Bioeconomic Modelling

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Spatial planning for lowland stream basins usinga bioeconomic model

Paul van Walsum a,*, JohnHelming b, LouisStuyt a,Eric Schouwenberg a, Piet Groenendijka

a Alterra, Wageningen University and Research Centre, PO Box 47, 6700 AA Wageningen, The Netherlandsb Agricultural Economics Research Institute (LEI), Wageningen University and Research Centre,

PO Box 29703, 2502 LS, The Hague, The Netherlands

Received 12 April 2006; received in revised form 28 August 2007; accepted 29 August 2007

Available online 23 October 2007

Abstract

Most lowland stream drainage-basins have a high population density and the land use is very intensive. The permeable subsoil acts as an

integrating medium, thus providing a widespread dispersal of leached nutrients and transmission of water-table lowering. This leads to eutro-

phication and desiccation of stream ecosystems. For providing suggestions with respect to cost-effective and sustainable spatial planning solu-

tions, the Waterwise bioeconomic model has been developed. It combines the accuracy of simulation models with the versatility of

optimization techniques to generate land-use patterns along with the appropriate water management, taking into account the preferences of

stakeholders with respect to peak discharges, nutrient loading on groundwater and surface water, the biological value of nature areas, and

the revenue from agriculture. Computational experiments with the model show, for instance, that a certain goal for the nitrogen load on surface

water can be reached at a 40% lower cost if the measures are tailored to the region instead of using generic-style measures towards the same

end.

2007 Elsevier Ltd. All rights reserved.

Keywords:Lowland hydrology; Agriculture; Nature desiccation; Flooding; Nutrients; Combination of simulation and optimization; Spatial planning; Bioeconomic

model; Stakeholder; EU Water Framework Directive

Software availability

Availability is limited to a reduced version, excluding the

DRAM model.

Name: WaterwiseDeveloper: P.E.V. van Walsum

Contact address: Alterra Wageningen UR, PO Box 47,

6700AA Wageningen, NL;[email protected]

First available: 2007

Minimum hardware requirements: Intel Pentium 4, 1 GHz,

256 Mb

Software required: Windows XP, DASH Xpress

Programming language: Mosel

Software: freely available from http://www.waterwijs.nl; full

version plus demo-version with data set (requires

the freely available Student edition of Xpress, obtain-able fromhttp://www.dashoptimization.com).

1. Introduction

Lowland stream basins have traditionally attracted many

dwellers, owing to their easy accessibility and high land-use

potential. A high population density and intensive land use

are the result. The dense network of channels and the perme-

able subsoil act as an integrating medium, thus providing* Corresponding author. Fax 31 317 419000.

E-mail address: [email protected](P. van Walsum).

1364-8152/$ - see front matter 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.envsoft.2007.08.006

Available online at www.sciencedirect.com

Environmental Modelling & Software 23 (2008) 569e578www.elsevier.com/locate/envsoft
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a widespread dispersal of leached nutrients and transmission of

water-table lowering. This endangers the drinking water supply

and leads to eutrophication and desiccation of stream ecosys-

tems, both the aquatic systems in the streams and the terrestrial

systems in the stream valleys. The biological value of the latter

is due to the presence of shallow water tables in combination

with calcium-enriched upward seepage that provides excellentconditions for vegetation requiring pH-buffered soils. Apart

from the degradation of nature areas and pollution of ground-

water, climate change is adding extra problems; especially

the increase of the flooding hazard is becoming manifest.

To combat the deterioration of river basins, the European

Community has issued a Framework Directive in the Field of

Water Policy (http://europa.eu/scadplus/leg/en/s15005.htm)

stipulating the achievement of several water-related environ-

mental objectives by the year 2015. For realising the ambitious

goals of the directive, it is clear that in many parts of Europe

a substantial reallocation of land use will be needed. This cre-

ates a demand for decision support systems that can provide

suggestions with respect to cost-effective and sustainable spa-tial planning solutions. The system described by, for instance,

