Towards the comparative analysis Towards the comparative analysis of the case studies: of the case studies: operative stepsoperative stepsCarlo Giupponi1,2 and Gretel Gambarelli2,3
1Università degli Studi di Milano 2FEEM3 PhD, Università Ca’ Foscari di Venezia
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“Cooking” a comparative analysis
5 very different CS
Models inputs Models outputs
Metadata (WP04)3 scenarios
Policy responses
The ingredients
COMPARATIVE ANALYSIS
The dish
DPSIR framework Sustainability indicators
The receipt
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How to cook this dish?
Option 1
- more rigorous
- more ambitious
Option 2
- less rigorous
- less ambitious
The difference between option 1 and 2 is about the relationship between scenarios and responses and the number of necessary models runnings
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WP10 objectives
To identify commonalities and differences and relate them to the specific regional setting;
To identify more generally applicable results that are invariant across the case studies;
To organize these finding in terms of a comparative policy assessment (existing and desirable, future ones) and best practice examples – contribution to sustainability.
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OPTION 1: 5 OPERATIVE STEPS
1) Definition of scenarios
2) Definition of responses (E,F)
3) Definition of sustainability indicators
4) We run the 3 scenarios with existing responses
5) We run the 3 scenarios with desirable future responses
CA on existing policies for each scenario
CA on proposed policies for each scenario
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OPTION 1: step 1
1) Scenarios are defined by COMMON VARIABLES representing DRIVING FORCES of all CS (Climate, Population, Land Use), NOT INCLUDING WATER POLICY RESPONSES.
Precipitation EEA D04.01
Temperature SMART D04.01
Population growth rate UNEP/MAP D04.01
Urban population UNEP/MAP D04.01
Rural population UNEP/MAP D04.01
Population density UNEP/MAP D04.01
Share of Urban area SMART% of total area LUC MODEL
Share of irrigated agricultural land UNEP/MAP
% of total area LUC MODEL
Share of Industrial area SMART% of total area LUC MODEL
Share of Portual area SMART% of total area LUC MODEL
Share of Tourism development area SMART
% of total area LUC MODEL
or: Number of turists per km of coastline UNEP/MAP turists/km2
national statistics
SOURCEPROPOSED
UNIT (2)TYPE INDICATORPROPOSED
BY
CLIMATE(D)
POPULATION (D)
LAND-USE(D)
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OPTION 1: STEP 2
2) Responses are organized in COMMON CATEGORIES for all CS (Water Demand, Water Supply, Water Quality), but single responses are SPECIFIC per CS (PARTICIPATION OF STAKEHOLDERS).
TYPE RESPONSEPROPOSED BY
Water demand management Water prices (domestic, agriculture, industry, tourism) EEAWater subventions SMARTWater distribution and use systems investments SMARTChange in irrigation systems SMARTChange in cropping patterns SMARTRising awareness for limiting abstraction SMARTMinimum flow for environmental purposes SMART
Water supply management Efficiency of water use EEAEfficiency in irrigation UNEP_MAPEfficiency in urban network UNEP_MAPWater leakage EEA
Water harvesting (lakes, reservoirs, small dams) SMART
Reservoir storage investments SMART
Groundwater exploitation SMARTMobilization of surface water SMARTBasin-out water supply (groundwater) SMARTWater imports SMARTRecycling of wastewater SMARTDesalination SMARTLimits to groundwater exploitation SMART
Water quality management Share of industrial wastewatertreated on site UNEP-MAPSolid waste management for avoiding illegal discharge in waterflows SMARTUrban waste water treatment EEAWater treatment investments SMARTShare of collected and treated wastewater by the public sewerage system UNEP-MAPRising awareness for limiting fertilization
SMARTLimit salinization through drainage systems SMARTExistence of monitoring programs concerning pollutants inputs UNEP-MAPNational regulations on wastewater SMART
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OPTION 1: STEP 3
3) Indicators for the CA are COMMON to all CS and address the 3 pillars of sustainability (Economy, Society, Environment) + cross-cutting themes
IMPACT INDICATORS:
D/S ratio for agriculture SMART % WaterWare
or: GDP from agriculture SMARTthousands euros
D/S ratio for industry SMART % WaterWare
or: GDP from industry SMARTthousands euros
D/S ratio for tourism SMART % WaterWare
or: GDP from turistic sector SMARTthousands euros
Economic efficiency of the system SMARTeuros/ mc H2O WaterWare
IMPACT INDICATORS:D/S ratio for domestic uses SMART % WaterWareor: number of days without drinking water SMART days/year WaterWare
IMPACT INDICATOR:D/S ratio for environmental uses SMART %
STATE INDICATORS: Nutrients in coastal waters EEA Telemac
Hazardous substances in transitional, coastal and marine waters EEA Telemac
or: Global quality of coastal waters UNEP - MAP class (I-IV) Telemac
or: Bathing water quality EEA class (I-IV) Telemac
PRESSURE INDICATOR: Water exploitation index (WEI) EEA and
UNEP-MAP: Mean annual total abstraction of freshwater / long-term average freshwater
%
CROSSCUTTING
SOURCE
ECONOMIC
SOCIAL
ENVIRONMENTAL
TYPE INDICATORPROPOSED
BY:PROPOSED
UNIT
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OPTION 1: STEP 4
4) Models are first run for the 3 scenarios, with the CURRENT RESPONSES for all CS. Values of sustainability indicators are derived.
