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Ecological Modelling 220 (2009) 2162–2172

Contents lists available at ScienceDirect

Ecological Modelling

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

ntegrating empirical and heuristic knowledge in a KBS to approach streamutrophication

sther Llorensa,∗, Joaquim Comasa, Eugènia Martíb, Joan Lluís Rierac, Francesc Sabaterc,anel Pocha,d

Chemical and Environmental Engineering Laboratory (LEQUIA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Catalonia, SpainCentre d’Estudis Avancats de Blanes (CSIC), Camí de Sta. Bàrbara s/n, 17300 Blanes, Catalonia, SpainDepartment of Ecology, Faculty of Biology, University of Barcelona, Avda. Diagonal 645, E-08028 Barcelona, Catalonia, SpainInstitut Català d’Investigació de l’Aigua (ICRA), Parc Científic i Tecnològic de la Universitat de Girona, C/ Emili Grahit 101, E-17003 Girona, Catalonia, Spain

r t i c l e i n f o

rticle history:eceived 7 October 2008eceived in revised form 17 May 2009ccepted 2 June 2009

a b s t r a c t

The nutrient enrichment of rivers and its consequences are among the most severe water quality problemsin Europe, causing eutrophication and other problems. The decision-making processes involved in themanagement of these problems require extensive human expertise from people who deal directly withday-to-day stream problems, as well as empirical knowledge based on scientific research. This means

eywords:utrophicationnowledge-based systemtream

that eutrophication is a complex problem, the optimal management of which requires an integrated andmultidisciplinary approach. This approach can be taken using a Knowledge-Based System (KBS) built uponthe concepts and methods of human reasoning. Accordingly, a KBS was developed within the STREAMESproject. In this KBS most of the knowledge needed for managing eutrophication problems was organised

m ofal strthe K

ater qualityediterranean

and structured in the forKBS, as well as the internobtained led to consider

. Introduction

The nutrient enrichment of rivers and its consequences aremong the most severe water quality problems in Europe (Drolc andagorc Koncan, 2002), causing eutrophication and other problems.allentyne (1978) defined eutrophication in freshwater as changes

n ecosystem conditions due to high nutrient inputs and result-ng in an excessive growth of aquatic plants and/or algal blooms.his process has a limited direct effect on human health but hasajor ecological and economic consequences. In ecological terms,

utrophication has direct effects throughout the entire river foodeb (Nijboer and Verdonschot, 2004), causing biodiversity losses.

n economic terms, the effects can be summarised as the degrada-

ion of fishing, the deterioration of water quality and, consequently,f water uses, and an increase in the cost of water treatment. Prettyt al. (2003) estimated that the costs of eutrophication in termsf water treatment for water supply and loss of biodiversity and

∗ Corresponding author. Current address: Environmental Engineering Division,epartment of Hydraulic, Maritime and Environmental Engineering, Technical Uni-ersity of Catalonia, c/ Jordi Girona 1-3, Mòdul D-1, E-08034 Barcelona, Catalonia,pain. Tel.: +34 934015952; fax: +34 934017357.

E-mail addresses: e.llorens@upc.edu (E. Llorens), quim@lequia.udg.es (J. Comas),ugenia@ceab.csic.es (E. Martí), jlriera@ub.edu (J.L. Riera), fsabater@ub.eduF. Sabater), manel@lequia.udg.es (M. Poch).

304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.06.012

a decision tree (DT). The methodology specially developed to build thisucture of the eutrophication decision tree, is presented here. The good DTBS a suitable tool to support the management of eutrophication.

© 2009 Elsevier B.V. All rights reserved.

amenity value would be around £100 million per year in Englandand Wales alone.

Since John R. Vallentyne began the eutrophication dialogue inthe early 1970s many research studies into eutrophication pro-cesses have been carried out (Nijboer and Verdonschot, 2004;Hilton et al., 2006). Despite this, and while the current understand-ing of the main processes governing eutrophication in lakes is verycomplete, there is still a lack of understanding of these processesin river systems. This situation is worst when one refers to riversystems located in the Mediterranean region. Alvarez-Cobelas et al.(2005) states most Mediterranean limnosystems are quite distinctfrom the contemporary limnological paradigm and that striking andexciting differences exist between Mediterranean and other tem-perate limnosystems. However, the limnological processes takingplace in Mediterranean regions are often poorly known (Alvarez-Cobelas et al., 2005). To this lack of knowledge should be addedthe fact that river responses to high nutrient inputs vary depend-ing on, for example, river alteration degree, i.e. pristine rivers canretain some of the nutrients in transport (Peterson et al., 2001), butlarge nutrient loads in polluted streams may exceed their retentioncapacity (Martí et al., 2004). The responses of rivers to high nutri-

ent loads and their susceptibility to eutrophication also depend onthe type of pollution source (point and/or diffuse) and on the mor-phological and biological characteristics of streams (Tong and Chen,2002; Nijboer and Verdonschot, 2004). However, there is a generalagreement in the literature that an increase in phosphorus, rather

E. Llorens et al. / Ecological Model

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edge was not only concerned with how the water managers identify

ig. 1. The management problem at catchment and regional scale (Comas et al.,002).

han nitrogen, is a pre-requisite for eutrophic conditions to develop,s phosphorus is usually the limiting nutrient (Carr and Chambers,998; Drolc and Zagorc Koncan, 2002).