Leon et al. (2000) cannot fully satisfy that demand because

the predictive functionality can only be used on a trial-and-error

basis: a spatial set of measures is specified through an interac-

tive user interface, and the effects on the objective functions are

then evaluated. This is repeated until the desired aspiration

levels for the objectives are met. Given the endless possibilities

for specifying spatial patterns of fertilization measures and

land-use reallocations, the achieved result is bound to be subop-

timal, meaning that it will not be cost effective. To achieve the

latter, the use of optimization techniques is required, as has

been done by for instanceNidumolu et al. (2007). But that ap-proach has the disadvantage that the representation of the phys-

ical-biological system has been greatly simplified, without

having any direct links to an underlying set of simulation

models. Such links to simulation models are available in the

RiverWare and WaterWare packages (respectively Zagona

et al., 2001; Jamieson and Fedra, 1996a,b), so that the versatility

of optimization techniques is combined with the accuracy of

simulation models. A similar approach is also followed in

Waterwise (Van Walsum et al., 2002a), a model for supporting

water and land-use planning in lowland stream basins. It is of

the holistic type as described byCai (2008)and Van Delden

et al. (2007). Holistic models are intended for providing deci-

sion support in basins where diverse interconnected problems

exist that are deeply rooted in the way stakeholders make their

living and also depend on environmental services. These prob-

lems are often very persistent and require for their solution

a high degree of cooperation between the stakeholders. One

of the hindrances to finding solutions is that downstream water

users tend to only see the negative effects of upstream activities,

and take the positive ones for granted. Holistic decision support

tools can make the underlying interrelationships between stake-

holders explicit and also suggest solutions that are efficient for

the stream basin community as a whole. They can thus be help-

ful in building the water-space partnerships that are needed

(Van Walsum et al., 2005a). In this article we give a brief

overview of Waterwise, followed by some results demonstrat-

ing its potential for supporting the implementation of the EU

Water Framework Directive.

2. Models

2.1. Representation of the regional system

For predicting effects of measures on a regional hydrologic

system and its dependent functions, the following models have

been coupled:

e SIMGRO (Van Walsum et al., 2004; Veldhuizen et al.,

2006) for regional hydrology; SIMGRO is an integrated

regional hydrologic model with a timestep-by-timestep

two-way coupling of submodels for soil water, groundwa-

ter and surface water;

e ANIMO (Groenendijk et al., 2005) for leaching of nitrates

and phosphates to groundwater and surface water;

ANIMO is a process-based simulation model that simu-lates all relevant components of soil chemistry, including

the carbon cycle; for reasons of computational efficiency,

use has been made of a simplified metamodel based on

regression analysis of a large number of computational

experiments (Schoumans et al., 2002);

e NATLES (Runhaar et al., 1999) for evaluating soil and wa-

ter site conditions in terms of the potential type of natural

vegetation that can develop;

e DRAM (Helming, 2005) for the development of agricul-

ture, modelled as a regional farm; DRAM is a regional-

ized mathematical programming model of agriculture

covering the whole of the Netherlands.

The coupling mentioned above is of the conventional type;

the models are run one after each other. Questions can be

answered of the type What is the effect of removing all agri-

cultural drainage on the (potential) value of wet nature areas?

The models can, however, not be used for answering questions

of the type What is the most cost-effective way to increase the

percentage of valuable wet mesotrophic natural grasslands by

10%? To answer such questions a model is needed that is

more fully integrated. We have developed such a bioeconomic

model using large-scale linear programming (LP, including the

use of binary variables) as the integration framework. The LP

model is the first-level model within a multi-level hierarchi-

cal system as illustrated byFig. 1. The idea is to service two

modelling objectives (e.g. Orlovski et al., 1986):

e achieve a form of model integration that is broad enough

for providing the desired decision support;

e maintain an acceptable predictive capacity of the (simpli-

fied) components.

In the terminology ofLetcher et al. (2007), at the level of

the integrated model the decisions are simulated under condi-

tions of perfect knowledge. If a more accurate estimation of

impacts is required, a cycle can be made via the complex

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models at the bottom of the knowledge pyramids in Fig. 1;

evaluation via this second level can be seen as expectations

based decision making (Letcher et al., 2007).

