Landscape Ecology 17 (Suppl. 1): 69–81, 2002.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
69
A decision support system for restoration planning of stream valley
ecosystems
N.M. Pieterse1,∗ , A.W.M. Verkroost1 , M. Wassen1 , H. Olde Venterink1,2 and C. Kwakernaak3
1 Department
of Environmental Science, Utrecht University, The Netherlands; 2 Present address: IHE, Wetland
Ecosystems, PO Box 3015, 2601 DA Delft, The Netherlands; 3 Department of Water and the Environment, Alterra
Green World Research, Wageningen, The Netherlands; ∗ Author for correspondence (Present address: Grontmij
Group, Department of Watermanagement and Hydrology. PO Box 203, 3730 AE De Bilt , The Netherlands; e-mail:
[email protected])
Key words: Decision support systems, Ecological economics, Ecological models, Landscape planning, Restoration
Abstract
Despite efforts that have been put into conservation, there is a continuing loss of species and ecosystems in Western
Europe. There is a growing awareness that restoration is an essential step to stop this tide. Unfortunately, there is
lack of understanding about the multitude of functions and the complexity of spatial interactions in a landscape.
The focus of this paper is to demonstrate that an Integrated Decision Support System (IDSS) is indispensable to
offer insight in this complexity and to design efficient restoration programmes. The IDSS is applied in a lowland
catchment on the border between The Netherlands and Belgium and leads to the following recommendations: (1)
The site conditions on the location where restoration is planned must be close to the range that is required for the
target ecosystem. (2) The manager has to decide for the most attainable targetecosystem, and accept the inevitable
loss of other ecosystems as a result from this choice. (3) Restoration planning involves that the optimal measure for
each catchment, subcatchment or region is assessed, being ecological, urban or agricultural. (4) For each ecosystem
an optimal set of measures must be selected. An analysis of the restoration efficiency (ecological gain divided by
economic costs) is crucial for this selection.
Introduction
Wetland ecosystems in Western Europe have been
lost or are threatened in spite of efforts for conservation, international agreements and national policies
(McCollin et al. 2000; Rich and Woodruff 1996; Stanners and Bourdeau 1995). While up to the 1950s
lowland valleys contributed to wetland biodiversity
with clean meandering streams and marshy woodlands, nowadays, large-scale reallotment, intensified
agriculture, urbanization and stream works have left a
small part of these ecosystems (Pedroli 1989; Stanners
and Bourdeau 1995). The remaining ecosystems are
continuously threatened by anthropogenic influences
such as the drop of water levels and chemical changes
of groundwater and surface water (Dodds et al. 1998;
Runhaar et al. 1996, 1997). During the last ten years
policy and nature protection organizations have be-
come aware that restoration of wetlands is an essential
step to stop this tide.
When the decision is made to restore a wetland,
a debate starts about the efficiency of restoration measures: what are the abiotic and ecological effects? How
do we optimally use our money and space? Answering
these questions is not easy in present day multi-land
use catchments. Measures meant to realize restoration targets interfere with existing economic functions;
they may not always yield the expected ecological
gain, or might even cause deterioration of existing
ecosystems. To make restoration projects successful,
information is needed about the trade-off between ecological gain of measures and the optimal use of limited
money and limited space.
Unfortunately, there is a lack of understanding
about the multitude of functions and the complexity of
70
Figure 1. Location and shape of the catchment of the river Dommel.
spatial interactions in a landscape. Turner et al. (2000)
argue that integrated wetland research combining social and natural sciences might be helpful to solve
the information problem. The aim of this paper is to
investigate if an Integrated Decision Support System
(IDSS) can be helpful to fill the information gap. The
value of the IDSS approach and its opportunities for
spatial planning are discussed.
An IDSS is built and demonstrated for the river
Dommel on the border between the Netherlands and
Belgium. Four different types of restoration measures
and two groups of target ecosystems were selected:
wet woodland ecosystems and aquatic ecosystems.
Models to calculate the environmental site conditions
for these ecosystems were integrated in a Geographical Information System (GIS). The IDSS spatially
quantifies the change of environmental site conditions,
the gain or loss for ecosystems and the economic costs.
Study area
The river Dommel is a tributary of the river Meuse
(Figure 1), draining an intensively agricultural and
heavily populated area (approx. 600,000 people) of
1,350 km2 . Groundwater hydrology is dominated by
infiltration in upland sandy areas and seepage in valleys. The mean annual rainfall is 740 mm a−1 , and
the reference evapotranspiration is 560 mm a−1 . The
average annual runoff is 190 mm a−1 , of which 70%
can be ascribed to groundwater seepage (Pieterse et al.
