Integrated modeling for river basin management planning

&*€*ry**€*# re*#*&***ry €** r*:g*x***** ffi&re&#*ffi#rc€ ry&**re*ffi#Eleftheria SafioleaResearch Scient is t , School of Civ l Engineer ing, Nat ional TechnicalUniversity of Athens, Athens, Greece

Sotiria BakiResearch Scient is t , School of Civ i l Engineer ing, Nat ional TechnicalUniversily of Athens, Athens, Greece

Christos MakropoulosAssistant Professor. School of Civ i l Engineer ing, Nat ional TechnicalUniversity of Athens, Athens, Greece

Jean Francois DelidgeDirector , Aquap6le, Univers i ty of L idge, L idge, Belgium

Paul MagermansResearch Engineer, Aquap6le, Univers i ty of L iege, L idge, Belgium

Complex spatio-temporal environmental problems call for an integrated assessment of environmental systems due totheir interdisciplinary nature. Within this context, the approach of integrated water resource management (IWRM) atthe catchment scale, which is at the heart of advanced European environmental policy such as the water frameworkdirective (WFD), argues for integrated modell ing tools and approaches. This paper examines recent technologicaldevelopments that facil i tate integrated modell ing and focuses on a novel approach, the open modell ing interface(OpenMl), which allows models built by different developers to communicate at runtime. The individual models,when made compliant with the OpenMl standard, can be considered as 'components' of an integrated model andseamlessly exchange data. Two OpenMl-enabled integrated modell ing case studies are presented and their resultscrit ically discussed. The paper concludes with opportunities and challenges towards the design of f lexible,component-based models supporting integrated environmental policy in general and the WFD implementation inparticular.

1. lntroductionIntegrated water resource management (IWRM) at the catchmentscale has been promoted as a method for integrated analysis anddecision making in view of the complex and evolving nature ofenvironmental systems and the high degree of underlyinguncertainty.. To make IWRM possible, integrated modelling

emerged as the approach of choice to produce the scientific basisthat environmental and water resource managers require to makeinformed decisions, therefore bridging the gap between scientistsand decision makers and encouraging more active commumca-tion (Liu et al., 2008). Clearly, the interpretation of modelintegration is a matter of degree. The US EnvironmentalProtection Agency (EPA, 2008) stated that integrated waterresources modelling has been traditionally addressed with thedevelopment of complex models focusing on single media andresearch areas rather than integration of simpler models across

different disciplines. This paper explores one of the availablepathways to achieve the next level of model integration. Itevaluates the use of the open modelling interface (OpenMI,

2011) to enable new or existing model components ofprocessesthat previously performed in isolation to interact with each otherin the same experiment and exchange data at runtime, as itactually happens.

Etienne EverbecqResearch Engineer, Aquapole, Univers i ty of L idge, L iege, Belgium

Anthoula GkesouliResearch Scient is t , School of Civ i l Engineer ing, Nat ional TechnicalUniversity of Athens, Athens, Greece

Anastasios StamouProfessor, School of Civ i l Engineer ing, Nat ional Technical Univers i ty ofAthens. Athens. Greece

Maria MimikouProfessor, School of Civ i l Engineer ing, Nat ional Technical Univers i ty ofAthens. Athens. Greece

As environmental legislation globally and the water frameworkdirective (WFD) in Europe in particular sets ever morestringent requirements on environmental targets, there is agrowing need for tools able to provide reliable predictions thatwill assist the EU in achieving new policy requirements such asgood ecological status. As a result, integrated models have tendedtd become more complex and all-encompassing. In recent years,

however, there has been a shift towards a component-basedapproach gradually replacing the use of single 'monolithic'

models that aim to simulate all the physical processes of a systemwithin a single code. Great importance has been given to modelreuse, flexibility, extensibility and usability in environmentalmodelling that can be achieved via module-based approaches,with different system components modelled as separate modules(Argent, 2005; Makropoulos et aI.,2010). To accommodate suchan approach, appropriate mechanisms and integrating modellingframeworks have been designed to enable components commu-nication and data flow and to deliver flexible component-basedsystems (Argent, 2004; Gaber et al., 2008). Such environmentalintegrated modelling frameworks (EIMFs) have the potential ofreducing repetitive modelling procedures and bringing back thefocus on model conceptualisation and system strucfure (Argent e/al., 2006; Rjzzoli et al., 2008). Integrated working environments

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Water ManagementVolume 164 lssue WMB

Integrated modelling for river basinmanagement planningSafiolea, Baki, Makropoulos et a/.

with modular components, separate data entry and data storageutilities that are shared between different modelling tools from

the same software provider (e.g. HEC, DHI, Waliingford Soft-

ware, USDA-SWAT) became increasingly popular in the late1990s and are extensively used in hydrological and hydraulic

applications today (Arnold and Allen, 1993; Butts et a1.,2004).