2.2. Bioeconomic model

For obtaining a LP model of a regional hydrologic system

and its dependent functions, there broadly are two techniques

available, as e.g. explained in Loucks et al. (1981) andGor-

elick (1983). The first technique is that ofembedding, which

involves the wholesale inclusion of (part of) a model. The

second is that of using a repro-function that reproduces the

behaviour of the simulation model for a specific type of mea-

sure. Both techniques have been used for constructing the bi-

oeconomic model Waterwise. A comprehensive run-down of

the model is given (in words) in Appendix A. In the

following we will highlight parts of it that we consider tobe methodologically interesting.

SIMGRO is a complex dynamic simulation model, requir-

ing the repro-function approach for including it in the bioeco-

nomic model. A simpledbut computationally very

intensivedmethod for deriving the repro-functions would be

to let a land and water management option walk through

the study region and then each time do a simulation run to reg-

ister the effects on the nature areas. Since one simulation run

with SIMGRO takes about 10 h, this method is not feasible. To

after all arrive at a (realistic) model of spatial interactions we

make an intermediate step using an analytical multi-layer

steady-state groundwater model. We use it for computing the

effect of raising (or lowering) the water table in a spatial plan-

ning unitsion the conditions in a planning unit sj. The analyt-

ical method is applied for each combination of spatial

planning units, which yields the so-called influence matrix

(see for instance Gorelick, 1983; Ahlfeld et al., 2005). The

matrix can be used for the superimposition of effects, due tothe linear nature of the differential equation describing the

steady-state groundwater flow. The influence matrix is cali-

brated on the results of sensitivity analysis runs with SIM-

GRO; in each of these runs a certain measure is uniformly

applied to the whole agricultural area in the region. A regres-

sion method is used for the calibration. The result is a model-

ling chain with the following components that are shown in the

scheme ofFig. 2:

(1) measures in agriculture areas, defined in terms of land use,

subsurface drainage, and sprinkling;

(2) effects of measures on local water-table conditions in ag-

riculture areas, in terms of effects on the Mean Spring Wa-ter table and the Mean Lowest Water table (from the

sensitivity analysis runs with SIMGRO);

(3) superimposed effects on the steady-state aquifer heads be-

low nature areas, by applying the influence matrix to the

water table effects calculated in step 2);

(4) effects on the dynamics of aquifer heads below nature

areas, by applying a regression function to the effects cal-

culated in step 3);

(5) effects on the water table conditions in nature areas, ex-

tracted in the form of tabular functions from the sensitivity

analysis runs with SIMGRO;

(6) effects of water table conditions in nature areas on the nat-ural vegetation that can develop, extracted in the form of

tabular functions from NATLES-evaluations of SIMGRO

sensitivity runs.

The prediction of head changes in the aquifer below the na-

ture areas is crucial for the validity of the method. An example

of a verification (using the SIMGRO model) is given inFig. 3.

The model NATLES for effects on the potential value of

vegetation in nature areas requires data with respect to the

management (mowing or grazing of grasslands), soil type,

and groundwater conditions. The groundwater conditions are

hydrology ecology economy

complex

Integrated bioeconomic model

simple

Fig. 1. Multi-level modelling with an integrated model connected to complex

models.

1) measures inagriculture area

6) effects innature area

2) water table 5) water tablechanges changes

3) superimposition of effects onaquifer heads

4) X calibration

aquitard

aquiferfactors

Fig. 2. Modelling chain for calculating hydrologic effects of measures in agriculture areas on nature areas.

571P. van Walsum et al. / Environmental Modelling & Software 23 (2008) 569e578


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given in terms of the so-called Mean Spring Water table, the

Mean Lowest Water table, and the gross seepage to the root

zone. (See Van Walsum et al. (2002b) for the coupling be-

tween SIMGRO and NATLES; the mean water table refers

to over-the-years averaging.) These data are then transformed

to suitability maps through a stepwise procedure involving

grids in an ArcView software shell (ESRI, 1996). NATLES

is incorporated in the bioeconomic model through the repro-function method in its most simple form: results of sensitivity

analyses with the regional hydrologic model SIMGRO are

routed through the ArcView shell of NATLES, and are stored

in tabular form for use in the bioeconomic model, in step (6)

of the procedure given above. A verification example of the

combined SIMGRO-NATLES model is given in Table 1

(Van Walsum et al., 2002a; see also Section 3). Results of

the NATLES model are presented in terms of nature goal re-

alizationW, i.e. the percentage of non-desiccated nature area.