Eutrophication management is, therefore, a complex task. Theact that management in Europe has to cope with the 2000/60/EC

ater Framework Directive (WFD), which aims to achieve a goodr very good ecological state for water bodies by 2015, makes itore complex still. This objective clearly goes beyond the water

uality concept. It encompasses many qualitative and quantita-ive features (biological, hydromorphological and physico-chemicaluality elements) besides solute concentrations. All of them areequirements of the WFD in the evaluation and establishment ofhe ecological status of river systems. They are also required byhe DPSIR (Driver, Pressure, State, Impact, Response) approach,hich proposes a methodology for the WFD implementation (Borja

t al., 2006). According to them (WFD and DPSIR), the optimumanagement of eutrophication requires an integrated and mul-

idisciplinary approach, i.e. taking into account the relationshipsetween chemical, physical and biotic descriptors in developingutrient criteria (Poch et al., 2004), as shown in Fig. 1. This approachan be taken using artificial intelligence tools.

The application of artificial intelligence tools in the river domains a relatively new discipline (Dzeroski et al., 1997; Cortés etl., 2000). Mathematical models, simulation models and decisionupport systems have been used for catchment management tovaluate water quality, to reduce inputs of nutrient loads or toolve eutrophication problems (Jørgensen, 1995; Young et al., 1995;hen et al., 1997; Lee and Wen, 1997; Jolma et al., 1997; Somlyódy,997; Chen and Chang, 1998; Davis et al., 1998; Xu et al., 1998;e Marchi et al., 1999; Rekolainen et al., 1999; Adriaenssens etl., 2006). Neural networks have also been used to evaluate theuality of river water (Zhang and Stanley, 1997; Maier et al., 1998;alley and Fontama, 1998; Llorens et al., 2008). However, the above

ited tools only considered descriptive parameters and none ofhem displayed a true integrated and multidisciplinary approach,hich should integrate diverse knowledge (heuristic/empirical and

ualitative/quantitative). Bayesian networks and Knowledge-Basedystems (KBSs) appear to be a good option because, unlike thereviously cited tools, they enable the integration of heuristicnowledge (basic for eutrophication management). A Bayesian net-ork consists of a graphical structure and a probabilistic description

f the relationships among variables in a system (Borsuk et al.,004). A KBS is consulting software based upon the methods andoncepts of human reasoning and designed to simulate decision-aking processes that would otherwise require human input andlot of mathematical calculation. Although both have been used

s decision-making methods, their approach is different. Examplesn literature show a high amount of Bayesian networks are focusedn effects prediction analysis of a management action done or to

e done (Borsuk et al., 2004; Farmani et al., 2009). On the contrary,BSs are focused on system state diagnostics and on the suggestionf solutions (management actions) to the problems that have beeniagnosed.

ling 220 (2009) 2162–2172 2163

The objective of the present study was the development ofa methodology that would allow the integration, representationand further implementation of empirical and heuristic knowledgerelated to eutrophication problem management in a KBS in orderto provide three main outputs: 1) diagnosis of the eutrophicationproblem; 2) detection of the problem’s causes; and 3) a list ofbest management strategies for solving or minimising the prob-lem in accordance with the cause/s detected. This paper presentsthe methodology specially developed for the integration of all thisknowledge in a decision tree (DT), its implementation in a KBS com-posed of IF-THEN rules, and a brief case study of eutrophication DTapplication.

The study is part of a larger project, the STREAMES (STreamREach Management, an Expert System) European project, whichaimed to develop an Environmental Decision Support System(EDSS) to support and assist water managers with river water qual-ity management decision-making processes (Comas et al., 2002;Llorens et al., 2004) taking into account the WFD’s objective and theDPSIR considerations for river systems of third order located in theMediterranean region. The components of the STREAMES EDSS area knowledge-based (or rule-based) system (constituted by the KBSpresented here and other KBSs related to other stream problemsmanagement), a numerical model and a Geographic InformationSystem (GIS).

2. The STREAMES KBS

The STREAMES KBS is a computer program that can emulatemany human decision-making processes to deal with a specificriver problem. The structure of this KBS comprises two main inde-pendent modules: a knowledge base (KB) and an inference engine.The KB contains the overall knowledge of the decision-making pro-cess codified by means of heuristic rules (IF-THEN). The inferenceengine is the software that controls the reasoning operations of theKBS and links the different IF-THEN rules.

The activities carried out in this study were all related to the KBdevelopment, not to the inference engine. More details about theSTREAMES KBS and the inference engine can be found in literature(Cabanillas et al., 2004).