1998a). Geo referenced land use data (Figure 2a) were
derived from satellite images in the Dutch part of
71
Figure 2. (A) Anthropogenic influences in the catchment of the river Dommel. Black cells represent urban areas (18% of the catchment), grey
cells represent agricultural areas (50% of the catchment), white cells represent natural or forested areas (32%). (B) Black cells represent the
river network, composed from a digital terrain model and river topology. (C) Black cells represent the riparian zones, defined as the zone around
the river banks with peat sediments or wet sandy soils. Cell sizes are 500 m.
the watershed (Thunnissen et al. 1992), and from the
national inventory of the vegetation of Belgium (De
Blust et al. 1985). Agriculture occupies 49% of the
catchment, 26% of the catchment is forested and 18%
is urbanized. Heather, semi-natural grassland, ponds
and the drainage network occupy the remaining 7%.
Cells defined as streams are shown in Figure 2b, cells
defined as riparian area are shown in Figure 2c. Only
808 ha of natural riparian woodlands are left in the
area (Anonymous 1990; De Blust et al. 1985) which
is 2% of the total of 31,950 ha riparian zones in the
catchment. For the occurrence of aquatic communities only a biotic index of stream communities was
available (Bervoets and Schneiders 1990). This index
showed that 5 out of 37 sampling stations are well developed, 15 are slightly disturbed and 17 are heavily
disturbed.
Figure 3. General layout of the integrated decision support system.
Methods
A GIS based decision support system
Most elements of the IDSS (Figure 3) were constructed in a GIS. The GIS provided the tools to
manage data; create hydrological-, hydrochemicaland ecological models; create the structure for the
logical sequence of modelling steps and visualize the
data. The GIS database contained raster-based input
maps with cell sizes ranging from 50 × 50 to 500 ×
500 m.
Choice of ecosystems
The first step in the IDSS was to select the appropriate
ecosystems to be modelled. The selection was based
on historical references (Pedroli and Borger 1990)
and biodiversity in the catchment. A subset of these
72
Figure 4. Schematic diagram of the integration of abiotic models in a GIS. Rounded boxes are results of input maps, fat outlined squares are
coupled models, open arrows indicate a sequence of operations in the GIS, closed arrows indicate a result or dependence.
Table 1. Environmental site conditions and class borders for ecosystems in lowland stream valleys.
Model
Environmental site conditions
Class borders
Riparian woodlands
(ALNION)
Average groundwater level
below surface.
Wet:
Drained:
Aquatic ecosystems
(ECOSTREAM)
Saprobic state
Intermittence
Flow velocity
Clean:
Intermediate:
Polluted:
Permanent:
Intermittent:
Stagnant:
Running:
≤0.25 m (peat soils) or
≤0.30 m (mineral soils)
>0.25 m (peat soils) or
>0.30 m (mineral soils)
0–0.2 mg l−1
0.2–0.5 mg l−1
>0.5 mg l−1
≤42 days year−1 dry a
>42 days year−1 dry
≤0.1 m sec−1
>0.1 m sec−1
a A stream is considered to be dry when discharge is less than 1 l sec−1 . All class borders were derived
from Olde Venterink et al. (1998a,b). Saprobic state is defined as Dissolved Organic Nitrogen (DON).
ecosystems is chosen, namely riparian woodlands that
depend on wet or moist conditions and aquatic ecosystems that depend on clean, fast running and permanent flowing conditions. The project that delivered
the data for this paper also included wet and moist
grassland ecosystems and included the terrestrial site
condition acidity for grasslands and woodlands. The
choices were according to policy goals and they were
supported by local nature protection organizations.
Ecological models
Over the last decades, several ecological response
models have been developed to assess the effects of
changing environmental site conditions on ecosystems
(e.g., Fitz et al. 1996; Latour and Reiling 1993; Olde
Venterink and Wassen 1997; Witte et al. 1993). Ecological models generate an output that is a ‘potential
ecological change’. This means that an ecosystem
will change upon changed site conditions given that
other site conditions, e.g., management or seed dispersal do not prevent this. Because there are many
73
The occurrence of wet and drained woodlands was
assessed with the decision tree model ALNION (Olde
Venterink et al. 1998a), based on average groundwater levels (c.f. Table 1). The occurrence of aquatic
ecosystems was assessed with the decision tree model
ECOSTREAM (Olde Venterink et al. 1998b), based
on intermittence of streams, flow velocity and saprobic
state of the stream water (c.f. Table 1).
Abiotic models
Figure 5. Spatial distribution of each of the four measures.
