SMART INCO-MED kick-off meeting, January 5/6 2003 CEDARE, CAIRO

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SMART INCO-MEDkick-off meeting,January 5/6
2003CEDARE, CAIRO
DDr. Kurt Fedra ESS GmbH, Austria kurt_at_ess.co
.at http//www.ess.co.at Environmental
Software Services A-2352 Gumpoldskirchen

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SMART Project Overview

• 3 year duration to August 2005
• Started September 2002
• Current PM 5
• 9 partners and countries
• 12 work packages
• 5 case studies TR,LB,JO,EG,TU

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SMART Objectives

• Develop policy guidelines for ICZM, emphasis on
  water resources
• Conflicting water use
• Resource economics
• Quantitative analysis using indicators, models,
  and expert systems
• Public information, Internet
• Case studies, collaborative network within and
  between countries

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SMART Technical Objectives

1. Model integration, linkage through expert systems
   technology
2. Linkage of models and aggregate policy level
   indicators
3. Linkage of models and public information
   (Internet)

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SMART Work Plan Phases

1. Requirements analysis, data availability,
   specifications
2. Data compilation, tool development
3. Parallel case studies
4. Comparative evaluation, dissemination.

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SMART Milestones

• 1 PM 09 End of preparatory phase, first
  workshop
• 2 PM 12 Methods and tools prototypes ready,
  start of operational phase
• 3 PM 18 Case studies implemented, first results
  of scenario analysis
• 4 PM 24 Analysis and assessment phase
  initiated
• 5 PM 30 Case studies completed, final
  comparative analysis
• 6 PM36 Project and reporting completed

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SMART work packages

• WP 0 Coordination and Administration
• ESS, PM 1-36
• Communication
• Mailing list smart_at_ess.co.at
• Web server www.ess.co.at/SMART
• Discussion board
• Meetings
• Output
• Reports
• Deliverables
• Cost statements
• Project Review

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SMART work packages

• WP 01 Requirements and constraints analysis
• FEEM, PM 1-6
• Deliverable due by
• February 2003 !

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SMART work packages

• WP 02 Socio-economic framework and guidelines
• UATLA, PM 3-12

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SMART work packages

• WP 03 Analytical tools, models
• SOGREAH, PM 3-18
• Subtasks for
• TELEMAC
• WaterWare, XPS

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SMART work packages

• WP 04 Data compilation and analysis
• TR, PM 6-24
• Includes parallel sub-tasks, one for each case
  study/country

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SMART WP04

1. Develop meta-data structure
2. Formats, technical specifications,
3. Coverage and resolution (space and time)
4. Develop checklists
5. Monitor compilation
6. Comparative analysis (completeness, consistency,
   plausibility)

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SMART WP04

• Common data base or data repository
• Extensive documentation !
• Accessible from ftp server at project web site
• Selected data available with interactive on-line
  tools (e.g., hydro- meteorological time series
  data)
• Map server at CEDARE

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SMART work packages

• WP 05 09 Case Studies
• Respective partner, PM 12-30,
• Overlaps with data compilation

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SMART work packages

• WP 10 Comparative analysis
• FEEM, PM 24-36
• Requires input from all case studies

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SMART work packages

• WP 11 Dissemination and exploitation
• ESS, PM 3-36

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SMART time table
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SMART
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Water management

• must be analyzed in a broad systems context
• Socio-economic aspects (costs and benefits,
  jobs, institutions, regulations)
• Environmental aspects (water quality, water
  allocation, alternative use)
• Technological aspects (constraints,BAT, clean
  technologies, water efficiency, reuse and
  recycling)

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Water management

• Conflicting water use and changing, stochastic
  constraints
• Multiple criteria, conflicting objectives
• Industrial water management
• Water demand
• Consumptive use
• Water pollution

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Environmental problems

• Water Management problems
• Not enough
• Too much
• At the wrong place
• At the wrong time
• Insufficient quality
• Problems of distribution of resources (clean air,
  water, land, biodiversity, )

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Environmental problems

• result from the local or short-term optimization
  of resource management strategies, ignoring some
  externalities (side effects, costs to others).
• All life degrades its environment.
• All living systems have self-regulatory
  capabilities within usually unknown limits.

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Environmental problems

• Increasing human population
• Increasing resource consumption
• Energy
• Materials
• Space
• And potentially irreversible destruction of
  information (biodiversity)

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Environmental problems

• Three laws of ecology
• Everything is connected to everything else
• Everything must go somewhere
• Nature knows best.
• 
  Barry
  Commoner,
• 
  The Closing
  Cycle.

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Environmental problems

• Root problem
• Uncoupling of feedback loops
• (to obtain local or short-term benefits)
• Tragedy of the Commons (Hardin, 1968)
• Social costs (Kapp, 1979)
• Limits to Growth (Meadows et al., 1971)
• Malthus (1830)

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Environmental problems

• IF quantity or quality, spatial or temporal
  distribution of environmental resources do not
  match our needs or expectations
• Environment (objective reality)
• Needs (objective-subjective reality)
• Expectations (subjective reality)

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Regulatory response

• Laws and regulations
• Emission control (water, air)
• Product standards (fuel, engines, BAT)
• Permitting, zoning
• Monetary instruments
• Taxes (waste tax)
• Subsidies (for mitigation)

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Regulatory response

• Planning requirements
• Environmental impact assessment
• Risk assessment
• Self-regulation
• ISO 14000, 9000
• EMAS, Eco-Audit
• Responsible Care
• Labeling (biological food)

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Water management problems

• are inherently multi-disciplinary
• Hydrology, geology, climatology, geography
• (Geo)physics, chemistry
• Biology, ecology, toxicology
• Engineering, economics
• Psychology, sociology
• Law, political sciences

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Water management problems

• are complex (many elements and interactions)
• dynamic (including delay, memory)
• spatially distributed (1, 1.5, 2 and 3D)
• non-linear (feedback, bifurcation, etc.)
• involve large uncertainties in
• - the physical domain
• - the socio-economic domain
• involve multiple actors and stake holders
• are always multi-criteria, multi-objective
  

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A river basin perspective

• Water can easily be accounted for, a mass budget
  approach is feasible
• The hydrographic unit of the catchment or river
  basin provides a naturally bounded well defined
  system
• Conservation laws (mass, momentum) are used to
  describe dynamic water budgets.

