Landscape planning to reduce coastal eutrophication: agricultural practices and constructed wetlands

Landscape and Urban Planning 67 (2004) 205–215

Landscape planning to reduce coastal eutrophication: agricultural practices and constructed wetlands B. Arheimer a,∗ , G. Torstensson b , H.B. Wittgren c a

b

Swedish Meteorological and Hydrological Institute (SMHI), SE-601 76 Norrköping, Sweden Department Soil Sciences, Swedish University of Agricultural Sciences (SLU), Box 7072, SE-750 07 Uppsala, Sweden c Swedish Institute of Agricultural and Environmental Engineering (JTI), Box 7033, SE-750 07 Uppsala, Sweden

Abstract Southern Sweden suffers from coastal eutrophication and one reason is the high nitrogen load through rivers. The major part of this load originates from diffuse land-based sources, e.g. arable soil leaching. Effective reduction of load from such sources demand careful landscape analysis, combined with changed behaviour of the stakeholders. This study describes a chain of methods to achieve trustworthy management plans that are based on numerical modelling and stakeholders participation and acceptance. The effect of some measures was unexpected when modelling their impact on the catchment scale. Management scenarios to reduce riverine nitrogen load were constructed in an actor game (i.e. role-play) for the Genevadsån catchment in southern Sweden. The game included stakeholders for implementation of a loading standard for maximum nitrogen transport at the river mouth. Scenarios were defined after negotiation among involved actors and included changes in agricultural practices, improved wastewater treatment, and establishment of wetlands. Numerical models were used to calculate the nitrogen reduction for different measures in each scenario. An index model (STANK) calculated the root zone leaching of nitrogen from crops at four type farms. This generated input to a catchment scale model (HBV-N) and farm economics. The economic impact of different sets of remedial measures was evaluated for each type farm and then extrapolated to the catchment. The results from scenario modelling show that possible changes in agricultural practices (such as tuning, timing of fertilisation and ploughing, changed crop cultivation) could reduce the nitrogen load to the sea by some 30%, while wetland construction only reduced the original load by some 5%. In the most cost-effective scenario agricultural practices could reduce the riverine load by 86 t per year at a cost of 1.0 million SEK, while constructed wetlands only reduced the load by 14 t per year at a cost of 1.7 million SEK. Thus, changed agricultural practices can be the most effective and less expensive way to reduce nitrogen transport from land to the sea, while constructed wetlands with realistic allocations and sizes may only have small impact on riverine nitrogen transport from land to sea. The overall experience is that actor games and numerical modelling are useful tools in landscape planning for analysing stakeholders’ behaviour and the impact of measures to reduce coastal eutrophication. © 2003 Elsevier Science B.V. All rights reserved. Keywords: Nitrogen; Transport; Catchment; Modelling; Scenarios; Agriculture; Wetlands

1. Introduction ∗

Corresponding author. Tel.: +46-11-495-8560; fax: +46-11-495-8001. E-mail address: [email protected] (B. Arheimer). 0169-2046/$20.00 © 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0169-2046(03)00040-9

Eutrophication of inland and coastal waters is a worldwide environmental problem and serious efforts are needed to reduce emissions and improve the

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situation (e.g. Ryding and Rast, 1989). It has been an environmental problem ever since the beginning of the industrial era, and it is strongly associated with urbanisation and efficient industrial and agricultural production. The effect of eutrophication is high production of plankton algae, excessive growth of weeds and macroalgae, leading to oxygen deficiency, which in turn may lead to fish kills, reduced biological diversity and bottom death. The use of water mainly as a transport medium and as a recipient for unwanted substances prevents a multiple use of the resource and is not a sustainable management strategy. The prevailing opinion is that the eutrophication problem is caused by high nitrogen (N) and phosphorus loads (Granéli et al., 1990; Rosenberg et al., 1990; Hansson and Rudstam, 1990). Trend analysis shows that the N concentration has increased within the Baltic Sea during the last 20 years (Sandén and Rahm, 1993), and the N load is estimated to be four times higher than it was 100 years ago (Larsson et al., 1985). The anthropogenic N emissions reach the Baltic Sea through rivers, atmospheric deposition, and coastal point sources. The rivers represent more than 60% of the total load on the Baltic Sea (Stålnacke, 1996). For southern half of Sweden 45% of this riverine N load originates from agricultural sources (Arheimer and Brandt, 1998). The first Swedish measures against eutrophication were introduced during the 1960s and mainly targeted the point-source emissions of nutrients. Especially for phosphorus point sources have been reduced successfully. The important task now is to reduce the discharges from diffuse sources. Although measures have been undertaken to reduce the agricultural leaching during the 1980s and 1990s (Johnsson and Hoffman, 1998) their net influence on the large-scale transport from the agricultural sector is quite small (Arheimer and Brandt, 2000). To achieve more efficient reduction of nutrient concentrations in the Swedish fresh-water system, new policies including catchment-based management plans for farmers have been suggested (Swedish Environmental Ministry, 1997). Nevertheless, catchment-based solutions of nutrient problems demand catchment-based knowledge of nutrient transport processes and appropriate tools for landscape planning, which may not be available at present. Reduction of nutrients from diffuse sources is difficult to achieve as the sources are difficult to monitor