As the range of EIMFs grew, the scientific community (Fiizzoli et

al., 2008) argued there was more benefit in improving andexpanding existing EIMFs rather than continuously developingnew frameworks. The modular modelling system of Leavesley e/

al. (1996) was one of the first attempts at module-based model-ling; the dynamic information architecture system (Sydelko et ol.,2001) has an object-based architecture in which the modules are'wrapped' and therefore do not need to be rewritten in a specified

system language; the spatial modelling environment (SME)(Costanza and Voinov, 2003; Voinov et al., 7999,2004) has the

ability to incorporate a series of integrated Stella (systems

thinking experiential learning laboratory with animation) models

through a user-friendly interface, as well as components devel-oped in C++. Catchment modelling toolkits with module librariesinclude the interactive component modelling system (ICMS,

formerly known as integrated catchment management system)(Reed et al., 7999; Rizzoli et al., 1998), which uses a simplifiedmodelling language and is aided by a visualisation interface andTarsier (Watson and Rahman, 2004; Watson et al., 1998) for

which new modules need to be written in C++. The comparativeanalysis of the SME, Tarsier and ICMS frameworks conducted byArgent et al. (2006) highlighted the advantages and applicabilityof each one. The invisible modelling environment TIME (Rah-

man et al., 2003, 2004, 2005) is a framework with advancedfunctionality and flexibility facilitating component-based model-

ling that was created by merging ICMS and Tarsier to benefitfrom the advantages ofeach individual framework.

On TIME's architecture, WaterCAST (former E2) was developed(Argent et al., 2009; Cook et al., 2009), which delivers an

environmental decision-support system for catchment manage-ment; its new version is called 'Source Catchments' (eWater,

2010). Another recent development is the object-oriented model-ling framework 'object modelling system' (OMS) (David er a/.,

2002; Kralisch et a1.,2005) with advanced spatial representation

capabilities; it requires modules to be rewritten in Java. The Jena

adaptable modelling system (Jams) (kalisch and Krause, 2006) isbased on the OMS, which was modified to accommodate the special

needs of the model developers. The two frameworks are focused

towards agro-environmental applications and the implementation

of the WFD (Fink e/ aL.,2007) with model components simulating

hydrological and nutrient cycles. The Liquid platform (Branger et

al., 2010) focuses on hydrologic modelling and is suitable for

applications within a wide range of spatial and temporal scales;

while it retains integration properties similar to those offered byJams and WaterCAS! it is also able to represent more complex

interactions and feedbacks between components. The framework

for understanding strucfural errors (Fuse) combined components

406

from four hydrological models in order to diagnose differences inthe resulting 79 unique model structures (Clarket a\.,2008).

These modelling frameworks have been mainly designed forbuilding custom integrated models through module libraries or bydeveloping new modules. A more generic type of modellingframework has been built especially for the integration ofexistingmodels (Voinov, 2010). Within this context, the common compo-nent architecture (CCA) (Bernholdt et al., 2004) developedjointly by the US Deparlment of Energy and various US nationallaboratories and universities, is a framework focusing at high-performance computing that facilitates module-based modellingby incorporating models by different groups that communicatebetween them via the Babel language interoperability tool.Frames (framework for risk analysis in multi-media environmen-tal systems) (Frames, 2010) is a platform that encompassescollections of models and modelling tools and facilitates commu-nication between them. 3MRA (multi-media, multi-pathway,multi-receptor) (3MRA, 2010) is a set of 17 science modules forthe simulation of release, fate and transport, exposure and riskassociated with hazardous contaminants disposed of in land-based

waste management units included within Frames. The combinedFrames-3MRA system (Babendreier and Castleton, 2005) wasdeveloped by the US EPA as an assessment technology for thehazardous waste identifi cation rule.

Finally, the open modelling interface (OpenMI) (Moore andTindall, 2005), whose application is presented and discussed inthis paper, represents a radically different approach in supportingmodel linking and communication. It was initially specificallydeveloped to assist in the implementation of the WFD as a toolfacilitating IWRM. The OpenMI standard is not a 'framework'

similar to the ones mentioned above. It is actually a softwarecomponent interface definition for the computational core (the

engine) of models that, after being made compliant, can exchangedata between them at runtime without any further programming(Gregersen et al., 2007) with each operating in their nativeenvironment. In this way, custom integrated models can be builtvia linkable model components using existing models fromdifferent providers and different domains according to the specificneeds of a problem, and interactions and feedbacks betweenlinkable components can be simulated. This is achieved byselecting 'exchange variables' between the models, with the'output exchange variables' of one model being linked to the'input exchange variables' of the other.

