1. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 1 HarmoniRiB Workshop on Uncertainty in Data and Models Brussels, September 21, 2006 Uncertainty in Water Resources Management Co-ordinator: Jens Christian Refsgaard (GEUS)

2. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 2 Outline of presentation What is the problem ?  Why is uncertainty important  Sources of uncertainty - Where does uncertainty occur in the IWRM cycle HarmoniRiB contribution - overview  Uncertainty  Databases  Data from 8 river basins for research purposes

3. Copenhagen County project on identification of suitable methods for assessing groundwater vulnerability (2000) Assessments from five consultants on areas vulnerable to nitrate pollution from diffuse sources Consultant # 1 Consultant # 2 Consultant # 4 Consultant # 5 Consultant # 3 Vulnerable areas Very vulnerable Vulnerable Less vulnerable Well protected Problem statement #1 Uncertainty should affect water management decisions

4. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 4 Problem statement #2 Uncertainty should affect the selection of appropriate modelling tool  “We do not need complex hydrological models which we do not understand and where the output is known to be uncertain”  “Instead we want simple models that are reliable” Statement from a water manager responsible for implementation of Water Framework Directive (Harmoni-CA conference, Brussels February 2004)  Uncertainty assessments of model predictions both for simple and complex models is essential for bridging the perception gap between scientists and practitioners

5. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 5 Problem statement #3 - Example from Guidance Document No 7 “Monitoring”  “The level of acceptable risk will affect the amount of monitoring required to estimate a water body’s status... It is likely that there will have to be a balance between the costs of monitoring against the risk of a water body being misclassified.” (p 17)  “The Directive has not specified the levels of precision required from monitoring programmes and the status assessments. This perhaps recognises that achievement of too rigorous precision and confidence requirements would entail a much-increased level of monitoring for some, if not all, Member States” (p 17)  When designing monitoring programmes the following needs to be considered: – uncertainties (risk of misclassification) – costs of monitoring (and of subsequent measures)  Similar examples in other parts of the WFD Guidance Documents

6. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 6 Uncertainty in Water Resources Management

7. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 7 Uncertainty in Water Resources Management Context - framing of decision problem Data Models

8. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 8 Sources of uncertainty in the Water Management Process  Data – physical, chemical, biological, etc. – scale problems (temporal and spatial)  Data – physical, chemical, biological, etc. – scale problems (temporal and spatial)  Model – parameter values – numerical solution (approximations) – bugs in model code – model structure (process equations)  Context - framing of problem – multiple framing (ambiguity) among decision makers and stakeholders, including differences in objectives – external factors not accounted for in study – legislation, regulatory conditions, etc.

9. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 9 The HarmoniRiB Project Co-ordinator: Jens Christian Refsgaard (GEUS)  Uncertainty (concepts and tools)  Database to handle uncertain data  Datasets for a network of representative river basins All with the perspective of practical application in connection with WFD implementation

10. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 10 Project Partners  Geological Survey of Denmark and Greenland (GEUS), DK - Coordinator  Institute of Inland Water Management and Waste Water Treatment (RIZA), NL  Water Research Institute (CNR-IRSA), IT  Centre for Environmental Research (UFZ), DE  Centre for Ecology and Hydrology (CEH), UK  University of Amsterdam (UVA), NL  DHI Water & Environment (DHI), DK  Technical University Crete (TUC), GR  Universidad de Castilla La Mancha (UCLM), ES  Povodi Moravi (PM), CZ  Alterra, NL

11. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 11 Sources of uncertainty in the Water Management Process  Data – physical, chemical, biological, etc. – scale problems (temporal and spatial)  Model – parameter values – numerical solution (approximations) – bugs in model code – model structure (process equations)  Context - framing of problem – multiple framing (ambiguity) among decision makers and stakeholders, including differences in objectives – external factors not accounted for in study – legislation, regulatory conditions, etc. HarmoniRiB Focus

12. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 12 Data Uncertainty PDF = Normal Mean =  SD = σ ? ? 0 1.01.0

13. Review of uncertainty in  Meteorological data  Soil physical and geochemical data  Geological and hydrogeological data  Land cover data  Discharge data  Surface water quality data  Ecological data  Socio-economic data