and the nutrients constitute a natural part of the soil and water environment. Moreover, the measures requested more directly affect people’s lifestyle and livelihood, asking for a policy that changes people’s behaviour (e.g. car driving and meat consumption) and involve the stakeholders in the management actions taken. The type of measures that reduce diffuse N leaching from arable land demand changed agricultural practices by the farmers. In addition, a wetland constructed downstream an agricultural catchment is also considered to be an effective and cost-efficient way to reduce N transport further down in the river system (e.g. Jørgensen et al., 1989; Mitsch, 1992; Fleischer et al., 1994; Raisin and Mitchell, 1995). Thus, large amount of money is spent in Sweden by the government to support wetland constructions for reduced eutrophication. This article presents a case study where both changed agricultural practices and constructed wetlands have been considered and compared for reduction of riverine N-load. The analysis is based on strategic landscape planning where farmers and stakeholders developed realistic management scenarios themselves (Wittgren et al., 2001). Moreover, it is based on the natural hydrometeorological dynamics and the present environmental conditions in a Swedish catchment, along with numerical modelling of the impact on N load when introducing different measures. 2. Material and methods 2.1. Site characteristics The study was performed in the Genevadsån catchment (224 km2 ) in southern Sweden (Fig. 1). The Genevadsån River is a natural reproduction area for salmon. It discharges into the Laholm Bay, which suffers from severe eutrophication problems. The upper part of the catchment is forested, while the lower part nearby the coast is intensively cultivated. Fig. 1 shows that there are few natural lakes in the area. Although there is only 200 m difference in altitude, the precipitation gradient is pronounced in the catchment, with 700 mm per year near the coast and 1200 mm per year in the inland. The specific runoff is on average 17 l s−1 km−2 , but varies largely both spatially and temporally, due to the precipitation pattern and the relatively few lakes present. The soil types

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Fig. 1. The Genevadsån catchment in southern Sweden, with some characteristics and sites suitable for wetland constructions marked.

are mainly sandy till-clays. The catchment has 4800 inhabitants, of which 3200 live in towns or villages. In addition there are some 100 summerhouses. Five wastewater treatment plants are present. The agriculture is dominated by livestock, cereals, potatoes and oil-plants, and the average commercial fertilisation is on 90 kg N ha−1 for the region (SCB, 1992). About 4238 animal unites are present, of which 60% are concentrated at 10 farms (Wittgren et al., 2000). The annual average N-concentration is normally 2–3.5 mg l−1 at the outlet where Genevadsån River enters the Laholm Bay. 2.2. Catchment modelling The catchment modelling of N transport was done with the dynamic HBV-N model, which has recently been used for estimation of Swedish N-load from rivers to the Baltic Sea (Arheimer and Brandt, 1998; 2000). HBV-N is based on the rainfall-runoff model HBV (e.g. Bergström, 1995). In the N-routine, leaching concentrations are assigned to the water percolating from the unsaturated zone of the soil to the groundwater reservoir of the HBV model (Fig. 2). Different concentrations are used for the categories: forest, urban, arable and other land. The arable land may be further divided based on soil type, crops and management practices (e.g. fertiliser applica-