OpenMI has the ability to provide a generic communicationprotocol for component-based modelling that can be used by othermore tightly coupled frameworks (Castronova and Goodall, 2010).Within this context, the Seamless integrated framework (Van

Ittersum et al., 2008), developed for the assessment of agro-environmental policies in the EU, utilises OpenMI for communica-tion between model components. Another example of this approachis the simple modei wrapper (Castronova and Goodall, 2010),

which uses processJevel hydrologic modelling components linked



through OpenMI. In addition, the community surface dynamicsmodelling system (Slingerland et al., 2008), dedicated to theprediction of fluid, sediment and solute movement in landscapes,

while operating within a CCA environment to take advantage

of high-performance computing capabilities, uses OpenMI as

the communication protocol for advanced information transfer.

OpenMI has already been applied in several case studies in water

management (Dewoede et al., 2008; Fotopoulos et al., 2010), as

well as for interdisciplinary analysis in the broader environmental

domain (Bulatewicz et a1.,2010; Knapen et a1.,2009).

The rest of the paper presents the application of OpenMI research

tool in fwo integrated modelling case studies relevant to the WFD- the Pinios catchment in Greece and the Scheldt catchment in

Belgium. The main objectives of the two case studies were to test

the use of the tool under real life conditions, to evaluate the

reliability of the results, to assess the current benefits and

weaknesses related to integrated modelling and to suggest further

improvements. The key criteria for model component selection

were current compliance with OpenMI (or with the opportunity to

become compliant by offering open source code), model special-

isation, extra feafures and existing expertise ofthe study end users.

Detailed assessments of the technical aspects and applicability of

the integrated modelling tool from both user and developerperspectives can be found in the literature (OpenMI, 2010a).

2. Case study A: Pinios catchment

3..3 Stzsdy *r*aCase study A focuses on the upper catchment of the Pinios River in

the Thessaly water district in Greece, which drains an area of10500km2 (Figure l). The catchment has a predominantly

0 4 5 9 1 8 2 7 3 6 k mE

7i7zsr* 1" Thessaly water distr ict, the Pinios River and study area

agricultural character, is intensely cultivated with water-demandingcrops and has significant livestock activities. Increased irrigationneeds within the area have 1ed to a considerable decrease ingroundwater levels and river flow.

The main objective of the work carried out was to examine how

OpenMI could be used to build an integrated model that wouldfacilitate investigation of the effectiveness of different interven-tions needed to achieve WFD-related water quality targets in thestudy area. Necessary steps for successful OpenMI linking weretested using a hydraulic and a water quality model developed andapplied by different research groups.

2.7 lVtadell ing appr*esh

The integrated modelling scheme set-up for the upper PiniosRiver catchment area combined the models Mike 11 and Otis, asshown in Figure 2. Mike 11 (DHI, 2009; Madsen, 2000) is awidely used river modelling system with numerous applications.In this study, it includes

(a) the Nam module, which is a deterministic, conceptual,lumped rainfall-runoff model to evaluate the runoffgenerated from each sub-catchment

(b) the HD module, which solves continuity and momentum

equations to determine hydrodynamic characteristics of theriver such as water depth and flow velocity.

Otis (one-dimensional transport with inflows and storage) calcu-lates the concentrations of waterborne conservatlve or non-conservative substances in streams and rivers by solving the

advection-dispersion equation for steady orunsteady flow condi-tions. Otis was developed by the US Geological Survey (Runkel,

' River network- Pinios River

C Ali Efenti

f f i Upper Pinios catchmenl

DEM Thessaly|:10-287 mffi zag-sga mffi s9t-g6g m& 969- l+s9m& tqsg-zlza m

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407


lntegrated modelling for river basinmanagement planningSafiolea, Baki, Makropoulos et a/.

Measured dischargeat catchment

KatnTail ano rt I oui lelfrom sub-basins Al i Efenti

F 4g g

Stage-discharge

relat ionshipCross-section atinformation Al i Efenti

E gg g

Measured

Ali Efentiand

Mesdanioueg

Pollut ion loadsfrom point sources

and non-pointsourceS

@

@

Water qual i tymeasurementS

in fourlocationson Pinios

@

DischargePinios River

C ross-sectionalarea

Pinios River

Figr.*r* ?. Case study A: simulated physical processes and modelsused (PET, potential evapotranspiration; BOD, biochemicaloxygen demand)

1998, 2000; Runkel and Broshears,1991); Mike l1 is released byDHI as an OpenMl-compliant model. Otis had to be migrated inOpenMI by the authors, which was possible since Otis isdistributed as open source.