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A river basin perspective

• Industrial water use is one of the demand nodes
  in a river basin network/graph
• Input nodes (sub-catchment, wells)
• Domestic demand nodes
• Agricultural demand nodes
• Industrial demand nodes
• Reservoirs, lakes
• Structural components (confluence)
• connected by river reaches, canals

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Water demand

• Depends on
• Production volume
• Production technology
• Recycling strategies
• Demand has quantitative and qualitative elements,
  usually involves water treatment
• For a given cost of water, an optimal strategy
  can be computed based on investment cost,
  discount rate, and project lifetime (NPV)

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Water demand
Consumptive use
Production process
intake
return flow
recycling
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Consumptive use

• Water demand consists of
• Consumptive use
• Process water (integrated in the product)
• Cooling (evaporation)
• Temporary use (return flow)
• But pollution can make the return flow unfit for
  subsequent use

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Conflicting use

• More than 70 of water is generally used for
  agriculture (irrigation)
• Added value per unit water used in industry is
  usually between 50 to 100 times higher than in
  agriculture
• Domestic use of water is comparatively small, but
  with high quality requirements and low
  elasticity.
• Environmental use (low flow, quality constraints).

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Water Pollution

• Industrial effluents incl.spills
• Domestic sewage
• Irrigation return flow
• Reduces potential utility for other down-stream
  users
• Endangers biological systems (fish kill)
• May accumulate over long periods (chemical time
  bombs in sediments)

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Waste management

• Waste allocation
• Utilizes the self-purification potential of
  natural water bodies (BOD, biodegradable
  substances)
• But many toxics and heavy metals are persistent
  (long term cumulative damage, bioaccumulation,
  sediments).

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WP 10 Comparative Analysis

• OBJECTIVES
• The comparative analysis of the set of scenarios
  for each case/scenario.
• The multi-criteria comparative analysis and
  selection of a non-dominated set of (Pareto
  optimal) alternatives
• The identification of the most promising scenario
  or small set of candidate scenarios from each
  test site

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WP 10 Comparative Analysis

• Scenario comparison and multi-criteria analysis
• Baseline Scenarios
• Common Scenarios
• Specific Scenarios
• ANY EXTERNAL CASES ?

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WP 10 Comparative Analysis

• METHOD
• Discrete MC
• Pareto set (half ordering)
• Reference point approach
• Set of criteria define constraints and
  optimization direction for each
• Optimum solution is the one nearest to reference
  point (utopia).
• Reference point location scales (criteria)
  dimensions.

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WP 10 Comparative Analysis

• SCENARIO
• INPUT Set of assumptions
• Decision and Policy variables
• Exogeneous variables
• OUTPUT Set of indicators or criteria

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Decision Support Methodology

• Reference point approach

utopia
A4
efficient point
A5
A2
criterion 2
A6
A1
dominated
A3
better
nadir
criterion 1
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WP 10 Comparative Analysis

• METHOD
• Modify the set of alternatives
• Select criteria
• Modify constraints
• Introduce reference point
• Reduce dimensionality

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WP 10 Comparative Analysis

• METHOD
• Combination of indicators by RULES
• or simple algorithms
• Quality, Quantity ? STATUS
• Dynamically generated combined indicators can be
  used for the benchmarking

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WP 10 Comparative Analysis

• RESULT
• Ranking order of alternatives
• Dominated/Pareto subsets
• Distance from reference point nearest best

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WP 10 Comparative Analysis

• The vocabulary
• List of indicators
• Derived indicators (rule-based)
• Constraints based on
• Standards (WQ, reliability, ?)
• Distributions (e.g., drop one SD)
• Preferences

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WP 10 Comparative Analysis

• Inidicator definitions
• Name, alias
• Unit
• Allowable range with symbolic labels
• Question/definition
• Inference Rules

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WP 10 Comparative Analysis

• Reliability
• A REL
• U
• V Low 0, 10, 20
• V Medium 21, 25, 40
• V High 41, 50, 100
• Q What is average reliability of meeting water
  demands on a daily basis ?

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WP 10 Comparative Analysis

• RULES
• IF condition
• AND/OR condition
• THEN conclusion
• Condition
• Descriptor Operator Value
• Quality high

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WP 10 Comparative Analysis

• RULES
• IF condition
• AND/OR condition
• THEN conclusion
• Condition
• Descriptor Operator Value
• Density , lt,gt,!,. high
• Conclusion
• Descriptor Assignment Value
• Density high

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WP 10 Comparative Analysis

• RULES
• IF quantity sufficient
• AND quality sufficient
• THEN status very_good
• Condition
• Descriptor Operator Value
• Reliability , lt,gt,!,. high
• Conclusion
• Descriptor Assignment Value
• Reliability high

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WP 11 Dissemination

• Web site
• Meeting, conferences, scientific and technical
  literature
• Local workshops

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WP 11 Dissemination

• Web site, other material ?
• Meeting, conferences, scientific and technical
  literature
• Local dissemination workshops (language)
• SMART the book ?