dynamic modelling of a shallow freshwater lake for ecological … · 2014-11-05 · ecological...

Ecological Modelling 167 (2003) 115–138

Dynamic modelling of a shallow freshwater lake forecological and economic sustainability

Burak Güneralp∗, Yaman BarlasDepartment of Industrial Engineering, Bogaziçi University, Bebek Istanbul 80850, Turkey

Received 5 October 2001; received in revised form 21 November 2002; accepted 17 April 2003

Abstract

This research deals with the dynamic simulation modelling of a shallow freshwater lake ecosystem and analysis of potentialsustainable management policies. The study region consists of a shallow freshwater lake and its surroundings, where fishing isa major commercial activity. The lake is under high nutrient loads, hence eutrophic with macrophyte dominance. The goal ofthis research is to find a balance between the ecosystem and economic activities in the region. To this end, a system dynamicsmodel of the wetland is constructed. The results obtained from model simulations show that there is no threat of a shift toalgal dominance in the near future. The major problem seems to be a potential decline in the welfare of the inhabitants, mainlydue to unsustainable population increase. Different scenario runs reveal that the lake would have become eutrophic with algaldominance, if the crayfish population did not collapse due to a fungus disease in 1986. One particular scenario analysis (therecovery of crayfish sometime in the future within the model time frame) results in increase in crayfish harvest; hence in incomefrom fishing, leading to betterment in social conditions. As for the alternative policies tested, ‘improved agricultural techniques’is the only policy that leads to better social conditions, through increased yield per hectare. It is hoped that the dynamic simulationmodel will serve as a laboratory to study the different features of the eutrophication problem in shallow freshwater lakes and toanalyse different policy alternatives with an integrated, systemic approach.© 2003 Elsevier B.V. All rights reserved.

Keywords: Shallow freshwater lakes; Eutrophication; Dynamic simulation modelling; Sustainable management; System dynamics method

1. Introduction

Wetlands, in general, are extremely important andrich ecosystems. Similarly, shallow lakes form one ofthe most fragile ecosystem types on earth, and theyare generally the first to perish under development ac-tivities (Barbier et al., 1997). Millions of people live

∗ Corresponding author. Present address: Department of NaturalResources and Environmental Sciences, University of Illinois atUrbana-Champaign, S. Goodwin Avenue, Urbana, IL 61801, USA.Tel.: +1-217-333-2770; fax:+1-217-244-3219.

E-mail address: [email protected] (B. Güneralp).

near the shores of shallow lakes and their lives dependon conditions of those lakes. They have complex eco-logical structures and high rate of productivity, bothof which support the bulk of diversity associated withfreshwaters (Jeppesen et al., 1997a). Furthermore,shallow lakes are generally situated on farmable low-lands, which makes them more vulnerable to humandisturbances.

Research on shallow lakes has intensified only re-cently. The report prepared byMoss (1998)has a ref-erence section that constitutes a long list of valuablestudies on shallow freshwater lakes. Amongst those,Jeppesen et al. (1990)deals with interactions between

0304-3800/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0304-3800(03)00172-8

116 B. Güneralp, Y. Barlas / Ecological Modelling 167 (2003) 115–138

Nomenclature

BOD : Biological Oxygen Demand (mg/l)Chloro a : chlorophylla. An indirect measure

of algal biomass (�g/l)DHKD : Dogal Hayatı Koruma Dernegi

(the Society for the Protectionof Nature)

DO : dissolved oxygenDSI : Devlet SuIsleri

(State Hydraulic Works)GIS : Geographical Information SystemsN : nitrogenNGO : Non-Governmental OrganisationNH4

+ : ammoniaNO2

− : nitriteNO3

− : nitrateP : phosphorusPO4

3− : phosphatePVI : Percent Volume Inhabited. A

measure of a water body’s volumethat contains aquatic macrophytes

SE : Secchi disk transparency (m)TKV : Türkiye Kalkınma Vakfı (the

Development Foundation of Turkey)

different trophic levels, an important contribution toour understanding of shallow lakes. Also,Scheffer(1998)has a very useful book on ecology of shallowlakes.

Studies on shallow lakes suggest that there may betwo alternative equilibria over a range of nutrient con-centrations: a clear state dominated by macrophytesand a turbid state dominated by high algal biomass.Switches between these two states are possible. How-ever, there are buffer mechanisms that may preventsuch a switch even if the conditions are favourableit to happen. The resistance of buffer mechanismsdepends on factors such as nutrient release from sed-iments, magnitude and duration of nutrient loading,hydraulic flushing rate and trophic structure, all ofwhich have important implications for managementof shallow lakes (Scheffer et al., 1993; Jeppesen et al.,1997b; Jeppesen, 1999).

Achieving both conservation and effective naturalresource utilisation requires careful analysis of the re-gion under study. The relations amongst wildlife, peo-

ple, economics, government, etc. almost always con-stitute highly complex dynamic systems. So a holis-tic point of view is to be adopted and considerableamount of effort must be devoted to field studies toanalyse the system and offer improvements.

Three sub-systems are essential elements in asustainable management practice:ecological, eco-nomic and social. The ecological sector considersthe ecological conditions on which life depends. Theeconomic sector considers economic developmentand other non-market activities that contribute tosocio-economic well-being. Finally, the social sectordeals with demographics, equity and disparity withinthe human population. Also, the boundaries of thesystem must be carefully determined not to breakany major links between elements. A crucial charac-teristic of a sustainable management practice is thecontribution of various disciplines and involvementof all interested groups (Hardi and Zdan, 1998).

2. Study region

The region modelled is in Temperate Climate Belt,the wetland under study being in Turkey. The regionconsists of a shallow lake named Uluabat (or Apoly-ont), rivers flowing in and out of the lake, groundwa-ter, and lands surrounding the lake. The precipitationin the region is characterised by wet–dry year cycleswith a more or less regular pattern (as revealed bydata). These cycles have important implications forthe hydrological dynamics of Lake Uluabat. They af-fect the depth of the lake, most importantly duringsummer months. The changes in the depth of the lake,in turn, lead to changes in the ecological dynamics—most notably the trophic state—of the lake. The lakeis an important nesting place for bird species, someof which are under the threat of extinction. Althoughthe lake meets the Ramsar Criteria,1 it was not un-der conservation status until recently. This situation

1 The Ramsar Convention states, “Wetlands should be selectedfor the List on account of their international significance in terms ofecology, botany, zoology, limnology or hydrology.. . . the RamsarCriteria include four clusters: (1) criteria for representative/uniquewetlands, (2) general criteria based on plants/animals, (3) specificcriteria based on waterfowl, and (4) specific criteria based on fish”(The Ramsar Convention Manual, 1997).

B. Güneralp, Y. Barlas / Ecological Modelling 167 (2003) 115–138 117

resulted in “development” activities, which damagedthe ecosystem including the habitat of endangeredbird species (ME, 1998). Crayfish (Astacus leptodacty-lus) population was an important element of the lakeecosystem until 1986, the year when the populationwas infected by fungusAphanomyces astaci (Yararand Magnin, 1997). The disease is commonly knownas the ‘crayfish plague’. The lake, under continuousload of organic wastes, is faced with the threat of fur-ther eutrophication (Inan et al., 1997). Presently, theorthophosphate concentration in the lake is around0.09 mg. According toJeppesen (1999), in most shal-low lakes there is a threshold value of 0.1–0.15 mg P /l,above which macrophytes cannot maintain clear waterstate. Clearly, there is a risk of a switch to the phyto-plankton dominated turbid state for the lake.

The inhabitants rely on fishing, agriculture and—until recently—crayfish harvest. Agriculture is themost pervasive way of subsistence among the in-habitants; on the other hand, fishing and its relatedindustry are quickly replacing the crayfish harvest andindustry (Inan et al., 1997). Tomato cultivation is mostsignificant. The tomato paste factories in the regionmostly process tomato from fields around the lake.The production equals 80% of Turkey’s tomato pasteproduction. The irrigation is done by water pumpedfrom the lake and its major inflow, M. Kemalpa¸saRiver. The intensive fertiliser and pesticide use maybe potential sources for the pollution of the lake,which would in turn affect the agriculture. However,no empirical evidence is available yet (Inan et al.,1997; Demir et al., 1998).