In the same catchment area but using different assumptions, Stamouet al. (2007) applied an alternative integrated model that involved

(a) the hydrological model Mike-She (DHI, 2011)(b) the hydrodynamic model Rish-lD (river and stream water

hydraulics in one dimension)(c) the water qualify model RISQ-ID (river and stream water

quality in one dimension).

Rish-lD and Risq-1D were developed in the National TechnicalUniversity of Athens, and a discussion on the two integratedmodels can be found in Makropoulos et al. (2010).

2"3 t&*4*Z e{**{*e4Csetierz e{v* Zt=.t**rate* {****elira*Zreg

River nodes and reaches were defined in both models based onavailable data, which included locations of tributary confluences,pollution point sources and river monitoring network points. Itshould be noted that extra model nodes had to be added toovercome the water quality model's limitation in simulatingmultiple point sources, as well represent tributary inflows. Both

408

these inputs to the river system were modelled as lateral flowsbetween adjacent nodes as shown in Figure 3.

Three input exchange variables were selected for Otis, corre-sponding to main channel discharge, cross-sectional area andlateral flow from the main tributaries. Discharge and velocitywere set as the output exchange variables for Mike 11. Since thedirect exchange of cross-sectional area between the two modelswas not feasible due to limitations in the Mike 11 migration, themddelling team had to code additional conversion routines withinthe Otis DLL wrappeq so that the area could be calculated fromdischarge and velocity. In total, 9l one-direction links wereestablished via the OpenMI configuration editor between the

WWTP/|ndustry or majortributary with pollution load

?Egtzr*S.Representation of pollution po,nt ,orr.., and majortributaries (WWTP, wastewater treatment plant)


Integrated modelling for river basinmanagement planningSafiolea, Baki, Makropoulos ef a/.

hydrologicArydraulic and the water quality model for the different

exchange variables at the various nodes along the river network.

The migration process and linking assumptions were evaluated

before the final model calibration by setting up and running a

simplified pollution scenario. Since only one-direction links wereused in the study, comparable standalone and linked model results

along the river confirm successful migration, smooth operation of

the linking process and correct data exchange as shown in Figure 4.

3..4 fsl*d*i s€t*#p *r:d ealibrati*r:

The individual model components were separately calibrated using

data from 1993 that included a low-flow and high-flow period. Thatperiod was chosen on the basis ofthe reliability and availability of

flow and water quality measurements, although several limitations

2 - 0 . ,

* Linkedsimulation

- ( o n r r r f o

slmutalron

Figure 4. BOD levels at Otis node 26 trom separate and linkedmooer runS

240

220

in the spatial and temporal variability ofthe datasets still had to beaddressed. However, Pinios fuver, as a chosen EU pilot basin, doesprovide one ofthe best comprehensive datasets in Greece that relateto a water district oftrue regional interest. The hydrologic/hydraulicmodel computations had a l0 min timestep and were calibrated attwo locations, Mesdaniou (53 km chainage from the first upstreammodel node) and Ali Efenti (77 km chainage from the first upstreammodel node) (Figures 5 and 6). The effect of temporary flowdiversions for (often unregulated) water abstractions during earlyautumn is apparent in Figure 6, with very low flows being recordedin the downstream node that cannot be reproduced by the modelling

exercise artd the related upstream measurements.

Pollution loads from point and non-point sources were importedin the water quality model. Point sources included wastewater

treatment plants (WWTPs), industries and untreated disposal ofdomestic sewage. BOD5 (5-day biochemical oxygen demand)loads were estimated using design information from past studiesand relevant literature (Stamou and Douka, 2010; Starnou et al.,2007). Non-point pollution, mainly originating from livestock,agriculture and landfills, was assumed to enter the river systemindirectly via the main tributaries. In the absence of more detaileddata, the pollution load corresponding to each tributary wasestimated using average values from the limited available fieldmeasurements with minor adjustments (Stamou and Douka, 2010;Stamou et a1.,2007). Otis was set at a 24h timestep; therefore,data between the two models were exchanged every 24h. Themodel was calibrated during the low-flow period and validatedfor the high-flow period at four monitoring stations. The relia-

bility and sufficiency of the water quality measurements wasquite low since only sporadic gab-sample measurements werecarried out once a month. A comoarison of measured and

