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Sustainable Water: Uncertainty, Risk and Vulnerability in Europe

Workplan

The generic methodology used to reach the defined objectives has been developed and applied according to the following scheme agreed by the consortium of researchers and end-users:

Because aspects of some Case Studies (CSs) appear in more than one WP, a full description of the CS will be given only in the WP for which it is a major component. See Table for the inter-relation between CSs and WPs:

Case Study Work Package
WP 1
(led by Team 4)

WP 2
(led by Team 5)

WP 3
(led by Team 2)

WP 4
(led by Team 3)

CS 1
(Team 1)
Required for all case studies (P)
Future planning
Risk of failure in drought Impacts of over-abstraction

CS 2
(Teams 1 and 4)

Improvements in CSO design (P)
Risk of pollution in storm overflows
Impacts of pollution on fisheries
CS 3
(Team 5)
(P)
Operation in future climates
Flood risk Impacts of abnormal levels, full economic costing
CS 4
(Team 5)
Operation in future climates (P)
Flood risk
Cost of shortfall in HEP
CS 5
(Teams 1 and 3)
Future changes in supply Risk of failure on agriculture (P)
Full economic costing and conflict avoidance
CS 6
(Team 3)
(P)
Future changes in supply and demand
- -
CS 7
(Team 2)
- (P)
Flood risk
Loss of navigation

CS 8
(Team 1)

Planning abstractions - (P)
Impacts of changes in regime on salmonids
Key (P) Primary case study
Secondary case study

PERT diagram for SWURVE

Description of diagram

A generic methodology is followed for each Case Study. This allows inter-comparison of impacts, identification of important factors and differences between cases, and validation of the transferability of the methods to applications elsewhere in Europe. The overall methodology may be described as follows:

  1. Develop and validate a deterministic simulation model of the resource system, river basin, eco-system etc. using observed data (in most of the proposed Case Studies, part or entire models already exist and are used in operational procedures).
  2. Analyse the outputs in terms of system performance measures, i.e. resilience, reliability and vulnerability, which can be compared with other systems in a general, transferable and meaningful way.
  3. Apply the simulation model with long series of inputs (e.g. 1000 years) to produce long series of outputs (a Monte Carlo simulation), for both a present day case (control) and a number of future (perturbed) cases. The future cases will be derived from representative GCM simulations.
  4. Apply scaling methods to determine changes in regional climate variables for different greenhouse gas emission scenarios and global climate sensitivities and establish statistical relationships between regional climate variables and the local rainfall statistics. This then allows a large range of possible future scenarios to be sampled statistically, without the major computational and time expense of generating explicit time series as in (3).
  5. Derive statistical descriptions of the hydrological or system model inputs (e.g. pdfs of rainfall amounts) and the outputs (e.g. flow duration curves or reliability of a system).
  6. Perform uncertainty analysis using these relationships and those from (4), accounting for the full range of possible GCM emission scenarios, model versus real climate sensitivity and climate variability.
  7. Estimate the vulnerability of various systems to the predicted future climate changes taking into account consequential economic and management impacts and produce plans for mitigating the predicted impacts.


Contact SWURVE Coordinator: c.g.kilsby@ncl.ac.uk

© School of Civil Engineering and Geosciences University of Newcastle 2003
Page updated August 26, 2003

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