Quality Assurance in Water Resources Modelling

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2 DANMARKS OG GRØNLANDS GEOLOGISKE UNDERSØGELSE RAPPORT 2009/16 Quality Assurance in Water Resources Modelling NOVANAQuA Anker Lajer Højberg & Jens Christian Refsgaard GEOLOGICAL SURVEY OF DENMARK AND GREENLAND MINISTRY OF CLIMATE AND ENERGY

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4 Table of Coes 1. Dansk Sammenfatning Projekt aktiviteter Projekt konklusioner Anbefalinger Iroduction Need for Quality Assurance (QA) in modelling The NOVANAQuA project The prese report Iernational state-of-the-art in QA in modelling Quality Assurance (QA) defined for modelling Guiding principles three differe approaches Type of QA guidelines for modelling Classification Discussion of QA in water resources modelling Key aspects in QA guidelines Organisational requiremes for QA guidelines to be effective Modelling Support Tool (MoST) The HarmoniQuA project Terminology and scieific philosophical basis The knowledge base MoST the supporting software tool Experiences with use of MoST in Denmark The LOOP 1 project The project and the procedure of MoST testing Water manager experiences and views Modeller experiences and views Reviewer experiences and views Geological mapping The project and the procedure of MoST testing Water manager experiences and views The DK-model at GEUS The project and the procedure of MoST testing Experience and views from the couies and environmeal ceres Modeller experiences and views Views on QA in the Danish modelling community Seminar on June 1 st The questionnaire survey...30 G E U S 3

5 7. Conclusions Need for QA in modelling MoST Recommendations on QA in water resources modelling in Denmark Vision Specific recommendations for action now References 42 Appendix A: Alectia report with modeller experience 45 Appendix B: NOVANOQuA kick-off seminar 55 Appendix C: Questionnaire 57 4 G E U S

6 1. Dansk Sammenfatning Nærværende projekt NOVANAQuA er finansieret under de tværgående indsatser under det national overvågningsprogram NOVANA. Hovedformålet med projektet har været at iroducere kvalitetssikring i modelleringen udført indenfor NOVANA, og undersøge muligheden for anvendelse af en standardiseret metode til kvalitetssikring indenfor de enkelte programområder såvel som på tværs af disse. Projektet har indeholdt en test af softwareprogrammet MoST, der er udviklet under EU-projektet HarmoniQuA (www.harmoniqua.org), til at understøtte kvalitetssikringen af hydrologisk modellering. Testen er gennemført indenfor grundvands- samt landovervågningsprogrammerne med henblik på en ideificering af mulige forbedringer til programmet for en tilpasning til danske forhold. 1.1 Projekt aktiviteter Projektet blev igangsat d. 1. juni 2005 ved afholdelse af et et-dags seminar på GEUS. Her blev MoST samt NOVANAQuA projektet iroduceret til en bredere kreds af aktører indenfor hydrologisk modellering i Danmark. I alt deltog 35 personer i seminariet repræseerende vandressourceforvaltere (9) rådgivere (14), ieresseer (3) samt forskere (9). Den overordnede konklusion fra dette seminar var, at der generelt var enighed om et behov for øget kvalitetssikring i forbindelse med hydrologisk modellering, og der blev udvist stor ieresse for testen af MoST. Under NOVANAQuA projektet blev MoST testet i to modelleringsstudier, ét indenfor landovervågningsprogrammet (LOOP), med deltagelse af vandressourceforvaltere, modellører og reviewer, og ét indenfor grundvandsprogrammet med deltagelse vandressourceforvaltere alene. Følgende institutioner deltog i testen af MoST under NOVANAQuA projektet: GEUS (koordinator, support til MoST, reviewer i LOOP model studiet) DMU (monitering af landovervågningsområderne, LOOP) Miljøceer Nykøbing-Falster (vandressourceforvalter i LOOP og grundvandsstudiet) Alectia (modellør i LOOP modelleringsstudiet) Udover testen af MoST i NOVANAQuA projektet, blev MoST indenfor samme periode afprøvet i forbindelse med opdateringen af den nationale vandressource model (DK-model), denne test var ikke finansieret af NOVANAQuA projektet, men erfaringerne herfra er medtaget i nærværende rapport. Endvidere blev der i projektet opstillet et spørgeskema omkring kvalitetssikring i hydrologisk modellering. Spørgeskemaet blev gjort offeligt tilgængelig via nettet, og i alt 84 personer fra hhv. miljøcerene, rådgivningsfirmaer, vandselskaber og forskningsinstitutioner blev via indbudt til at deltage i spørgeskemaet. Sammenlagt gennemførte 38 personer spørgeskemaet fordelt på miljøcere (14), rådgivere (13), vandselskaber (3) og forskningsinstitutioner (8). G E U S 5