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Water ManagementVolume 164 lssue WMS


m m m m an m m m m m m m m m m m m m rq m m cn m m mol or or ol ol or ol or ol ot o) ol ot ol ol ot ol or ol ol ol or ol ol ol\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \O I O t O T O I O t O I O O O O O O O - - - N N N N N No o o o o o- (o - (o - (o .- \o - (o - (o - n o n o h o n o n o n oO O - - N N O O - - N N m O r - N N r f l O - . - N N m

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simulated BOD5 values along the Pinios River for November andDecember is given in Figwes 7 and 8 respectively. Some BOD5

concentration peaks that varied significantly from average values(which could be attributed to illegal discharge of tmtreated

domestic and industrial wastewater) could not be adequatelysimulated. The variation in BOD5 concentration from September

to December 1993 is shown in Fieure 9.

2.5 F*l!a*t i*n c{e$e.;*gFollowing satisfactory set-up and calibration of the integrated

model, three pollution scenarios addressing typical water qualityproblems in the catchment were investigated. Pollution scenariosI and 2 dealt with pollution incidents caused by point sources at

different locations along the river (failure of a WWTP's second-

35000 40000 4s000 50000 ss000 60000 65000

Node chainage: m

Fig*re 7" Observed and simulated BOD levels along the Pinios

River in November

35000 40000 45000 50000 55000 60000 65000Node chainage: m

*ig*r* *. Observed and simulated BOD levels along Pinios River inDecember

ary treatment and lack of wastewater treatment at selectedindustries). Pollution scenario 3 examined the effects from anincrease in diffirsed pollution due to different livestock andagricultural practices. Under scenarios I and 2, the linked-runanalysis showed greater impact during low-flow periods (Figure

10). In scenario 3, water quality was affected during both low-

flow (September to mid-November approximately) and high-flow(mid-November to December approximately) periods (Figure 1l),

which demonstrates the importance of adopting environmentallysound agricultural practices within the catchment (see alsoPanagopoulos et al. Q0O7)).

2.4 C**ela:sia*s

Integrated modelling through the OpenMI standard was success-fully applied to the upper Pinios catchment by linking a

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hydrologic,&ydraulic model with a water quality model in orderto investigate various interconnected issues affecting water quan-tity and quality in the area. Depending on the model migrationprocess, selected exchange variables may not be available to usein the OpenMI environment. The present study was able toovercome such issues by adding extra calculations in the wrapperof the model migrated by the authors. Current OpenMI-compliance rules demand that developers provide an .xml filewith exchange parameters and model-related information so thatmodellers can see whether a migrated model satisfies their needs(OpenMI, 2011). The linked scheme minimised the input datasetpre-processing time for the pollution scenario runs. However, theintegrated runs were significantly longer than the standalone runs;this can be partially contributed to additional calculationsembedded in the water quality wrapper.

3. Case study B: Scheldt catchment

3"3 *7zs4V ar*aThe Scheldt fuver originates in France and flows through Belgiumand the Netherlands into the North Sea. It is a significant interna-tional waterway, 350 hn long, that drains anarea of 21860 km2 andis navigable from its mouth to the French city of Cambrai. Signifi-cant environmental degradation concerns in the Scheldt and itstributaries over the last century may be partly attributed to theincreasing population density (currently more than ten millioninhabitants). The Scheldt case study focused on a sub-basin oftheScheldt: the Dijle River basin in central Belgium. More particularly,it involved linking Infoworks RS software (MWH Soft, 2010) andMike 11 (DHI, 2009) river flow models (simulating two differentsections of the Dijle fuver) to the Pegase.river quality model(Delidge et a1.,2009), simulating the greater Dijle and the Demercatchments (see Figure 12). Extra modelling was required tosimulate the details of the waterway in the centre of the city ofLeuven. Thus, for the purpose of this feasibility study, it wasdecided to evaluate the non-tidal upstream and the tidal downstreamlinked systems of Dijle separately, not connected to each other.

The main goal of this work was to improve the tools that arealready used to simulate river basin management plans producedfor the purposes of the WFD. This goal was obtained by linking,in real time, the models already used by the stakeholders andproducing an integrated system on which other models could beadded in the future to evaluate different aspects of river basinmanagement. Two conditions were specifically addressed: lowflows in a particular$ dry period and high flows in a particularlywet period, but without extreme flooding.