3. Development and implementation of a decision tree foreutrophication river problems

A DT is defined as a representation of a causal chain of inter-actions from symptoms to problems, causes and solutions of aproblem under evaluation (Comas et al., 2003b). In this case theproblem being evaluated is the eutrophication of rivers. The DT thatis developed, together with DTs for other river problems, constitutesthe KB of the STREAMES KBS after being codified into some IF-THENrules.

3.1. Knowledge acquisition

The bottleneck in the development of a DT, and, in consequence,of a KB, is the knowledge acquisition phase. It is not possible to con-struct a useful and reliable DT without good knowledge sources. Inorder to ensure success, a lot of effort was focused on this phase.Both general knowledge (related to river systems) and specificknowledge (related to the eutrophication problems), divided intoheuristic and empirical knowledge, must be acquired. This knowl-

eutrophication problems, but also with how they deal with themand how the scientists think their management could be improved.

In order to obtain robust and consistent knowledge abouteutrophication and to identify those key parameters that control

2164 E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172

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campaigns to acquire quantitative data. It is an important issue inboth cases as it enables the question “Is there an eutrophicationproblem?” to be answered.

In relation to the parameters considered in the quantitative(left) branch, the most relevant are the nitrogen and phospho-

Fig. 2. Strategies used within

ts development, different knowledge acquisition strategies weresed (see Fig. 2) (more detailed information can be found in Comast al., 2003a; Llorens, 2004):

1. Literature review: a wide and detailed bibliographical reviewconcerning eutrophication management was carried out (e.g.papers such as Rekolainen et al., 1999; European EnvironmentAgency, 2000; Sabater et al., 2003).

. Survey of stream experts and face-to-face interviews: the objec-tive was to acquire information to understand the reasoningmechanisms and procedures of experts when tackling anyproblem to do with eutrophication, and to obtain first-handinformation/data from these people. This information was essen-tial for building a reliable knowledge-based tool (Comas et al.,2003a).

. Experimental campaigns: eleven streams in various Euro-pean draining catchments with different land-use compositions(ranging from agricultural to forest-dominated) were selected(Morais et al., 2004; Merseburger et al., 2005).

. Discussion forum and workshops between STREAMES partners(Llorens, 2004): water managers and scientists involved in theproject shared their knowledge within the forum and sent theiropinions, comments and suggestions concerning the DT and theKBS, with the aim of ensuring the development of a useful, reli-able and scientifically based tool.

.2. Knowledge representation

Once the knowledge acquisition phase had been completed,fforts were concentrated on organising, structuring and repre-enting the knowledge gained in the previous phase in the formf a decision tree. The main difficulty was the decision about howo represent and structure knowledge from different sources (i.e.iterature, empirical and heuristical) and with different structuresi.e. qualitative and quantitative) as it establishes and determineselationships between the different variables.

The eutrophication DT developed was divided into other partialTs, as shown in Fig. 3. Each partial DT refers to the topic that it dealsith (i.e. how to diagnose eutrophication problems, how to detect

heir causes and their effects, suitable management strategies, etc.).his organisation was considered the best way to ensure optimumifferentiation of the reasoning steps carried out within the KBS.he general eutrophication DT was composed of:

a “Problem Diagnosis DT” containing key knowledge to diagnose

the existence and the severity of any eutrophication problem andin which both quantitative and qualitative data are crucial;a “Cause Detection DT” where key knowledge for detecting andevaluating the cause/s of the problem diagnosed is structured;and

nowledge acquisition phase.

- a list of suitable management strategies to deal with the causesof the eutrophication problem or to minimise their effects on thequality and state of the river ecosystem.

The development of the eutrophication DT was carried out bymeans of an iterative process in which experts played an importantrole in revising and modifying the decision tree branches.

3.2.1. Problem Diagnosis DTAll knowledge considered to be useful in the diagnostics step

was structured into the partial Problem Diagnosis DT developed.The knowledge was represented, organised and grouped in twodifferentiated groups according to the data type (i.e. qualitativeor quantitative). Each group corresponded to one main branch ofthe DT as shown in Fig. 4: the quantitative data was grouped intothe left branch, and the qualitative data into the right. This distri-bution is especially important since it enables the integration ofphysical, chemical, morphological and biological parameters intothe diagnosis of the eutrophication problem in rivers and becauseit allows the end-user to obtain a diagnosis of the scenario stud-ied through the consideration of one or both types of data. In thissense, the qualitative and the quantitative branches enable fasterand deeper diagnostics respectively. As a result, the end-user canrun the KBS (and, therefore, the DT) with some qualitative dataand then, depending on the results, prepare or not experimental

Fig. 3. Decision tree structure composed of different partial DTs related to diagnosisof the problem, cause detection and planned actions for better management.

E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172 2165

F utroph[ y).