7 1.2 Projekt konklusioner Både testen af MoST samt resultatet af spørgeskemaet viste, at vigtigheden af kvalitetssikring i forbindelse med hydrologisk modellering er erkendt af samtlige aktører. Det blev endvidere fundet, at der generelt er en accept af, at en dokumeeret kvalitetssikring medfører en øget omkostning, og at der er villighed til at betale en sådan meromkostning. Spørgeskemaet viste imidlertid også, at der generelt ikke er specifik fokus på kvalitetssikring ved udførelse af modelstudier i praksis. I spørgeskemaundersøgelsen blev det fundet, at udviklingen af nationale vejledninger for kvalitetssikring generelt blev bifaldt. Særlig udtalt var dette for ansatte i Miljøcerene, hvor en sådan udvikling blev eydigt efterspurgt, ligesom der var stor tilslutning til udvikling af et software program til understøtning heraf. Udbydere af modelopgaver har et særligt ansvar i forbindelse med at opnå et øget fokus på kvalitetssikring af modelleringen i praksis, da disse har muligheden for at efterspørge dette og allokere de nødvendige ressourcer hertil. Årsagen til misforholdet mellem erkendelsen af vigtigheden af kvalitetssikring og den fokus der er herpå i praksis, skal sandsynligvis søges i manglen af nationale vejledninger. Uden vejledende principper samt understøttende værktøjer, kan det være svært for model udbydere at sikre en tilstrækkelig fokus på kvalitetssikringen. Hovedparten af deltagerne i spørgeskemaundersøgelsen (82%) var bekendt med kvalitetssikringsværktøjet MoST, heraf besvarede mellem 11 og 17 spørgsmålene der adresserede MoST specifikt. Designet af guidance delen i MoST, dvs. principperne for god modelleringspraksis samt den udviklede modelleringsprotokol, blev vurderet som værende værdifuldt af langt hovedparten af deltagerne. Indholdet af guidancen delen, dvs. den skriftlige vejledning, blev ligeledes fundet velegnet, men dog ikke i samme udstrækning som designet. I modsætning hertil blev moniterings- samt rapporteringsdelen generelt ikke fundet velegnet. Resultaterne fra spørgeskemaet er i overensstemmelse med de erfaringer, der blev opnået i fobindelse med afprøvningen af MoST. Her blev guidance delen ligeledes fremhævet som værende meget værdifuld. For den erfarne modellør kan den tjene som en tjekliste, mens personer med mindre erfaring kan støtte sig til den skriftlige vejledning. Model protokollen og vejledningen var endvidere vigtige til opnåelse af en fælles forståelse mellem samtlige aktører af en modelopgave. Mht. moniterings- og rapporteringsdelen af MoST var erfaringerne, at denne del var meget kompleks og svær at overskue, specielt for personer der ikke arbejde med tekniske programmer til daglig. Det blev poieret, at det ikke var umiddelbart klart, hvor de enkelte delelemeer af opgaven skulle dokumeeres i MoST, og programmet havde generelt en meget stejl læringskurve. Endvidere blev det fremført, at programmet spændte for vidt mht. funktionalitet, da det foruden en monitering af de udførte aktiviteter, indeholder funktioner for projektstyring. Denne funktionalitet er dog ikke tilstrækkelig for en samlet projektstyring, og det vil være nødvendigt at anvende et mere detaljeret værktøj, hvilket resulterer i dobbelt bogføring. I danske modelleringsopgaver er det gennem de senere år blevet mere almindeligt at opdele opgaven i faser, hvor hver fase afsluttes med et milepælsmøde. På disse møder præseeres og diskuteres fremdriften af modelleringen og eveuelle ændringer i modelstudie planen besluttes. Forud for møderne udarbejdes en skriftlig rapport der dokumeerer modelleringsprocessen. Testpersonerne var meget opmærksom på vigtigheden af en detaljeret dokumeation af det udførte arbejde 6 G E U S