Another problem about the wetland is intensivefishing. This is one of the main activities that haveadverse effects on the fragile wetland ecosystem(MARA, 1994).

The industry in the wetland depends on agriculture.The—mostly organic—waste of these factories alsothreatens the wetland ecosystem. The installation oftreatment facilities is the most important method toreduce the amount of wastes. Apart from these pollu-tion sources, rivers carry toxic and organic wastes offactories and households in their catchments, furtherworsening the state of the lake (Inan et al., 1997).

There are also projects (some in construction phase)of Devlet SuIsleri (DSI, State Hydraulic Works) putforward to regulate the hydrology of the lake. Thoughthe inhabitants are somewhat aware of most of these

problems, it is still important to educate them andthus raise the awareness to desired levels with the en-deavours of local organisations, and NGO’s such asDogal Hayatı Koruma Dernegi (DHKD, the Societyfor the Protection of Nature) and Türkiye KalkınmaVakfı (TKV, the Development Foundation of Turkey).

The ultimate goal of this research is to find a bal-ance between the ecosystem and human activities inthe region, so as to secure a continuous improvementin well-being of the inhabitants while improving (ormaintaining) the ecosystem. To this end, a systemicdynamic model of the wetland system is constructedby identifying the main elements and the interactionsamong them. After validation of the model, variouspolicies are analysed by altering some parametersand/or relations in the model.

3. Model description

The model is constructed using system dynam-ics methodology, an effective tool in dealing withdynamic problems (such as tropical deforestation,chronic high levels of inflation, unsustainable popu-lation growth or in this study, excessive nutrient load-ing) (Forrester, 1968; Sterman, 2000). Understandingof how the existing physical processes, informationflows and management strategies interact in creatingthe dynamics of the variables of interest is the focalpoint of this methodology. These elements and therelationships between them constitute the ‘structure’of the system. Thus, it is said that the ‘structure’ ofthe system, operating over time, generates its ‘dy-namic behaviour patterns’ (such as S-shaped growth,exponential growth or decline). The totality of modelequations constitutes the ‘structure’ of the model.It is essential in system dynamics methodology thatmodel structure should provide a valid description ofthe real system (Forrester and Senge, 1980; Barlas,1996). The purpose in a typical system dynamicsstudy is to reveal how and why the problematic be-haviour is generated and to find leverage points inthe system that would be effective in eliminating theproblematic behaviour. These leverage points are thenused to generate ‘policies’ (such as fish or agriculturalmanipulation) to improve the situation.

The main building blocks of a formal systemdynamics model arestocks, flows and auxiliary


(converter) variables. Stocks (symbolised by rectan-gles) are also known as levels or state variables. Theyrepresent major accumulations in the system. Flows(symbolised by valves), also known as rates, changethe value of stocks. Stocks in a dynamic system in turninfluence the values of flows. Flows represent activi-ties that fill in or drain the stocks. Intermediate con-cepts or calculations are known as auxiliary variablesor converters. Converters are computed from stocks,constants, data, and other converters—unless they areconstants. (SeeFig. 1efor illustrations of these threetypes of variables.) The focus and the time frame ofthe study are important in deciding which elementsof the system are to be represented as stocks, flows orconverters.

Most system dynamics studies are concerned withthe long term rather than the short term, and hence,they are all related to sustainable management to a cer-tain extent. There is considerable amount of study inthe system dynamics literature on creating sustainablesolutions to social, economic and organisational prob-lems (Saeed and Radzicki, 1998). For instance,Wils(1998)evaluates the relative advantages of extractionand end-use efficiencies in natural resource manage-ment. More recently,Saysel et al. (2002)focus on aregional sustainable management application of sys-tem dynamics. On the other hand, this paper presentsa more local application of the methodology concen-trating on a lake’s ecosystem.

The model has three sectors as mentioned ear-lier: lake ecosystem, economic activities and socialstructure on the periphery of the lake. Each consistsof several sub-sectors (Güneralp, 2000) (Fig. 1a).The variables of the model are represented in italicsthroughout the paper.

The ecological elements of the lake and their in-teractions are modelled in thelake ecosystem sector.In construction of the ecosystem sector, limited stud-ies done on shallow lakes have been used. The timeunit is month and, for the purpose of this research,the lake ecosystem is modelled in just enough de-tail to reflect the fundamental system dynamics andto have a meaningful input–output exchange with theother two sectors. Theeconomic activities sector in-cludes all activities of the inhabitants with significanteconomic value, such as industrial facilities, farmingaround the lake and fishing. Thesocial structure sec-tor deals with the demographics of the inhabitants,

tightly related with the functioning of the other twosectors. The acronyms used in the model are listed inNomenclature.

3.1. Main assumptions for the model

There are 11 major assumptions of the model:

• There are two major fish species in the lake: pike(Esox lucius) and carp (Cyprinus carpio).

• Regeneration of fish and crayfish takes place all yearlong.

• Flood plain mechanism (flooding of the lake’sshoreline during peak seasons of the lake volume)is ignored in the model.

• Immature pike/carp and immature crayfish are notlanded.

• Macrophyte cover area is based on lake surface area,since there is no available updated data on the lakebottom.

• Dynamics of nitrogen fixation by organisms and dis-solved oxygen (DO) in the lake are ignored in themodel.

• 1995 real prices are used.• In initial conditions, 50% of crops processed in fac-

tories come from within the region and the rest fromoutside the region.

• The effect of rainfall on irrigation is assumed neg-ligible.

• Only rural population in the region is considered.• Random effects are ignored in the model because of

the lack of available data and because randomnessis unlikely to change the major behaviour patternssignificantly. Therefore, average values are assignedto all parameters and inputs.

3.2. Lake ecosystem sector

The sector consists of four sub-sectors:hydrology,nutrients and other indicators, chloro a–zooplankton–macrophyte andfish and crayfish. A broad, summarycausal-loop diagram of the ecosystem sector is givenin Fig. 1b.

Thehydrology sub-sector is the skeleton of thelakeecosystem sector. It is the sub-sector in which thephysical state and the hydrological dynamics of thelake are modelled. This sub-sector consists of standardstock-flow equations that calculate natural inflows and


Fig. 1. (a) Model structure showing the sectors, sub-sectors and sections. (b) Broad causal-loop diagram of thelake ecosystem sector. (c)Fish-fishing effectiveness graph. (d) Broad causal-loop diagram of tomato cultivation from theeconomic activities sector. (e) Diagram ofthe social structure sector.


Fig. 1. (Continued ).


outflows, mass balance (volume), surface area, depthof the lake, and the irrigation flow (which interactswith the economy sector). Groundwater is not includedin the model due to the lack of available data.

Phosphorus (P) and nitrogen (N) are the two majornutrients, bound by plants during photosynthesis. InLake Uluabat, nitrogen is mainly present in the formof NO2

−, NO3− and NH4

+, whereas phosphorus ismainly present in the form of PO43−. Their concen-trations in the lake water and the ratio of nitrogento phosphorus are important parameters dictating the“trophic” state of the lake. Total nitrogen, the sum ofnitrogen from all three sources, is used in the model.Orthophosphate is used in the model since data werenot available on total phosphorus. The amounts ofthese two nutrients in the lake—both in the waterand bound by the macrophyte—are represented asstocks N, and O-P, in milligrams. The concentra-tions of phosphorus and nitrogen in the lake waterare calculated by dividing the corresponding stockvalue, O-P and N respectively, by theLake stockand subtracting the portion bound by the macro-phyte (Eqs. (1a) and (1b); Fig. 1b). Nutrient use bymacro in Eqs. (1a) and (1b)represents the percent-age of P and N bound by the macrophyte in thelake and reflects the role of macrophyte in regulat-ing the nutrient levels—and thus algal biomass—ofthe lake. Actual macrophyte is defined as percentlake volume inhabited and is assumed to consumethe same percentage of nutrients in the lake. Thus,nutrient use by macro is assumed equal toactualmacrophyte, which competes with algal biomass fornutrients. In brief,O-P in lake andN in lake will giveP and N concentrations, available to algae, respec-tively.