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ig. 4. The Problem Diagnosis DT for the eutrophication problem (EUTROP. refers to ePs] to soluble phosphorus concentration, and v. H2O refers to current water velocit

us (total and soluble) concentrations of the stream water that isoing to be evaluated in agreement with the literature (Nijboer anderdonschot, 2004). Within the DT they are used to calculate the:P molar ratio and assess which nutrient, at a particular point in

ime, is the limiting one (i.e. N or P). The threshold values for thisatio vary depending on the author (Ryding and Rast, 1992; Kelly,998). Taking these into account, and in accordance with the char-cteristics of the rivers evaluated within the STREAMES project, aew threshold was defined: a threshold higher than 16 indicateshat P is the limiting nutrient. Another piece of quantitative dataonsidered is pH and type of riverbed substrate. Depending on allf these values the DT is able to determine different diagnosticsccording to the severity of the problem (i.e. hyper eutrophication,igh eutrophication, moderate eutrophication, low eutrophicationnd pristine). The thresholds of the different chemical parame-ers are based on the results of the surveys distributed amongTREAMES project participants (most of them ecologists) with thexception of the soluble phosphorus concentration values. In thisase, the thresholds were obtained from the European Environmentgency (European Environment Agency, 1999) and then modified

or siliceous substrates in accordance with the existing relation-hips between the nature of the riverbed substrate and the pHarameters.

As noted previously, the qualitative (right) branch of the DTnables a eutrophication problem to be identified even if the end-ser of the KBS does not have any analytical values from the studyite, since this branch only uses qualitative data (e.g. presence oflgae). The data evaluated is all related to the streambed: presence

f filamentous algae (e.g. Cladophora), light limitation, water veloc-ty, sediment/substrate stability and presence of macrophytes. Algalnd macrophyte production is a qualitative parameter to take seri-usly into account as it is known that it can greatly increase asresult of an eutrophication problem (Stevenson, 1997). For this

ication; [N] refers to nitrogen concentration, [PT] to total phosphorus concentration,

reason the presence of filamentous algae, as well as a macrophytepresence, is considered to be a good indicator of the eutrophica-tion state of a river and its effects within the biotic community.However, the absence of filamentous algae depends on certain fac-tors that could limit algal growth. Some of these factors are lightlimitation (Kelly and Wilson, 2004), substrate stability (mobile sub-strate makes the development of algae communities difficult) andcurrent water velocity (which influences phosphorus retention anduptake), as shown in the results from the experimental campaigns(published in some technical reports), the workshops and the sur-veys carried out within the STREAMES project. This fact has beenrepresented in the DT developed and fits well with some authors,who claim that some of the main local stream features are stronglyrelated to eutrophication effects (D’Angelo et al., 1991; Uehlinger etal., 2002; Nijboer and Verdonschot, 2004; Hilton et al., 2006). How-ever, the type of qualitative data available determines whether ornot the DT’s qualitative branch is able to diagnose the problem. Insome cases the diagnosis is not so easy and, for this reason, quan-titative data such as soluble phosphorus concentration is required.Another weak point of the Problem Diagnosis DT is that the qual-itative branch does not allow the severity of the eutrophicationproblem to be determined once it has been diagnosed. This is thecase when a strong presence of filamentous algae is observed: theDT indicates an eutrophication problem but, as it is very subjec-tive, if end-users want to know its severity they will have to collectmore data, basically quantitative, and enter it into the system. Thissituation is repeated when there is a strong presence of macro-phytes in the stream. However, it should be emphasised that no

visual detection does not mean the stream has no eutrophicationproblem. This is the case when no algal presence is observed in aplace with light limitation, which affects algae and plant growth, asphosphorus concentrations can be high but the riverbed conditionsdo not allow their growth. In these last cases qualitative data is not

2166 E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172

F astewt

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ig. 5. The Cause Detection DT for the eutrophication problem (WWTP refers to wotal nitrogen concentration, and PT refers to total phosphorus concentration).

nough to make a diagnosis and, as noted above, quantitative datas required.

Nijboer and Verdonschot (2004) considered that the idealtream eutrophication model should combine relationshipsetween local stream characteristics, nutrient input, nutrientptake versus transport and their effects on the biotic community.t the moment, a model that integrates all of these parameters doesot exist as the existing river water quality models have been espe-ially developed for predicting nutrient levels. In this sense our KBSries to both go beyond and cover some of the required parameters.

.2.2. Cause Detection DTAll information on eutrophication obtained from the knowledge

cquisition phase and considered as useful in the cause detectiontep was structured into the Cause Detection DT (Fig. 5). The causeetection step starts once the previous step (Problem Diagnosis) hasiagnosed an eutrophication problem. Its aim is to identify whatollution sources could be responsible for the problem within thevaluated scenarios.

