Evaluating an integrated approach to catchment management to ...

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An integrated approach to catchment management

Soil Use and Management (2002) 19, 386±394

DOI: 10.1079/SUM2002150

Evaluating an integrated approach to catchment management to reduce soil loss and sediment pollution through modelling G. Verstraeten1,2*, K. Van Oost1, A. Van Rompaey1,2, J. Poesen1 & G. Govers1

Abstract. Soil erosion and sediment delivery cause many environmental problems posing a substantial ®nancial burden upon society. Policy makers therefore look for a strategy to minimize their impact. The spatial nature of soil erosion and sediment delivery, as well as the variety of possible soil conservation and sediment control measures, requires an integrated approach to catchment management. To evaluate such management, a spatially distributed soil erosion and sediment delivery model is necessary. Such a model (WaTEM/SEDEM) was applied to three agricultural catchments in Flanders (Belgium). The model was ®rst used to identify where the measures to control soil loss should be taken. Secondly, a scenario analysis was used to select the most effective set of techniques. The ®ndings showed that soil conservation measures taken in ®elds are not only effective in reducing on-site soil loss, but also in drastically reducing sediment yield. Off-site sediment control measures appear to be much less effective in reducing sediment yield than previously thought. The results also suggest that data from ®eld experiments cannot be extrapolated to a catchment scale. Keywords: Erosion control, watersheds, sediment, models, soil conservation, agricultural land, Belgium

I

INTRODUCTION

ntense soil erosion on arable land has many detrimental impacts, both on-site as well as off-site. Among the onsite problems related to soil erosion are the loss of topsoil and fertilizers, the decrease in crop yield (where plants are eroded or covered with sediment deposits) and accessibility (in case of gullies) in the short-term and a decrease in soil productivity in the long-term. The off-site problems, however, are often more obvious and include the pollution of surface water with suspended sediment and other pollutants attached to the sediment particles like phosphates or heavy metals, silting of riverbeds, reservoirs and ponds requiring costly dredging operations, as well as intense muddy ¯oods in local villages and public infrastructure with substantial ®nancial and psychological damage as a result (e.g. Clark et al. 1985; Boardman et al. 1994; Verstraeten & Poesen 1999). Most of these problems are present in the Belgian loess belt. A rough estimate of the annual off-site costs of excessive soil erosion on arable land in Flanders (Belgium) is C, corresponding to 40 to 120 = C for each ha 25 to 75 million = of all arable land, whether or not there is signi®cant soil loss. 1

Laboratory for Experimental Geomorphology, K.U.Leuven, Redingenstraat 16, B±3000 Leuven, Belgium. 2Fund for Scienti®c Research ± Flanders, Belgium. *Corresponding author: Fax: 0032 16 32 64 00 E-mail: [email protected]

Most of these off-site costs are born by the various authorities (regional and local). In recent years, policymakers have become increasingly aware that these costs are related to soil loss from upstream ®elds, and more and more efforts are now made to reduce the off-site related problems of soil loss. In particular muddy ¯oods in villages as well as sediment loads in river channels need to be reduced. The questions posed are what kind of measures need to be taken and where to place them.

Many soil conservation and sediment control techniques are known and widely studied, including for instance reduced tillage or zero tillage, cover crops, grass buffer strips between ®elds or along rivers and sediment retention ponds. The effects of most of these techniques have been analysed using experimental ®eld plots, i.e. under controlled conditions. Their impact at a catchment scale generally remains unclear. However, this information is crucial if one needs to evaluate the effect of a technique on the reduction in sediment delivery to a river. Soil erosion and sediment delivery processes also vary in space with different kinds of processes operating at various locations. Most soil conservation and sediment control measures are only effective in combating a speci®c soil erosion or sediment delivery process. Therefore, the general implementation of one single conservation measure to a whole catchment will not be as effective as its application in those locations where it is most suited. A conservation strategy should therefore integrate a variety of suitably

G. Verstraeten et al.

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Figure 1. Location, topography and soil use map of the study area. The numbers refer to individual catchments: 1=Molenbeek; 2=Cicindria; 3=Melsterbeek.