The results have been aggregated for nature subareas, each

containing several tens of groundwater model nodes; thus

the modelling errors shown in Fig. 3 are largely averaged

out. As can be seen from the table, the results computed by

the simulation models are in nearly all cases better (i.e.

have a higher Wvalue) than those computed by the optimiza-

tion model. These better than differences are due to the fact

that the aspiration levels for the nature goal realizations (W

values) are specified by the user; the optimization model

then seeks to comply with these constraints. It can happen

that strict constraints for some of the nature subareas lead to

non-binding constraints for some of the others. This has to

do with the proximity of other nature areas with stricter con-

straints. So the computed better than values are not caused

by model defects. To further improve the accuracy of themodel, the parameters have also been made dependent on

the desired level of goal realization W itself; this yields

a higher accuracy than when a general relationship is used.

This is especially important for regions where there is

a non-linear relationship between the water table effects of

step (2) and the effects on the aquifer heads below the nature

areas of step (4). Such a non-linearity is usually caused by the

presence of surface water channels that become more actively

draining due to the higher upward seepage caused by higher

aquifer heads. An example involving the mentioned non-line-

arity is provided in Van Walsum et al. (2006).

The model DRAM (Helming, 2005) is a national model for

agriculture in the Netherlands. Most of the model equationsare in a linear form. That made it possible to realize a style

of integration symbolized inFig. 4: Waterwise overlaps with

a substantial part of DRAM. So here the embeddingtechnique

has been used. The embedded part DRAM-WW concerns the

land balances, the manure balances, and the nutrient balances

in terms of the nutritional value of N and P for crops, the bal-

ances of the fodder for livestock, and finally the objective

function in terms of total revenue. The latter contains terms

for the yield of arable land crops, the yield of intensive live-

stock farming, the yield of dairy farming, and the subsidies

on special types of agricultural land-use that are nature

friendly. The revenue function also contains terms for thecosts of chemical fertilizer, the costs of manure application,

the costs of manure export to other regions, the local costs

of changing the type of land-use, and the regional costs of ex-

panding a certain type of production. The latter term is derived

using the PMP approach (Positive Mathematical Program-

ming, see Howitt, 1995), taking into account the simulated

markets at a national scale. Since this part of DRAM is not ex-

plicitly included in the bioeconomic model, the functions are

delivered to it in the form of quadratic regional cost functions.

The resulting convex form of the total revenue function re-

flects the law of decreasing marginal returns on increasing pro-

duction; the implementation is done using a piece-wise linear

function (Loucks et al., 1981). The manner in which the qua-

dratic cost term is derived and included in the model is a typ-

ical example of the repro-function method.

For relating land and water management measures to peak

discharges of the streams, the sensitivity analysis runs with

y= 0.90x + 0.0006

r = 0.94, r2 = 0.88

-0.2 -0.1 0.0 0.1 0.2

Ha,WATERWISE

(m)

Ha,SIMGRO (m)

-0.2

-0.1

0.0

0.1

0.2

Fig. 3. Verification of the optimization model for water quantity interactions.The predicted changes of the aquifer head as computed by the optimization

model (Waterwise) are plotted against those predicted by the simulation

model (SIMGRO).

Table 1

Comparison between the computed indicator for nature goal realization ( Win %) by the simplified optimization model and by the SIMGRO-NATLES simulation

models

Nature area 1 2 3 4 9 10 11 13 15 17 18 19 20

Optimization model 49 15 74 25 48 29 12 21 45 68 91 95 56

SIMGRO-NATLES 60 (24) 15 (4) 77 (33) 25 (2) 48 (8) 31 (7) 20 (9) 24 (10) 62 (30) 69 (40) 91 (42) 93 (34) 53 (35)

The simulation results for the current state are given in parentheses.