="A 7€c*d*E€l*zq z*?{*e{?zAs shown in Figure l2,Iwo integrated model schemes were set upand tested in different sub-basins. For each scheme, a river modelcalculated the hydraulic variables using hydrological and topo-graphic input data. The upstream sub-basin, which involved thenon-tidal part of the Scheldt tributary was modelled using Info-works RS. Infoworks RS includes a 1D and a 2D simulation ensine

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Node '14

Node 22Node 26Node 28Node 10

?Zgasr*33. Simulated BOD levels: pollution scenario 3

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Water ManagementVolume '164 lssue WMB


Figure 12. Case study B area catchments

with the ability to perform geographical analysis and data manage-

ment. It can model open channels, culverts, floodplains, embank-

ments and hydraulic strucfures, and can simulate event-based and

continuous rainfall-runoff to satisff its hydrological input de-

mands (Ghani et a1.,2010; MWH Soft, 2010). The downstream

sub-basin related to the tidal part of the Dijle River was modelled

using Mike I 1. The calculated hydraulic river variables, water

temperature and pollution loads were the main inputs for the

evaluation of physical and chemical water quality characteristics

conducted by the Pegase river quality model. The Pegase model

calculates water quality (-30 of the main parameters involved in

water quality, including BOD, chemical oxygen demand (COD),

02, NO2, NO3, NH4) in a deterministic way under differentpollution loads, for both stationary and non-stationary hydrological

conditions (Delidge et al., 2009). Furthermore, it includes a

hydraulic calculation module based on an efficient flow recalcula-

tion from observed flow gauge data. Some of the ecosystemprocesses involved in Pegase are shown in Figure 13.

3.3 tul*del Echen'latisation end intesratcd msdel

l ink ing

The two studied sub-basins are situated upstream and down-

stream of the city of Leuven. The two commercial hydraulic river

412

models were already OpenMl-compliant while the scientiflc

water quality river model had to be migrated by its developers

during the study to become compliant. A preliminary task was toidentify the possible technical issues. This task focused on the

compatibility of variables exchanged and the computer perform-

ance related aspects ofthe integrated model. For example, actions

were taken to

(a) allow extra parameters (such as velocity) to be a standard

OpenMI exchange item(b) relax a low-flow threshold that a river model used to apply to

overcome potential instabilities(c) expand RAM memory to support a single large-scale

modelling run(d) provide exchange values during a model's internal calculation

trmestep.

All the models were set up by different research groups (devel-

opers). In order to create meaningful links between the models,

the following characteristic datasets should match

(a) river digitisation (also including main tributaries and

secondarv branches)

Water ManagementVolume 164 lssue WM8


?iqzsr* -3. Processes and variables involved in the ecosystemmodule of the river oualitv model

(b) location ofexchange nodes along the river

(c) main river characteristics such as cross-section profile, slope

and roughness(d) initial condition assumptions for the same parameters(e) initial releases and abstractions to the river.

Wherever necessary extra nodes were introduced to satisfr proper

data exchange among the models.

3.4 lr:?*greted {tle'**Z 1c=Zt{c* *'}r?{c* e "?.s*z

e?t* * *?Eperiod

A series of scenarios of increasing modelling complexity was

designed to allow gradual understanding ofkey integrated model-

ling aspects and provide suggestions for improved integrated

modelling techniques. The first two scenarios (Table l) tested

integrated model linking during characteristic high- and low-flow

conditions. The simulations resulting from the integrated model

runs were also compared with the equivalent standalone runs. All

model runs were evaluated in selected locations where observed

data were available. Representative model run results for scenar-

ios 1 and 2 are shown in Fisures 14-19.

The flows of integrated modelling runs matched the measured

data in a satisfactory way for both wet and dry periods. Hydraulic

river models tended to overestimate peak flows and underestimate

low flows, especially when model calibration focused on high

flow and did not take place close to the studied nodes. Standalone

runs ofPegase, using its own internal hydraulic calculations from

measured flow data, also provided reasonable results; it appeared

to match low-flow conditions better since it also accounted for

different releases (wban, industrial, etc.) in the river network.

However, during wetter seasons, the water quality model estima-

tions of flow (already accvrate in the standalone mode) couldgain from the hydraulic input of calibrated river flow models. The

evaluation of water quality under the specific scenarios did not

significantly change, especially compared with the actual varia-

tion and uncertainty ofmeasured data.

3.= *<crz*:z*s *€ ltz<rca*** Ea:t*grat** nz***Z?Erzg

< tzzV7*zztV

In the previous section, the two integrated models upstream and

downstream of Leuven exchanged one parameter (flow) at six

locations. After testins the models under different conditions and

413


lntegrated modelling for river basinmanagement planningSafiolea, Baki, Makropoulos et a/.