located control techniques into a catchment management plan. To evaluate the impact of a series of possible management scenarios, a spatially distributed soil erosion and sediment delivery model is a necessary tool. In this study, a scenario analysis was conducted for evaluating conservation management in three catchments (1100±2300 ha) in the Belgian loess belt using WaTEM/ SEDEM, a spatially distributed soil erosion and sediment delivery model (Van Oost et al. 2000; Van Rompaey et al. 2001a). It is part of a pilot project set up by the Flemish Government which aims at reducing soil loss on ®elds as well as muddy ¯oods in villages in Flanders (Verstraeten et al. 2001). STUDY AREA AND MODEL DESCRIPTION The study area Three catchments were selected in the Belgian loess belt, all within the municipality of Gingelom, located in the southeast of Flanders (Figure 1). Each of these catchments is named after the main river running south to north: the Molenbeek (2317 ha), the Cicindria (1718 ha) and the Melsterbeek (1117 ha), from west to east respectively. The landscape consists of a smooth undulating plateau in the south (maximum 145 m a.s.l.), whereas in the north, the rivers have incised the plateau to as low as 70 m a.s.l.,

creating a rolling topography with steep slopes close to the rivers. Land use is predominantly arable (656% of the area), orchard (12%), pasture (6%) and forest (2%). The most important crops are winter wheat, sugarbeet, chicory, potatoes, maize and some vegetables. Built-up areas cover approximately 24% of the catchment area. The 12 settlements within the selected catchments are all in the valleys and are frequently confronted with muddy ¯oods, either directly from the arable land or from ¯ooding of the rivers (Mermans 1997). Soils are loess-derived luvisols (typically 12% clay, 80% silt, 8% sand) and, in combination with a very low topsoil organic matter content of 1±2%, they are very susceptible to soil erosion by rain and runoff. In the past, four retention ponds have been constructed to prevent ¯ooding, two on a permanent river and two in a small dry valley. The WaTEM/SEDEM model Simulations were carried out using the spatially distributed soil loss and sediment delivery model WaTEM/SEDEM (Van Oost et al. 2000; Van Rompaey et al. 2001a). A detailed description of the model can be found in Van Rompaey et al. (2001a) and only the general outline and input requirements will be given here. The model predicts mean annual soil loss and sediment delivery values. Sediment yield data from 21 small catchments (7±4873 ha) spread throughout the Belgian loess belt (Verstraeten & Poesen 2001) were used to calibrate

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and validate the model (Van Rompaey et al. 2001a). It should be stressed that the WaTEM/SEDEM model only predicts long-term average annual soil loss and sediment delivery by using mean annual values for each input parameter. For individual years or storm events, the response may be quite different as local conditions may vary substantially through space and time. This also implies that under extreme circumstances (e.g. heavily compacted and sealed soil), certain soil conservation measures will have no effect at all, although model predictions may show a signi®cant impact in the long-term. Thus, the simulations with the model only give a general idea about the response of catchment management on soil loss and sediment delivery. Model structure. The model is pixel-based with a resolution of 20 3 20 m. First, soil loss was predicted using the RUSLE methodology (Revised Universal Soil Loss Equation; Renard et al. 1997), taking into account the two-dimensional calculation of the topographic factor (LS) using the algorithms of Desmet & Govers (1996). Previous research has illustrated that the RUSLE and its earlier version (USLE or Universal Soil Loss Equation) is effective for use with loess soils in Western Europe in general, and in Belgium in particular (e.g. Bollinne 1985; Biesemans et al. 2000). Secondly, an annual transport capacity map was calculated assuming that this capacity is proportional to the potential for concentrated ¯ow erosion using a transport capacity coef®cient. Later, the runoff pattern was calculated with a multiple ¯ow algorithm incorporating the effects of road infrastructure. Sediment was routed along this runoff pattern towards the river, taking into account the local transport capacity (TC) of each pixel. If the local TC was smaller than the sediment ¯ux, sediment deposition was modelled. TC is also dependent on the land use, whereby TC is greater on arable land than in forests and grasslands. This is re¯ected in the TC coef®cient, which was calibrated at 75 m for arable land and 42 m for forests and grasslands (Van Rompaey et al. 2001a). When the sediment reached the river, it was directly delivered to the outlet of the catchment. The model does not predict riverbank erosion or channel storage, and predicted sediment delivery values therefore need to be interpreted as sediment delivery towards the complete length of the river in the catchment. Model requirements and input parameters used in this study. Several raster data layers with a resolution of 20 3 20 m are necessary to run the model: a digital elevation model (DEM), land use, ®elds, roads (optional), rivers, a layer with the location of pools, reservoirs or ponds, soil erodibility (K-factor in RUSLE) and the crop management (C-factor in RUSLE). A DEM was constructed by: (1) digitizing all contours (2.5 m interval) of the of®cial 1:10 000 maps of the National Geographic Institute: and (2) interpolating a raster DEM using the contours in IDRISIq. For Flanders, C values for the most common crop rotation schemes were calculated using crop data (Ghekiere 1997) and the RUSLE-methodology (Renard et al. 1997). The C-factor takes into account the impact of crop cover, surface cover, soil roughness, soil moisture and prior land