O-P in lake = If (Lake > 0) then((1 − nutrient use)

× (O-P/Lake)) else 0 (1a)

N in lake = If (Lake > 0) then((1 − nutrient use)

× (N/Lake)) else 0 (1b)

A portion of both phosphorus and nitrogen inputsis stored in sediments. Moreover, phosphorus and ni-trogen in sediments are released back to water at acertain rate. This forms a feedback structure that mayact as a buffer mechanism in certain cases. In general,

sedimentation rate is higher, which results in nutrientexcess in sediments and an internal nutrient supplyto water body even if external input no longer exists.The result is prolonged and intensified eutrophication,which may be the case in Lake Uluabat.

Sewage from urban settlements, industrial areas,and nutrient flow from drainage basins are majorsources for both nutrients. Nutrient flow from thesesources are formulated as graphical functions andextrapolated into the future. However, another sourcefor nitrogen is agricultural areas. Excess nitrogen,originating from fertilisers and not bound by plantsor in the soil, leaks to the lake, whereas all the ex-cess phosphorus from fertilisers is stabilised. Nutrientflow from agriculture depends on the cultivated areaand the type of crop. It is worth noting that nitrogenfixation mechanism by organisms, which plays a rolein nitrogen removal from water body, is not includedin the model.

The concentration of bor, a toxic element (bor inlake, in mg/l) and Biological Oxygen Demand (BOD)concentration (BOD in lake, in mg/l) are computedin this sub-sector simply as water quality indicators.DO is ignored, since the decline in DO is significantonly at organic waste discharge points and thus thecomplexities involved in its inclusion in the modelare not justified. The suspended solid concentra-tion in the lake,SS in lake in mg/l, is computed inthis sub-sector; it affects transparency of the lakewater.

Algal, macrophyte and zooplankton biomasses arecalculated in thewater plants and zooplankton sec-tor (related parts inFig. 1b). Algal biomass is repre-sented by thepotential chloro a content of the lakewater, which is found by a logarithmic relationshipbetween algal biomass (potential chloro a), P and Nconcentration of the lake (O-P in lake andN in lake)(Jørgensen, 1994). There are three such empirical re-lationships, each accepted valid for one of three casesdepending on the value of the N:P ratio (Eqs. (2)–(4))(Jørgensen, 1994):

for the caseN in lake:O-P in lake ratio >12, therelationship is:

log10(potential chloro a)

= 1.45× log10[(O-P in lake) × 1000]− 1.14

(2)


for the case the ratio<4:


= 1.4 × log10[(N in lake) × 1000]− 1.9

(3)

for the case the ratio >4 and the ratio<12:


= min(1.4 × log10[(N in lake)

× 1000]− 1.9, 1.45× log10[(O-P in lake)

× 1000]− 1.14) (4)

whereN in lake andO-P in lake are expressed asmg/l; unit of potential chloro a is �g/l.

However, algae are controlled by its predator,zoo-plankton whose unit is mg/l. So, theactual chloro ais the algal biomass that can be observed in lake, al-thoughpotential chloro a level “indicated” by P andN concentrations may be higher. A portion of theac-tual chloro a is continuously flushed out by the out-flow of the lake (Fig. 1b). The algal growth sub-modelmay seem simplistic compared to more detailed for-mulations in the literature, but from our perspectivethe current detail level is sufficient for the purposes ofthis study. More complex and detailed eutrophicationformulation will be considered in the future versions.

Macrophyte biomass unit is expressed as PVI (Per-cent Volume Inhabited). It is a function of P and Nconcentration of the lake and the N:P ratio. It is alsoaffected by thechloro a level in the lake, since al-gae is more efficient in getting nutrients from waterthan macrophyte (Fig. 1b). As thechloro a level getshigher, macrophyte can absorb fewer nutrients. Al-gal biomass has also an indirect effect on macrophytebiomass via lake water transparency.Transparency isdictated byactual chloro a level and measured asSecchi disk transparency (SE) in metres (Jørgensen,1994). Macrophyte is able to colonise the lake to adepth of three times the SE although the changes inlake transparency are not instantly reflected in theac-tual macrophyte biomass. It takes sometime for macro-phyte to colonise newly available regions and to perishfrom regions with insufficient light conditions. Thissituation is represented in the model by the use of athird order exponential smoothing, with a delay con-stant of 12 months. The same is not the case for al-

gae, since they are able to respond to changes in theirenvironment almost instantly.

The struggle between macrophyte and algae is verysignificant for the overall ecosystem. The outcome ofthis struggle dictates the trophic state of the lake andlargely depends on the positive feedback loop, formedmainly by nutrient concentrations (i.e.N in lake andO-P in lake), algae (i.e.actual chloro a), actual macro-phyte, andtransparency. The darker arrows inFig. 1bdepict this phenomenon.

The fish and bird sub-sector constitutes the top ofthe food chain. It is this sub-sector of thelake ecosys-tem sector that is directly linked to theeconomy sec-tor via the fish industry. There are a number of fishspecies in the lake but two fish species are chosen asthe indicators of the lake’s trophic state: pike and carp(Fig. 1b). Fish that are between zero- and two-year-oldare treated as separate immature fish stocks and arenot reproductive. This allows both to differentiate thefood needs/sources and to isolate the young fish fromreproduction. Reproduction of fish is assumed to takeplace all year long since the climate is suitable. Theunit of fish and crayfish stocks is kg/ha.

Pike is a predatory fish species.Mature pike preyson other fish including carp and crustaceans, such ascrayfish and it prefers clear waters. Hence, reproduc-tion and death of pike depend on the availability of suf-ficient food sources and transparency of the lake. Bothof these factors are reflected in the model as graphi-cal functions. On the other hand, the presence ofma-ture pike means grazing pressure onimmature/maturecarp/crayfish forming a predator-controlled mecha-nism. The crucial point is thatmature pike grazes onmore abundant prey. In other words, when density ofa prey species falls, grazing pressure on that speciesalso decreases proportionate to the ratio of it to the to-tal density of prey species. Thoughactual macrophytecontributes to clear water conditions favourable formature pike, it also provides refuge for prey species,represented as a dimensionless variablerefuge againstpike for carp in the model.

Carp is a planktivorous fish; that is, it feeds mainlyon zooplankton. The dynamics of carp is similarto pike. Food availability affects both reproductionand death rate of carp via graphical functions. How-ever, since carp prefers turbid waters, its reproductivecapacity increases as lake water becomes less trans-parent. Unlike pike, bothimmature carp and mature


carp stocks experience grazing pressure. Since carpfeeds onzooplankton, high carp density means lowzooplankton density which means suitable growthconditions for algae. Algae (actual chloro a) causeturbidity, which favours reproduction of carp and putsmore grazing pressure onzooplankton. The result isa turbid lake dominated by carp and algal blooms.

Fishing is an external, anthropogenic factor that hasthe potential of altering the functioning of the ecosys-tem. The amount of fish that is landed depends on thenumber of fishermen and fishing effectiveness.Fishingeffectiveness for pike/carp is dimensionless and it is afunction ofmature pike/carp density (Fig. 1c). Fishingeffectiveness is also affected by macrophyte to reflectthe negative effect of macrophyte on fishing nets.

Although a fungi attack in 1986 has driven thecrayfish population near extinction, crayfish is alsoincluded in the model. One reason is that there isalways a possibility for the crayfish to recover inwhich case its economic and ecological consequencesare worth analysing. Another is to test a highlydebated policy option, the introduction of crayfishto the lake. Lake depth and macrophyte cover, themain food for crayfish, determine its reproductivecapacity. Its death rate is a function of macrophytecover too. There is a grazing outflow from bothimmature crayfish and mature crayfish stocks as inthe carp, due to pike predation.Crayfish harvest, inkilograms per month per hectare, is a function ofma-ture crayfish stock and is also adversely affected byactual macrophyte.