8 men fandt, at den dokumeation de ville foretage via MoST også skulle fremgår af de modelrapporter der skulle udarbejdes i forbindelse med milepælsmøder, hvorved dokumeationen i MoST bliver overflødig. Endelig blev det vurderet, at MoST ikke i tilstrækkelig grad er gearet til at facilitere en løbende dialog mellem klie og rådgiver, en aktivitet der i øvrigt blev fundet meget væselig i forbindelse med kvalitetssikring. 1.3 Anbefalinger Det anbefales, at udviklingen indenfor for kvalitetssikring af hydrologisk modellering stiler mod en situation der er karakteriseret ved: a) Modellering udføres i henhold til en vejledning for god modelleringspraksis, der er generelt accepteret og videnskabeligt funderet. Vejledingen skal sikre en fælles forståelsesramme blandt alle involverede aktører og minimere risikoen for konflikter opstået pga. uklarheder om procedurer og metoder. b) Dokumeationen af modeljobbet skal sikre gennemskuelighed mht. de anvendte metoder, tilgrundliggende aagelser samt kommearer fra alle involverede parter. Endvidere skal de dominerende usikkerhedskilder angives, og deres betydning for modelresultaterne skal fremgå klart. c) Der skal tilknyttes en ekstern reviewer til modelopgaven. d) Der skal skabes optimale betingelser for en aktiv dialog mellem alle aktører med fokus på at opnå en fælles forståelse samt skabe konsensus om den udførte modellering. e) Der skal ske en dokumeation så modellen er reproducerbar. Dette er nødvendigt for at tredjemand kan foretage en auditering af modellen for vurdering af dennes kvalitet, udføre nye scenarier simuleringer samt ved opdatering af modellen eller tilstødende modeller. Kvalitetssikring af hydrologiske modeller indenfor Danmark har været genstand for en betydelig fokus og udvikling i de senere år. Der er dog endnu behov for udvikling indenfor flere områder, før ovennæve forhold vil være standard for modelopgaver. Som led i udviklingen i den skitserede retning anbefalinger vi følgende aktiviteter, der vurderes at kunne gennemføres indenfor en overskuelig fremtidig. Nationale vejledninger. Udvikling af nationale vejledninger til at sikre, at modellering udført indenfor NOVANA samt de regionale Miljøcere er baseret på et fælles sæt vejledninger for god modelleringspraksis. For at gøre disse vejledninger attraktive skal de: Være accepteret bredt af vandressourceforvaltere og rådgivere Blive opdateret regelmæssigt så de afspejler nyeste viden og erfaring indenfor hydrologisk modellering Være let tilgængelige på en måde, der sikre at brugeren altid er sikret den seneste version. Guidance delen i MoST blev vurderet værdifuld af såvel personerne deltagene i testen af MoST samt deltagere i spørgeskemaundersøgelsen, og danner således et godt udgangspunkt for udarbejdelsen af nationale vejledninger. De nationale vejledninger bør dog udbygges med eksisterende tekniske og administrative vejledninger udviklet på nationalt ni- G E U S 7

9 veau. Byggestenene for et nationalt sæt af vejledninger eksisterer således, men bør struktureres så de præseeres for brugerne på en ensartet form. For at sikre en bred konsensus om vejledninger, bør de uarbejdes som offelige ieraktive vejledninger, dvs. de skal opstilles gennem opbygning af konsensus mellem aktørerne indenfor hydrologisk modellering, og inkludere såvel tekniske aspekter samt ieraktionen mellem klie og rådgiver. I praksis betyder det, at de bør udvikles i et fællesskab mellem vandressourceforvaltere, rådgivere og forskningsinstitutioner. Vejledningen i MoST foreligger på engelsk, hvilket kun blev kommeeret af få i spørgeskemaundersøgelsen. Der var dog forslag om, at der som minimum skulle være en ordbog for de tekniske gloser. Det bør overvejes hvorvidt de nationale vejledninger skal foreligge på dansk, ulempen herved er, at det er mere tidskrævende at foretage en opdatering baseret på nye iernationale udviklinger. En regelmæssig opdatering af de nationale vejledninger samt en let tilgang hertil gør en web løsning mere favorable en udarbejdelsen af en sammenhængende rapport. Vi foreslår derfor, at der udvikles en hjemmeside, hvorfra den seneste version kan hjemtages. Hjemmesiden skal inkludere en historik for udviklingen af vejledningerne samt give mulighed for, at brugere kan komme med forslag til forbedringer. Der bør udpeges én institution til at varetage vedligeholdelsen af hjemmesiden samt sikre opdateringen af vejledningerne. Hjemmesiden skal ligge på en ceral server hvortil der er fri adgang. Kommunikationsforum. En væseligt del af kommunikationen under en modelopgave foregår via s, men det bliver mere og mere almindeligt, at der oprettes dedikerede web hoteller til lagring og udveksling af information. Ofte er disse løsninger struktureret af en projektadministrator, og strukturen for disse web hoteller er derfor varierende, hvilket gør, at det ikke altid er lige nemt at navigere til det relevae materiale. Endvidere indeholder disse løsninger ikke informationer relevae i forbindelse modellering og er derfor ikke medvirkende til at øge kvaliteten af modelstudiet, men anvendes alene som en ekstern lagring. Vi anbefaler, at der udvikles en standard web løsning for et kommunikationsforum. Foruden at give mulighed for udveksling af materiale, bør den seneste version af de nationale vejledninger være gjort tilgængelig via løsningen. Der eksisterer en række freeware web løsninger, der med en relativ beskeden indsats kan tilpasses en struktur, der er i overensstemmelse med de opgaver der indgår i et modeljob, så brugeren bliver præseeret for de dele af vejledningerne, der er relevae for den aktuelle fase af modelleringen. På baggrund af kommearerne omkring kompleksiteten af MoST og dens stejle læringskurve anbefales det, at web løsningen udformes relativt simpel. Web løsningen bør gøres tilgængelig fra den hjemmeside, hvorpå de opdaterede vejledninger ligger. Dokumeation af modelleringsprocessen. Testen af MoST og vurderingerne fra spørgeskemaet viste, at en detaljeret dokumeation af modelleringsprocessen er meget omfattende, og ofte fører til en dobbelt dokumeation, hvorved arbejdsbyrden og økonomien for et modelstudie øges betydeligt. Denne type af dokumeation er primært releva under to situationer: 1) i koroversielle studier, der har omfattende socioøkonomiske konsekvenser, og 2) ier i en organisation, hvor flere arbejder på samme modelopgave samtidigt. Til håndtering af sidstnæve forhold har flere rådgivere i forskelligt omfang udviklet egne metoder og procedurer. Vi foreslår, at detaljeringen af dokumeationen af modelleringspro- 8 G E U S