The COMPARATIVE ANALYSIS assesses how current responses perform in different case studies in each scenario.
Policy questions to be answered:
How effective are existing water policies with respect to the management of water supply, water demand and water quality?
What are the current effects of existing water policies on economic performances, the quality of life, the environmental quality?
Are the abstractions from our water resources sustainable over the long term?
What are the differences and communalities in current practices of the 5 CS?
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WP10: STEP 5
5) Models are run PER EACH SCENARIO, PER EACH CATEGORY OF RESPONSES. Each response impacts on a pressure or a state indicator, thus modifying models’ inputs. Values of sustainability indicators are derived.
The COMPARATIVE ANALYSIS assesses how common types of future responses perform in different case studies in each scenario.
Policy questions to be answered:
How effective are proposed water policies with respect to the current practices in improving the management of water supply, water demand and water quality?
How effective are proposed water policies with respect to the current practices in improving economic performances, the quality of life, the ecological quality?
Are the abstractions from our water resources sustainable over the long term if the proposed policies are implemented?
What are the differences and communalities in proposed practices of the 5 CS?
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OPTION 1: MODELS RUNNING
3x4 = 12 runnings of models per each CS
12 different results registered by sustainability indicators
3 scenarios, 1 Existing +3 Future Responses (WD, WS, WQ)
Hence, for each CS:
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OPTION 1: pros and cons
PROS:
- there is a LOGICAL DISTINCTION between external variables (i.e. climate conditions, population growth, etc.) and decision variables (i.e. water policies).
- more consistent with DPSIR: D define scenarios, for each scenario we have different effects on P,S,I indicators and R try to improve P, S, I indicators
CONS:
- rather complex
- many models runnings
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WP10: EXAMPLE
EXAMPLE:
Evaluation of one sustainability indicator (D/S ratio for agriculture):
• 1 scenario (pessimistic)
• 1 variable defining scenario (share of irrigated agricultural land)
• 1 type of response (water demand management. In particular: sprinkler irrigation)
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DF: Increased share of irrigated agricultural land
PESSIMISTIC SCENARIO
INDICATOR BASELINE BAU OPT PESSShare of irrigated area 50% 0% -3% +5%
LUC MODEL
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P: Increase in water demand for agriculture
DF: Increased share of irrigated agricultural
land
P m3/yearWater demand for agriculture
Current irrigation methods, crops etc.
Possibilities for the derivation of sectoral water demand:- water demand derived through a decision table having land use and population growth as inputs- direct derivation of water demands (coherent with land-use).In both cases the sum of sectoral water demands should be equal to the regional water demand for each scenario, as calculated by the LUC model.
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P: Water demand for agriculture
DF: Increase in irrigated surface
S: Total water availability for
agriculture
WATER RESOURCES MANAGEMENT MODEL
S Total water availability for agriculture
m3/y
Aggregation of daily data
Allocation strategies & other inputs
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I: D/S ratio in agriculture
P: Water demand for agriculture
DF: Increase in irrigated surface
S: Total water availability for
agriculture
S Total water availability MC/y
WATER RESOURCES MANAGEMENT MODEL
I D/S ratio in agriculture %
PWater demand for agriculture
MC/Y
Input for CA of existing responses
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P: Increase in Water demand for agriculture
DF: Increase in irrigated surface
S: total water availability for
agriculture
I: D/S ratio in agriculture decreases
P: Increase in Water demand for agriculture
Input for CA of future WDM responses
R: Sprinkler use
P: Decrease in Water demand for agriculture
S: Total water availability for
agriculture unchanged
I: D/S ratio in agriculture improves
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OPTION 2: OPERATIVE STEPS
1) Definition of scenarios, including responses
2) Definition of sustainability indicators
4) BAU scenario (including existing responses)
6) Optimistic scenario (including desirable future responses)
Answer to policy questions
6) Pessimistic scenario (including undesirable future responses)
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OPTION 2: pros and cons
CONS:
- NO LOGICAL DISTINCTION between external variables (i.e. climate conditions, population growth, etc.) and decision variables (i.e. water policies).