use on the soil loss risk. The mean C value for the crop rotations within the study area was estimated at 0.36 with a high value of 0.45 for a monoculture of maize and a low value of 0.31 for a rotation of winter wheat, potatoes and sugarbeet. Soil erodibility (K-factor) was estimated from soil texture data provided by the digital soil survey map of Flanders. This map only makes a distinction between heavy clay, clay, clayey silt loam, sandy silt loam, light sandy loams, loamy sands and sandy soils according to the Belgian soil classi®cation (Verheye & Ameryckx 1988). For each of these soil texture classes, the mean % silt, clay and silt were estimated from the texture triangle and these values were used to calculate the geometric mean particle diameter Dg (mm) (Shirazi & Boursma 1984): " # X 0:01fi 1nmi 1 Dg exp i

with fi the particle size fraction in % and mi the arithmetic mean of the particle size limits (mm) of that size. The K-factor was assessed using the equation of RoÈmkens et al. (1986), which was developed using data from 249 soils worldwide: " # logDg 1:519 2 2 K 0:0035 0:0388exp ÿ0; 5 0:7584 Declercq & Poesen (1992) concluded that the use of Dg in calculating soil erodibility for medium textured soils in Belgium with silt content greater than 70% (which is the case in our study area) is more appropriate than the use of the original nomograph of Wischmeier et al. (1971). The calculated K value for the dominant soil type in the study area (silt loam soil in Belgian nomenclature) is 0.042 t h MJ±1 mm±1. Other soil types occupy 7.5 t ha±1 yr±1) could be selected for implementing reduced tillage or cover crops. The subcatchments with a sediment yield larger than 2 t ha±1 yr±1 would be the ®rst areas in which to implement sediment control techniques such as small sediment ponds or grass buffer strips. Depending on the available budget and the objectives to be achieved, the soil loss and sediment yield thresholds can be changed, thereby increasing or decreasing the number of selected subcatchments. To combat high levels of soil loss, one needs to look at a smaller spatial scale, i.e. the individual ®eld (Figure 3). Even subcatchments with a mean soil loss level below the selected threshold value, may have some ®elds that experience soil loss rates well above the threshold value; these can then be selected for the application of soil conservation measures. The type of measure should be dependent on the severity of the soil loss. For instance, ®elds estimated to be losing >20 t ha±1 yr±1 might be taken out of production and put under grassland or forest, whereas ®elds losing between 10 and 20 t ha±1 yr±1 may still be used for agriculture but with cover crops and/or reduced tillage. Figures 2 and 3 also illustrate that the selection of priority subcatchments or ®elds will depend on the scale one decides to implement a management plan. If, for instance, the plan will be on the scale of a municipality, which is the case in Flanders, more efforts need to be directed to the Molenbeek catchment than to the other two catchments. SCENARIO ANALYSIS FOR A CATCHMENT MANAGEMENT Selected scenarios In total 26 scenarios for integrated catchment management have been analysed, plus the present-day situation. Table 1 gives a brief explanation of each scenario. All the scenarios were based on what is achievable in the framework of the current agri-environmental policy in Flanders and the European Union (EU). In the context of the Common Agricultural Policy of the EU, farmers are ®nancially supported if they take a certain fraction of their arable land out of production. This set-aside

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Table 1. Modelling results for the simulated scenarios for a catchment management. Scenario number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Scenario

E

DE

SY

TSY

DTSY

DSY

SDR

Sp

DSp

unchanged situation 5% set-aside using probability model of Van Rompaey et al. (2001b) 10% set-aside using probability model of Van Rompaey et al. (2001b) 15% set-aside using probability model of Van Rompaey et al. (2001b) 20% set-aside using probability model of Van Rompaey et al. (2001b) 5% set-aside using soil loss rate map 10% set-aside using soil loss rate map 15% set-aside using soil loss rate map 20% set-aside using soil loss rate map ®lling ditches and installing grass waterways 6 sediment retention ponds 6 sediment retention ponds and 7 water retention ponds soil conservation measures on all ®elds soil conservation measures on ®elds with high soil losses grass buffer strips downstream ®elds with high soil losses grass buffer strips along all rivers 12 and 13 12 and 15 12 and 16 13 and 15 13 and 16 15 and 16 13, 15 and 16 12, 13, 15 and 16 10 and 13 10, 13, 15 and 16 6, 10, 13, 15 and 16