Specific data on population dynamics and feedinghabits of birds is very limited. Further, they are as-sumed to have an insignificant effect on their preyspecies compared to other effects. Two bird speciescould be selected as indicators of bird presence on theshores of the lake. These are coot (Fulica atra), an her-bivorous species and pygmy cormorant (Halietor pyg-meus), a carnivorous one. Coot feeds on macrophyte;and pygmy cormorant feeds on fish. Therefore, sincethe conditions of macrophyte and fish in the modelalso give insight on the condition of birds, these latterare not included in the model.

Macrophyte provides certain degree of refuge forzooplankton against predation. Dimensionlessrefugeeffect for zooplankton increases with increasingactualmacrophyte biomass. However, when planktivorousfish biomass is high enough, the refuge effect dimin-

ishes dramatically no matter how high the macrophytebiomass is (Schriver et al., 1995).

Finally, note that there are a number of balancing(negative) feedback loops in the food chain in ad-dition to the large reinforcing (positive) loop men-tioned above. One can conjecture that the stability ofthe ecosystem would depend on the relative strength(dominance) of these two types of loops in the system(May, 1977; Scheffer, 1990). Most data specific to thelake are unfortunately unavailable, including carp andpike stocks in the lake. Hence, related parameter val-ues were estimated either from literature or based oninterpretations of incomplete data. For example, ini-tial fish and crayfish stocks were estimated based onthe amounts of fish and crayfish sold per year in thelocal market.

3.3. Economic activities sector

Three sub-sectors constitute theeconomy sector:fishing, agriculture and industry. The structures ofthese sub-sectors resemble each other.

There aretomato, vegetable, fruit, andgrain culti-vation sections in theagriculture sub-sector. The val-ues for parameters such asyield per hectare (in kg/ha)andmarket price (in US$) are estimated from availableliterature (Madran, 1991; Ergüler, 1994; SIS, 1997).The structures of these sections are also similar toeach other. Hence, only thetomato cultivation sectionis explained in detail as an example (Fig. 1d).

Tomato Cultivated per Hectare is a function of ir-rigation and tomato harvest (Total Tomato Cultivated)depends on the land allocated to tomato cultivation(Tomato Cultivation Area). Total income increases astotal tomato harvested increases (Market price is con-stant). This income increases the welfare—in US$per person per month—of the inhabitants relying ontomato cultivation, which also defines the attractive-ness of tomato cultivation for other people. This at-tractiveness is normalised by dividing it by the sumof attractiveness values from all economic activities.This facilitates comparison amongst the attractivenessvalues of all economic activities. Thus, these nor-malised values represent how people prefer differenteconomic activities, depending on their relative attrac-tiveness. The multiplication of normalised attractive-ness of tomato cultivation (a dimensionless numberbetween 0 and 1) with total workforce determines the


workforce in tomato cultivation (Fig. 1d). The land onwhich tomato is cultivated is an increasing functionof the workforce. The maximum value it can reach isthe total arable land around the lake (Total AvailableCultivated Area).

The main interactions between theagriculturesub-sector and the lake are the irrigation of fieldsand excess fertiliser returning back to the lake fromfertilised lands. It is assumed thatfertiliser use permonth is currently three times more than the actualneed to represent the excessive use by the farmers.Total actual irrigation, in litre per month, is affectednegatively when the water level in the lake dropsdown.

Fishing sub-sector has a similar structure. It is clearfrom Fig. 1cthat increased income draws more work-force to fishing, subsequently increasing the pressureon fish. This in turn inevitably reduces total fish landedas a result of diminished fish stocks since the lakedoes not have the capacity to sustain extreme levelsof fishing. Hence, fishing from the lake is affected bythe fish stock in the lake. This is exactly what is ob-served in the lake today. This will either exterminatefish from the lake or reduce thefishing workforce toa sustainable level depending on the prevailing condi-tions.

In thefishing sub-sector, total fish landed is affectedby fishing effectiveness. Fishing effectiveness is di-mensionless and reflects the effect of fish density andmacrophyte.Actual fishing is the sum ofcrayfish har-vest, pike andcarp landings, computed by multiplyingfishing pike/carp (in kg/ha per month) with thelakesurface area (in ha).

Factories near the lake either process crop from agri-cultural lands or fish. For different reasons, fish pro-cessing factories get fish from outside the region sincealmost all fish landed in the lake is sold in the domes-tic market. An increase in agricultural yields in theregion directly implies an increase in the productionrate of factories. Therefore, the expansion of the agri-cultural industry depends on the amount of harvestsand available workforce. If there occurs a decline inharvests, this affects the factories negatively.

The fundamental balancing (negative) feedbackstructure applies also here, with slight modifications.For example, the market price (e.g.market price TPin the tomato paste industry) is constant and the pro-duction (tomato paste production) is a linear function

of it. In addition, the workforce (Actual Wf NeedTP) depends on the production amount. If workforceis greater than workforce supply (Wf Supply TP),then this indicates a labour shortage in the area andit is assumed that this shortage is compensated byhiring from the unemployed workforce (Surplus WfTotal) and if necessary, workforce from outside theregion which is assumed always available (Eq. (5)).If workforce supply is greater than actual workforcethen there is a surplus labour (Surplus Wf TP), whichhas the same meaning as it has in theagriculturesub-sector. (However, there is no concept of “unmetworkforce need” in the agriculture sub-sector, sincea certain level of cultivation implies that there musthave been enough people to cultivate it. The same istrue for the fishing sub-sector.)

Unmet Wf Need TP

= If ((Wf Supply TP − Actual Wf Need TP) < 0)

then(Actual Wf Need TP − Wf Supply TP)

else 0 (5)

The main interaction between factories and the lakeis the discharge of the factories. Since discharges aremainly organic, it is assumed that they are only com-posed of organic wastes (e.g.Organic Waste DischargeTP, in metre cubed per month, in the tomato paste in-dustry). These discharges may play a significant role inthe probable further eutrophication of the lake. Also,the discharges of some other industrial facilities out-side the periphery but inside the catchment of the lakealso reach the lake via the main inflow river (M. Ke-malpas a River). These discharges are represented asexternal inputs in thenutrients and other indicatorssub-sector.

3.4. Social structure sector

Demographic indicators are modelled in thesocialsector (Fig. 1e). Population dynamics,total workforce,average income per workforce of the regional popula-tion are all computed in this sector.

Only the rural population is considered in thestudy.Net birth fraction for the region is 1.75% peryear (TKV, 1998). Assuming an average of five per-sons per family of which one male is in the workingage, thetotal workforce in the region is one fifth


of the population. The difference between total sur-plus workforce (Surplus Wf Total) and total unmetworkforce need (Unmet Wf Need Total) gives theunemployed in the region. Surplus workforce fromsub-sectors with full employment is employed insub-sectors with non-zero unmet workforce need.However, unmet workforce need may be higher thansurplus, in which case it is assumed that hiring work-force from outside the region meets the remainingworkforce need. This is a common practice in the Ulu-abat region. On the other hand, if unmet workforce isless than surplus than there is non-zerounemployedin the region (Eq. (6)). This sector also providesparameters required to calculate the allocation ofworkforce amongst competing economic activities,such as fishing, agriculture or industrial processing.The allocation is done for each sub-sector using theratio of the attractiveness value of that sub-sector (e.g.Attractiveness TP) to Attractiveness Total (the sum ofthe attractiveness values of all economic activities).

Unemployed in the Region

= If ((Surplus Wf Total−Unmet Wf Need Total)>0)

then(Surplus Wf Total − Unmet Wf Need Total)

else 0 (6)

4. Validation and the base run

Verification and validation testing, done by carry-ing out some standard procedures, follow the modelconstruction stage (Barlas, 1996). In verification, theequations were checked against careless errors and di-mensional consistency was ensured on both sides ofall equations. Control/trace variables were also usedin the model to prevent formulation errors.