As Drolc and Zagorc Koncan (2002) point out, the study of var-ous activities that lead to nutrient fluxes and releases into thenvironment is an important basis for total phosphorus manage-ent in a basin. These authors state that phosphorus pollution is

rimarily the consequence of wastewater discharges, agriculturalctivities on land and atmospheric deposition. Novotny (1999) addsand-use changes, feedlands, treated wastewaters and urban zones

ithout sewers in the list of pollution sources. In the Cause Detec-

ion DT four causes of pollution and eutrophication developmentere considered (two related to point sources and two related toon-point sources), taking into account that nutrients from humanctivity can reach streams and rivers from discrete locations such asoint sources or through diffuse surface and subsurface pathways.

ater treatment plant, [N,P] refers to nitrogen and phosphorus concentration, NT to

As point sources, wastewater discharges and treated wastewaterswere considered and then included within the industrial wastewaterand urban wastewater treatment plant (WWTP) effluents groups. Onthe other hand, agricultural activities developed on land, land-usechanges and feedlands were grouped under the title of agricul-tural and shepherding activities. These were considered as non-pointsources because they contribute to soil erosion and sediment runoff,which, in turn, cause not only soil losses but also water and nutri-ent losses (Wang et al., 2004). Urban areas without sewers werealso integrated in the Cause Detection DT as non-point sources.The only source suggested by the literature that was not taken intoaccount was atmospheric deposition because of the complexity ofevaluating it (in the sampling scenarios this was not considered).Moreover, besides the causes mentioned, the system also takesinto consideration two other possible causes, both related to themalfunctioning of the stream ecosystem: riparian dysfunction andstreambed dysfunction. The first is strongly related to the nutrientbuffer capacity of the river bank of the study site and the second isobtained from the comparison between the self-purification capac-ity and the theoretical potential capacity value of the stream beingstudied.

The thresholds of total nitrogen and total phosphorus concen-trations of WWTP effluents were determined using the expertise ofSTREAMES participants and taking into account European Directive91/271/CEE.

As the cause of the problem could be more than one, the runningperformance of the KBS in the diagnosis problem and cause detec-

tion steps was structured in a different way. While in the problemdiagnosis step the KBS follows a unique branch of the Problem Diag-nosis DT, in the cause detection step it follows each branch of theCause Detection DT. This different running performance gives anopportunity to detect one or more possible causes.

E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172 2167

Table 1Part of the management strategies stored in the KBS’ Access database related to the eutrophication problem organised and structured according to the application scale, theaspects affected by the action once this is applied, the actuation description and the techniques proposed. Each action is linked to the severity of the eutrophication problem(He: Hyper-eutrophication, Ee: High eutrophication, Me: Moderate eutrophication, and Le: Low eutrophication) and the causes detected in the cause detection step (1: urbanWWTP or industrial WWTP, 2: factory effluent not treated in a WWTP, 3: urban area without WWTP, 4: agricultural activities, 5: streambed dysfunction, and 6: ripariandysfunction).

Scale Aspects affected Type of actuation Technique Causes He Ee Me Le

Catchment/subcatchment Hydraulics Controlling water levels andflows

Managing water levels withinthe channel and adjoiningriparian zone

4, 5 � � � �

Catchment/subcatchment Hydrological features Reduction of stream lengthaffected by water diversions

Water pumping 4, 5 � � � �

Catchment/subcatchment Chemistry Construction of a new WWTP Planted systems (plant soiltreatment)

2, 3 � � � �

Lagooning or wastestabilisation ponds

2, 3 � � � �

Constructed wetlands (or reedbed filters)

2, 3 � � � �

Catchment/subcatchment Chemistry WWTP improvement Optimization of thedenitrification treatment

1 � � � �

Implement a phosphorusremoval treatment

1 � � � �

Implement a nitrogen removaltreatment

1 � � � �

Optimize the biologicalphosphorous removaltreatment

1 � � � �

Catchment/subcatchment Best practices Education and training Environmental educationalprograms

1, 2, 3, 4 � � � �

Catchment/subcatchment Best practices Best management practices Best management practices(BMPs) in agriculture

4 � � � �

Alluvial zone Biota Restoring the alluvial zone Removing and setting backartificial flood banks

1, 2, 3, 4, 6 � � � �

Alluvial zone Biota Restoring riparian wetlands Restoring riparian wetlands 1, 2, 3, 4, 6 � � � �Alluvial zone Biota Buffer strips Buffer strips 1, 2, 3, 4, 6 � � � �Alluvial zone Hydrological features Floodplain restoration Restoration of floodplain

scrapes4 � � � �

Alluvial zone Hydrological features Restoring meanders tostraightened rivers

Restoring meanders byexcavating or by using softengineering techniques

3, 4 � � � �

River channel Hydrological features and Biota Restoring stream sinuosity Using vegetation to restorestream sinuosity

1, 2, 3, 4, 5 � � � �

River channel Hydrological features Creation of in-channel spatialheterogeneity

Use of current deflectors 1, 2, 3, 4 � � � �

Building breakwaters 1, 2, 3, 4 � � � �Construction of artificial rifflesand pools

1, 2, 3, 4 � � � �

Creation of stone cascades 1, 2, 3, 4 � � � �

River channel Biota Control of aquatic vegetation Mechanical control 1, 2, 3, 4 � � � �River channel Hydrological features Narrowing the normal flow

channelChannel modification usingbio-engineering methods toincrease flow velocity

1, 2, 3, 4 � � � �

Streambed Hydrological features Creation of a depositional area Introduction of gravel beds 4, 5 � � � �Simulated bedrock outcrops 4, 5 � � � �

S nnel s

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ltatatomtbc

treambed Hydrological features Creation of in-chaheterogeneity

.2.3. List of suitable management strategiesAll knowledge acquired that could be useful for the sound

anagement of eutrophication problems was stored in an Accessatabase, which was integrated within the KBS.

