., 2011a) are most likely mainly a result of human impacts. 4.2.2. Mediterranean catchments Whereas the Non-Mediterranean catchments considered in this study showed a clear scale-dependency in terms of HIF, this was not the case for the Mediterranean catchments (Fig. 7). Evidently, this may be due to the limited number of catchments available,

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Fig. 5. Scatter plots of the contemporary sediment yield (SYa) and estimated degree of human impact (HIF) versus the fraction of arable land (AL) in each catchment, the catchment area (A) and the average annual air temperature (T) for the 165 considered catchments (see Fig. 1). Circles indicate the median simulated SYa and HIF values for Mediterranean and non-Mediterranean catchments, while error bars indicate the 95% confidence interval (see Section 2.2).

covering a limited range of catchment areas. Furthermore, the calibration of our pristine SY model (Eq. (2)) was based on only few data from Mediterranean catchments (Fig. 1). Hence, these results

should be interpreted with caution. Nonetheless, it is interesting to point out that also physical and environmental factors may explain this lack of scale-dependency.

M. Vanmaercke et al. / Global and Planetary Change 130 (2015) 22–36

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Fig. 6. Scatter plots of the contemporary sediment yield (SYa) and the estimated degree of human impact (HIF) versus the catchment area (A), subdivided according to the fraction of arable land (AL) for all Non-Mediterranean catchments (according to the LANMAP2 classification; Mücher et al., 2010). Circles indicate the median simulated SYa and HIF values, while error bars indicate the 95% confidence interval (see Section 2.2).

Firstly, the overall impact of contemporary land use on hillslope erosion rates in Mediterranean environments may often be smaller than in non-Mediterranean environments. Because, the pristine vegetation cover in (semi-arid) Mediterranean areas is generally lower than in Western and Central Europe (e.g. Bohn et al., 2003), the cultivation of Mediterranean hillslopes may have led to relatively smaller disturbances in vegetation cover and hence a relatively smaller increase in hillslope erosion rates (e.g. Vanacker et al., 2014). However, also in absolute terms, the increases in hillslope erosion rates induced by land use changes may be fairly limited. Contrary to what is often assumed, current hillslope erosion rates in Mediterranean environments are often relatively low due to the stony nature of the soils (e.g. Poesen and Lavee, 1994; Govers et al., 2006; Cerdan et al., 2010; Maetens et al., 2012). This high rock fragment contents of Mediterranean soils may be partly due to the long and intense cultivation of many of these hillslopes resulting in soil depletion (i.e. the fine fraction has already been eroded away), but also due to the fact that soil formation rates in a Mediterranean climate are relatively slow (Clapp et al., 2000; Bintliff, 2002; Hooke, 2006; Lasanta et al., 2006; García-Ruiz, 2010; Dusar et al., 2011). Secondly, SY in Mediterranean catchments is often strongly controlled by landsliding and river bank erosion (e.g. de Vente and Poesen, 2005;

Vanmaercke et al., 2012a). The contribution of these sediment sources to SY can be expected to be scale-independent or, in the case of river bank erosion, even increase with catchment size (e.g. Birkinshaw and Bathurst, 2006). This is also indicated by the fact that SY often shows an insignificant or positive correlation with A in Mediterranean regions, even for strongly cultivated catchments (e.g. de Vente and Poesen, 2005; de Vente et al., 2007; Vanmaercke et al., 2011a). The large contribution of riverbank erosion may in some cases be a legacy effect of a long and intensive history of cultivation (leading to the storage and current evacuation of alluvial sediments) but is also strongly controlled by geomorphic and climatic factors, such as the relatively frequent occurrence of (flash) flood events (e.g. Poesen and Hooke, 1997; Bintliff, 2002; Dusar et al., 2011) or the lack of natural vegetation, resulting in poorly protected river banks and intense bank gully erosion (Oostwoud Wijdenes et al., 2000). 4.3. The role of climate Our results showed no significant correlations between HIF and variables relating to the average rainfall or runoff in the catchment (Tables 2 and 3). The same is true for our SYa observations. To some extent, this may be attributable to the uncertainties associated with these

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Fig. 7. Scatter plots of the contemporary sediment yield (SYa) and the estimated degree of human impact (HIF) versus the catchment area (A), subdivided according to the fraction of arable land (AL) for all Mediterranean catchments. The distinction between Mediterranean and non-Mediterranean catchments is based on the LANMAP2 classification (Mücher et al., 2010). Circles indicate the median simulated SYa and HIF values, while error bars indicate the 95% confidence interval (see Section 2.2).

