General Framework for Scenario Analysis

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General Framework for Scenario Analysis
EVK3-CT-2002-0084
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INTRODUCTION
Scenario A coherent, internally consistent and
plausible description of a possible future state
of the system under investigation

• Scenario characteristics typology (EEA, 2000)
• Forecasting scenarios study future alternative
  development
• Backcasting scenarios Describe the desired
  future and explore strategies to reach the
  desired goals
• Descriptive scenarios set out a sequenced set
  of possible events in a neutral way
• Normative scenarios incorporate different
  interests, values and ethics
• Quantitative scenarios Computable model-based
  exercises
• Qualitative scenarios rely solely on narratives
• Trend (Bussines As Usual) scenarios
  extrapolation of current trends
• Extreme scenarios incorporate unlike and/or
  extreme evants and their consequences

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• GENERAL SCENARIOS
• BUSSINES AS USUAL (BAU), which is based on the
  assumption of the continuation of current trends
  into future. In this case prevailing trends will
  be allowed to continue without major
  intervention.
• POLICY TARGETS (PT), which is based on the
  assumption that in the future all current
  regulatory standards and targets will be met.
  This is equivalent to the implementation of the
  Water Framework Directive with all its
  requirements.
• DEEP GREEN (DG), which is based on the
  assumption that there is a shift in EU society
  towards environmental conservation goals in
  preference to economic growth-related objectives.
  In this case, the goal will be the
  re-establishment of pristine conditions subject
  to the historical legacies.

From EUROCAT Project
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THRESHOLDS project
Non-linear cause-effect relationship between
driver and state
Corridors of Sustainability
EUROCAT project
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DPSIR Framework Analysis for DITTY sites
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DPSIR Framework Analysis for DITTY sites
Relevant watershed driving forces and impacts
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DPSIR Framework Analysis for DITTY sites
Relevant lagoon driving forces and impacts
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Linking Scenario Analysis to Models
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Scenario Analysis Quantification

• Effects of nutrient loads, Catchment modelling

Joint investigation by modellers and social
scientists to study the feasibility,
effectiveness and practicability of changing
loads and quality improvements, together with
enabling measures and instruments (e.g. improved
sewage treatment altered agricultural practices,
fertiliser taxes, etc.)
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Scenario Analysis Quantification

• Effects of nutrient loads, coastal lagoon
  modelling results

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Scenario Analysis Quantification

• Bacteria of sanitary concern, Catchment modelling

• Bacteria of sanitary concern, lagoon modelling

Bacteria concentration will serve as a forcing
function in the lagoon model or as a source into
the lagoon.
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Scenario Analysis Quantification

• Climate Change, Catchment modelling

A climate change scenario assuming the indicated
timeframe in the WFD (2015) will be generated for
each lagoon taking into account long term climate
variation, i.e.increase in air temperature and
precipitation variability, from the IPPC (Europe)
by modifying weather time series. All the other
scenarios will use this synthetic weather time
series (check if SWAT can be do it)
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PRedICT Description(A GIS-DSS for Watershed
Management,)

• PRedICT (Pollution Reduction Impact Comparison
  Tool) was developed as a companion software tool
  for use with AVGWLF

http//www.orser.psu.edu/gissupport/predict.htm
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• PRedICT evaluates the implementation of both
  agricultural and non-agricultural pollution
  reduction strategies at the watershed level.
• This tool allows the user to create various
  scenarios for future conditions that reflect
  the use of different pollution reduction
  strategies such as agricultural and urban best
  management practices (BMPs).
• PredICT can evaluate the conversion of septic
  systems to centralized wastewater treatment, and
  upgrading of treatment plants from primary to
  secondary to tertiary.
• A rather simple cost-accounting approach is used
  to estimate load reductions and their associated
  costs.