- less consistent with DPSIR: D and R are mixed in defining scenarios, so the effect of R on P,S.I indicators is less transparent because other variables (climate, population, etc.) change at the same time
PROS:
- less complex
- less models runnings
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Discussion….
For both option 1 and option 2 we have to agree on
- scenarios
- responses (included or not in scenarios)
- sustainability indicators
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1) SCENARIOS
Precipitation EEA D04.01
Temperature SMART D04.01
Population growth rate UNEP/MAP D04.01
Urban population UNEP/MAP D04.01
Rural population UNEP/MAP D04.01
Population density UNEP/MAP D04.01
Share of Urban area SMART% of total area LUC MODEL
Share of irrigated agricultural land UNEP/MAP
% of total area LUC MODEL
Share of Industrial area SMART% of total area LUC MODEL
Share of Portual area SMART% of total area LUC MODEL
Share of Tourism development area SMART
% of total area LUC MODEL
or: Number of turists per km of coastline UNEP/MAP turists/km2
national statistics
SOURCEPROPOSED
UNIT (2)TYPE INDICATORPROPOSED
BY
CLIMATE(D)
POPULATION (D)
LAND-USE(D)
TELEMAC:- sources of pollution
- type of pollution
- concentration of pollution
WATERWARE: Metadata (WP04)?
- Income increase per sector
- Per capita water consumption by sector, etc.
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2) RESPONSES
TYPE RESPONSEPROPOSED BY
Water demand management Water prices (domestic, agriculture, industry, tourism) EEAWater subventions SMARTWater distribution and use systems investments SMARTChange in irrigation systems SMARTChange in cropping patterns SMARTRising awareness for limiting abstraction SMARTMinimum flow for environmental purposes SMART
Water supply management Efficiency of water use EEAEfficiency in irrigation UNEP_MAPEfficiency in urban network UNEP_MAPWater leakage EEA
Water harvesting (lakes, reservoirs, small dams) SMART
Reservoir storage investments SMART
Groundwater exploitation SMARTMobilization of surface water SMARTBasin-out water supply (groundwater) SMARTWater imports SMARTRecycling of wastewater SMARTDesalination SMARTLimits to groundwater exploitation SMART
Water quality management Share of industrial wastewatertreated on site UNEP-MAPSolid waste management for avoiding illegal discharge in waterflows SMARTUrban waste water treatment EEAWater treatment investments SMARTShare of collected and treated wastewater by the public sewerage system UNEP-MAPRising awareness for limiting fertilization
SMARTLimit salinization through drainage systems SMARTExistence of monitoring programs concerning pollutants inputs UNEP-MAPNational regulations on wastewater SMART
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3) SUSTAINABILITY INDICATORS
IMPACT INDICATORS:
D/S ratio for agriculture SMART % WaterWare
or: GDP from agriculture SMARTthousands euros
D/S ratio for industry SMART % WaterWare
or: GDP from industry SMARTthousands euros
D/S ratio for tourism SMART % WaterWare
or: GDP from turistic sector SMARTthousands euros
Economic efficiency of the system SMARTeuros/ mc H2O WaterWare
IMPACT INDICATORS:D/S ratio for domestic uses SMART % WaterWareor: number of days without drinking water SMART days/year WaterWare
IMPACT INDICATOR:D/S ratio for environmental uses SMART %
STATE INDICATORS: Nutrients in coastal waters EEA Telemac
Hazardous substances in transitional, coastal and marine waters EEA Telemac
or: Global quality of coastal waters UNEP - MAP class (I-IV) Telemac
or: Bathing water quality EEA class (I-IV) Telemac
PRESSURE INDICATOR: Water exploitation index (WEI) EEA and
UNEP-MAP: Mean annual total abstraction of freshwater / long-term average freshwater
%
CROSSCUTTING
SOURCE
ECONOMIC
SOCIAL
ENVIRONMENTAL
TYPE INDICATORPROPOSED
BY:PROPOSED
UNIT
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