1. Why do models fail? Problems, problems …
John Rees
BGS Head of Policy and Science Co-ordination
Andrew Hughes
BGS Groundwater Modeller
and NMPI Co-organiser

2. Ideal vs reality

3. Generic issues. as seen from a government institute
Generic issues as seen from a government institute - the British Geological Survey

4. BGS and numerical modelling
Groundwater - Model Developer and user Coastal change - Poacher and gamekeeper Contamination - often the ‘honest broker’

5. Typical reasons for failure
Lack of consistency of approach
Turnover and education of staff in client organisations
Language between user and modellers, and between modellers
Appropriate and sufficient data
Trust between parties
Unrealistic expectations
Honesty about the limitations of models
Cost of taking-up new developments

6. \
Policy makers \
Modellers \
Resource Managers

7. Key organisations Universities Consultants Government Institutes
Regulators
Utilities
Government Ministries
Pan-governmental

8. Regulator issues Limited number of staff that have modelling skills
Accessibility of models is often poor – they are often not easy to run
The maintenance and updating of models has a high cost (the leaky roof syndrome)
Inadequate data to support development or understanding
Expectations are often not met
Stakeholders are not adequately consulted

9. Resource manager issues
Common uncertainty about the specification and scale of model required
Lack of clarity about the needs of regulators
Find that models have too much uncertainty for detailed use
Inadequate timely stakeholder involvement
Find that models are not as flexible as managers would like

10. Model developer issues
Models are driven by policy and not the other way round e.g. Habitats Directive
Few examples of models being trusted by all parties
Stakeholders are recognised as important, but involvement is very variable
Personalities are important in defining how much stakeholders interact
Managing expectations is very important
Those consultants who deliver to spec and on time may not be the best to drive modelling forwards

11. Broader implications
Lack of efficiency associated with development encourages adoption or tweaking of older ‘industry standard’ models instead of adoption of newer models.
Drive to conservatism encourages usage of ‘tried-and-tested’ consultants who focus on delivery, rather than more innovative scientists who will introduce new concepts and stretching the modelling.
Acceptance of limitations (e.g. empirical constants, black-boxes) instead of driving better modelling.

12. Towards solutions
Need common understanding before attempting to formulate solutions
- Climate change models are not taken up by some national governments that do not accept reality!
Language – differs markedly between disciplines
Definition of problem type, organisations involved and geographic extent
Problem is potentially huge, so need boundaries
Guidelines are needed

13. Summary

14. Questions ?