tion). In addition to soil leaching, N is also added from rural households and point sources, such as industries and wastewater treatment plants. Atmospheric deposition on water surfaces is also added, while N-deposition on land is included in the soil leaching. The HBV-N model simulates N residence, transformation and transport in groundwater, rivers, lakes, and surface-flow wetlands (ponds). A time-step of 1 day is used. The equations used to account for N-retention processes are based on empirical relations between physical parameters and concentration dynamics (Arheimer and Brandt, 1998; 2000). Normally, inorganic N and organic N are modelled separately. However, in the present application, only the routines for inorganic N were used since organic N is a minor fraction in the Genevadsån River. The model was calibrated against observed time-series of water flow in a nearby river, while the N-routine was calibrated in four river branches within the Genevadsån catchment. Further general descriptions of HBV-N model structure and calibration strategies may be found in Pettersson et al. (2001). Source apportionment for the riverine load is achieved by adding sources of different categories in the catchment. This is made separately for gross and net load to illustrate the influence of retention processes. Net load is the remaining part of the gross load, which eventually reaches the sea after

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Fig. 2. Schematic structure of the process-based catchment model HBV-N, which were used for calculation of riverine N load and the impact of management scenarios. The catchment is divided into several coupled subbasins along the river network where each subbasin is modelled separately.

accumulated N removal in sinks downstream the specific source and subbasin (Wittgren and Arheimer, 1996). The model was set up for a 10-year period (1985–1994) for the results to be flow normalised. When applying the model, the river basin may be divided into several coupled subbasins, for which the water balance and N concentration is computed separately. The Genevadsån catchment was divided into 70 subbasins to account for locations suitable for wetland constructions. For each subbasin, input data on meteorology, physiography and emissions, as well as hydrological coupling to other subbasins, is needed to run the HBV-N model. In the present study, this was achieved from earlier applications (i.e. Arheimer and Brandt, 1998; Arheimer and Wittgren, 2002) except from the leaching concentration for arable land. 2.3. Modelling agricultural practices Agricultural leaching was calculated separately by using the index model STANK, which is developed by Hoffmann et al. (1999). The agriculture of the Genevadsån catchment was classified into four type farms, and the root zone leaching from different crops at each farm type was calculated. The N leaching (kg ha−1 per year) in the model is based on five key factors, and is calculated as: Nleaching = NG gf bf Nm Ni

(1)

where NG is the basic leaching, which is affected by average runoff from the area, soil texture, geographic location and use of manure, gf the relative leaching potential of the crop in relation to a spring cereal fertilised according to recommendations. The factor gf is based on leaching experiments and measurements from observation fields, bf the time for soil tillage and the use of under-sown ley or catch crops give another factor, which modifies the basic leaching, Nm manure application. A part of the ammonium-N in the manure is assumed to leach. The leached amount depends on the time of manure spreading, the soil texture and the growing crop, Ni fertilisation intensity, in relation to the demand of the crop. If the amount of fertiliser exceeds the demand, the leaching increases according to a progressive scale. Different scales are used for different soil textures. The impact of changed agricultural practices was modelled by changing these key factors. The results from the index model (for present conditions and scenarios) was compared and adjusted to previous results from the more detailed process description of the SOIL-N model (Johnsson and Hoffman, 1998). However, the STANK model was chosen for the scenario modelling to reduce computational times, as quick results were requested. The type farms in Genevadsån included the categories “pig”, “milk”, “crop” and “part-time”, which were defined on the basis of detailed studies in a small (10 km2 ) agricultural catchment, Prästabäcken, to the south of the Genevadsån