5.9 5.6 5.1 4.8 4.4 5.1 4.6 4.2 3.7 5.9 5.9 5.9 3.9 4.8 5.7 5.8 3.9 5.7 5.8 3.7 3.7 5.7 3.6 3.6 3.8 3.6 3.2

± ±5 ±13 ±19 ±25 ±13 ±22 ±30 ±38 ± ± ± ±35 ±18 ±3 ±2 ±35 ±3 ±2 ±37 ±37 ±4 ±38 ±38 ±36 ±38 ±46

0.7 0.7 0.6 0.6 0.5 0.6 0.5 0.5 0.4 0.6 0.6 0.6 0.4 0.6 0.6 0.5 0.4 0.5 0.5 0.4 0.3 0.5 0.3 0.3 0.4 0.3 0.3

3451 3378 3151 3021 2828 2866 2574 2371 2257 3036 3046 2969 2313 2988 3213 2788 1996 2756 2465 2113 1799 2653 1712 1506 1977 1534 1317

± ±71 ±298 ±428 ±621 ±583 ±875 ±1078 ±1192 ±413 ±403 ±480 ±1136 ±461 ±236 ±661 ±1453 ±693 ±984 ±1336 ±1650 ±796 ±1737 ±1943 ±1472 ±1915 ±2132

± ±2 ±9 ±12 ±18 ±17 ±25 ±31 ±35 ±12 ±12 ±14 ±33 ±13 ±7 ±19 ±42 ±20 ±29 ±39 ±48 ±23 ±50 ±56 ±43 ±56 ±62

11 12 12 12 12 11 11 11 12 10 10 10 10 13 11 9 10 9 8 11 9 9 9 8 10 8 8

516 490 450 437 377 465 385 352 335 410 1028 1377 352 447 497 407 913 1316 1121 330 263 398 257 693 271 208 193

± ±26 ±66 ±79 ±139 ±51 ±132 ±164 ±181 ±106 +512 +861 ±164 ±69 ±19 ±109 +397 +800 +605 ±186 ±253 ±108 ±259 +177 ±245 ±308 ±323

E = average soil loss rate (t ha±1 yr±1); n E = % change in soil loss rate; SY = sediment yield (t ha±1 yr±1); TSY: total sediment delivery (t yr±1); n TSY = change in total sediment delivery (t yr±1); n SY= % change in SY; SDR = sediment delivery rate (%); Sp = annual amount of sediment deposited in the ponds (t yr±1); n Sp = absolute change in Sp (t yr±1)

Figure 4. Spatial pattern of ®elds selected for a given set-aside scenario (5% of cropland) using two different selection procedures: probability according to Van Rompaey et al. (2001b) (A) and the soil loss map (B).

programme aims to reduce crop production and at present, the farmers in Flanders need to set-aside at least 10% of their arable land (Sibbesen 1997). They can, however, also receive ®nancial support if they wish to increase this setaside percentage, up to a maximum of 40%. The impact of additional set-aside percentages (5%, 10%, 15% and 20% extra) on the reduction in soil loss and sediment yield was determined. The ®elds in set-aside in the simulations were selected using two different procedures. One of these used a

probability model developed by Van Rompaey et al. (2001b) that incorporates soil texture, soil hydrology and topography, to select the ®elds for set-aside. This model was set up using data from a farmers survey to identify the ®elds already set-aside at the actual minimum percentage of 10% (scenarios 2±5). The other procedure used the soil loss rate map (Figure 3) with 5%, 10%, 15% and 20% of the most erosive ®elds selected, (scenarios 6±9). Figure 4 shows the spatial patterns of the selected ®elds for 5% additional set-