Validation tests were carried out both on individualsub-sectors in isolation and on the whole model. Themodel passed thesestructural and behavioural vali-dation tests (Güneralp, 2000). Structural tests assessthe logical validity of model equations, by evaluatingthem one by one and by testing their behaviour underextreme/stress conditions. In behaviour validation, themodel-generated behaviour patterns of major vari-ables were evaluated based on available data on LakeUluabat and the literature on shallow freshwater lakes.The validity tests showed that the model-generated

behaviour is consistent with the literature and limitedavailable data. Clearly, it would be possible to estab-lish a stronger case if more numerical time-series datawere available (Güneralp, 2000). Therefore, exten-sive validation tests should be carried out with moredata in the future. The present level of validation ofthe model is not enough to draw exact conclusions.However, lack of recorded data severely restricts ourcurrent options and collecting new dynamic data ne-cessitates long time periods. It should also be kept inmind that the main purpose of the model is to cap-ture the broad dynamic behaviour patterns of the realsystem, not provide “point” predictions.

4.1. The base run

The base run of the model was simulated underthe pre-mentioned set of assumptions, graphical func-tions and parameter values which reflect the currentconditions in the region (Güneralp, 2000). The timeframe of the study is 34 years, from the year 1982 to2016. Time axis is in months in model-generated out-put graphs; a second scale in years is added below theaxis for explanatory purposes. The discussion in thetext is done using years as the time unit.

4.1.1. Lake ecosystem sectorA striking output obtained from the base run is

that a shift to algal dominance in the lake will notoccur, although eutrophication continues. This state-ment is crucial since there is a widespread acceptancethat such a shift will occur in the near future if cur-rent conditions persist. However,mature pike popula-tion controlsimmature and mature carp population,and actual macrophyte takes out the nutrients fromthe lake water, controlling algal growth. So, clear wa-ter conditions persist in the lake at least for the next15 years.

Two types of oscillations are observed in lake hy-drological dynamics. The smaller ones are seasonaloscillations caused by fluctuations in the major in-flow source, i.e.M. Kemalpas a River. The largeroscillations are the result of wet–dry year cycles.1988–1996, 2003–2008, 2014–2015 periods are dryyears (i.e. months 72–168, 252–312 and 384–396).The only notable point inirrigation is the slight de-crease in water drained because of the decline in thelake depth. Mean nitrogen concentration,N in lake and


mean phosphorus concentration,O-P in lake are notcritically high: 0.5393 and 0.0744 mg/l, respectively.The mean phosphorus concentration is well belowthe approximate threshold values, 0.1–0.15 mg P/l,stated inJeppesen (1999). However, the increase inphosphorus concentration is worth noting until thecrayfish population ails and collapses, i.e. month 60.Furthermore, its mean during the period 1982–1986(between months 0 and 60) is 0.1024 mg/l (Fig. 2a).Although nitrogen is the limiting nutrient until 1994,the increase in phosphorus concentration is criticaland deserves a scenario analysis on “what if the cray-fish never go infected”? (referSection 5.1.1). After1994, phosphorus is the limiting one during spring,and nitrogen during fall (Fig. 2b). Also, suspendedsolid concentration in the lake is around 100 mg/l;again a reasonable value due to reduced suspendedsolid loading from the major inflow in recent years.The fluctuations in these variables are caused byfluctuations in their loading, except suspended solidfluctuations, which are the result of seasonal fluctua-tions in inflow and outflow sources of the lake. Also,BOD and borax concentrations are well below thedangerous levels.

Actual macrophyte cover is approximately 55 PVIduring dry years and 40 PVI during wet years. Over-all, the averageactual macrophyte cover in the lakebottom is around 50 PVI.Actual chloro a level in thelake is always below 50�g/l, except for a period justbefore the collapse of the crayfish population in whichactual chloro a concentration reaches 84.7�g/l. Ac-tual chloro a level is controlled by bothactual macro-phyte, via nutrient concentrations, andzooplankton,which does not experience a high grazing pressurefrom planktivorous fish (Fig. 2c).

Transparency is below 1.5 m most of the time ina year and is the same as the lake depth for certainperiods. Its average value is slightly more than 1 m.Although averagetransparency is greater in wet yearsthan dry years, the portion of the lake transparent indry years is larger than that in wet years (Fig. 2d).

The mature crayfish stock is near zero due to thefungal disease, but not totally extinct. This providesan opportunity for the crayfish population to overcomethe disease. Since pike grazes on carp and crayfish, thecollapse of crayfish stock causes the grazing pressureon carp to increase. This results in increased control oncarp.Mature pike population is about 3.5 times more

than that ofmature carp. Considering that it is enoughfor pike to control carp in a lake when its populationis twice that of carp, the lake is secure from becomingeutrophic with algal dominance. In addition, the lake isnot turbid favouring reproduction of pike over that ofcarp.Fig. 2eshows the population dynamics of the fishspecies. It is also worth mentioning that macrophyteprevalence causes approximately a 30% decrease infishing effectiveness.

The fast decrease inactual macrophyte togetherwith the algal bloom and the up and down inmaturecarp population, coinciding with the peak in phospho-rus concentration just before the crayfish collapse de-serves closer inspection. Smaller oscillations inactualmacrophyte are caused by oscillations in nutrient con-centrations (Fig. 2f). Oscillations inimmature/maturepike/carp, actual chloro a, and transparency arelargely due to seasonal fluctuations in hydrologicalcycle and to a smaller extent as a result of oscillationsin nutrient concentrations. Neither small nor largeoscillations are the result of predator–prey dynamics.

Consequently, pike is able to suppress carp through-out the simulation time frame, in spite of a decrease inits population during the first few years, preventing theso-feared algal blooms, and hence yielding high levelof transparency and broadactual macrophyte coveron the lake bottom. This result points to the potentialvital role fish manipulation may play in fighting eu-trophication.

4.1.2. Economic activities sectorIn the agriculture sub-sector, inefficiencies in irri-

gation (e.g.Irrigation Tomato) during dry years causea slight fall in yield per hectare (e.g.Tomato Culti-vated per Hectare), which is reflected in income perworkforce (e.g.Income per Wf Tomato). Cultivatedareas (Total Actual Cultivated Area) increase as work-force in agriculture increases as a result ofpopulationgrowth. On the other hand, a decreasing trend beginsin normalised attractiveness of each crop asTotalActual Cultivated Area reaches the available arablelands (Total Available Cultivated Area) in the year1996 (month 168). The reason is that while workforcesupply in agriculture keeps increasing,Total AvailableCultivated Area stays constant. As a result, income perworkforce diminishes and, consequently,attractive-ness drops. However, since the increase intotal work-force is greater than the decrease inattractiveness,


Fig. 2. (a) Base run behaviour of phosphorus concentration in the lake. (b) Base run behaviour of phosphorus over nitrogen concentrationratio. (c) Base run behaviour ofchloro a level in the lake water. (d) Base run behaviour oftransparency as compared tolake depth. (e)Base run population dynamics ofmature pike, carp and crayfish. (f) Base run behaviour ofactual macrophyte cover in the lake. (g) Baserun behaviour offishing workforce. (h) Base run behaviour ofaverage income per workforce in the region.



workforce supply keeps increasing. Furthermore theworkforce also increases; in fact it is equal towork-force supply in this run. Namely, every person whois willing to work in theagriculture sub-sector finds

opportunity to do so. This means the criticalIntensityLimit for Cultivated Area, which defines the maxi-mum workforce that can be supported, is not reachedin any of the areas in theagriculture sub-sector.


The situation in theindustry sub-sector is quite dif-ferent. Although the production in factories (exceptfish processing) experiences a drop during dry years(as a result of drops in yields), this does not affectin-come per workforce. The reason is that workforce tobe employed each year, i.e.Actual Wf Need is dictatedby the level of production in the factories. So,attrac-tiveness in this sub-sector stays the same. However,normalised attractiveness increases sincenormalisedattractiveness of the agriculture sub-sector decreases.