(A) Measure 1: stopping groundwater extraction wells, represented
by dots or circles. (B) Measure 2: locations where infiltration in
urban areas is enhanced (black cells). (C) measure 3: locations
where natural stream properties are restored (black cells). (D) Measure 4: locations with reduction of organic compounds from diffuse
sources. Grey cells is a reduction of 50%, black cells a reduction of
100%. White cells are not affected by the measures.
site conditions that might limit or affect occurrence
of ecosystems, a selection of adequate site conditions
was required. The criteria for the selection of site conditions were: (1) site conditions should have a major
influence on biota of the target ecosystem; (2) it should
be possible to adequately simulate site conditions on
a landscape scale; (3) restoration measures should be
influential for the state of the site conditions. The
selected site conditions are listed in Table 1.
The models to simulate site conditions were integrated
in a GIS (c.f. Figure 4). The site condition intermittence was simulated with the model STREAMFLOW
(Pieterse et al. 1998a). The core of STREAMFLOW
is a dynamic water balance, carried out for every cell
in the catchment for every ten days during a sevenyear period. Dynamic stream discharge includes the
components fast runoff, base flow and effluent from
households and industries. The water balance model
incorporates climatic influences, the influence of vegetation type on the soil moisture content and dynamic
groundwater levels.
Average groundwater levels were simulated with
the finite element groundwater flow model MODFLOW (Harbaugh 1990). The geology in the Dommel catchment was schematised in cells of 500 ×
500 m over 9 model layers (Pieterse et al. 1998b).
Average groundwater levels were derived by subtracting groundwater simulations from a detailed digital
terrain model with a cell size of 100 × 100 m.
Because both hydrological models are mutually dependent (STREAMFLOW provides recharge rates to
MODFLOW and MODFLOW provides annual seepage rates to STREAMFLOW), iteration steps were applied. The iteration criterion was an identical average
base flow within a 5% range.
Flow velocity was estimated with the Manning formula, being a function of water depth, stream width,
slope, and roughness of the streambed (c.f. Pieterse
et al. 1998a). Basic assumptions were that the hydraulic slope and stream width remain constant over
time and that one value for the roughness parameter
could be used for the whole catchment.
Saprobic state of the stream water was represented
by Dissolved Organic Nitrogen (DON). Basic properties are (Pieterse et al. (1998a): (1) Spatial assessment
of the sources (loads), being individual households,
industries and wastewater plants. (2) Estimation of
nitrification of DON during transport in the river network, as a function of the residence time in a cell and a
74
Figure 6. Summary of the simulated impact of four measures on the Dommel catchment. Values in the figure are derived from maps. The
four graphs A–D indicate the four site conditions in Table 1. The numbers 1 to 4 on the X-axis refer to the four measures (see also Table 2
and Table 3). Change of site conditions is represented by a high-low bar at the bottom of each of the four graphs, showing the highest- and
lowest change in the catchment. Ecological change is represented by filled bars at the top of each graph and shows gain (positive values) or loss
(negative values) of the corresponding ecosystem. Unit is km2 (woodlands) or km (aquatic ecosystems).
75
Table 2. Selected measures in the Dommel catchment.
Code
Measure
Cell type
Action
1
Reduction of ground
water extractions
–
2
Enhance infiltration
in urban areas
Urban
3
Restore natural
stream properties
Designated for
naturea and nature
4
Stop effluent from
individual households,
not connected to
wastewater plants
Designated for
naturea and nature
Termination of water extractions for industry with a
total pumping capacity of 15 million m3 a−1 .
Reduction of water extractions for drinking water
with 20%, in total of 10 million m3 a−1 .
Separating rainwater from sewerage in already
existing urban areas. The number of sewers is
decreased with 15%.
1. Removal of weirs (increase of the hydraulic head).
2. Re-meandering: length of stream increased with 50%.
3. Depth of riverbed was set to 0.4 m below the riverbank.
100% purification of effluent by local treatment plants.
Rural areas
50% purification of effluent by local treatment plants.
a Areas that are ‘designated for nature’ are areas with a present-day economic function, but will be used to restore natural
conditions in the future.
Table 3. Economic costs of various measures. Unit is US$. i.e. = one inhabitant equivalent. Values are derived from
Kwakernaak et al. (1998).
Code
Measure
Specification
Costs per unit
Amount
of units
1
Reduction of ground water
extractions for:
Enhance infiltration in
urban areas
Restore natural stream
properties
Stop effluent from
individual households,
not connected to
wastewater plants
Industry
Drinking water
–
1.5 US$ m−3 yr−1
3.0 US$ m−3 yr−1
9,700,000 US$ km−2
12,100,000 m3
11,600,000 m3
190 km2
Development in the stream
Acquisition of land
Urban
59,400 US$ km−1
2,376,000 US$ km−2
568 km2
59 km2
0 i.e.
Designated for naturea
nature
Other
1188 US$ i.e.−1
80656 i.e.
2
3
4
20609 i.e.
a Areas that are ‘designated for nature’ are areas with a present-day economic function, but will be used to restore
natural conditions in the future.
decay constant. Residence time was derived from flow
velocity calculations. (3) Concentration is load divided
by discharge.