G. Verstraeten et al. aside using the two procedures. Since the most erodible ®elds are in the Molenbeek catchment, more set-aside ®elds were selected in this catchment using the soil loss rate as a criteria compared to the probability procedure. With the simulations, it is assumed that ®elds that are taken (temporarily) out of production are put under grassland or another cover crop that gives good protection to the soil surface. C values for grassland are therefore used in all the set-aside scenarios, as well as a lower transport capacity coef®cient. For a few subcatchments in the southern part of the Molenbeek catchment, sediment delivery ratios (i.e. the ratio between total soil loss in the catchment and sediment yield at the outlet of the catchment) are 15±25%, which is much larger than for the other subcatchments where it is only 1± 5% although they have the same topographic characteristics, i.e. a plateau with gentle slopes and wide valleys that promote sediment deposition. However, in some of the Molenbeek subcatchments, runoff is concentrated in a man-made ditch thereby facilitating sediment delivery. Researchers have shown before that incised channels in an agricultural area, whether they are man-made (ditches) or natural (e.g. gullies), can be very ef®cient pathways for sediment transport to rivers or reservoirs (e.g. Stall 1985; Steegen et al. 2000). In scenario 10, the small man-made ditches were ®lled, levelled and seeded with grass forming a grassed waterway in the valley axis. In order to reduce the impact of muddy ¯oods on local communities, ¯ood retention ponds have been a classical control measure. This practice will continue in the immediate future as it is one of the measures that is most easily implemented by local authorities and for which substantial subsidies are available. Some of the simulations were therefore carried out with new ponds. Scenario 11 has six ponds located where the sediment delivery from arable land to villages was high. Their main aim was to reduce muddy ¯oods (i.e. sediment retention ponds). Scenario 12 has the six ponds of scenario 11, and also 11 ponds that should reduce the risk to ¯ooding in the villages but where there is less sediment involved (i.e. water retention pond). Scenario 13 involves soil conservation measures applied to all the ®elds in agriculture (i.e. 3167 ha). The soil conservation measures were: (1) a cover crop to protect the bare soil in winter after the harvest of a summer crop like maize, sugarbeet or potatoes; (2) leaving the previous crop residues on the ®eld without incorporation; and (3) drilling or planting summer crops like maize or sugarbeet into the cover crop or the cover crop residue to protect the bare soil in spring against storms. This measure reduced the overall long-term C value from 0.36 to 0.28. Scenario 14 used soil conservation measures but at a more limited scale than scenario 13; only ®elds with a soil loss rate larger than 10 t ha±1 yr±1 were treated (560 ha). On ®elds with a soil loss rate between 5 and 10 t ha±1 yr±1 (1130 ha), a cover crop was to be sown once every three years (long-term C value drops to 0.31). No soil conservation measures were taken on the remaining ®elds (1447 ha).

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Grass buffer strips are often considered one of the most effective measures for reducing the sediment delivery to rivers (e.g. Haan et al. 1994). They can be installed at a downstream ®eld edge or along a river. Both types of grass buffer strip have been evaluated. In scenario 15, buffer areas of 0.1 to 0.2 ha at the downstream part of ®elds with a net soil loss above 5 t ha±1 yr±1 or ®elds that deliver more than 50 t to the downstream ®eld, river or road are evaluated. Since the WaTEM/SEDEM model routes runoff along the downstream ®eld edge so that runoff ¯ows to another ®eld at a single location, a grass buffer area around this location was modelled instead of a strip along the whole ®eld edge. In total 35 ha of grass buffer areas were simulated in 199 ®elds. The effect of a 20 m wide grass buffer strip along a river channel was evaluated only at those places where arable land borders the river (scenario 16). The treated river length was 33 km (i.e. 86% of total river length or 65 ha of buffer strips). Since integrated catchment management should not focus on one single soil conservation or sediment control measure, some combinations have also been evaluated in scenarios 17± 27. Results of scenario analysis and discussion on the effectiveness Table 1 lists the main results from the modelling. For each scenario the following results are given: the mean soil loss rate (E), sediment yield (SY), sediment delivery ratio (SDR), the amount of sediment deposited in the retention ponds (Sp) as well as the change in all these parameters compared to the unchanged control (scenario 1). The results are shown for the three catchments together. For the unchanged situation (scenario 1), the four existing water retention ponds are included. In general, it is clear that soil conservation measures taken at the ®eld scale, whether it be set-aside or modi®ed crop management, were relatively effective in reducing both soil loss and sediment yield. Sediment control measures, e.g. grass buffer strips or retention ponds, also reduced sediment yield, although by somewhat less, but did not decrease soil loss signi®cantly. Set-aside scenarios. When ®elds were taken out of production (scenarios 2±9), their exact location was very important. This becomes clear when the set-aside scenarios are compared (Table 1 and Figure 5). With the probability model, the % decrease in soil loss was slightly more than the % area in set-aside, whereas if the most erodible ®elds were selected ®rst, the decrease in soil loss was almost double the area percentage they occupied. The difference in the reduction in sediment yield between the two selection methods was even greater. Setting the 5% most erodible ®elds aside was as effective in reducing sediment delivery to the rivers as taking 20% of the arable land out of production using the probability model (i.e. 17 and 18%, respectively). The results, and in particular the soil loss map, can thus be of great help in giving advice to farmers on which ®elds to select for set-aside. However, these results may not apply in other areas. Using their probability model for selecting setaside ®elds, Van Rompaey et al. (2001b) estimated that the average soil loss rate (X) for an area of 850 km2, of which our study area (51 km2) is a part, decreased with set-aside