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catchment. The average value from several farms was considered for each category. The calculated load from arable soils was divided by the calculated runoff so that concentrations were achieved that could serve as input to the HBV-N model (cf. Fig. 2). The type farms of Prästabäcken were assumed to be representative regarding leaching and distribution of crops, so that this could be extrapolated to the whole Genevadsån catchment. The crop distribution for the whole catchment was based on multisource satellite data classified by Michelson et al. (2000). Calculations of the economic impact of changed agricultural practices were made for the four type farms (Wittgren et al., 2000). Animal stock, crop distribution and fertilisation intensity were given by the different sets of measures, and calculations were made for the change in profitability when going from the starting conditions and successively introducing different measures. Throughout the study, the price was assumed to be 1 SEK kg−1 for cereals, 1.85 SEK kg−1 for oil plants, and 7.10 SEK kg−1 for N in fertiliser. Costs involved for catch crops were estimated at 400 SEK ha−1 , while subsidies for catch crops in e.g. cereals was 900 SEK ha−1 . 2.4. Modelling constructed wetlands The impact of constructed wetlands on riverine N load was calculated by the HBV-N model. The type of wetland that is normally constructed on Swedish arable land is surface-flow wetlands or ponds. In the HBV-N model such wetlands are viewed as completely mixed batch reactors, where N retention is assumed to be area dependent. The N retention in such a reactor is described by: d(cvwet ) = −kaT cAwet dt

(2)

where c is the N concentration (g m−3 ), vwet wetland volume (m3 ), Awet wetland area (m2 ), kaT area-based and temperature dependent retention rate coefficient (m per day). The retention-rate coefficient (kaT ) was calibrated based on empirical time-series from surface-flow wetlands (ponds) within 100 km from the Genevadsån catchment (Arheimer and Wittgren, 2002). Data was available from eight shallow ponds, constructed for N retention from agricultural runoff in southern Sweden.

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Monitoring programmes for the ponds have been run for 2–5 years during 1992–1997 by county or municipal authorities. Potential areas for created wetlands in the Genevadsån catchment included 40 sites and 0.92 km2 in total, according to a topographically realistic wetland plan developed from landscape analysis (Wessling, 1991). Allocation and size of wetlands were based on expected load and water residence time, as well as the risk for flooding of surrounding arable land due to increased groundwater table: the nearby waterlogged area should not exceed the size of the wetland itself. When simulating the wetland impact on riverine N transport, the potential wetlands were included in the HBV-N input files for the appropriate subbasins. The corresponding wetland area was withdrawn from the area of arable land. In the water-balance simulation, the wetlands were treated as small lakes with a general rating curve, and the retention (Eq. (2)) affected the N load during its residence in the wetland. The economic costs involved in wetland constructions were based on an analysis of 53 wetlands by Söderqvist (1999), where the costs showed large variation between wetlands (range: 900–66600 SEK ha−1 per year). In the present study the average annual cost on 16300 SEK ha−1 wetland was used for the economic calculations. 2.5. Definition of management scenarios Management plans including measures to reduce riverine N load were constructed in an actor game (i.e. role-play) for the subbasin Prästabäcken and then extrapolated to the whole catchment. The game considered implementation of environmental quality targets for N, where one of the targets was a loading standard for maximum riverine-N transport (200 t per year) at the mouth of the Genevadsån River. This would reduce the anthropogenic part of the load by 50% (Wittgren et al., 2000), which has been a Swedish target ever since the Helsinki Commission and the North Sea Conference/Paris Commission during the 1980s. The other target considered groundwater concentration but is not emphasised in this study on eutrophication control. The actor game was performed in three sessions of 2 days each. The 16 actors represented stakeholder groups to which they belong, or are closely related

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to. The following interests were represented: farming, sports fishing, groundwater extraction, wastewater discharge, environmental protection (local authorities and NGOs), the regional watershed authority, the water court and media. A steering group headed by a game leader prepared the game and led the sessions. A reference group and a team of scientists assisted in preparing the background material. This material consisted of game rules, legislation including environmental quality standards, a catchment description, and monitoring data/model calculations for the starting conditions (see more details in Wittgren et al., 2000). During the game, a team of scientists calculated the outcome of the suggested set of measures, both in terms of their contribution to achieving the target, and their economic impact on the type farms. Based on the outcome of the game, the team of scientists formulated three alternative management scenarios, all of which were estimated to largely meet the environmental targets.