Policy analysis and restoration measures
A policy analysis was carried out to investigate what
kind of measures were feasible to apply and on which
location these measures could be realized (Kwakernaak et al. 1998). The selection of measures was
based on the merits that they are generally accepted
to lead to nature restoration and at the same time are
acceptable to policy actors and landowners in the area.
From the ten selected measures in the Dommel project,
only four are discussed in this paper (Table 2). The
spatial distribution of each measure is shown in Figure 5 (A–D). The four measures were: (1) Stopping
of groundwater extraction wells, the first measure to
consider in most restoration projects. (2) Enhance infiltration in urban areas. This measure should provide
both a quality enhancement in the stream and cause
higher groundwater levels due to enhanced groundwater recharge. (3) Restore natural stream properties. The
idea behind this measure was to go back in time, and
rewind human interferences as much as possible. Most
Dutch streams have been straightened, which caused
76
should be compared with each dollar spent. The IDSS
provides all the information to calculate restoration
efficiency (Equation 1). To optimise restoration planning, restoration-efficiency can best be calculated for
every measure per ecosystem.
Restoration − Efficiency =
Ecological gain − Ecological loss
. (1)
Economic cost
Economic costs is in US$ and ecological gain and -loss
in km2 (woodlands) or km (aquatic ecosystems).
Results
Figure 7. Economic costs per measure. Measure codes ‘1’ to ‘4’ on
the x-axis refer to the measures in Table 2. Measure 1 is expressed
as US$ a−1 ; for all other measures money is spent once, unit is US$.
erosion and subsequently lowering of the riverbed.
Moreover, weirs in the stream channel influence fish
migration and stream velocity (Pedroli 1989; Verdonschot 1990). (4) Stop effluent from households.
This measure is directed on the site-condition saprobic
state. The aim of this measure was to reduce point
loads and diffuse loads. In this paper only the effect
of reduction of loads from individual households was
analysed.
Abiotic and ecological change
The actual situation of various environmental site conditions (c.f. Table 1) was simulated as a reference for
the effect of restoration measures. Abiotic change is
presented as the difference in simulation results. Ecological change is presented as an increase (gain) or
decrease (loss) of the number of cells where potential
occurrence of an ecosystem was predicted, relative to
the predicted actual situation.
Economic costs
The assessment of economic costs (Kwakernaak et al.
1998) was based on an analysis of acquisition costs,
management costs and opportunity costs, where the
latter are economic losses that occur when a measure interferes with existing economic functions (van
Ierland and de Man 1996). Table 3 lists these economic costs per measure per unit. Only the costs that
surpassed existing policy were taken into account. Because space and money is limited, ecological gain
Dynamic stream discharge, including intermittence,
was calibrated at the outflow point of the catchment
(Nash-Suthcliffe R 2 = 0.78) and 9 other locations
within the catchment (Nash-Suthcliffe R 2 of −0.60;
0.33; 0.47; 0.60; 0.68; 0.72; 0.78; 0.80 and 0.80,
respectively). The spatially distributed capability of
the year-average discharge yielded an R 2 of 0.82.
Correlation between simulated and observed average
groundwater levels yielded an average discrepancy of
+0.12 m with a standard deviation of 0.69 m (n =
139). Simulated DON concentrations matched adequately with field data (R 2 = 0.6, n = 31). Stream
velocity could not be calibrated because there were no
field data available.
Validation of simulated actual occurrence of
ecosystems was only qualitative because the number
and size of natural ecosystems in the Dommel catchment was insufficient. A rough validation for woodlands reveals that the dry conditions in the catchment
were adequeately reconstructed: 4% of the riparian
zones could be classified as wet woodlands whereas
field observations (anonymous 1990; De Blust et al.
1985) show that 2% of the riparian zones are wet
woodlands (Olde Venterink et al. 1998a). According to observations of aquatic ecosystems (Bervoets
and Schneiders 1990), simulations adequately separated the clean channels from polluted channels (Olde
Venterink et al. 1998b).
The model exercises (Figure 6A–D and Figure 7)
show differences in ecological gain, abiotic change
and economic costs. The amplitudes of abiotic change
for each of the measures vary per measure and per site
condition. If it were the aim to enhance groundwater
levels (Figure 6A), the most effective measure is to
stop groundwater extraction wells (measure 1). The
least effective measure is reduction of diffuse effluent
(measure 4), which did not yield any effect (c.f. de Wit
77
Figure 8. Restoration efficiency of each of the measures. (A) For wet woodlands. (B) For aquatic ecosystems depending on clean stream water.
(C) For aquatic ecosystems depending on permanent running water. (D) For aquatic ecosystems depending on high flow velocities.