Decision analysis techniques were applied in this phase, fol-owing the procedure described by Reichert et al. (2007). Whilehe decision problem was well-defined and the objectives tochieve were identified easily, the difficult was on the identifica-ion and selection of alternatives. The knowledge was organisednd structured in different management strategies according tohe objectives to achieve when they are applied, the severity

f the problem to confront, the scale of application (i.e. catch-ent/subcatchment, alluvial zone, river channel or streambed) and

he aspects affected by the applied action (i.e. chemistry, biota,est practices and hydrological features), as Table 1 shows. Theharacteristics of the management strategies as well as the pre-

Weirs 1, 2, 3, 4 � � � �

patial Creation of in-channel pools 1, 2, 3, 4 � � � �

diction aspects considered are detailed in Section 4.2. A rankingof alternatives was initially defined according to the data obtainedfrom the survey of stream experts and the face-to-face interviewsto water managers (Comas et al., 2003a). However, the projectparticipants did not agree the relevance of the strategies pro-posed by the KBS and the ranking was removed into the system.The assessment of results is done in the STREAMES EDSS, notinto the KBS, thanks to the application of a numerical model anda GIS.

As this list is also considered an output of the system, moredetails about it are provided in Section 4.2.

3.3. Implementation of the eutrophication decision tree

Implementation of the eutrophication DT was performed bytransferring the two partial DTs and some data from the list of suit-

2168 E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172

ne bra

aT

IsiiIc

Fig. 6. Brief example of the common and new rules structure of o

ble management strategies to a set of IF-THEN production rules.hese rules became the core of the KBS.

The common structure of IF-THEN rules is usuallyF<condition>THEN<conclusion>. Although their structure is

imple they have the disadvantage that the knowledge representedn one branch of a DT must be given by a set of IF-THEN rulesnstead of by one single rule. For this reason the structure of theseF-THEN rules was modified to a more suitable structure (i.e. (valueonclusion) (condition 1) (condition 2). . . (condition n)). This

Table 2Some definition data from the brief case study, where P1 refers to upstream, P2 reP0 data is not presented here (quantitative values were internally calculated by t

General data of the streamevaluated

Current water velociFlow: 1 650 m3/daySubstrate geology: siWidth: 1.5 mLength: 3 700 mDepth: 0.2 m

Riverbed characteristicsSlope: 0.09%Channel sinuosity: sMain substrate: bedr

Sample points data

P1

Quan

Qual

P2 Quan

P3

Quan

Qual

nch of the Problem Diagnosis DT for the eutrophication problem.

new structure provided us with the opportunity to gather all theknowledge contained in each branch of each DT in a single rule(see Fig. 6). Therefore, each rule with the new structure refers toone DT branch. This change in the rules structure simplifies the

KB structure and, in turn, should increase user understanding.Moreover, the more simplified the KB structure, the more able tobe easily updated. Walters (1986) states models must be able to beeasily updated to reflect evolving scientific knowledge and policyneeds.

fers to WWTP effluent and P3 refers to downstream of the stream studied.he KBS by means of mass balance).

ty: 0.035 m/s

licate

traightock, rocks

titative data

[NH4+] = 0.6 mg N/l

[NO3−] = 0.3 mg N/l

[NT] = 1.28 mg N/l[PO4

3−] = 0.15 mg P/l[PT] = 0.18 mg P/lpH 7

itative data Algal presence: noMacrophytes presence: yesLight limitation: no

titative data

Flow: 1 800 m3/day[NH4

+] = 0.62 mg N/l[NO3

−] = 0.1 mg N/l[NT] = 2.28 mg N/l[PT] = 3 mg P/lpH 6.9

titative data

[NH4+] = 0.5 mg N/l

[NO3−] = 0.35 mg N/l

[NT] = 2.6 mg N/l[PO4

3−] = 1.23 mg P/l[PT] = 1.69 mg P/lpH 7

itative dataAlgal presence: yesMacrophytes presence: yesLight limitation: no

Modelling 220 (2009) 2162–2172 2169

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.4. Validation

The laboratory testing of the part of the KBS related to eutroph-cation problem management consisted of a series of experiments

ith real data collected within the STREAMES project. The main aimf these tests was to verify the consistency and completeness of theystem. Syntactic and semantic aspects of the rules generated in themplementation step were checked. The results obtained enabledhe validation of the knowledge acquired and the methodologyeveloped in that particular case.