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Figure 5. Relative change in soil loss and sediment delivery for a range of set-aside percentages and two selection procedures.

percentage (Y) according to a power function (X=2.3Y0.91). Taking 20% of the arable land out of production would in that case have resulted in a soil loss reduction of 35%. The study area of Van Rompaey et al. (2001b) in general has a complex topography and soil pattern, such that the most erodible ®elds are more likely to be selected for set-aside. Our study area, in contrast, has a much more homogeneous topography and very little soil variation. Modi®ed crop management. The implementation of modi®ed crop management on all ®elds (scenario 13) resulted in a larger estimated reduction in soil loss (i.e. 35%) than the decrease in average C value (from 0.36 to 0.28=22%). This was because the measures reduced the runoff, thereby reducing erosion in downslope ®elds. Furthermore, this also decreased the transport capacity of the runoff, reducing the sediment delivery to the river as well. This effect was more limited when the measures were taken on a selected number of ®elds (scenario 14 or set-aside scenarios 2±9). In these cases, the `clear water effect' became important at some locations. Downstream of a ®eld where soil conservation measures were taken, the runoff contains less sediment and hence has greater potential to carry eroded soil when it ¯ows over a ®eld with no conservation treatment. Hence, the sediment delivery ratio (soil loss divided by sediment yield) of scenario 14 was higher than for scenario 13. Furthermore, the reduction in soil loss for scenario 14 was only half of that of scenario 13, which at ®rst sight was expected as the latter involves measures on almost double the area. However, if one considers that for scenario 14 only the ®elds with a high soil loss were selected, it should be clear that a greater reduction in soil loss should be expected. A non-continuous or patchy pattern of treated ®elds was therefore less effective than management covering the complete area. Grass buffer strips. Whether constructed along a river channel or between ®elds, grass buffer strips did not signi®cantly reduce soil loss. This is quite logical for buffer strips next to the river. For grass buffer strips or areas between ®elds it is more surprising as it is often believed

that they form a break in runoff allowing partial in®ltration in the strip. This would then reduce the runoff erosivity downstream. However, a grass buffer strip will also trap some sediment and consequently downstream of a strip, the runoff will be capable of transporting more sediment. The reduction in sediment yield for scenario 15 was therefore very small. For the grass buffer strips along the rivers (scenario 16), the reduction in sediment yield was more important than for strips away from the river because there is no further possibility for transporting sediment by overland ¯ow. Nevertheless, even the 19% reduction in sediment yield for scenario 16 is quite small compared with experimental results from grass buffer strips. Field experiments often show that a grass buffer strip can trap as much as 70±90% of the in¯owing sediment (e.g. Dillaha et al. 1987; Magette et al. 1987; Haan et al. 1994). These experimental results were obtained on isolated linear slopes, which cannot be extrapolated to a catchment scale. Indeed, rather than approximate diffuse ¯ow over a combination of linear slopes, catchments concentrate runoff in zero and ®rst order valleys, thereby reducing the number of points in the landscape where the runoff from arable land enters rivers. Furthermore, at sites where runoff is concentrated, depth of ¯ow will be larger, submerging the grass and, hence, seriously inhibiting the effectiveness of buffer strips (Dillaha et al. 1989; Vuurmans & Gelok 1993). For scenario 16 the model predicted that 41% of the sediment that ¯owed into grass buffer strips would be trapped. However, in the absence of a grass strip 11% would be deposited already due to the change in slope. The net effect of all the grass buffer strips was therefore limited to 30%. Since a lot of sediment enters the river system through roads and sewers, the effect of grass buffer strips along the river was only 19% for the whole catchment. Removing ditches. Disconnecting the upstream parts of the catchment from the river network by ®lling a few ditches (scenario 10) was, given the small spatial extent of the control measure, very effective. For the three catchments together, sediment yield was reduced by 12% but for the Molenbeek catchment, which is the only catchment where this control measure was simulated, the reduction was 20%. For the subcatchments in which the ditches were removed, the reduction in sediment yield was between 85 and 96%. This reduced the sediment delivery ratio of these subcatchments to 0.5±2%, a value that is comparable to sediment delivery ratios for other subcatchments with a similar topography. Retention ponds. The sediment retention ponds that were speci®cally constructed to store runoff with high sediment loads (scenario 11), also reduced the sediment yield by 12% for the whole study area and by 19% for the Molenbeek catchment in which all the six ponds were located. Their effect was thus similar to that of scenario 10 although ®nancial costs would be greater. If the water retention ponds are included (scenario 12), the reduction in sediment yield is not much more (14 versus 12%). It is also clear that the total amount of sediment that was trapped in the extra ponds was much greater than the total reduction in sediment yield. For