10 cessen aftales i forbindelse med det enkelte modelstudie, frem for udvikling af en standardiseret obligatorisk procedure. Teknisk dokumeation. Den færdige model bør indlægges i den nationale modeldatabase. I denne forbindelse skal modellen dokumeeres på et detaljeringsniveau, der gør det muligt at genskabe modellen og de udførte simuleringer. Denne dokumeation skal ligeledes indlægges i den nationale modeldatabase. Et igangværende projekt har til formål at udrede og præcisere detaljeringen af denne dokumeation. Yderligere aktiviteter i denne forbindelse vurderes således ikke at være nødvendige. G E U S 9

11 2. Iroduction 2.1 Need for Quality Assurance (QA) in modelling Models describing water flows, water quality, ecology and socio-economics are increasingly being used to support water manageme functions and decisions. Stakeholders and decision makers are therefore often confroed with results from models whose underlying assumptions they, especially those without modelling background, have difficulty assessing. At the same time insufficie atteion is generally given to documeing the predictive capabilities, the uncertaiies involved and the limitations of models used (Refsgaard et al., 2005; Jakeman et al., 2006). As a result of this developme many modelling studies have produced results that were not considered credible or understandable by the water managers and stakeholders and that therefore were not utilised. This tendency is reflected in the increasing number of guidelines for good modelling practise that are appearing in these years (Refsgaard et al., 2005). A summary of reasons for the growing ierest in Quality Assurance includes: Ambiguous terminology and a lack of understanding between key-players (modellers, clies, auditors, stakeholders and concerned members of the public); Bad practice (careless handling of input data, inadequate model set-up, insufficie calibration/validation and model use outside of its scope); Lack of data or poor quality of available data; Insufficie knowledge on the processes; Poor communication between modellers and end-users on the possibilities and limitations of the modelling project and overselling of model capabilities; Confusion on how to use model results in decision making; Lack of documeation and clarity on the modelling process, leading to results that are difficult to audit or reproduce; Insufficie consideration of economic, institutional and political issues and a lack of iegrated modelling. In the Danish water resources manageme community there is an increasing atteion on quality assurance aspects in connection with modelling. This is probably most pronounced with respect to groundwater modelling, which by far is the largest modelling field in terms of turnover and number of jobs outsourced. The quality of the terms of references for modelling jobs has improved significaly, and the use of external reviewers has increased during the past 5-10 years. The groundwater modelling handbooks (Henriksen et al., 2001; Sonnenborg and Henriksen, 2005) and the courses in groundwater modelling, which have materialised based on initiatives from the water managers, originally in the couies now in the environme ceres, have played a major role in this process of bringing more atteion to quality aspects of modelling. 10 G E U S