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SIMGRO are analysed in terms of the incremental flow contri-

bution that each spatial planning unit makes in a period with

high rainfall. These contributions are then stored as coefficients

of the bioeconomic model. For relating land-use measures to

the leaching of nutrients, the results of the sensitivity analyseswith SIMGRO are routed through the metamodel of ANIMO

(Schoumans et al., 2002) using nutrient surpluses derived from

the agricultural model DRAM. Nutrient surpluses are defined

in terms of land application minus the crop uptake. The regis-

tered effects on the nitrate and phosphate leaching to surface

water and groundwater are stored as coefficients. For model-

ling the spreading of leachates in the aquifers a simple mixing

cell model is used as shown inFig. 5. The nitrate mass-balance

equations of the cells (for the equilibrium state) are embedded

as equality constraints in the LP model; the concentrations are

included as decision variables, i.e. as the unknowns. In order to

keep the model linear, the water flows are handled as coeffi-cients that are fixed at the beginning of a model run. The equa-

tions include a decay term for the denitrification of nitrate

under anaerobic conditions. The justification for computing

the steady-state equilibrium is that only in this manner can

asustainableplanning solution be found.

For handling the multiple objectivefunctions the simple con-

straint method is used. The user/stakeholder supplies aspiration

levels for the nature goal realization (reduction of desicca-

tion), the reduction of the peak discharges (taking also into ac-

count the possible effects of climate change), and the reduction

of the nitrogen leaching. The bioeconomic model first ascertains

whether there is a solution at all, and (if there is one) then finds

the land and water use pattern that satisfies the constraints and at

the same time optimizes the revenue from agriculture.

The bioeconomic model has been implemented in the Mo-

sel language of the Xpress-mathematical programming pack-

age of DASH (2006). The Newton Barrier algorithm is used

for solving the resulting linear programming problem. Com-

pared to simplex this interior point method drastically re-

duces computation times of large-scale problems.

3. Results and discussion

The Waterwise model has been applied to the Beerze and

Reusel stream basin in the Netherlands. The (twin) basin

covers an area of some 45,000 ha. The nature areas cover

roughly 15,000 ha of the area; about 22,000 ha are in use by

agriculture. For the bioeconomic model the study region was

divided into 4000 spatial units. The implemented model has

roughly 200,000 functional decision variables, 60,000 active

equations, and about 2 million coefficients in the LP matrix.

On a P4, 2.4 GHz PC, the solution time is about 0.5 h. To dem-onstrate the model we have made a sequence of runs for strat-

egies as listed inTable 2.

The objectives for nature goal realization, peak dis-

charges and nitrogen loading have been converted into con-

straints, and the model was then run to optimize the total

economic revenue. It is especially interesting to analyse the re-

sults in terms of synergies (or conflicts) between the men-

tioned objective functions that are converted into constraints.

For instance, strategy 4 involving the reduction of desiccation

and of peak discharges leads to a loss of revenue of 9.7 MV/

year. The total costs of the component strategies 1 and 2

are respectively 2.1 and 1.2, totalling 3.3 MV/year. This yields

a synergy of 3.3 9.7 6.4 MV/year. Such a negativevalue reveals the conflict between combating desiccation on

the one hand and the reduction of peak flows on the other;

to reduce desiccation the field drainage around the nature areas

should be removed. But to reduce peak flows (here with a re-

turn period of 10 years) the field drainage should actually be

expanded. The reason for the latter is thatdalthough drainage

increases the discharge having a return period of e.g. 0.5

yeardthe drainage increases the storage capacity of the soil

for when a really extreme precipitation event occurs. So the

drainage lowers the discharge having, e.g. a return period of

10 years by reducing the surface runoff from saturated soils.

For many policy makers this result is counterintuitive, andthis analysis therefore contributes to understanding the conflict

between the two objectives. More details of results with re-

spect to desiccation, peak flows and climate change are given

inVan Walsum et al. (2005b).

The generated land-use pattern for the integrated strategy of

run 7 (of Table 2) is shown in Fig. 6. The model includes

consequences of environmental

constraints for agricultural activities

DRAM

restrictions on total areas of crops

regional quadratic cost functions

Waterwise

DRAM-

WW

Fig. 4. Embedding of part (DRAM-WW) of the national agricultural model

DRAM in the regional bioeconomic model Waterwise.