Sub-basin Links/nodes Simulation period2002-2003: days

Total runtimeTimestep: s Saveinterval

Scenario 1 Non-tidal (upstream)Wet period Tidal (downstream)

Scenario 2 Non-tidal (upstream)Dry period Tidal (downstream)

?*bl* 5. Characteristics of scenarios 1

Unidirectional/6Unidirectional/6

U nidirectional/6U nidirectional/6

and 2

6030

6030

t h1 5 m i n

t h1 5 m i n

1 8 1442(but analysed only thewet period part)184184

13 h 47 m in4 h 28 min ( fortotal simulationperiod)1 3 h 2 9 m i n2 h 2 8 m i n

20

1 8

1 6

1 4

-< 12E. . 1 n= ' "

L d

6

4

2

001/1

Fig*r* ' !4. Scenario 1 (wet period): comparative analysis ofstandalone (s.a) and l inked runs of the upstream sub-basin

I

Pegase s.a.Pegase-lnfoworks R5Measurements

1/2002 01/12/2002 31/12/2002 30/01/2003 01/03/2003 31/03/2003 30/04/2003Date

120

Fig*r* 35. Scenario 1 (wet period): comparative analysis of

standalone (s.a.) and l inked runs of the downstream sub-basin

* Pegase s.a.* Pegase-Mike 11

a Measurements

30/01/2003 01/03/2003 31/03/2003Date

100

80

Er 6 0t

r

40

20

001/1 30t04/20031/2002 01/12/2002 31/12/2002

recognising best practices that would improve the integratedmodel set-up, two extra scenarios were designed to furtherchallenge each individual linked scheme. Scenario 3 demanded

the exchange ofthree parameters (floq stage and velocity) in all

414

common nodes of the non-tidal (upstream basin) part of the DijleRiver. Scenario 4 tested the use ofbidirectional links to exchangerelease discharges in the tidal (downstream sub-basin) part of theDijle River (Table 2).

Water ManagementVo lume '164 lssue WMB


8

7

6

5

4

3

2

1

001/11/2002 01/12/2002 31/12/2002 30/01/2003 01/03/2003 31/03/2003 30/04/2003

Date

figa:r* €S. Scenario 1 (wet period): BOD variations at thedownstream sub-basin

5 . 4 .

* Pegase-Mike I1a Measurements

Eoo

9

B

7

6

5

4

3

2

1

001/05/2003 01/07/20

{Egzsre j3. Scenario 2 (dry period): comparative analysis ofstandalone (s.a.) and linked runs of the upstream sub-basin

- Pegase s.a.

.* Pegase-lnfoworks RS

a Measurements

E;

u

31/08/2003 31/10/200301/07/2003Date

I60

50

40

30

20

1 0

0

+ Pegase s.a.+ Pegase-Mike 11

4 Measurements

EI

L

01/05/2003 01/07/2003

Figure tS. Scenario 2 (dry period): comparative analysis ofstandalone (s.a.) and linked runs of the downstream sub-basin

Date31t08/2003 31/10/2003

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Water ManagementVolume '164 lssue WMB

lntegrated modelling for river basinmanagement planningSafiolea, Baki, Makropoulos ef a/.

* Pegase s.a....'e Pegase-Mike 11

a Measurementse Less than detection l imit

001/05/2003 01/0t/2003

{ igz;t* 3*" Scenario 2 (dry period): BOD variat ions at the

downstream sub-basin

31/10/2003

Among the issues raised during this testing phase was the

significant increase of integrated model runtime. The intensive

data exchange between the two models of scenario 3 resulted in a

25o/o increase in runtime compared with only exchanging one

variable at six nodes for the same simulation period. Similarly,the addition of four bidirectional links in scenario 4 resulted in a

67Yo increase in runtime compared to a six-node unidirectional

link run. On the other hand the bidirectional links seemed to

improve the low-flow simulations of the integrated system,

feeding additional information in the interaction of both models.

In general, the standalone (water-quality-only model) and linked

runs in both scenarios matched well.

3.& {*rzr1tseE*rza

Case study B tested progressively different aspects of integrated

modelling using OpenMI and highlighted the strong necessity to

address physical processes when implementing new integrated

methodologies to improve water quality modelling. Good coop-

eration ofmodel/software developers and end users was necessary

to overcome challenges related to the novel integrated modelling

approach and produce satisfactory model runs. Aspects such as a

clear prior problem definition, the building of satis$ing concep-tual integrated model and appropriate model selection were found

essential for improved integrated model linking. Standalone

models are still effective tools to quickly answer selectedquestions focusing on their specific strengths. The two-model

linking case study was chosen to reveal important points of

integrated modelling to consider later in larger integrated model-

ling schemes where any chosen process interaction could bemodelled and evaluated when needed, by linking the most

appropriate available modelling tools.