Moreover, the results obtained from the sampling scenariosere fed back into the KBS in order to refine the understanding of

he relationships between river communities and nutrients, and inurn, to improve the decision-making process involved in the KBS.he close cooperation between water managers and researchers ofhe STREAMES project was crucial to this step and provided themith the opportunity of a wealth-producing knowledge exchange

mong them.The participation of key water managers within all phases of the

resent work developed (even in the validation step) ensures thathe final KBS highly suits the end-user needs and expectations.

. Outputs of the KBS

The main outputs provided by the KBS give important informa-ion to the end-user, who should be able to use it to manage theutrophication problem.

.1. Outputs from the problem diagnosis and the cause detectionteps

Two main outputs are given at this phase, once the end-useras provided the set of data required to run the system. The issuesevealed by these outputs are the following:

) Does our stream/river suffer an eutrophication problem? TheKBS gives a diagnosis of the severity of the problem in thestream/river being studied.

) What are the possible causes that could have led to the eutroph-ication problem? The system gives the end-user a list of possiblecauses.

In addition, the system also gives a qualitative characterisationf the stream water according to its nutrient concentration values.he KBS compares these values with the standard thresholds fixedy the water agency of the country where the stream is locatednd then it characterises them according to the WFD categorisa-ion (i.e. very poor, poor, moderate, good, and very good). In thisense, a quantitative value can be translated into a qualitative one,hereby facilitating the comparison between reaches from differ-nt countries (with different standard thresholds). Another issueo note is the integration of certain physical and chemical val-es to give a kind of ecological status value, since the WFD doesot define clearly how this should be calculated. The integratedualitative value given is considered to be the minimum qualita-ive value of the ammonium, nitrate and phosphate concentrationategories.

.2. Management strategies

When considering the content of the outputs given in the previ-

us steps, the KBS offers another main output. This is a list of bestanagement strategies aimed at solving or minimising the prob-

em detected in the particular reach studied, always in accordanceith the cause/s that have been determined. All the best manage-ent strategies should allow not only the attainment of improved

Fig. 7. Part of the action proposed to restore stream sinuosity.

water quality, but also at least a good ecological status for the streamecosystem, from an integrated WFD perspective.

These strategies are based on the assumption that, when themain cause of the problem is a point source, one of the first thingssuggested is controlling (reducing or removing) the nutrient loadswhich are being dumped into the river (Davis et al., 1998). In com-parison with the control of point sources, the control of diffusesources is much more complex and difficult. The same happens withtheir abatement. While treatment is the most effective method fortraditional point sources or “end of the pipe pollution”, the abate-ment of diffuse pollution focuses on land use, good agriculturalpractices and surface runoff management, as Novotny (1995) andIce (2004) have stated. The KBS takes into account all of these man-agement strategies and includes other more innovative actions toincrease stream self-cleaning capacity (Petts and Calow, 1996; RiverRestoration Centre, 2002) and to restore the riparian and alluvialzones and their functions (Novotny, 2003; Gallagher, 2004; USDA,2004a,b,c), among others. Accordingly, the strategies provided bythe system include land-use practices within the catchment, waysof controlling point and non-point nutrient sources, and mea-sures to increase in-stream nutrient retention and riparian buffercapacity. The list as an output is structured in order to provide auser-friendly layout.

Fig. 7 shows a part of one strategy proposed by the KBS once a

eutrophication problem has been detected. As can be seen, the KBSprovides information related to a set of features for each proposedstrategy: name of the action, specific technique, description of theaction, benefits, limitations, comments, user comments, scale ofapplication, a ratio of environmental benefit versus effort (based on

2170 E. Llorens et al. / Ecological Modelling 220 (2009) 2162–2172

Table 3List of management strategies proposed by the KBS to manage the low eutrophication detected in the stream evaluated and taking into account that a WWTP is responsiblefor the eutrophication problem.

Scale Aspects affected Type of actuation Technique

Catchment/subcatchment Chemistry WWTP improvement

Implement a phosphorus removal treatmentOptimize the biological phosphorous removaltreatmentOptimize the physico-chemical phosphorus removaltreatmentImplement a nutrient removal treatmentOptimize the nutrient removal treatment

Catchment/subcatchment Best practices Education and training Environmental educational programsAlluvial zone Biota Restoring riparian wetlands Restoring riparian wetlandsAlluvial zone Biota Restoration of riparian vegetation Restoration of riparian vegetationRiver channel Hydrological features and Biota Restoring stream sinuosity Using vegetation to restore stream sinuosity

River channel Hydrological featuresCreation of in-channel spatialheterogeneity

Building breakwatersConstruction of artificial riffles and poolsCreation of stone cascades

River channel BiotaRiver banks stabilisation usingbio-engineering methods

Live stakesWillow spilling

River channel Biota Restoring bank vegetation Re-creation of natural vegetation on channel banksR al andS vegeS ositionS nnel

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6. Potential end-users

Potential end-users of the KBS are not only river institutionsand river management agencies that directly use the KBS. The KBS’potential target audience is wide and can be conveniently subdi-

iver channel Biota Control of margintreambed Biota Control of aquatictreambed Hydrological features Creation of a deptreambed Hydrological features Creation of in-cha

he possibility of success, the feasibility of implementing the solu-ion and the response time of the action) and bibliographic and/oreb references.