12 2.2 The NOVANAQuA project The prese project, NOVANAQuA, is funded by the research and developme programme under the Danish National Monitoring and Assessme Programme for the Aquatic and Terrestrial Environme (NOVANA). The project was funded in 2004 and activities were carried out during the period 2005 to The objectives of NOVANAQuA were to iroduce QA of the modelling carried out within the NOVANA programme and to explore the possibility of using a harmonised methodology for QA in individual projects as well as across the various sub-programmes in NOVANA. The NOVANAQuA project utilised the software programme Modelling Support Tool (MoST) that was developed in the EU project HarmoniQuA to support quality assurance in hydrological modelling. MoST was tested in real-life, ongoing projects. Based on these experiences recommendations for the developme of QA in hydrological modelling in Denmark are provided. Apart from the LOOP and the groundwater case study, experience on use of MoST in the update of the Danish national water resource model (DK-model) has been included in the prese report. Use of MoST in the DK-model study has, however, not been support financially by the NOVANAQuA project. The partners involved in the NOVANAQuA projects were: GEUS (co-ordinator, MoST support, reviewer in LOOP case) DMU (agricultural monitoring (LOOP) Environmeal Cere of Nykøbing Falster (MC-NYK) (water manager role) Alectia (modellers in the LOOP case) 2.3 The prese report The prese report is organised in eight chapters. Following this iroductory part a state-ofthe-art in QA in modelling is given in chapter 3 that iroduces some principles, terminologies and key aspects of QA in hydrological modelling. Chapter 4 provides a short description of the EU-project HarmoniQuA and preses the building blocks of the software programme MoST developed in HarmoniQuA. The studies in which MoST were tested are described in chapter 5 along with the experiences and views on using MoST for the differe parties involved in the MoST testing phase. Beyond testing MoST the views on QA in hydrological modelling of a broader group was studied in the NOVANAQuA project by the organisation of one-day seminar and a web-based questionnaire. Results from these activities are prese in chapter 6. Conclusions from the test of MoST and from the broader group are drawn in chapter 7. Based on the conclusions the report finalises by providing recommendations on QA in water resources modelling in Denmark, and ideify the necessary activities. G E U S 11

13 3. Iernational state-of-the-art in QA in modelling 3.1 Quality Assurance (QA) defined for modelling Quality assurance (QA) can be defined as the procedural and operational framework used by an organization managing the modelling study to build consensus among the organizations concerned in its implemeation, to assure technically and scieifically adequate execution of all tasks included in the study, and to assure that all modelling-based analysis is reproducible and justifiable (Scholten et al., 2007). This includes the organizational, technical and scieific aspects as well as the need to build consensus among the organizations concerned. 3.2 Guiding principles three differe approaches Differe approaches have been used to ensure good quality in water resources modelling. Inspired by de Groof et al. (2005) and Hansen and Grøn (2006) the approaches may be categorised according to three differe guiding principles: Regulation driven. Performance is regulated by detailed standards on how to perform a study. These regulations often combine technical guidance and policy governed instructions linked to certain legislation. The regulations are typically issued by public authorities and have a status that makes it compulsory to subscribe to them. So this principle results in a ceralistic top-down approach. The advaages of such regulations are that they often provide a transpare and operational set of rules for how to perform a given task for water managers, stakeholders and consultas. The disadvaages are that to provide detailed descriptions of what to do in all possible cases they become rather resource demanding and rigid. This approach is typically adopted for work carried out within public authorities. An example of regulatory driven guidelines is the UK Flood Estimation Handbook (IH, 1999; Packman, 2002) that provides detailed prescriptions on how to use various models to perform flood estimation. Another example is the set of Common Implemeation Strategy (CIS) guidance documes aiming at supporting the WFD implemeation (EC 2004a; EC 2004b). These CIS guidance documes are, however, broader than modelling and not very detailed, so they leave room for many differe ierpretations about how to do things and should therefore be combined with other approaches to assure good quality. A Danish example from this category is the guidance on how to make zonation in connection with detailed mapping of areas for groundwater protection (Miljøstyrelsen, 2000). Expertise driven. Performance is here governed by knowledge and experience from experts. This will result in delegation of modelling tasks to modelling specialists, who have documeed experience and a well proven positive track record. In its ultimate way it may result in certification of certain tasks to individuals or organisations. This is a deceralised approach where the responsibility is erusted to specialists. The advaage of this approach is that it allows knowledge and experience to be utilised in a flexi- 12 G E U S

14 ble manner. The major disadvaage is that it is subjective in nature. Differe experts may have differe experiences and assessmes, so the procedure is usually not reproducible and transpare. This approach is typically used in connection with consultancy services, where the clie fully trusts the consulta. Quality driven. Modelling is here performed according to detailed technical guidelines, including a documeation of the quality of work. Examples of such guidelines are norms and standards. Such standards can ensure that all the importa elemes are considered. Furthermore, it may ensure that the study is carried out within a formal framework involving the eire organisation. The major disadvaage is that the standards can be very detailed and the formal documeation of quality typically requires a lot of bureaucratic work and hence considerable resources. 3.3 Type of QA guidelines for modelling Classification QA in the modelling process has two main compones: (a) QA in developme of model codes and (b) QA in relation to application studies. Here we focus on the second compone only. QA in model application studies includes data analyses, methodologies of good modelling practice, reviews and administrative procedures. Such QA guidelines can be classified according to how much focus is put on the consensus building process between the modeller and the water manager in the following three classes (Refsgaard et al., 2005): Iernal technical guidelines (Type 1) established and used iernally by the modeller s organisation. Public technical guidelines (Type 2) established as public guidelines and used iernally by the modeller s organisation. Public ieractive guidelines (Type 3) established as public guidelines and based on regulation of the ieraction between the modeller and the water manager throughout the modelling process. If we refer to the three guiding principles (Section 3.2) both the iernal and the public technical guidelines are typically quality driven, while the public ieractive guidelines also to some exte are expertise driven. Type 1: Iernal technical guidelines Most organisations involved in modelling studies have some kind of iernal QA procedures. They usually focus on the technical aspects, i.e. to ensure that the modelling work itself is done without making unqualified judgemes or errors. The betters of these are based on the modelling protocols and similar scieifically based procedures originating from the research community. These procedures are iernal in nature because they have been established or adopted unilaterally by the modeller s organisation, and because they seldom deal with the ieraction between modeller and end-user. Examples of Type 1 guidelines include: Iernal QA procedures, common in many companies. G E U S 13