Table 2

Optimization results for the indicator total economic revenue, for 7 strategies

Strategy Description of strategy Revenue loss

(MV/year)

Synergy

(MV/year)

1 nature goal realizationsas given inTable 1 2.1 e

2 reduction of peak discharges

(with a return period of 10 years)

by 20%

1.2 e

3 reduction of nitrogen loading on

surface waters by 50%;

10.4 e

4 nature, peak discharges 9.7 6.4

5 nature, nitrogen loading 11.2 1.3

6 peak discharges, nitrogen loading 12.1 0.5

7 nature, peak discharges, nitrogen

loading

17.2 3.5

Current situation: 95 MV/year. The synergy is computed for strategies 4e7

by comparing the total of the revenue losses of the component strategies (1, 2,

and 3) with the loss of the combined strategy. For instance the synergy of

strategies 1 and 2 is computed as 2.1 1.2 9.7 6.4.



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a penalty term for deviating from the current state; this sym-bolizes the costs of a transition, and avoids generating model

results that are too far removed from the current state to be re-

alistic. It is interesting to note that the model apparently finds

it efficient to remove some of the agricultural land and convert

it to nature area; this can be seen from the large expanses of

new natural grassland and of new forest. The conversion

to new natural grassland is used for reducing the desiccation

of natural grasslands. This particularly concerns the low-lying

spots that in the past have been drained to make them suitable

for agricultural production. The next best option for reducing

desiccation is by introducing low intensity grassland around

the wet natural grasslands, because this option does not in-volve the use of field drainage. The low intensity grassland

also has a low manuring intensity, and thus also contributes

to reducing the nitrogen loading. New forest is popular

with the model because it contributes to two of the three ob-

jectives: reduction of nitrogen loading and reduction of peak

flows. The latter is achieved through the increased evapotrans-

piration, which creates more storage capacity in the soil; this

mechanism is similar to that of field drainage, as mentioned

earlier. But the increased evapotranspiration also has a lower-

ing effect on the water tables, so the model keeps the new for-

est at a safe distance from the (wet) natural grasslands. Apart

from reducing the intensity of (agricultural) land use it is inter-

esting to note that the model also generates locations with the

highest intensity of grassland use, involving high levels of ma-

nure application. The economic sense of this tendency is that it

simply is very efficient for a farmer to get a high return per

unit of area, because that reduces a whole array of costs, espe-

cially of labour. We will take a closer look at this by making

a comparison between these model results and the results for

policies currently being considered.

In order to reduce nitrogen leaching it has been considered

to set a maximum loss of 60 kg N ha1 year1 for each and

every location in the Netherlands; this limit refers to thetotal

loss of nitrogen to groundwater and surface water from a

certain field location. When applied to the study area this

generic-style measure reduces the nitrogen load on surfacewater from 9.5 mg N l1 to 4.5 mg N l1 at the outlet of the ba-

sin. Roughly half of this load consists of nitrogen that reaches

surface water by surface runoff and by shallow leaching to

groundwater and subsequent drainage to surface water at the

field scale. The other half reaches the surface water after

deep infiltration to groundwater, transport through the ground-

water (modelled with the mixing cells of Fig. 5), upward

seepage, and finally drainage to surface water in the streams.