G. Verstraeten et al. scenario 11 this difference is 110 t (513 t ± 403 t) and for scenario 12 it is 381 t (861 t ± 480 t). This discrepancy is due to the fact that much of the sediment would be deposited anyway in the lower valley bottoms, even in the absence of ponds. Combined scenarios. The reductions in soil loss and sediment yield were in general larger for the combined simulations, although not as high as the sum of the reductions of the individual scenarios. For example, sediment yield is only reduced by 42% in scenario 17 while one would expect 47% (i.e. 33% for scenario 12 + 14% for scenario 13). When sediment control and soil conservation measures were combined, this usually resulted in a larger decrease in sediment yield than the decrease in soil loss. Under such circumstances, the sediment delivery ratio was also reduced substantially. Which scenario should be implemented? The model offers the possibility of selecting one of the most technically effective scenarios for management of a catchment. Decisions will, however, also depend on the ®nancial ef®ciency of the measures. Although the implementation of soil conservation measures on the majority of the ®elds would be one of the most effective actions, it might require such a large ®nancial subsidy to farmers that the bene®ts would be less than the costs. Therefore, a complete costbene®t analysis for each scenario is required to complement the analysis presented in Table 1. This, however, is dif®cult to achieve and previous attempts have not resulted in standardizing soil erosion related costs (e.g. Crosson 1995; Pimentel et al. 1995; Pretty et al. 2000). Further research on this is in progress but is not suf®ciently advanced to include in this paper. CONCLUSIONS In this study, a spatially distributed soil loss and sediment delivery model (WaTEM/SEDEM) was used to evaluate the effectiveness of a variety of soil conservation and sediment control measures at the catchment scale. It was shown that for catchments, the effect of most sediment control measures is much less than would be expected from results of ®eld experiments. This suggests that sediment control measures, like grass buffer strips or sediment retention ponds, should only serve a supplementary role in controlling the off-site impacts of soil erosion, such as muddy ¯oods or surface water pollution. In terms of soil loss and sediment delivery reduction, soil conservation measures are much more effective. The spatial modelling of the integration of various conservation and control measures into an overall plan for catchment management also showed that the combined effect of the measures is less than the sum of the individual effects. A spatial soil loss and sediment delivery model is potentially a powerful tool for land managers, allowing them to select the most technically effective plan for catchment management. Further research on how to achieve realistic cost-bene®t analysis of the management options is required. Finally, once a speci®c plan is executed, soil loss