15 Text books. Many textbooks coain chapters with recommended modelling protocols. Manuals to software packages with his on the best way to use a model. Type 2: Public technical guidelines These guidelines often coain the same substance as the iernal technical guides. However, they differ in the sense that they have been prepared through a consultative and consensus building process involving many persons and organisations. They focus on the technical aspects and give no or little emphasis to the ieraction between the modeller and the end-user. Examples of Type 2 guidelines include: Standards from American Society for Testing and Materials (e.g. ASTM, 1994). The USGS guidelines for evaluating of ground-water flow models (Reilly and Harbaugh, 2004). Type 3: Public ieractive guidelines These guidelines have, like the public technical guidelines (Type 2), been established through a public consultative and consensus building process. However, they differ from the Type 2 guidelines by an additional focus on regulating the ieraction between the modeller and the water manager, who often have the roles of consulta and clie, respectively. Importa elemes in public ieractive guidelines are reviews that, in addition to QA in the sense of technical guidance, can facilitate the consensus-building process between the parties. Experience shows that such a process is crucial for the overall credibility of the modelling process. Examples of such QA guidelines include: The Dutch guidelines (Van Waveren et al., 1999; Scholten and Groot, 2002). The Australian groundwater flow modelling guidelines established by the Murray- Darling Basin Commission (Middlemis, 2000). The Danish groundwater modelling guidelines (Sonnenborg and Henriksen, 2005). Californian guidelines prepared by Bay-Delta Modelling Forum (BDMF, 2000). The HarmoniQuA guidelines build io MoST (www.harmoniqua.org). 3.4 Discussion of QA in water resources modelling Key aspects in QA guidelines Guiding principles Quality assurance procedures are composed of a mix of the three guiding principles outlined in Section 3.2. For instance regulatory driven guidelines often prescribe use of (quality driven) standards for some of the detailed regulation. Most of the iernational guidelines listed above are quality driven, but with importa expertise driven elemes such as peer reviews and stakeholder ieraction. As these guidelines are often used within a regulatory coext, such as implemeation of the Water Framework Directive (WFD), the quality assurance in reality becomes a mix of all three principles: (a) they are based on quality driven 14 G E U S

16 guidelines providing recommendations on how to perform the technical details and how to manage the ieraction between modeller, water manager and stakeholders; (b) they include subjective expertise in terms of peer reviews performed by independe external experts; and (c) they are most often carried out within the coext of legislation prescribing that the work has to be carried out within the framework of certain regulatory guidelines. A particular problem with some of the regulatory driven guidelines is that they are prescriptive with respect to methodologies including some times even which model code to use. As new knowledge appears the old methodologies and codes will inevitably become outdated. Therefore in order for such guidelines to be in accordance with state-of-the-art it is required that they regularly be updated. Ieractive guidelines The dialogue between the differe players is crucial to ensure that the output from the modelling process is understandable for stakeholders and beneficial for the clie. The importance of involveme of stakeholder and public opinions is emphasised in many of the guidelines. In HarmoniQuA/MoST (Chapter 4), each of the five major steps is therefore concluded with a dialogue task, in terms of either coract negotiation (first step) or reviews (last four steps). A dialogue task encourages the assessme of the prese step and provides the opportunity to redefine the coe of the model study plan for the next step based upon the results and findings of the prese step. These dialogue steps provide flexibility to the modelling study and ensure that the tasks that have yet to be performed can be modified according to the achieved results and perceptions of modeller and clie. Peer review According to our experience the single most importa instrume to ensure a good quality of the modelling process is to perform independe reviews of the modelling process. Like in the scieific tradition, peer review of the modelling work is the best way of evaluating the quality. Peer review is based on the principle of expertise driven (Section 3.2) and is subjective by nature, which may be considered a weakness as compared to the more objective quality driven guidelines with step by step descriptions of what to do. However, if we were not allowing the subjective eleme, we would deprive the process of the knowledge of experienced professionals and such knowledge is virtually impossible to convert to guideline text, because each modelling case is unique, while the guidelines have to be generically formulated. By combining the generic guidelines and the peer review we believe that we achieve the optimal mix of quality and expertise driven QA, and most of the existing guidelines emphasise peer review. Transparency and reproducibility Transparency and reproducibility are importa, especially for large studies involving use of complex models. This is recommended in most guidelines. However, to efficiely impleme this requires a supporting tool which enables modelling teams, consisting of modellers, managers and auditors, to be guided through the modelling process, to monitor all modelling activities and to oversee the status of each task to perform. With an increasing tendency to reuse existing models or rebuild them with additional data, modified conceptual models (revised model structure and/or inclusion of additional processes) and improved calibration and validation tests, such functionality becomes very importa. To our knowledge the only existing tool dedicated for this purpose existing today is MoST (Chapter 4). G E U S 15