4. Conclusions and future workIntegrated modelling using OpenMI supports the seamless linking

of the best available compliant model components identified for thepurpose of a specific analysis. It thus assists integrated modelling

to become a dynamic tool actively supporting the efforts ofdecision makers to improve their understanding of the cause-effect

relationships between intervention and results at river basin scale,

which is of central importance to implementation of the WFD.

The work discussed in this paper presents the linking of different

river flow and water quality models applied on different sub-basins,

and reveals challenges and strengths of integrated modelling while

answering questions about the water quality status on selectedrivers. The study recognises that integrated modelling has thepotential to provide significant added value to water quality

assessment and WFD planning in general by explicitly modellingcomplex interactions between models through bidirectional links(e.g. backwater effect) and the coupling of model components

from different disciplines (including groundwater, WWTPs and

economics). Furthermore, it is suggested that OpenMI saves

significant time on data pre-processing as well as possible errors

Parameter exchange Type of l inks Timestep:5

Simulation Total runtime:period h

Saveinterval: h

Scenario 3, non-tidal basin Flow, velocity and stage at 269 nodes One directionScenario 4, t idal basin Flow at 6 nodes Two directions

Release discharges at 4 nodes

Ta*E* ?, lncreased model comolexitv scenarios

1 0 0bU

Year 2000 30.5Year 2006 5.0

416



caused by the transformation of ouQut files to specific input format

files that would otherwise be requested by individual models.

Using OpenMI, models developed and set up in isolation can be

integrated into a single (integrated) model, using a drag and drop

approach that does not require additional programming skills.

This apparently easy-to-use approach, howeveq should be handled

with care: depending on the scientific discipline and expertise,

different modellers may have adopted different spatial conflgura-

tions or followed different initial assumptions or boundary

conditions. In such cases, particular attention has to be paid when

deciding which parameters of each model will be linked and

where, in order to create physically meaningful data exchanges.

OpenMI promotes the reusability of existing models and facil-

itates swapping models in existing schemes. However, the notion

of facilitating integrated modelling by linking diverse components

at runtime, built or converted to be OpenMl-compliant, is a new

one. Therefore, end users and model developers have to rethink

some of their established modelling techniques and method-

ologies. Unsuitable or obsolete coding approaches or poor

migration choices might contribute to significant time overheads

during linked runs, thus affecting the performance of a whole

system of model components. In an integrated system, the

numerical and physical limitations of one model, once integrated,

may directly influence other models' performance at runtime and

accuracy. Thus, even reliable standalone models could be pushed

beyond their design limits and produce instabilities and/or errors

when coupled in an unbefitting way.

As understanding of these and other challenges develops, the

standard itself will evolve through applications of OpenMI such

as those discussed in this paper. The latest version 2.0 of the

OpenMI standard (OpenMI, 2010b) defines tools to carry out

linked system calibration, improves quantitative and qualitative

data manipulations, and supports the integration of both spatial

and time-dependent model components.

As integrated model linking becomes more powerful and easy to

implement, with the computer world moving towards cloud com-puting and web services and as international geospatial standards

become widely accepted, a number of technical, scientific and

institutional issues that prohibit the unobstructed and accurate

exchange of data and knowledge between databases, models and

organisations can be overcome. This brings possibilities for envir-

onmental professionals to address complex environmental problems

in the presence of undiminished natural uncertainty and rising

societal expectations, and opens new horizons for operational use of

integrated modelling and research in the integrated modelling field.

AcknowledgementsThe work was financially supported by the European Commission

under the LIFE programme and contributing to the implementa-

tion of the thematic component LlFE-Environment under the

policy area 'Sustainable management ofground water and surface

water management' (contract No. LIFE06 ENVruK/000409).

Case study A participants were the Applied Hydraulics Labora-

tory and the Hydrology and Water Resources Management

Laboratory of the School of Civil Engineering of the National

Technical University of Athens. Case Study B participants

included the Unit River Basin Management of the Flemish

Environment Agency (VMM-Staf AD-IWB), the Division Opera-

tional Water Management of the Flemish Environment Agency(VMM-AO\rD, Flanders Hydraulics Research and University ofLidge Aquap61e. \MM provided the datasets used in case study

B. Research for the review section of this paper was co-financed

by the European Union (European Social Fund) and Greek

national funds through the operational programme 'Education

and lifelong learning' of the National strategic reference frame-

work - Research funding programme: Heracleitus II investing in

knowledge society through the European Social Fund.

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Integrated modeling for river basin management planning

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