1999). If it were the aim to enhance water quality (Figure 6B), the best measure would be to reduce diffuse
effluent. The least effective measure is restoration of
natural stream conditions (measure 3) that even caused
deterioration. If it were the aim to reduce the intermittence of streams it would be best to apply infiltration in
urban areas. Restoration of natural stream conditions
caused deterioration. If it were the aim to restore fast
flowing streams (Figure 6D) only restoration of natural stream conditions (measure 3) produced an effect,
which was positive in some channels and negative in
other channels.
By translating the absolute abiotic change into
ecological gain some ecosystems do not respond in
accordance (Figure 6A–D). The most apparent are wet
woodlands, benefiting by rising groundwater levels
that were best restored with measure 3 ‘restoring natural stream properties’. All other measures did not, or
only slightly, contribute to ecological gain. Some measures show a positive effect on the restoration chances
in some channels and at the same time a negative effect
in other channels. This is clearly demonstrated with
measure 3 ‘restoration of natural stream properties’ in
Figure 6B–D.
78
Figure 9. Spatial distribution of the predicted gain of wetland
ecosystems. (A) The effect the measure 3 ‘restore natural stream
properties’. Black cells are cells where recovery of wet woodlands is
predicted. Grey cells indicate loss or did not change. (B) The effect
of measure 4 ‘stop diffuse effluent’. Black cells are cells where recovery of clean aquatic ecosystems is predicted. Grey cells indicate
loss or no change.
Economic assessment of each of the four measures
shows that, when applied on the catchment scale as
in Figure 5A–D, the second measure (enhance infiltration in urban areas) is by far the most expensive with
1,700 million US Dollar. The cheapest measure, with
15 million US dollar, is reduction of diffuse effluent.
Restoration-efficiency refers to the ecosystem
profit that is expected for every dollar spent (Figure 8). According to this figure, restoration of wet
woodlands (Figure 8A) can be achieved for the lowest
price by restoring natural stream properties. Restoration of clean aquatic ecosystems (Figure 8B) can best
be achieved by reducing point sources of effluent.
Permanent aquatic ecosystems (Figure 8C) only cost
money, except from a small gain that can be achieved
by infiltrating water in urban areas. Efficient restoration of fast running aquatic ecosystems (Figure 8D)
can only be achieved by restoring the natural stream
properties.
Discussion and conclusions
It is important for policy makers and managers of
nature reserves to make the right decisions for nature reserves under their custody. However, decisions
about possible measures are not easy if we take into
consideration the large uncertainty in assessing ecological gain and that money is limited. We show that
integration of abiotic models, ecological models and
economic cost assessment is crucial to solve the information problem as observed by Turner et al. (2000) in
their search to quantify the trade off between ecology
and economy of wetlands. Unfortunately, we cannot
simply add up the ecological gain and loss that is calculated for each measure and then select the best for
restoration planning. There are several reasons for this.
Firstly, simply adding up gain or loss would be double
counting the area of aquatic ecosystems. For instance,
a simple measure like removing a weir in a channel
improves flow velocity and enhances intermittence at
the same time. This doesn’t mean that there is gain for
one ecosystem that is responding on flow velocity and
loss for another ecosystem that is responding on intermittence. Instead, the changes of both site conditions
affect only one ecosystem in the channel. Secondly,
adding up different habitats (e.g., aquatic ecosystems
and terrestrial ecosystems) would be like comparing
apples and oranges. Thirdly, researchers working with
an IDSS should not aim to put everything in one single
number, but should instead aim to present objective
data and allow managers to make the best decision
on restoration of those ecosystems that fit best in the
landscape or are considered to be historically correct.
This study must be seen as a tentative study for
the quantification of the opportunities and constraints
in spatial planning. We expect that models will have
an error due to the model concept (Connolly et al.
1997; van Horssen et al., 1999), due to propagation of
errors (Heuvelink et al. 1989) and due to scale problems (Burrough 1995; Clemen 1998; Fitz et al. 1996).
And the error of some of the elements of the IDSS
cannot be quantified at all (e.g., the economic assessment). However, De Wit (1999, 2000) demonstrated
that interdisciplinary analyses on a landscape scale
with complex underlying processes can be performed.
Three obvious conclusions can be drawn. Firstly,
the results show that for some measures increase of
ecological values is not proportional to abiotic change.