Fishing workforce, while increasing during firstfew years as a result of highattractiveness of fishingamongst inhabitants andpopulation growth, experi-ences a sharp decline at the end of year 1986 (month60) when the fungi hit the crayfish stock in the lake(Fig. 2g). Although it does not follow a smooth pat-tern, normalised attractiveness of fishing declinesthroughout the simulation time-frame. However,fish-ing workforce increases slightly after the year 2000(month 216) mainly due to an overall increase in fishstocks in the lake (Fig. 2e).

The most notable dynamics of this sector is the de-cline in fishing workforce as a result of the collapseof crayfish population in the year 1986. Also, the de-cline in total normalised attractiveness of theagricul-ture sub-sector as a result of the crowding of cultivatedareas (compared to an increase in total normalised at-tractiveness of theindustry sub-sector) are worth men-tioning. On the other hand,normalised attractivenessof fishing stays more or less constant after the year1986, while it was increasing prior to that year.

4.1.3. Social structure sectorThe population andtotal workforce are increasing.

There is an increase in unemployed in the rural pop-ulation, which is expected because the arable landsare limited and businesses have limited capacity (TotalAvailable Cultivated Area is constant). Fishing can-not absorb this unemployed workforce. Attractivenessof all three main income sources (i.e. agriculture, in-dustry and fishing) in the region is contracting. Like-wise, average income per workforce in the region isalso declining (Fig. 2h). Thus, the result of the baserun simulation is that the welfare of people in the re-gion worsens over time. In addition to the first declinein 1986, there is a steeper decline after about 1997.One reason is that the demand for arable lands (To-tal Indicated Cultivated Area) increases so much that

it exceedsTotal Available Cultivated Area, resultingin less land for each crop type. Another reason is theincrease in the unemployed workforce. These are re-flected in the general welfare of people in the regionas a decline inaverage income per workforce. It isalso worth noting that dry–wet year cycles affectaver-age income per workforce, hence the welfare to someextent. In dry years, the fishing yield and the incomefrom fishing are relatively low. Theaverage incomeper workforce in the beginning of the year 2000 is ap-proximately US$200 in 1995 base prices (Güneralp,2000).

Consequently, it is observed that there is an increasein the number of unemployed and a decline in welfareof inhabitants especially after the year 1997 (month180), as a result of limited arable lands and limitedcapacity of businesses. This is the most notable dy-namics in this sector. The saw-toothed pattern is theresult of dry–wet year cycles. It is lower in dry yearsbecause both fish landing and agricultural yield dropdue to less water available in the lake.

5. Scenario and policy analyses

Through scenario and policy analysis, a number ofmodifications on the base model are discussed in or-der to gain a better understanding of the system andimprove it with policy recommendations.

5.1. Scenario analysis

5.1.1. No crayfish population collapse in 1986The fast increases in phosphorus concentration,

carp population and the high algal bloom just aroundthe crayfish population collapse in the beginningof year 1987 (month 60) together with the fast de-cline in pike population deserves closer inspection(refer Section 4.1.1). Therefore, a scenario analysisregarding “no crayfish population collapse in 1986”was done to find out what would happen in the lakeecosystem in that case.

The result is surprising. The carp becomes thedominant fish species with the decline of pike and thelake loses its clear water state, the insufficient grazingpressure on carp and also the high fishing pressureon pike playing the main roles. It becomes turbidand switches to a eutrophic state characterised by


high algal blooms and sparse or no macrophyte cover(Fig. 3a). Run names sustain2015 and sustain2015noDdenote the cases of the crayfish being infected andnot being infected, respectively. The transition takesplace in 1990 (month 96). Thus, surprisingly, it can beconcluded that the collapse of crayfish is not a purelynegative event as it has typically been considered.

5.1.2. Crayfish recovers in the futureAlthough the fungi still affect the crayfish popula-

tion, it is reported that there is considerableimmaturecrayfish population in the lake. The fungi affect thespecies by preventing them from changing their outer

Fig. 3. (a)Chloro a level dynamics in case of nocrayfish disease anddisease. (b) Dynamics ofmature crayfish in case of recovery in 2000and no recovery in the time-frame of the study. (c)Fishing workforce in case of recovery in 2000 and no recovery in the time-frame of thestudy. (d) Hydrology sub-sector when all three policies are implemented (policy-related variables are underlined). (e)Actual macrophytecover in the lake with and without dam. (f) Behaviour ofmature carp in case of water level regulation and no regulation. (g)Transparencyin case all three policies implemented and no implementation. (h)Average income per workforce in case ofimproved productivity and noimprovement. (i) Behaviour ofmature crayfish in case ofcrayfish introduction and no introduction. (j)Chloro a level in case of harvestingall every week and no harvest at all. (k)Transparency in case of harvesting all every week and no harvest at all.

skeleton. As a result, the crayfish is not totally extinctfrom the lake and this gives the hope that it may beable to re-colonise the lake sometime in the future.The recovery, if realised, will most probably have ma-jor implications on fishing and the lake ecosystem.The time the crayfish will recover, if it ever recovers,is uncertain. Hence, possible recovery scenarios areanalysed with three different recovery times. It is as-sumed that recovery takes place in a very short timerather than being a gradual process.

First, “recovery in the beginning of the year 2000(month 216)” scenario is analysed. With the disap-pearance of the fungi,mature crayfish stock shows a


Fig. 3. (Continued )

fast increase and seems to reach steady state at ap-proximately 750 kg/ha around the year 2013 (month372) (Fig. 3b). Accordingly, immature crayfish stockalso shows an increase. Pike also flourishes, as doescrayfish since more crayfish is available in addition tothe present carp stock as a food source. Final equilib-rium values of both species are higher than their ini-tial stock values before thedisease. There is a slight

decrease inactual macrophyte cover due tocrayfishfeeding but transparency is virtually not affected.

The result explained above is entirely different thanthe result presented inSection 5.1.1, although there isabundant crayfish population in the lake in both cases.The reason for this distinction is the “pike to carpratio”. If it is less than or equal to three, as was thecase before and during the year 1986 (months 48–60)



(refer Section 5.1.1), the presence (or recovery) ofcrayfish favours carp population. If the ratio is largerthan or equal to four, as has been the case after the year1986, the presence (or recovery) of crayfish favourspike population.

With the increase in crayfish and pike, thefishingworkforce reaches almost 600 people toward the year2015 (month 396) (Fig. 3c). The increase infishingworkforce has a positive impact on the social structure.The number ofUnemployed in the Region decreases


substantially. Scenarios in which recovery takes placein the beginning of the years 2005 and 2010 gen-erate similar results. InFig. 3b and c, run namessustain2015-p8a and sustain2015-p4 are for the caseswhere the recovery takes place in the year 2000 andno recovery ever takes place, respectively.

Finally, it is concluded that the overall effect of thecrayfish recovery on the lake ecosystem, trophic andsocial structure is positive as long as the pike to carpratio is at least four. The runs are done not on thebase model but on a modified version in which theplanned DSI projects are assumed to be operationalin the beginning of the year 2000 (month 216) (referSection 5.2.1).

5.2. Policy analysis

Policy runs are done based on those policies pro-posed both in reports on the lake and in literatureregarding hydrological regulations on the lake, agri-culture and fishing. The results are used to designrecommendations for the management of the lakeregion. All policies are introduced in the model inthe beginning of the year 2000 (month 216) and thetime horizon is taken to be 16 years (i.e. until thebeginning of year 2016).

5.2.1. Policy options related to hydrologicaldynamics of the lake

The dramatic decrease in thelake water level dur-ing dry years causes several problems regardingir-rigation, actual macrophyte cover, andfishing. Sincethe water level is diminished, it becomes hard to pumpwater out forirrigation. Theactual macrophyte covertends to increase in dry years due to increased lightpenetration to the lake bottom. The combined effectof increasedactual macrophyte cover and low waterlevel is decreased navigation ability for fishing boats.

The differences in average water levels between wetand dry years are caused by the fluctuations in themain inflow source,M. Kemalpas a River. To controlthese fluctuations, DSI has begun the construction ofa dam on the main inflow source. When completed,the dam will reduce the fluctuations between dry andwet years to a great extent. In addition, the regulatedflow will contain virtually no suspended solids.