17 Accuracy criteria Establishme of accuracy criteria for a modelling study is a very importa, but difficult, issue. Modellers often establish numerical accuracy criteria in order to classify the goodness of a given model (Henriksen et al., 2003). These attempts are very useful in making the performance more transpare and quaitative, but do not provide an objective means to decide what the optimal accuracy criteria really should be in a given case. According to Refsgaard and Henriksen (2004) no universal accuracy criteria can be established, i.e. it is generally not possible from a natural scieific poi of view to tell when a model performance is good enough. Such acceptance criteria will vary from case to case depending on the socio-economic coext, i.e. what is at stake in the decisions to be supported by the model predictions. An appropriate question may be: how do we translate the soft socioeconomic objectives to hard-core model performance criteria? This is obviously a challenge that can not be solved by natural science alone, but needs to be addressed in a much broader coext including aspects of economy, stakeholder ierests and risk perception. Performance statistics must comprise quaifiable and objective measures. However numerical measures can not stand alone. Often expert opinions are necessary supplemes. Uncertaiy assessmes Quality assurance and uncertaiy assessmes are two aspects that are very closely linked (Refsgaard et al., 2007). Initially, the manager has to define accuracy criteria from a perception of which uncertaiy level he/she believes is suitable for a particular case (see accuracy criteria above). Subsequely, as the modelling study proceeds, the dialogue between modeller and manager has to coinue with the necessary trade off between modelling accuracy and the cost of the modelling study. In the uncertaiy assessmes it is very importa to go beyond the traditional statistical uncertaiy analysis. Thus, e.g. aspects of scenario uncertaiy and ignorance should generally be included and in addition the uncertaiies originating from data and models often need to be iegrated with socio-economic aspects in order to form a suitable basis for the further decision process (Van der Keur et al., 2008). Uncertaiy assessme is included in almost all new guidelines. However, most of them only consider uncertaiy assessme as something that needs to be done late in a modelling study together with the scenario simulations. The guidelines which most strongly emphasise that uncertaiy should be on the agenda from the very first day and throughout a modelling study are HarmoniQuA s modelling guidelines. Assessme of uncertaiy due to errors in the model structure is a particularly difficult task and is most often neglected. One way of evaluating this source of uncertaiy is through the establishme of alternative conceptual models (Refsgaard et al., 2006). Among the existing guidelines this aspect is most strongly emphasised in HarmoniQuA s modelling guidelines. Model validation Although experience shows that models generally perform poorer in validation tests against independe data than they do in calibration tests, model validation is in our opinion a neglected issue, both in many modelling guidelines and in the scieific literature. Maybe many scieists have not waed to use the term validation due to the scieific philosophically related coroversies, but in any case many scieists are not advocating the need for 16 G E U S

18 model validation. One of the unfortunate consequences of this lack of ierest is that not much work has been devoted to developing suitable validation test schemes since Klemes (1986). In our opinion further developme of suitable testing schemes, particularly for nonlinear models and for applications comprising extrapolations beyond the calibration data basis, is a major future challenge. Supporting software tools Modelling is a complex process and QA is far from trivial. Therefore there is an obvious need for software tools to support QA of the modelling process. Such support is first of all required to docume what is going on in the modelling process, so that the transparency and reproducibility can be improved. To our knowledge the only existing software tool dedicated for this purpose existing today is MoST Organisational requiremes for QA guidelines to be effective Modelling studies involve several parties with differe responsibilities. The key players are modellers and water managers, but often reviewers, stakeholders and the general public are also involved. To a large exte the quality of the modelling study is determined by the expertise, attitudes and motivation of the teams involved in the modelling and QA process. QA will only be successful if all parties actively support its use. The attitude of the modellers is importa. NRC (1990) characterises this as follows: most modellers enjoy the modelling process but find less satisfaction in the process of documeation and quality assurance. Scholten and Groot (2002) describe the main problem with the Dutch Handbook on Good Modelling Practice as they all like it, but only a few use it. The water manager, however, has a particular responsibility, because he/she has the power to request and pay for adequate QA in modelling studies. Therefore, QA guidelines can only be expected to be used in practice if the water manager prescribes their use. It is therefore very importa that the water manager has the technical capacity to organise the QA process. Often, water managers do not have individuals available with the appropriate training to understand and use models. An external modelling expert should then be sought to help with the QA process. However, this requires that the manager is aware of the problem and the need. G E U S 17