This can be explained when we take into account
that the tolerance of ecosystems for environmental
conditions is often narrow (e.g., Bio 2000; Runhaar
et al. 1997). Consider, for instance, a location where
groundwater levels are 4 m below the surface. Managers aim to restore wet woodlands on that site. Even
with a measure that causes an increase of 3 m the
aimed ecosystem will not return because wet woodlands require waterlevels of at minimum 0.3 m below
79
the surface. In contrast, some measures that yield only
small effects might be more effective if they are implemented on locations where site conditions are already
at or close to the critical range for the desired ecosystem (Luijendijk and Helmich 1997; Luijendijk and
Straatman 1997; Oomes et al. 1996). Hence, restoration must be tailor-made in respect to the right measure
on the right location. Secondly, results also show that
restoration measures will not always lead to ecological gain but also can lead to loss of ecosystems. It
has already been demonstrated that management influences ecological communities in the affected area
(e.g., Grootjans 1980; Grootjans et al. 1985). Comparably, we show that a measure meant to restore one
ecosystem may be conflicting with other ecological
functions. For instance, measures meant to enhance
flow velocity in a stream by removing weirs might
cause low groundwater levels along this stream, and
vice versa. This means that restoration of aquatic
ecosystems that depend on a permanent water flow
might lead to loss of groundwater dependent ecosystems in the vicinity of the stream. The idea of modelling conflicts between ecological functions due to
management is not new (Garritsen 1993; Kluge et al.
1994), but we are to our knowledge the first who try
to quantify the ecological gain and loss of different
ecosystems at the same time. Other studies or available
models were mostly confined to one habitat (Latour
and Reiling 1993; Miller 1994; Olde Venterink and
Wassen 1997; Qiong et al. 1997; Witte et al. 1993).
So the message is: a policy maker or manager of
a nature reserve should bear in mind that any measure aiming at restoration of a specific ecosystem may
cause deterioration of other already existing ecosystems. The manager has to make a decision in favour of
the most attainable ecosystem, and take the loss that
comes with it. Thirdly, a cost benefit analysis (Figure 7 and 8) shows that even if the abiotic effects of
a measure yields high ecological profit, the measure
might be too expensive to implement and therefore be
a sub optimal choice. This is demonstrated by comparing the ecological effects of measure one (stopping
groundwater extraction wells) and measure two (enhance infiltration in urban areas). Measure 2 yields a
higher ecological gain, but is it also the better choice?
Even if the investment for measure one would return
every year for ten years to come, it will still have
a higher restoration efficiency than measure 2 (Figure 8). This leads to the conclusion that not only the
right measure on the right place for the right ecosystem need to be considered, but that also the price-tags
need to be examined carefully (e.g., Costanza 1996;
Grossmann 1994).
Calculations for restoration-efficiency have so far
not taken into account that every subcatchment might
offer another potential for different ecosystems. It
is argued that the efficiency of restoration planning
would increase considerably if an optimised set of
measures could be defined for the catchment, offering great opportunities for spatial planning. As an
example, detailed spatial results of the effect of two
restoration measures are shown in Figure 9A and B.
One can easily imagine that the modeller can select
regions in these two maps (9A & B) that offer the highest ecological gain for one ecosystem and combine
it with other measures that enhance the overall effect. This optimisation might lead to restoration plans
for different ecosystems in every channel or subcatchment, or even might lead to the conclusion that, for
instance, agriculture is by far the most beneficial for
that subcatchment. In other words, restoration planning involves that a selection must be made of the
optimal function for each catchment or region.
From our study the conclusion is drawn that it is
most likely that not one single measure will be the
best for restoration of ecosystems. Instead, an IDSS
approach must be used to define a set of measures that
is optimised for location, target ecosystem and costs.
Acknowledgements
The research described in this paper was funded by
the EU LIFE program ‘Demonstration project for
the Development of Integrated Management Plans
for Catchment Areas of Small Trans-Border Lowland
Rivers: the River Dommel’. Waterboard de Dommel,
the province of North Brabant, The Flemish Environmental Company and the Flemish Institute of Nature
Conservation, provided the necessary data, which is
greatly acknowledged.
References
Anonymous, 1990. Vegetatieonderzoek. 5: Kartering Midden- en
Oost-Brabant. Karteerhandleiding, Provincie Noord-Brabant.
Reeks Landschapsonderzoek no.13, Den Bosch, The Netherlands.
Bervoets L. and Schneiders A. 1990. Onderzoek naar de Verspreiding en de typologie van de ecologisch waardevolle waterlopen in
het Vlaamse gewest. Algemene Methodologie, Rapport Universitaire Instelling Antwerpen, Antwerpen, Belgium.
80
Bio A.M.F. 2000. Does Vegetation Suit our Models. Data and
Model Assumptions and the Assessment of Species Distribution in Space. Ph.D. Thesis, Utrecht University, Utrecht, The
Netherlands, 192 pp.
Burrough P.A. 1995. Opportunities and limitations of GIS-based
modeling of solute transport at the regional scale, applications of
GIS to the Modeling of Non-Point Source Pollutants in the Vadose Zone, ASA-CSSA-SSSA Bouyoucos Conference, Mission
Inn, Riverside, CA, USA.