3. Results 3.1. Catchment modelling The water flow simulated by HBV (Fig. 3a) could not be validated in the Genevadsån River, as no water gauges are present. However, in the nearby Fylleån River, where the hydrological parameters were calibrated against measured flow, 87% of the variance was explained by the model (i.e. R2 = 0.87, based on Nash and Sutcliffe, 1970). Concentrations of N are monitored at four sites in the catchment, and Fig. 3b show the agreement between modelled and observed daily values at the river mouth. Source apportionment based on the model results showed that 77% of the riverine load originated from agricultural sources (Fig. 3c). The HBV-N model also estimates N retention during the water transport from the sources to the sea. However, this natural retention capacity was found to be small in the

Fig. 3. Results from catchment modelling using HBV-N: (A) water flow at the river mouth; (B) N concentrations at Tönnersa close to the river mouth, where monitored time-series are available; (C) contribution from various sources to the river load. Full bars represent gross load within the catchment, while the black part shows the actual input to the sea after natural retention in the fresh-water system.

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Table 1 Characteristics and calculated N retention for wetlands modelled Pond type

Real wetlands Average Median Potential wetlands Average Median

No.

Area (ha)

Depth (m)

Water flow (m3 per day)

Residence time (days)

Load (kg N ha−1 per year)

(kg N ha−1

R2

Retention (%)

per year)

8 0.8 0.5

1.3 1.3

8900 3200

3 3

64200 34300

580 450

3 2

0.64 0.85

2.0 1.7

2.0 2.0

30000 3400

17 11

21700 3200

200 180

8 6

– –

40

“Real wetlands” show the existing wetlands, which were used for calibration of Eq. (2) in the HBV-N model. “Potential wetlands” refer to the hypothetical wetlands included in the modelling of management scenarios for the Genevadsån catchment.

Genevadsån River and only 20% N removal was detected. Reasonable N-removing capacities were achieved when calibrating the wetland routine of HBV-N against time-series from monitored wetlands (Table 1). Two wetlands could be considered as outliers and was not well simulated by the model (Arheimer and Wittgren, 2002), which explains the difference between average and median R2 values in Table 1. One of the outliers was extremely effective for N removal but included a pumping-system, which was not accounted for in this study. The other outlier showed hardly any N removal at all. The modelling of potential wetlands in Genevadsån catchment showed large variation in water inflow, and compared to the real wetlands, the water residence-time was longer and the load lower. In general, they were estimated to be less effective for N retention, but specific removal rates ranged from 57 to 466 kg ha−1 per year for individual wetlands, depending on residence time (size and hydraulic loading) and N concentration in inflow. Due to temperature dependence and seasonal variation in hydraulic loading, significant decrease in N concentrations mainly occurred during summer periods, which were rather dry. Catchment-scale modelling with the HBV-N model showed that the 40 potential wetlands would reduce the N transport to the coast with some 5–6%. 3.2. Management scenarios The actor game was divided into three phases, when different kinds of measures were suggested according to policy settings. The first adaptation phase implied

that the substantive standards in the environmental legislation should be enforced. This was made by tuning of agricultural practices (Fig. 4). Fertilisation was then better adjusted to specific crop needs, catch crops was cultivated with cereals, rye followed potatoes in the crop rotation, and livestock nutrition changed to lower protein content. Model calculations showed that these measures alone reduced the total N load on the coast by 18%, and half of the target regarding the anthropogenic load was reached. The tuning did not include any economic cost but only gain. Since the target was still not met, the implementation phase followed. The regional river-basin authority could now choose between passive or active implementation. First, the actors chose passive implementation, which means that the authority refrains from detailed regulations and relies on voluntary action. Focus was taken on improved urban and rural wastewater treatment and ecological management of spill water. In addition, voluntary co-operation among farmers for manure redistribution was suggested to improve manure storage and timing of fertilisation. However, formal environmental co-operation to implement collective measures in a catchment was not seen by farmers as an attractive alternative. The farming actors were positive to a more even distribution of manure in the catchment, but they rather wanted to achieve this through contracts between a few, trustworthy individuals. Altogether, the passive implementation reduced the N load by 15 t per year (Fig. 4). Active implementation implies general restrictions and demands for reductions. Two main strategies to achieve the ultimate target were elaborated during the game. The first laid an embargo on manure

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Table 2 Costs and N-reducing capacity for changed agricultural practices and constructed wetlands, respectively, based on the three management scenarios to reduce coastal eutrophication (cf. Fig. 4) Measure

Scenario no.