(The concentrations are in fact loads, and not the real concen-

trations; nitrogen retention processes in surface water are

not modelled). The computed loss of revenue from agriculture

is 19%.A computational experiment was made with the bioeco-

nomic model to see whether the same 4.5 mg N l1 could be

achieved at a lower cost. The model showed that the 4.5 mg

N l 1 could also be achieved at a revenue loss of only 11%

(run 3 of Table 2). As can be seen from the comparison of

the nitrate concentrations inFig. 7, the generic-style measures

produce a concentration pattern (Fig. 7a) that is much more

evenly distributed than if the measures are tailored for a mini-

mum loss of agricultural revenue (Fig. 7b). In the latter case the

concentrations in uplands are much higher, because in these

parts of the region the bioeconomic model does not remove

all of the medium- and high-intensity dairy farming like the

generic-style measures do. The reasoning behind this strategy

is that if the high nitrogen concentrations are in the uplands, the

travel times through the deep subsoil are the longest, and there-

fore the denitrification of the nitrate can reduce the concentra-

tions by the time the water reaches the surface water system

through upward seepage and drainage: in the right hand map

(Fig. 7b) the concentrations near the streams are in general

lower than in the left-hand map (Fig. 7a) of the generic-style

measures. By making use of the groundwater as a denitrifica-

tion machine the bioeconomic model achieves the same envi-

ronmental goal (4.5 mg N l1 at the basin outlet) at a 40%

lower cost (11% loss of revenue instead of 19%). The model

coefficients with respect to nitrogen loading and denitrification

CN,s,i

CN,g,i,2

layer 1

layer 2

layer ..cell i

Fig. 5. Mixing cell scheme for simulating the transport of nitrate in surface water and in groundwater; the nitrate mass balances of the cells are embedded as

equality constraints in the bioeconomic model, with the concentrations in surface water ( CN,s) and in groundwater (CN,g) as the decision variables (i.e. as the

unknowns in the equations).



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are, however, very uncertain, because much is not yet known

about the underlying processes. But this example does demon-

strate how the bioeconomic model can take advantage of the

way a regional system functions in order to achieve environ-

mental goals at the lowest possible cost.

4. Concluding remarks

A bioeconomic model has been developed for spatial plan-

ning of integrated land and water management in lowland

stream basins. The technique of linear programming (and its

extension involving the use of binary variables) has been

used as a framework for the integration of models. The system

owes its practical relevance to:

ethe possibility for the user/stakeholder to specify goals and

constraints for the desiccation of nature areas, the nutrient

loading on groundwater and surface water, peak dis-

charges, and the revenue from agriculture;

e the predictive accuracy of the simplified submodels incor-

porated in the bioeconomic model, based on results of

simulations with complex dynamic models; simulation

models are also used for verification of the spatial solu-

tions found by the bioeconomic model;

ethe use of state-of-art optimization technology, providing

a spatial resolution of 10 ha for basins of up to

50,000 ha, within acceptable computation times that are

needed for facilitating a decision-making process.

The latter point has been demonstrated by the successful

implementation for the Beerze and Reusel basin in the Nether-

lands. A series of computational experiments demonstrates the

potential of the model for revealingdand quantifyingdcon-

flicts and synergies between regional objectives. Suggested

solutions can be counterintuitive, thus deepening the insight

into the regional system functioning. It can for instance be

economically efficient in some parts of a basin to relax the en-

vironmental constraints on agriculture, in order to reach goals

Fig. 6. Generated land-use pattern for the integrated strategy (run 7 ofTable 2) involving reduction of desiccation in existing nature areas, reduction of peak dis-

charges, and reduction of nitrogen loading on surface waters.



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at the stream basin scale. Such a differentiated approach can

also help in obtaining stakeholder support. The message here

is to not think one-dimensionally about how to achieve envi-

ronmental goals.

Crucial for the success of the simplified modelling in the

bioeconomic model is that the submodels are accurate enough

to facilitate the computational process of searching for a cost-

effective solution. For the final estimation of the goals that

actually are achieved the simulation models are needed. The

verification with SIMGRO-NATLES for the effects of mea-

sures aimed at combating desiccation showed that the results

of the simplified submodel were accurate within 10e15% of

the simulation models. But the only verification of a simplified

optimization model thatreallycounts is whether the model can

come up with solutions that make better use of the regional

system than is the case with hand-made sets of spatial mea-

sures. For obtaining formal proof of that it would be neces-

sary to define the level of intelligence behind the hand-made

alternatives. We have confined ourselves to giving a compari-

son between the (lack of) cost efficiency of generic style mea-

sures and the efficiency of measures obtained through

optimization aimed at reducing the nutrient loading on surface

waters.

The model is now being further developed (Van Walsum,

2007; Mysiak, 2007) within the scope of the Newater project

(Pahl-Wostl and Kabat, 2004), which is focused on developing

new approaches to adaptive water management under uncer-

tainty. For handling the latter, the model is being extended

to include multiple events, meaning that the decision simula-

tion will simultaneously take into account different possibili-

ties of external conditions for climate and socio-economy.

We expect that in the near future the model will play a role

in discussions about the implementation of the EU Water

Framework Directive. A combination with decision support

systems like MULINO (Giupponi, 2007) and Watersketch

(Ulvi et al., 2007) could enhance its application potential by

structuring the interaction with the stakeholders.