Clemen T. 1998. The use of scale information for integrating simulation models into environmental information systems. Ecol.
Model., 108(1–3): 107–113.
Connolly R.D. Silburn D.M. and Ciesiolka C.A.A. 1997. Distributed
parameter hydrology model (ANSWERS) applied to a range of
catchment scales using rainfall simulator data. III. Application to
a spatially complex catchment. J. Hydrol. 193(1–4): 183–203.
Costanza R. 1996. Ecological economics: reintegrating the study of
Humans and Nature. Ecol. Applic. 6(4): 978–990.
De Blust G., Froment A., Kuyken E., Nef L. and Verheyen R. 1985.
Biologische waarderingskaart van België. D/1985/2505/21, Ministerie van Volksgezondheid en van het Gezin, instituut voor Hygiëne en Epidemiologie. Coördinatiecentrum van de Biologische
Waarderingskaart.
De Wit M. 1999. Nutrient Fluxes in the Rhine and Elbe Basins.
Ph.D. Thesis, Utrecht University, Utrecht, The Netherlands, 163
pp.
De Wit M. 2000. Modelling nutrient fluxes from source to river load:
a macroscopic analysis applied to the Rhine and Elbe basins.
Hydrobiologia 410: 123–130.
Dodds W.K., Hones J.R. and Welch E.B., 1998. Suggested classification of stream trophic state: Distributions of temperate stream
types by chlorophyll, total nitrogen, and phosphorus. Water Res.
32(5).
Fitz H.C. et al. 1996. Development of a general ecosystem model
for a range of scales and ecosystems. Ecol. Model., 88: 263–295.
Garritsen A.C. 1993. Linking hydrological and ecological models,
The use of hydro-ecological models in the Netherlands: CHOTNO meeting, Ede, The Netherlands, pp. 67–79.
Grootjans A.P. 1980. Distribution of plant communities along
rivulets in relation to hydrology and management. In: O.
Wilmanns and R. Tüxen (eds.), Berichte über die internationalen
symposien der I.V.V. 1979. Cramer verlag, Germany, pp. 143–
170.
Grootjans A.P., Schipper P.C. and van der Windt H.J. 1985. Influence of drainage on N-mineralisation and vegetation response in
wet meadows I. Calthion palustris stands. Acta Oecol., 6(20):
403–417.
Grossmann W.D. 1994. Socio-economic ecological models: criteria for evaluation of state-of-the-art models shown on four case
studies. Ecol. Model. 75/76: 21–36.
Harbaugh A.W., 1990. A computer program for calculating subregional water budgets using results from the U.S. Geological
Survey modular finite-difference ground-water flow model, U.S.
Geological Survey, Reston, Virginia, USA.
Heuvelink G.B.M., Burrough P.A. and Stein A., 1989. Propagation
of errors in spatial modeling with GIS. Internl. J. Geogr. Inform.
Syst. 3(4): 303–322.
Kluge W., Muller-Buschbaum P. and Theesen L. 1994. Parameter
acquisition for modelling exchange processes between terristrial
and aquatic ecosystems. Ecol. Model. 75/76: 399–408.
Kwakernaak C., Van der Windt N., Van der Gaast J., Van Os J. and
Pieterse N.M. 1998. Demonstration Project for the Development
of Integrated Management Plans for Catchment Areas of Small
Trans-Border Lowland Rivers: The River Dommel. 7. Naar een
ecologisch herstel van het Dommeldal; Scenario’s voor het EUproject LIFE-Dommel, Winand Staring Centrum for Integrated
Land, Soil and Water Research (SC-DLO), Wageningen, The
Netherlands.
Latour J.B. and Reiling R. 1993. A multiple stress model for vegetation (‘MOVE’); a tool for scenario studies and standard setting.
Sci. Total Environ. Suppl: 1513–1526.
Luijendijk J. and Helmich F.A.M. 1997. Hydrologische bufferzones tegen verdere verdroging natuurgebieden. Praktijkstudie
provincie Noord-Brabant. H2 O 30(4): 100–103.
Luijendijk J. and Straatman R. 1997. Ecohydrologische effectvoorspelling winplaats Luyksgestel. H2 O 30(23): 705–708.
McCollin D., Moore L. and Sparks T. 2000. The flora of a cultural
landscape: environmental determinants of change revealed using
archival sources. Biol. Conserv., 92: 249–263.
Miller D. 1994. Coupling of process-based vegetation models to
GIS and knowledge-based systems for analysis of vegetation
change. In W.K. Michenen, J.W. Brunt and S.G. Stafford (eds.),
Environmental Information Management and Analysis: Ecosystem to Global Scales. Taylor & Francis, New York, NY, USA,
pp. 497–509.