rainfall and runoff estimates. However, also several other studies have indicated that spatial patterns of SY are very weakly or not correlated to average rainfall or (measured) runoff depths (e.g. Aalto et al., 2006; Syvitski and Milliman, 2007; de Vente et al., 2011; Vanmaercke et al., 2014a,b), suggesting that these factors are often only of secondary importance for understanding average SY at regional scales. On the other, we did observe significant positive correlations between the HIF-values of our selected European catchments and the average annual air temperature (T), even after controlling for A and AL (Fig. 4c; Fig. 5). Also when only the Non-Mediterranean catchments were considered, a highly significant positive correlation between T and HIF was observed (Spearman r = 0.52, p b 0.0001). Since the observed correlation is fairly weak (Fig. 5) and cannot be straightforwardly explained by a clear mechanism, this result should be interpreted with caution. Nevertheless, it is likely that average annual air temperature to some extent reflects the climatic sensitivity of catchments to human disturbances in terms of sediment export. The intensity of extreme precipitation events can be expected to increase exponentially with mean air temperature (Berg et al., 2009). T may therefore reflect the likely occurrence of short but very intense rainfall events, causing soil erosion and floods with large geomorphic impacts (e.g. Poesen and Hooke, 1997; Markus and Demissie, 2006; Vanmaercke et al., 2010). Also Syvitski and Milliman (2007) report that variability in SY at a global scale is correlated to average annual air temperature, which they partly attribute to the occurrence of torrential rainstorms. Furthermore, the 146 pristine catchments used for the calibration of our SYp model (Fig. 1) had a very similar range in average annual air temperature (from − 2 °C to + 12 °C) as our 165 non-pristine catchments (−2 °C to +15 °C). Nonetheless, the observed SY of these catchments showed no significant correlation with T (Vanmaercke et al., 2014a). This further indicates that T reflects to some extent the importance of extreme rainfall events, since we can expect that such events will have a much smaller geomorphic impact in these densely vegetated pristine catchments. 4.4. Uncertainties and the role of other factors Apart from land use, spatial scale and potentially climate, no other factors could be identified that significantly correlated with HIF (Fig. 4d). In other words, our analyses provide no evidence that HIF also depends on topography, lithology, seismic activity or historical land use. It should be emphasized that this does not necessarily imply

that these factors can have no influence on the degree of human impact on SY. The lack of significant correlations between HIF and the considered variables may also be attributed to the inherent limitations of our approach. Especially the large uncertainties on both the estimated HIF-values and several of the considered variables (Table 2) may impede the identification of other relevant factors. For instance, we considered estimated values of historical deforestation (Table 2; Kaplan et al., 2009, 2012) to evaluate the potential effects of historical land uses. These estimates are not only of a coarse resolution and subject to important uncertainties, but also allow insufficient differentiation between different land uses and land management strategies (e.g. cropland cultivation versus grazing land, land abandonment versus reforestation). Such differences can lead to completely different impacts on SY that are not captured by the average deforestation rates considered in this study (e.g. De Brue and Verstraeten, 2014). Also the lithology data used in this study may be too crude to identify potential influences of rock/soil characteristics on HIF. Furthermore, the variables considered in this study provide only a spatially lumped description of potentially relevant factors. Recent modelling studies clearly show that also the spatial configurations of different land use types within a catchment can have a very large influence on the resulting SY (De Brue and Verstraeten, 2014). Such spatial patterns are likely also important to understand differences in HIF, but are not captured by the variables considered in this study (Table 2). This is not only the case for land use, but also for other factors (e.g. climate, topography, lithology). Likewise, also temporal variations in land use or weather conditions may significantly influence the estimated HIF values. These difficulties may to some extent be solved by considering more refined (e.g. spatially and temporally explicit) descriptions of catchments characteristics. Likewise, future research may benefit still from considering a larger number of catchments. Despite the fact that we used data from 165 catchments (Fig. 1), which is significantly more than many other contemporary models that aim to simulate spatial differences in SY at the regional or continental scale (de Vente et al., 2013), this number may still be too small to identify the impact of all relevant factors and their potential interactions. Especially considering more small (b10 km2) catchments in the analyses could help, since human impacts are strongly scale-dependent (Figs. 5 and 6). Additional correlations may further be revealed by subdividing catchments according to their environmental or climatic conditions. However, also in that case, a sufficiently large number of catchments in each subgroup will be required.

M. Vanmaercke et al. / Global and Planetary Change 130 (2015) 22–36

Nonetheless, the fact that variables related to topography, lithology, seismicity and historical land use do not show clear correlations with HIF (Fig. 4) strongly indicates that their importance in understanding human impacts on SY at regional scales is limited compared to the role of contemporary land use and spatial scale. This also concurs with findings of other studies indicating that conditions affecting SY or erosion rates at a local scale are not necessarily relevant at a regional or intra-continental scale (e.g. Van Rompaey and Govers, 2002; Syvitski and Milliman, 2007; Maetens et al., 2012; de Vente et al., 2013). 5. Summary, conclusions and implications Our findings can be summarized in a conceptual model (Fig. 8). Firstly, a distinction should be made between Mediterranean and NonMediterranean catchments (i.e. catchments in central and Western Europe). SY values from Non-Mediterranean undisturbed catchments can be expected to be scale-independent or to slightly increase with catchment area (because of the evacuation of previously deposited alluvial sediments by river erosion processes or simply because of intercorrelations between catchment area or other factors controlling SY). Cultivating these catchments can, depending on local conditions and the proportion of the catchment cultivated, increase hillslope erosion rates by up to two orders of magnitude. However, a fraction of these eroded sediments will be deposited as colluvium or alluvium. This fraction will be proportional to the catchment area. Likewise, with increasing catchment area, the contribution of hillslope erosion to SY may be diluted by the influence of other erosion processes such as landslides and riverbank erosion. Although the latter can also be influenced