19 4. Modelling Support Tool (MoST) 4.1 The HarmoniQuA project The HarmoniQuA project 1 aimed at developing a common, generic methodology, terminology and guidelines to support multidisciplinary model-based water manageme by developing a modelling Knowledge Base (modelling KB), building a Modelling Support Tool (MoST), testing these products, extending the project results to the wider professional community and raising awareness to use the developed methodology and tool to facilitate quality assurance and to make modelling projects more transpare (Refsgaard and Henriksen, 2004; Refsgaard et al., 2005; Scholten et al., 2007) Terminology and scieific philosophical basis A key problem in relation to establishme of a theoretical modelling framework is confusion on terminology (Refsgaard and Henriksen, 2004). For example the terms validation and verifications are used with differe, and some times ierchangeable, meaning by differe authors. The confusion arises from both semaic and philosophical considerations (Rykiel, 1996). Another importa problem is the lack of consensus related to the so far nonconclusive debate on the fundameal question concerning whether a water resources model can be validated or verified, and whether it as such can be claimed to be suitable or valid for particular applications (Konikow and Bredehoeft, 1992; De Marsily et al., 1992; Oreskes et al., 1994). Therefore, HarmoniQuA has developed an iernally consiste terminology and a glossary comprising more than 1000 terms. The terminology and methodology are based on Refsgaard and Henriksen (2004). The most importa definitions are: A model code is a generic software program, which can be used for differe study areas without modifying the source code. A model is an application of a model code to a particular study area, including input data and parameter values. A model code can be verified. A code verification involves comparison of the numerical solution generated by the code with one or more analytical solutions or with other numerical solutions. Verification ensures that the computer programme accurately solves the equations that constitute the mathematical model. Model validation is here defined as the process of demonstrating that a given sitespecific model is capable of making accurate predictions for periods outside a calibra- 1 HarmoniQuA is an acronym for Harmonizing Quality Assurance in model-based catchme and river basin manageme. From 1 January 2002 to 31 December 2005, this research project was supported by the European Commission under the Fifth Framework Programme and coributed to the implemeation of the Key Action "Sustainable Manageme and Quality of Water" within the Energy, Environme and Sustainable Developme Programme. Coract n : EVK1-CT See for a complete list of publications: 18 G E U S

20 tion period. A model is said to be validated if its accuracy and predictive capability in the validation period have been proven to lie within acceptable limits or errors. 4.3 The knowledge base Guidelines for good modelling practise are included in the Knowledge Base (KB) of MoST. The modelling process has been decomposed io five steps, see the flowchart in Fig. 1. Each step includes several tasks. Each task has an iernal structure i.e. name, definition, explanation, ierrelations with other tasks, activities, activity related methods, references, sensitivity/pitfalls, task inputs and outputs. The KB coains knowledge specific to seven domains (groundwater, precipitation-runoff, river hydrodynamics, flood forecasting, water quality, ecology, and socio-economics), and forms the heart of the tool. The most importa QA principles incorporated in the KB are: The five modelling steps conclude with a formal dialogue between the modeller and manager, where activities and results from the prese step are reported, and details of plans for the next step (a revised work plan) are discussed. External reviews are prescribed as the key mechanism of ensuring that the knowledge and experience of other independe modellers are used. The KB provides public ieractive guidelines to facilitate dialogue between modellers and the water manager, with options to include auditors (reviewers), stakeholders and the public. There are many feed back loops, some technical involving only the modeller, and others that may require a decision before doing costly additional work. The KB allows performance and accuracy criteria to be updated during the modelling process. In the first step the water manager s objectives and requiremes are translated io performance criteria that may include qualitative and quaitative measures. These criteria may be modified during the formal reviews of subseque steps. Emphasis is put on validation schemes, i.e. tests of model performance against data that have not been used for model calibration. Uncertaiies must be explicitly recognised and assessed (qualitatively and/or quaitatively) throughout the modelling process. MoST coains descriptions of 90 differe Methods that can provide support to the user on how to perform an activity. Examples illustrating the variety of Methods are Moe Carlo Simulation comprising advice on how to perform Moe Carlo analyses and BMW Toolbox, which simply is a hyperlink (http://www.rbm-toolbox.net/bmw/index.php) to guidance on selection of an appropriate model code developed under another EU research project. 4.4 MoST the supporting software tool MoST supports multi-domain studies and working in teams of differe user types (water managers, modellers, auditors, stakeholders and members of the public). The associated glossary is accessible via hyperlinked text. The key functionality of MoST is to G E U S 19

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