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and sediment yield should be monitored in order to test the model results. Although WaTEM/SEDEM has already been validated using sediment yield data for 27 catchments, this work needs to be extended. Since the model predicts long-term annual average values, the monitoring period should last for several years. ACKNOWLEDGEMENT The authors would like to thank the Land Division of the Flemish Community for ®nancial support for the scenario analysis conducted for the municipality of Gingelom. REFERENCES Biesemans J Van Meirvenne M & GabrieÈls D 2000. Extending the RUSLE with the Monte Carlo error propagation technique to predict long term average off-site sediment accumulation. Journal of Soil and Water Conservation 55, 35±42. Boardman J Ligneau L de Roo A & Vandaele K 1994. Flooding of property by runoff from agricultural land in north-western Europe. Geomorphology 10, 183±196. Bollinne A 1985. Adjusting the universal soil loss equation for use in western Europe. In: Soil erosion and conservation. Eds SA El Swaify WC Moldenhauer Soil and Water Conservation Society, Ankeny, 206±213. Clark EH Haverkamp JA & Chapman W 1985. Eroding Soils: the off-farm impacts. The Conservation Foundation, Washington, DC. Crosson P 1995. Soil erosion estimates and costs. Science 269, 461±463. Declercq F & Poesen J 1992. Evaluation of two models to calculate the soil erodibility factor K. Pedologie 42, 149±169. Desmet PJJ & Govers G 1996. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation 51, 427±433 Dillaha TA Reneau RB Mostaghimi S Shanholtz VO & Magette WL 1987. Evaluating nutrient and sediment losses from agricultural lands: vegetative ®lter strips. US Environmental Protection Agency. Dillaha TA Reneau RB Mostaghimi S & Lee D 1989. Vegetative ®lter strips for agricultural nonpoint source pollution control. Transactions of the American Society of Agricultural Engineers 32, 513±519. Ghekiere G 1997. Het begroten van het actuele erosierisico in het stroombekken van de Kemmelbeek. Unpubl. Diss. Ir., R.U.Gent. (in dutch) Haan CT Bar®eld BJ & Hayes CJ 1994. Design hydrology and sedimentology for small catchments. Academic Press San Diego. Magette WL Brins®eld RB Palmer RE Wood JD Dillaha TA & Reneau RB 1987. Vegetated ®lter strips for agricultural runoff treatment. US Environmental Protection Agency. Mermans H 1997. Water- en modderoverlast in Zuid-Limburg: wachtbekkens als symptoombestrijding. Unpubl. Diss. Lic. Geography, K.U.Leuven, Faculteit Wetenschappen, Leuven. (in dutch) Pimentel D Harvey C Resosudarmo P Sinclair K Kurz D McNair M Crist S Shpritz L Fitton L Saffouri R & Blair R 1995. Environmental and economic costs of soil erosion and conservation bene®ts. Science 267, 1117±1123. Pretty JN Brett C Hine RE Mason CF Morison JIL Raven H Rayment MD & van der Bijl G 2000. An assessment of the total external costs of UK agriculture. Agricultural Systems 65, 113±136. Renard KG Foster GR Weesies GA McCool DK & Yoder DC 1997. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture Handbook 703, USDA, Washington, DC. RoÈmkens MJM Prasad SN & Poesen JWA 1986. Soil erodibility and properties. In: Proceedings of the 13th Congress of the International Soil Science Society 5, 492±504. Shirazi MA & Boersma L 1984. A unifying quantitative analysis of soil texture. Soil Science Society of America Journal 48, 142±147. Sibbesen E 1997. Set-aside and land use regulations with relation to surface runoff in Finland, Denmark, Scotland, Belgium, France and Spain. SPreport no. 14, Danish Institute of Agricultural Science. Stall JB 1985. Upland erosion and downstream sediment delivery. In Soil

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erosion and conservation. Eds SA El Swaify & WC Moldenhauer Soil and Water Conservation Society, Ankeny 200±205. Steegen A Govers G Nachtergaele J Takken I Beuselinck L & Poesen J 2000. Sediment export by water from an agricultural catchment in the Loam Belt of central Belgium. Geomorphology 33, 25±36. Van Oost K Govers G & Desmet PJJ 2000. Evaluating the effects of changes in landscape structure on soil erosion by water and tillage. Landscape Ecology 15, 579±591. Van Rompaey A Verstraeten G Van Oost K Govers G & Poesen J 2001a. Modelling mean annual sediment yield using a distributed approach. Earth Surface Processes and Landforms 26, 1221±1236. Van Rompaey A Govers G Van Hecke E & Jacobs K 2001b. The impacts of land use policy on the soil erosion risk: a case study in central Belgium. Agriculture, Ecosystems and Environment 83, 83±94. Verheye W & Ameryckx J 1988. Mineral fractions and classi®cation of soil texture. Pedologie 38, 215±225. Verstraeten G & Poesen J 1999. The nature of small-scale ¯ooding, muddy ¯oods and retention pond sedimentation in central Belgium. Geomorphology 29, 275±292.

Verstraeten G & Poesen J 2001. Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate. Geomorphology 40, 123±144. Verstraeten G Van Oost K Van Rompaey A Poesen J & Govers G 2001. Integraal land- en waterbeheer in landelijke gebieden met het oog op het beperken van bodemverlies en modderoverlast (proefproject gemeente Gingelom). Report to the Land Division of the Ministery of the Flemish Community. (in dutch). Vuurmans KJ & Gelok AJ 1993. WeerstandscoeÈf®cieÈnten en maximaal toelaatbare stroomsnelheden in grasbanen voor waterafvoer in hellende gebieden. Landinrichting 33, 11±17. (in dutch). Wischmeier W Johnson C & Cross B 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation 26, 189±193. Received February 2002, accepted after revision July 2002.

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British Society of Soil Science 2002