Fig. 8. Conceptual model illustrating the expected scale dependency of sediment yield (SY) and relative human impacts on sediment yields (HIF) for Western and Central Europe and the Mediterranean region. Spatial scale ranges from hillslopes (HS) to large catchments (LC) of more than 1000 km2. Whereas Fig. 6 provides a first quantification of these scale-dependencies for Western and Central Europe, the indicated relationships for the Mediterranean region remain to an important extent hypothetical.

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by land use changes, they are to a large extent controlled by nonanthropogenic factors. As a result, HIF can be expected to decrease with catchment area (Fig .8). Our results provide one of the first robust quantifications of this effect and clearly indicate that human impacts on SY are indeed strongly scale-dependent in Western and Central Europe (Fig. 6). This scale-dependency most likely also explains why SY correlates negatively with A in intensively cultivated catchments. Nonetheless, the latter correlation will be somewhat weaker than the scale dependency of HIF, since SY is also controlled by non-anthropogenic factors. Our results were less clear for Mediterranean catchments (Fig. 7). Future research based on a larger number and range of Mediterranean catchments as well as better constrained estimates of the pristine SY under Mediterranean conditions is required. Nonetheless, there are several reasons to expect that the contemporary cultivation of Mediterranean catchments has a smaller and less scale-dependent influence on SY (Fig. 8). Firstly, cultivation of Mediterranean slopes will in many cases lead to much smaller increases in hillslope erosion rates, due to the often stony soils and the smaller difference in vegetation cover before and after cultivation. Secondly, the contribution of other erosion processes (such as landslides and river bank erosion) to SY will be larger. Whereas (historical) land use may influence these processes (especially in the case of riverbank erosion), they are to a large extent attributable to non-anthropogenic factors (i.e. topography, lithology, seismicity and climate). The contribution of these processes to SY are also likely scale-independent or may (in the case of riverbank erosion) even increase with catchment area. As a result, SY in Mediterranean catchments will be generally higher than in Western and Central Europe, less dependent on the degree of cultivation and less likely to decrease with catchment area (Fig. 8). Apart from land use and catchment area, no other factors could be clearly identified that control the degree of human impact on SY. We observed a correlation between average annual air temperature and HIF (Fig. 6), which may be related to the occurrence of extreme rainfall events and could indicate that also climate is significant in explaining human impacts on SY at a continental scale. Further research may clarify this. Variables related to lithology, topography, seismicity and historical land use showed no significant effect. To some extent, this may be explained by the uncertainties and limitations associated with our approach. For example, the use of more accurate and spatially distributed descriptions of catchment properties could probably reveal additional significant controls on HIF. Nonetheless, it may also indicate that that their importance in understanding regional variations in human impacts on SY is limited, compared to the role of current land use, catchment scale and climate. This does not imply that these factors are irrelevant to understand human impacts at a local scale. Likewise, this does not mean that they are irrelevant for our understanding of SY in general. Regarding the latter it is important to point out that variations in SY within Europe seem mainly controlled by topography, lithology and seismicity (Table 3). It is only after controlling for these factors that the effects of land use and catchment scale become apparent (Fig. 5). Our findings have important implications for catchment management strategies. Most importantly, they demonstrate that changes in hillslope erosion rates are not necessarily reflected in the corresponding contemporary catchment sediment export, especially not if the catchment is relatively large (e.g. N 1000 km2). Increases in erosion rates may be buffered by an increase in colluvial or alluvial sediment storage. This can be an important advantage (e.g. when aiming to minimize water reservoir capacity losses due to sedimentation; Vanmaercke et al., 2011b). However, also reductions in hillslope erosion rates (e.g. due to reforestation or the implementations of soil and water conservation measures) may be compensated for by an increase in channel erosion processes. This is a well known issue that has been documented and discussed in several studies (e.g. Trimble, 1999; Phillips, 2003; Nyssen et al., 2008; Vanmaercke et al., 2011b). Hence, catchment

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management strategies should try to take this into account. I.e. they should not only focus on a reduction of hillslope erosion rates but consider all potentially relevant sediment sources. Acknowledgments M. Vanmaercke acknowledges his postdoctoral research grant from the Research Foundation Flanders (FWO), Brussels, Belgium. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). We thank Matthias Demuzere for his help with extracting data from the E-OBS database and Bastiaan Notebaert for his constructive comments during discussions. Jed Kaplan is acknowledged for providing data on estimated rates of historical deforestation. We thank an anonymous reviewer for his/her valuable suggestions on this work. References Aalto, R., Dunne, T., Guyot, J.L., 2006. Geomorphic controls on Andean denudation rates. J. Geol. 114, 85–99. 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