Economics (million SEK)

N-reducing capacity

Cost

Gain

Balance

(t N per year)

+1.9 +2.0 +1.8

+0.9 −47.5 −47.7

−86 −107 −90

−1.7 −2.3

−14 −15

Changed arable practices

1 2a 2b

−1.0 −49.5 −49.5

Wetland constructions

1 2b

−1.7 −2.3

– –

application prior to autumn sowing and on ploughing of ley in early autumn, and requested wetland constructions in suitable sites. The second laid an embargo on commercial cultivation of potatoes in combination with: (a) no manure application prior to autumn sowing, or, (b) establishment of wetlands in suitable sites. According to the model results presented in Fig. 4, all three management scenarios could be accepted for reaching the loading standard of maximum riverine-N transport on 200 t per year. However, the costs involved differed significantly. The removal of potato fields was found to be a relatively expensive measure. Table 2 summarises the results from the scenario modelling and shows that possible changes in agricultural practices (such as tuning, timing of fertilisation and ploughing, changed crop cultivation) could reduce the N load to the sea by some 30%, while wetland construction only reduced the original load by some 5%. In the most cost-effective scenario (i.e. no. 1) agricultural practices could reduce the riverine load by 86 t per year at a cost of 1.0 million SEK, while constructed wetlands only reduced the load by 14 t per year at a cost of 1.7 million SEK. 4. Discussion

Fig. 4. Modelled N load at the river mouth when introducing various measures, and the gains (+) or costs (−) involved. The impact on riverine N load is accumulated from top to bottom in the figure, while the economics consider each specific measure. Modified from Wittgren et al. (2000) and Wittgren et al. (2001).

As expected, the Genevadsån River was found to be in large need of a management plan emphasising diffuse N leaching. Arable land is the overall-dominating source of N load to the river, and the retention in the river system is very small. Only 20% of the gross load is removed during the transport to the sea, which is very little, and should be compared to the average

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N removal for the catchments draining to the eastern coast, which is 55% (Arheimer and Brandt, 1998). The scenario modelling indicated that it is possible to fulfil even quite ambitious environmental targets if the stakeholders are involved. It was interesting to note that the farmers preferred legislation, general restrictions and demands for N reductions by authorities instead of voluntary co-operation. This was mainly because of the risk of being forced to co-operate with actors they feared would be free riders. Several of the suggested measures also did not have a character that made them more profitable if implemented collectively. These circumstances should be paid more attention in the future and need further investigations. The model calculation shows that the coastal N loads could be reduced with less costly measures than previously anticipated, at least in areas with significant animal production. The tuning was much more effective than expected. However, the measures involved demand special knowledge and interest from the farmers and it should be noted that the costs for advising and educating farmers was not included in the economical calculations. In contrast to the agricultural practices, the constructed wetlands were found to be less effective for N removal than expected. This was the case even though the basic equation for wetland retention rather overestimated the retention capacity as no resuspension or net outflow was taken into account, although this is frequently noted in nature. It has been argued that the wetlands would have been more effective if they had been introduced earlier in the chain of measures, and not last when the concentrations have already been reduced (Wittgren et al., 2000). However, when calculating the wetlands first in the chain they reduced the load by 6% (Arheimer and Wittgren, 2002) instead of 5% in the present study. Two other reasons may explain the low annual N-reducing capacity: Firstly, high N load to the sea appears during winter-time in this region when there is a high hydrological load, but unfortunately this coincide with low activity of biochemical processes. Secondly, the summer loads to the wetlands were often far too low, which lead to unnecessary long residence-times. It seems to be difficult to get optimal conditions for efficient N reduction in wetlands when there is such large hydrometeorological variability during the year.