In conjunction with the dam, it is planned to con-struct a new regulator to the outflow of the lake. While

the dam regulates the fluctuations of the inflow be-tween dry and wet years, the regulator will serve a sim-ilar purpose to regulate the seasonal fluctuations in thelake. In addition to these, an extension to the existingUluabat irrigation is in construction phase. When theextension is completed, 10,828 ha of cultivated landswill be irrigated in addition to the currently irrigatedlands.

The policies are brought into the model one by one.The resulting hydrology sub-sector when all three poli-cies are introduced is provided inFig. 3d. The timelag required for the dam to stock water is ignored.Also the fact that regulated flow enters the lake froma different point is not represented in the model.

The effect of the dam on the lake is as expected. Theaverage lake water level in dry years is increased andhas a value close to that of wet years. Thesedimenta-tion rate slows down considerably. Theactual macro-phyte cover is about 45 PVI throughout dry years dueto increased water level, while it used to be more than55 PVI in dry years (Fig. 3e). However, this is notcompensated by an increase inactual chloro a level asmight be expected at first, since the suspended solidflow into the lake is diminished by the dam affect-ing carp negatively and furnishing conditions forzoo-plankton growth. The overall increase intransparencyas a result of the decrease insuspended solid concen-tration is the reason for the carp’s decline. InFig. 3e,run names sustain2015-p1 and sustain2015 refer to thecases where the new dam is operational and no damexists (i.e. base run), respectively. The effect of thedam onirrigation from the lake is also positive. Themean value ofirrigation from the lake increased from1193 million to 1231 million litres per month.

Secondly, the activation of the new regulator in theoutflow of the lake is analysed. The seasonal-regulatedlake water levels are proposed byAltınayar (1998)soas to resemble the seasonallake water levels in wetyears (Table 1). These proposed levels are to be at-tained by controlling the major outflow via the newregulator. The effect onactual macrophyte cover issimilar to that of the dam construction. Sincesus-pended solid concentration is not affected as much,light conditions are only slightly improved as a resultof the effect of outflow regulation onchloro a flushout. This policy has the greatest effect on carp amongstthe three policies analysed in this section (Fig. 3f).There is not much change insedimentation rate from


Table 1Proposed monthly regulatedlake water levels (Altınayar, 1998)

Month 1 2 3 4 5 6 7 8 9 10 11 12

Elevation (m) 5 5.5 5.5 5.5 5 4.5 3.8 3.3 3.1 2.7 2.9 3.3

the base run and thelake water level is close to theone in the presence of the dam case. InFig. 3f, runnames sustain2015-p3 and sustain2015 refer to thecases where the regulator is implemented and no reg-ulator is implemented (i.e. base run), respectively.

The analysis of the last component, the irrigationextension proves that the lake has a huge irrigationpotential. New irrigated lands subject to an irrigationextension project are outside the region. Therefore, theactivation of the extension project affects only thelakedepth, though not substantially.

When all three policies are implemented in themodel simultaneously, it is observed that the dam hasthe biggest effect amongst all (Fig. 3g). The majoreffect of the policies is on the lake water conditionsand the lake ecosystem. Their effect on thesocialsector is minor. However, it should be noted thatflood plain mechanism, which plays an essential rolein fish spawning, is not included in the model. Thedam would affect both the lake ecosystem and so-cial structure negatively leading to a reduction in thenatural flood plain area of the lake with an expecteddecrease in the fish recruitment. InFig. 3g, run namessustain2015-p4 and sustain2015 refer to the caseswhere all considered policies are implemented andnone implemented (i.e. base run), respectively.

5.2.2. Policy options related with agriculturesub-sector

The yield per hectare (e.g.Tomato Cultivated perHectare) values in the base model reflect the averagevalues.Population is increasing while theTotal Avail-able Cultivated Area is limited. Therefore, trying toincrease yield per hectare through improved agricul-ture technologies stands as a viable policy option.

The yield per hectare variables are multiplied withimproved productivity, a dimensionless variable addedto the base model to reflect the effect of improvedagricultural techniques. A 30% increase in yields perhectare is analysed. The increase in agricultural yieldalso causes an increase in production in related in-

dustrial sub-sectors. The workforce demand in thesesub-sectors increases, resulting in a decrease in thevalue of unemployed in the region and an increasein average income per workforce (Fig. 3h). Sincethe increase in yield per hectare is assumed to occurwithout additional use of fertilisers, the nutrient con-centrations in the lake are unaffected. Also the amountof water used is the same as before. Therefore, thepolicy has no effect on the lake ecosystem and hy-drology. In Fig. 3h, run names sustain2015-10a andsustain2015-p4 refer to the cases where improved pro-ductivity is achieved beginning from year 2000 and noimprovement in productivity takes place, respectively.

Bringing the amount of fertiliser use to normallyrequired levels by the crop is also analysed indepen-dent of the former policy. The local farmers use morefertiliser than normally required believing more fer-tiliser means more yields, which is not necessarilytrue. So it is assumed in the base model that fertiliseruse is three times the required amounts. When the basemodel is modified accordingly it is seen that the nutri-ent concentrations decrease as expected. The averageactual macrophyte cover decreases down to around41.5 PVI while there occurs no significant change inactual chloro a level. This is because the macrophytemakes use of the excess nutrients in the lake at present.Hence, a decrease in nutrient loading first strikes themacrophyte domination in the lake. The decrease inrefuge effect of macrophyte for carp cause higher graz-ing pressure onimmature/mature carp stocks. Theslight increase infishing effectiveness as a result ofdecreasedactual macrophyte cover does not cause aremarkable change in the fishing sub-sector. Conse-quently, the effect of the policy is not significant onany of the sectors.

5.2.3. Policy options related with fishing sub-sectorThe effect of the fungal disease, which hit the cray-

fish stock, on thefishing sub-sector has been detri-mental. Thedisease is still prevalent within crayfishpopulation and there is no known cure to get rid of it.


The introduction of healthy crayfish from otherlakes is offered as a policy option by some decisionmakers. However, taking such an action, while thedisease is still prevalent in the lake, would fail (Dr.Meryem Beklioglu, personal communication). Theexperiments conducted on the model are in agreementwith this view.

First, the introduction of 750 tonnes (50 kg/ha) ofmature crayfish per week for one year is analysed.They do not generate the desired result, that is there-establishment ofmature crayfish stock in the lake(Fig. 3i). The introduction of the same amount ofim-mature crayfish instead ofmature crayfish is also afailure. Thedisease keeps theimmature crayfish fromgrowing and causes them to die. InFig. 3i, run namessustain2015-p9b and sustain2015-p4 refer to the caseswhere new crayfish is introduced to the lake and nocrayfish is introduced, respectively.

In the light of these analyses, it is concluded thatit is best to help the lake ecosystem to recover fromthe disease by its internal dynamics, instead of an-thropogenic interventions, especially when the cost ofsuch interventions is taken into account.

5.2.4. Macrophyte clearance from the lake bottomMacrophyte clearance is a long debated policy

option in order to control eutrophication in the man-agement of shallow lakes. There have been someexperiences related to the harvest of macrophyte fromthe lake bottom. Such practices in shallow freshwa-ter lakes have resulted in undesirable outcomes (Dr.Meryem Beklioglu, personal communication). Firstof all, clearance of macrophyte is a burdensome pro-cess. It requires special equipment developed for thispurpose. Even with the equipment, the efficiency islow. The reason is that macrophyte is able to spreadfast, especially if the conditions favour macrophytepresence. Second and more importantly, even if theclearance succeeded, the result is typically a switchto a eutrophic state with turbid waters and high al-gal blooms. This also proves that macrophyte pres-ence acts, in fact, as a buffer before eutrophication(Schriver et al., 1995).