Olde Venterink H., Pieterse N.M., Wassen M.J. and Verkroost
A.W.M. 1998a. Demonstration Project for the Development of
Integrated Management Plans for Catchment Areas of Small
Trans-Border Lowland Rivers: The River Dommel. 5. Alnion, an
Ecohydrological Response Model for Wet and Moist Woodlands
in Brook Valleys. Department of Environmental Science, Utrecht
University, Utrecht, The Netherlands.
Olde Venterink H., Pieterse N.M., Wassen M.J. and Verkroost
A.W.M. 1998b. Demonstration Project for the Development
of Integrated Management Plans for Catchment Areas of
Small Trans-Border Lowland Rivers: The River Dommel. 6.
Ecostream, A Response Model for Aquatic Ecosystems in Lowland Streams., Department of Environmental Science, Utrecht
University, Utrecht, The Netherlands.
Olde Venterink H. and Wassen M.J. 1997. A comparison of six
models predicting vegetation response to hydrological habitat
change. Ecol. Model. 101: 347–261.
Oomes M.J.M., Olff H. and Altena H.J. 1996. Effects of vegetation
management and raising the water table on nutrient dynamics
and vegetation change in a wet grassland. J. Appl. Ecol. 33: 576–
588.
Pedroli G.B.M. 1989. The Nature of Landscape. PhD Thesis,
University of Amsterdam, Amsterdam, The Netherlands.
Pedroli G.B.M. and Borger G.J., 1990. Historical land use and hydrology, a case study from eastern Noord-Brabant. Landscape
Ecol. 4: 237–248.
Pieterse N.M., Olde Venterink H., Schot P.P. and Verkroost A.W.M.
1998a. Demonstration Project for the Development of Integrated
Management Plans for Catchment Areas of Small Trans-Border
Lowland Rivers: The River Dommel. 2. Streamflow, a GIS-based
Environmental Assessment Tool for Lowland Rivers, Department of Environmental Science, Utrecht University, Utrecht, The
Netherlands.
Pieterse N.M., Schot P.P. and Verkroost A.W.M. 1998b. Demonstration Project for the Development of Integrated Management
Plans for Catchment Areas of Small Trans-Border Lowland
Rivers: The River Dommel. 3. Simulation of the Regional
Hydrology of the Dommel Catchment (in Dutch, with English Summary), Department of Environmental Science, Utrecht
University, Utrecht, The Netherlands.
Qiong G., Ning L. and Xuejun D. 1997. A modelling analysis
on dynamics of hilly sandy grassland landscapes using spatial
simulation. Ecol. Model. 98(2–3): 163–172.
81
Rich T.C.G. and Woodruff E.R. 1996. Changes in the vascular
plant floras of england and scotland between 1930-1960 and
1987-1988: The BSBI monitoring scheme. Biol. Conserv. 75:
217–229.
Runhaar J., van Gool C.R. and Groen C.L.G. 1996. Impact of hydrological changes on nature conservation areas in the Netherlands.
Biol. Conserv., 76: 269–276.
Runhaar J., Witte J.P.M. and Verburg P.H. 1997. Ground-Water
level, moisture supply, and vegetation in the Netherlands. Wetlands 17(4): 528–538.
Stanners D. and Bourdeau P. 1995. Europe’s Environment, The Dobris Assessment. European Environment Agency (EEA), Copenhagen, Denmark.
Thunnissen H.R., Olthof P., Geltz P. and Veltz L. 1992. Grondgebruiksdatabank van Nederland vervaardigd met behulp van
Landsat Thematic Mapper opnamen. 168, DLO-staring centrum,
Wageningen, The Netherlands.
Turner R.K. et al. 2000. Ecological-economic analysis of wetlands:
Scientific integration for management and policy. Ecol. Econ.,
35: 7–23.
van Horssen P.W., Schot P.P. and Barendregt A. 1999. A GIS-based
plant prediction model for wetland ecosystems. Landscape Ecol.,
14: 253–265.
van Ierland E.C. and de Man, N.Y.H. 1996. Ecological engineering:
First steps towards economic analysis. Ecol. Engin. 7: 251–371.
Verdonschot P.F.M. 1990. Ecological Characterization of Surface
Waters in the Province of Overijssel. PhD Thesis, Wageningen
Agricultural University, Wageningen, The Netherlands, 255 pp.
Witte J.P.M., Groen C.L.G., van der Meijden R. and Nienhuis
J.G. 1993. DEMNAT: a national model for the effects of water
management on the vegetation. In: J.C. Hooghart and C.W.S.
Posthumus (eds.), The use of hydro-ecological models in the
Netherlands. TNO Committee on Hydrological Research, Ede,
The Netherlands, pp. 31–51.