. Application of the eutrophication decision tree—a briefase study

The application of the eutrophication DT in a brief case study isresented here. A more detailed description of a more complex casetudy using the KBS (and, therefore, the eutrophication DT) can beound in Llorens et al. (2008).

.1. Scenario characteristics

A stream of a third order river located in the Mediterranean zoneas evaluated by means of the eutrophication DT developed in this

tudy. The stream’s catchment drained an area of 1600 km2 and wasonstituted by siliceous substrates. The main land uses were non-ntensive agriculture and forest. One point source was identified

ithin the stream studied: a WWTP that treated the wastewater ofvillage located 5 km from the river.

The sampling campaign to obtain data to run the eutrophicationT (and therefore the KBS) was performed at three different points

P1, P2 and P3) along the stream studied. The first one (P1) wasocated 800 m upstream from the point where the effluent of therban WWTP was dumped into the river (called P0). The secondoint (P2) corresponded to the WWTP effluent and, the third pointP3) was located 1200 m downstream from the P0.

The main characteristics of the scenario are summarised inable 2.

.2. Diagnostics and cause detection

A minor eutrophication problem was identified at P3, while noroblem was identified at P1 (Fig. 8). The KBS was not able to givediagnosis for P1 on the basis of qualitative data only, as quantita-

ive data was required. However, it did identify an eutrophicationroblem at P3. The quantitative data made it possible to define

ts severity (low eutrophication) and identify pristine conditionspstream. An integrated value of good water quality was given forhe upstream and a value of very bad quality for the downstream,

in-channel vegetation Control by shadingtation Biological controlal area Weirs

spatial heterogeneity Creation of in-channel pools

basically due to the increase in phosphorus concentration. The highamount of phosphorus in the WWTP effluent evaluated was con-sidered to be responsible for the eutrophication problem and thepoor water quality.

5.3. Management strategies

A list of twelve types of strategies was provided to manage thelow eutrophication detected in the evaluated stream (Table 3). Oneof them focused on the improvement of the WWTP operation anddesign. One was related to plans for raising awareness of the issueamong the population. The remaining ten focused on direct actionsin the stream that would necessarily affect biotic and hydrogeo-morphological features at riverbank and riverbed scales.

Fig. 8. Branches of the Eutrophication Problem Diagnosis DT run by the KBS for thecase study and diagnostics (EUTROP. refers to eutrophication; [N] refers to nitrogenconcentration, [PT] to total phosphorus concentration, [Ps] to soluble phosphorusconcentration, and v. H2O refers to current water velocity).

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ided into three main end-user groups: 1) public and private wateranagers, public authorities; 2) students; and 3) municipalities and

eneral public (like local citizen action groups and environmentalon-professional associations). While the first group may be inter-sted in the use and application of the KBS to assist in the selectionf management strategies, the general public might be interested inhe results obtained from the KBS implementation. The KBS couldlso be used for educational purposes (then the end-users might betudents).

The way of organising, structuring, representing and integrat-ng the knowledge gained in the knowledge acquisition phase in aT together with its implementation in a KBS by means of simple

ules syntax makes possible the use of the KBS by the above citedotential end-user groups.

. Conclusions

This paper presents the development of a knowledge-basedpproach aimed at helping water managers confront eutrophi-ation management problems in river ecosystems of third order,specially located in the Mediterranean region. It is the result oflose cooperation between stream managers and researchers.

The main contribution of this study is the methodology whichas been developed and applied. It is based on a multi-perspectivepproach that includes knowledge acquired from literature, expe-ience and empirical data obtained from scientific research, itsepresentation in a decision tree and its implementation in a KBS.he good DT obtained suggests that the KBS model is a suitable arti-cial intelligence model for facing out eutrophication management.owever, it should be stressed that the model’s development waso easy task. The first difficulty was the acquisition of all the knowl-dge related to the problem (in this case, eutrophication) and itsanagement, as a close relationship exists between the knowledge

ources and the data acquired from these, and the development of aeliable DT, which is not possible without good knowledge sources.herefore, a lot of effort was focused on the knowledge acquisi-ion phase. Another difficulty was the processing and subsequentepresentation of all this knowledge from different sources andith different structures (i.e. qualitative and quantitative) in a DT

n order to establish and determine correct relationships betweenhe different variables. Close cooperation between water man-gers and researchers, in the knowledge acquisition and in the toolequirements definition, ensured the development of a useful toolnd provided, as an added value, a wealth-producing knowledgexchange among them.

One of the strengths of the work is the list of best managementtrategies. This is highly relevant as the KBS provides a specificist for a specific situation (in accordance with the severity of theroblem, its causes and local stream features).

Finally, although the methodology specially developed has beenpplied here to the problem of eutrophication, it is important tomphasise that it could be applied to any other stream problem.

cknowledgements

The authors would like to acknowledge the economic supportf the European Commission (EVK1-CT-2000-00081, STREAMESroject).

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