In spite of the facts delineated earlier, this policyis still being proposed by some decision makers tocontrol eutrophication in the shallow lakes of Turkey.Therefore, to demonstrate the extent of its infeasi-bility, the policy is tested in the model. In the first

case, half ofactual macrophyte cover is harvested permonth from the beginning of the year 2000 (month216). This resulted in a decrease in macrophyte coverbut the decrease is far from being satisfactory.Actualmacrophyte cover drops to just below 40 PVI, whereasactual chloro a concentration is unaffected. The pol-icy does not cause major changes in other parts of themodel. Then, a much more ambitious course of actionis adopted: To harvest all macrophyte in the lake bot-tom every week from the beginning of the year 2000to the beginning of the year 2008. This resulted in adramatic decrease inactual macrophyte (down to 15PVI) and after a short period of time, all macrophyte islost from the lake bottom. The macrophyte is cleared,however, just to open way for the dominance of algae(i.e. algal blooms), high turbidity, the loss of pike andthe establishment of a stable carp population (Fig. 3jand k): The characteristics of an eutrophic lakedominated by phytoplankton! Thefishing workforceincreases since the productivity of the lake is in-creased resulting in an increase in the total biomass.However, one should keep in mind that carp doesnot have a high economical value: a fact not rep-resented in the model. In reality the effort requiredto clear all macrophyte cover may be lower. How-ever, it is evident that the eventual outcome will bethe same.

In Fig. 3j and k, run names sustain2015-p7ff andsustain2015-p4 refer to the cases where a harvest of allmacrophyte cover per week takes place and no suchharvest takes place, respectively.

5.3. Comparison with related studies

Asaeda and Van Bon (1997)developed a dynamicmodel for eutrophication to understand the effectsof macrophytes on algal blooming in shallow lakes.The model is applied to a lake in the Netherlandsand displays satisfactory agreement with the realdata. In a related study,Xu et al. (1999)developedtwo dynamic models that describe phosphorus-foodweb dynamics with and without macrophytes in LakeChao ecosystems. Results from both models are insatisfactory agreement with the observed data. Themain result of our study—that macrophyte restorationcan decrease phytoplankton biomass and increase fishbiomass leading to improved lake ecosystem health—is in agreement with both of these studies (Asaeda


and Van Bon, 1997; Xu et al., 1999). Hongpingand Jianyi (2002)proposed a similar eutrophicationmodel with four main algae species instead of onefor a shallow eutrophic lake in China. Their modelshows that removing phosphorus in sediment is beststrategy to fight the eutrophication. This result isin agreement with our observation that sedimentmay be a crucial source of phosphorus for the lakeecosystem.

Janssen (2001)focused on managing lake eutrophi-cation using an approach similar to our study; namely,through the integration of ecological and social dy-namics. His model too includes the dynamics of thelake, the behaviour of agents using phosphorus foragricultural purposes, and the interactions betweenecosystem and farmers. However, he adopted a moredetailed economic representation including measures,such as tax manipulation and higher target level forreturns on the use of phosphorus. Again with a similarapproach,Tyutyunov et al. (2002)assessed economicand ecological dynamics of the Azov Sea simultane-ously using a stochastic simulation model. They foundthat current fishing rates induce a high risk of popula-tion decline in the fisheries, somewhat similar to thecase in Lake Uluabat. Their model further reveals thatthe extinction risk remains high even in the absence offishing, suggesting that additional conservation mea-sures are necessary to improve the environmental con-ditions.

There are also models that simulate both spatialand temporal variability in a lake to represent the un-steady and three-dimensional form of eutrophication(Soyupak et al., 1997; Tufford and McKellar, 1999;Cioffi and Gallerano, 2000). Such a detailed verticalspatial representation is not needed for shallow lakeslike Lake Uluabat. Time frames of the above modelsare generally short compared to our model, rangingfrom a few days to one year.McDermot and Rose(2000)concentrated on fish population dynamics in alake. Unlike ours their individual-based model tracksthe daily activity of individuals of several fish species.It is used to evaluate the effects of biomanipulation(piscivore enhancement) for its ability to sustain im-proved water quality. Predicted total zooplankton con-sumption is used to predict effects on algae and waterquality.

The well-established aquatic ecosystem model‘Rostherne’ was recently improved as to incorporate

fish and zooplankton dynamics (Krivtsov et al., 2001).The model includes features such as seasonal changesof solar radiation and water temperature, chemicalbudgeting, stratification of the water column, dynam-ics of detritus and its chemical constituents, whichare absent in our model (being relatively irrelevantfor our specific research questions). Concentratingmore on aquatic biogeochemical cycling, the studyof Krivtsov et al. naturally lacks social and economicdynamics, which are imperative in our case. Ourmodel presented in this paper is unique in its systemicand comprehensive approach. It represents ecological,economic and social dynamics in detail levels justenough to allow a time horizon and research scopemuch larger than most of the eutrophication modelsfound in the literature.

6. Conclusions and further research

The major aim of this study was to construct aframework to analyse the interactions between theecological, economic and social factors in a wetland.To this end, a system dynamics model of a shallowfreshwater lake and its surroundings is built. Partialvalidation tests done with available data and qualita-tive tests indicate that the model constitutes a plau-sible and coherent hypothesis. Some of the neededdata from the region are either unavailable or unreli-able. So, although the available validation results arean important initial step in the right direction, thereis a need for more data for better validation, to ob-tain more exact, implementable results. The conclu-sions listed below should be regarded with this factin mind.

The interesting result obtained in the base run is thatthe so-feared switch to a eutrophic state with seasonalalgal blooms is not likely to occur in the near future,and there is no sign that it will in the more distantfuture. The main undesirable outlook, mainly the resultof the unsustainable population increase, is a decreasein welfare of the rural inhabitants over time (Güneralp,2000).

Two scenario analysis runs are conducted. Thefirst asks what if crayfish population did not col-lapse due to the fungus disease in 1986. The answeris that the lake becomes turbid and eutrophic withdominance of carp and seasonal algal blooms. The


second scenario investigates what results will emerge,if crayfish recovers from thedisease sometime inthe future. Fishing becomes a major income sourceagain, improving social conditions. Pike increases,suppressing carp further.Transparency is almost notaffected.

Next the projects, planned or being currently con-structed by DSI are introduced into the model. It isobserved that they have no significant effect on theecosystem, except the decrease in macrophyte cover.There is no major betterment in socio-economic con-ditions.

The introduction of healthy crayfish in the modelto overcome the existingfungal disease proves to beineffective in re-establishing of healthycrayfish stockin the lake. Simulation runs also show that the appli-cation of macrophyte clearance for a short period oftime is not enough. In addition, application for a longperiod of time leads to the eutrophication of the lakewith dominance of phytoplankton, and turbid waters.These findings are in agreement with evidence foundin the literature.

None of the above-mentioned policy options resultsin the betterment of social conditions. On the otherhand, the use ofimproved agricultural techniques doesprovide better social conditions, with no adverse ef-fects on the lake ecosystem and hydrology.

The model is expected to provide a laboratory,where different management strategies regardingshallow freshwater lakes in particular and wetlandsin general can be tested. The research results arealso hoped to make some contribution to the generaltheory/literature of sustainable management. Theseresults are being shared with Ramsar ConventionBureau, DHKD, and TKV. The model itself willalso be made available to the interested institutions.Further research may concentrate on improved rep-resentations of theeconomic activities and socialstructure sectors. Collecting field data to improve theestimates of certain model parameters and integratedanalysis with GIS would also be beneficial. Thelakeecosystem sector may also be enhanced, as more in-formation in shallow lake ecology becomes available.The inclusion of processes, such as nitrogen fixationmechanism by organisms, inbreeding in case of lowpopulation densities and further elaboration of themodel performance under random effects also standsas a challenge for future study (Güneralp, 2000).

Acknowledgements

We thank all those who provided data for this ef-fort and acknowledge the support of the Society forthe Protection of Nature (DHKD). We would like tothank Dr. Meryem Beklioglu of Middle East Techni-cal University for her valuable comments and supportthroughout the study. We also thank Dr. George Gert-ner and Sarah Gertner for final edits to the manuscript.

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