14. Data Uncertainty Engine (DUE)  Characterisation and assessment of uncertainty in data  Propagation of data uncertainty through models  Free download from www.harmonirib.com 14

15. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 15 Data Uncertainty – Example #1 Monitoring of heavy metal in stream  Frequency: monthly  Sample size: 100 ml (”a bottle of water”) Relevant uncertainty information  How well does a sample represent the monthly mean concentration in stream Upscaling from measurement support to scale of interest  Time: Second  month  Space: Point  river crosssection Uncertainty – characterised as standard error  Instrument/sampling uncertainty: 5%  Uncertainty due to upscaling: factor 4  Total uncertainty: 20%

16. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 16 Data Uncertainty – Example #2  What is the uncertainty of precipitation data if one station should represent average precipitation over an area  Uncertainty is dependent on support scale 16

17. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 17 Identification of the problem Good status failed Identification of spatial and temporal scale of problem Concentration on a few problem fields Identification of decision maker List of evaluation criteria Identify objectives Define criteria List of alternative actions Environment-related measures Actor-related measures Combine to alternatives (bundles of measures) ???… AmAm …………… … A2A2 … A1A1 Alternative CnCn …C2C2 C1C1 Criteria Physical impact analysis Modelling Estimations Cost calculation Costs of investments Operating costs External costs 92,63…20,68A m organic farming …………… 69,12…51,53A 2 riparian buffer 58,35…63,25A 1 conventional m. Alternative Total costs …Mor- ph. Nitrate mg/l Criteria Cost-effectiveness analysis Check applicability of CEA Uncertainty analysis Multicriteria analysis Selection of a MCA method Uncertainty analysis Framing the problem Impact analysis Decision making under uncertainty Evaluation Decision support Results: Ranking of alternatives A 4 > A 1 >…> A m > A 2 Information on uncertainties Decision support

18. Uncertainty related to  Economical characterisation of river basins  Future water supply and demand  Selection of cost-effective programmes of measures  Cost recovery of water services

19. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 19 River Basin Network

20. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 20 Data Collected from River Basins  All data necessary to conduct research projects of relevance for WFD implementation  Time series – meteorological data – rivers (quantity, quality and ecology) – lakes (quantity, quality and ecology) – groundwater (quantity, quality and ecology) – transitional and coastal waters (quantity, quality and ecology)  Spatial data (GIS themes) – land use – pressures – socio-economic data (water users) – system characteristics

21. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 21 HarmoniRiB River Basin Network  Collect data from eight river basins  Assess and add uncertainty to data  Make data available freely to the scientific community for future research projects  Which European organisation will host our data in the future ?

22. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 22 Limitations of current databases  No possibility to store information on data uncertainty  Mostly national datasets – different terminology (dictionaries) across countries – different variables and measurement techniques across countries  Mostly single domain data – need for all domain data (meteorology, hydrology, geology, ecology, socio-economics, etc) in one database allowing easy cross-sectoral analysis

23. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 23 Database Design and Software Objective:  A database design for WFD data that allows uncertainty information to be associated with each data item Innovation - challenge:  How to store a measure of uncertainty along side each data value?  How to organise data that are heterogeneous with respect to – different national traditions terminology measurement practice – different practices across domains

24. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 24 Case Studies  Test methodologies and tools - proof of concept  Illustrate uncertainty aspects related to selection of programmes of measures – framing of the decision process – uncertainty on effects of measures – uncertainty on costs of measures

25. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 25 Conclusions  Uncertainty is important – it should be considered in WFD implementation – Uncertainty assessment should influence the entire project approach right from the beginning - and not only after a comprehensive modelling study – All sources and types of uncertainty should be considered in decision making (not only statistical uncertainty) Uncertainty in context / problem framing Data uncertainty Model structure uncertainty  Operational tools for handling of uncertainty exist – uncertainty assessment is possible in practise – HarmoniRiB has contributed with new concepts and tools

26. HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 26 More information www.HarmoniRiB.com  Software  Publications  Reports  Newsletters