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The wetland allocation was based on topographically realistic siting of 40 wetlands covering in total 0.92 km2 , which would be easy to construct without raising the groundwater level too much (Wessling, 1991). This figure corresponds to 0.4% of the total catchment area. Mitsch and Gosselink (2000) suggested that at least 1% of a catchment should be considered for wetlands to trap nutrients. If it would be accepted to have more land waterlogged in Genevadsån the wetland impact would also be higher, but this would significantly increase the costs involved, as far more surrounding arable land would have been waterlogged in relation to water surface gained. It has already been shown that rather large acreage is requested for wetlands to be effective at the regional scale in Sweden. Previous calculations in another catchment suggested that 5% of the area is needed to be converted into wetlands to achieve 40% reduction of riverine-N load (Arheimer and Wittgren, 1994). These figures are probably site specific and, hence, landscape analysis including hydrometeorological dynamics must precede estimation of the wetland area requested for each catchment. Constructed wetlands have been considered as a cheap alternative to traditional water treatment for the last 15 years. However, the present study clearly shows that wetland establishment must also be compared to alternative measures within agriculture, which also gives additional values by improving groundwater status. In this study changed agricultural practices were found to be the most cost-effective way to reduce riverine-N load in a region with high livestock density. The study shows the importance of careful catchment analysis and landscape planning to find the best management practices to reduce diffuse sources and combat coastal eutrophication. However, the exact model results are based on several basic assumptions, which involve uncertainties, and for more trustworthy results it would be interesting to: (i) further evaluate the wetland model against more wetlands with flow proportional monitoring, (ii) exchange the simplified STANK model against the process-based SOIL-N model, and, (iii) further elaborate the economic assumptions of wetland constructions. It must be emphasised that the conclusions would radically change if the basic assumption of costs involved for wetlands were different. It would also be interesting to examine whether it was justified to extrapolate the management plans

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and economic assumptions developed for the subbasin Prästabäcken to the whole catchment. The actor-game was found to be an efficient method to develop management strategies, which included people’s behaviour and could be accepted by stakeholders. The models and calculation tools showed to be very use-full and appreciated by the actors in the game. At first, some scepticism was present, but then the model results were widely accepted. The game would not have been possible without quantification tools that could give quick answers on complicated and nested environmental processes. However, the tools should be further elaborated, especially regarding the temporal aspect of measure impacts and uncertainty estimates. To address the eutrophication problem seriously it is also important to consider the phosphorus transport and similar scenario tools for P must thus be developed. 5. Conclusions • Actor games and numerical modelling are useful tools for landscape planning in practice, when analysing the stakeholders’ behaviour and the impact of measures to reduce coastal eutrophication. • Changed agricultural practices can be the most effective and less expensive way to reduce nitrogen transport from land to the sea in regions with high livestock density. • Constructed agricultural wetlands may only have small impact on riverine nitrogen transport in some regions, due to natural hydrometeorological dynamics. Acknowledgements This work is part of the Genevadsån study, which was performed within the Swedish Water Management Research Program (VASTRA), financed by the Swedish Foundation for Strategic Environmental Research (MISTRA). The Swedish Ministry of Environment provided additional funding as well as the Swedish Meteorological and Hydrological Institute (SMHI). Our sincere thanks to Markus Hoffmann, Lars Jonasson, Anna Pettersson, and everybody who participated in the actor game!

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the leading group of the national Swedish Water Management Research Programme, VASTRA. She is interested in operationalisation of hydrochemical models for national and regional environmental analysis. Her speciality is modelling of large-scale nutrient transport and transformations in terrestrial and aquatic systems. Her research includes spatial and temporal variability of nutrient leaching in both forested and agricultural environments, and retention processes in lakes and wetlands. She has 10 years experience in interdisciplinary research and holds a Ph.D. in Water and Environmental Studies from Linköping University.

Gunnar Torstensson is a senior research scientist at the Swedish University of Agricultural Sciences (SLU). His scientific interests covers identification and quantification of underlying processes for nitrogen leaching from arable land, and evaluation of counter-measures against leaching. His speciality is long-term field experiments and analyses of the impact from various management methods, e.g. catch crops, manure application rates and times, spring ploughing, ley compositions and time of incorporation. He is also involved in numerical modelling of nitrogen leaching from arable land. He holds a Ph.D. in Agronomy.

Hans B. Wittgren is a senior research manager for systems analysis at the Swedish Institute for Agricultural and Environmental Engineering (JTI) since 2001. Earlier, he was programme director for Swedish Water Management Research Programme (1997–2000), VASTRA and scientist/group manager at Swedish Meteorological and Hydrological Institute (SMHI) (1990–1996). He holds an M.Sc. in Chemical Engineering from Lund University and a Ph.D. in Water and Environmental Studies from Linköping University. His research interests include: ecotechnological systems for wastewater treatment and resource recovery, nutrient transformations and transport in terrestrial and aquatic systems, systems analysis of organic waste management.