Acknowledgements

This research was funded by the Strategic Expertise Devel-

opment Fund of the former Directorate for Agricultural Re-

search (DLO) that is now part of the Wageningen University

and Research Centre. Use has been made of experience gained

during IIASAs Regional Water Policies Project in the period

1983e1985, led by S.A. Orlovski. Research funding for the

current model development is from the EU NeWater project

(http://www.newater.info ).

N

(b)(a)

NO3-N(mg/l)

0-2

2-55-10

10-25

>25

2 Kilometers0

Fig. 7. Generated patterns of the nitrate concentration in the first aquifer (second layer of the subsoil schematization) for generic-style measures to decrease the

amount of nitrogen losses to the environment (a) and for tailor-made measures using the bioeconomic model (b).

http://www.newater.info/http://www.newater.info/


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Appendix A. Brief description of Waterwise

A.1. Introduction

A linear programming (LP) model consists of three types of

components: decision variables, constraints, and objective

functions. The list of decision variables does not only includevariables that directly relate to the actual policy decisions, but

also includes a large number of auxiliary variables that are

needed for describing the system functioning. The land-use

options are described using continuous variables on the inter-

val [0,1] instead of binary ones. Most model results for these

decision variables are anyhow equal to zero or to unity; subse-

quent rounding of those that are not does not significantly af-

fect the values of objective functions. Strict binary variables

are used for representing non-linear aspects of the surface wa-

ter system. The term constraint is also used for the system

equations, i.e. that part of the matrix that describes the func-

tioning of the regional system with all its interdependencies.

The method used here for handling the multi-objectivity isthat of the simple constraint method, meaning that all but

one of the objective functions are converted into constraints

using the desired aspiration level on the right-hand side. The

regional economic revenue (as far as it is related to the deci-

sion variables) is the objective function that gets optimized.

A.2. Decision variables

All decision variables are non-negative:

eFractions of land use type lwith water management option

w in planning unit ieIncrements and decrements of land use, as compared to the

current state

eChemical fertiliser applications using nutrient n on land

use lin planning unit i

eManure applications using manure type m on land use lin

planning unit i

eManure transports to other regions

eIntensities of animal husbandry type h in planning unit i

e Locally induced changes of the mean spring water table

and the mean lowest water table in all planning units i

e Regionally induced changes of the mean spring aquifer

head and mean lowest aquifer head below planning units

i that are nature areas

ePeak surface water flows through the trajectories o

eConcentrations of nitrogen and phosphorus in surface wa-

ter trajectoryo

e Concentrations of nitrogen in groundwater of planning

unit i and aquifer a

A.3. Constraints

eSum of land use fractions per planning unit i

e Diverse interrelationshipsbetweenlandusetypes, describing the

farming system per planning unitiand in the region as a whole

eManure balances per planning unit i

eManure export/import to/from other regions

eNutrient balances per planning unit i and land-use type l

eBalances of locally produced and consumed animal feed-

stuffs (maize, grass)

eAuxiliary equations for constructing piece-wise linear

agricultural cost functionse Influences of locally induced water-table changes on re-

gional aquifer heads

eConstraints on regionally induced aquifer-head changes, to

comply with the aspiration levels of the wet nature area

indicators for natural grasslands (see objective functions)

e Summation of peak discharge contributions (of land use

and water management options) in downstream direction

of the stream network

eConstraints on peak discharges at key points in the stream

network (see objective functions)

eSummation of nutrient loading (of land use and water

management options) on surface water trajectories

eSummation of nutrient loading in downstream direction ofstream network

eLoading of nitrogen on groundwater

eNitrogen mass balances in mixing cells of groundwater

and surface water for computing steady-state equilibrium

eConstraints on concentrations, according to the aspiration

levels at key points in the stream system (see objective

functions)

A.4. Objective functions

eTotal regional economic revenue (related to the decisionvariables), including costs of land and water management

practices, costs of changing land use type, subsidies for

multifunctional land use

eGoal realization in wet natural grasslands

ePeak discharge at key points in the stream network, with

a return period of 10 years

eNitrogen and phosphorous concentrations at key points in

the surface water system

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