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© K.Fedra 20071
DSS for Integrated DSS for Integrated Water Resources Water Resources Management (IWRM) Management (IWRM)
DSS for Integrated DSS for Integrated Water Resources Water Resources Management (IWRM) Management (IWRM)
Problems, data, instrumentsProblems, data, instrumentsProblems, data, instrumentsProblems, data, instruments
DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.atEnvironmental Software & Services A-2352 Gumpoldskirchen
DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.atEnvironmental Software & Services A-2352 Gumpoldskirchen
© K.Fedra 20072
IWRM: what to decide ?IWRM: what to decide ?IWRM: what to decide ?IWRM: what to decide ?• Water allocationWater allocation (sectoral: agriculture, (sectoral: agriculture,
domestic, industrial, recreational, environmental, domestic, industrial, recreational, environmental, hydropower, shipping, or geographic: hydropower, shipping, or geographic: upstream/downstream)upstream/downstream)
• Development projectsDevelopment projects (investment) (investment)– Structures, supply, demand, quality, Structures, supply, demand, quality,
land use …..land use …..• Strategic planningStrategic planning: : regional/national regional/national
development, security, sustainability development, security, sustainability (climate change)(climate change)
© K.Fedra 20073
IWRM: which scope ?IWRM: which scope ?IWRM: which scope ?IWRM: which scope ?
• Bounding the system, what to Bounding the system, what to • INCLUDE INCLUDE (part of the system state)(part of the system state)
• EXCLUDEEXCLUDE (treat as boundary conditions, (treat as boundary conditions, initial conditions, dynamic inputs)initial conditions, dynamic inputs)
Examples:Examples:• Fisheries managementFisheries management• Watershed management, Watershed management, land use, erosion controlland use, erosion control
• Public health, sanitationPublic health, sanitation
© K.Fedra 20074
Generating alternativesGenerating alternativesExplore the consequences of
alternatives, test feasibility, evaluate scenarios:
• by simulation modelling
Design alternatives given some goals, objectives, constraints:
• by optimization modelling
Explore the consequences of alternatives, test feasibility, evaluate scenarios:
• by simulation modelling
Design alternatives given some goals, objectives, constraints:
• by optimization modelling
© K.Fedra 20075
Model representationModel representation
Conservation laws:Mass conservation, mass budget
inputs - output - storage change = 0Water is neither generated nor lost within
the system, but can change state (evaporation, ice) or be incorporated into products (crops, beverages).
Conservation laws:Mass conservation, mass budget
inputs - output - storage change = 0Water is neither generated nor lost within
the system, but can change state (evaporation, ice) or be incorporated into products (crops, beverages).
© K.Fedra 20076
Model Data requirementsModel Data requirements
• Physiography• Hydro-meteorology• Drainage network, structures• Demand areas (nodes)• Pollution sources• Socio-economics (demography)
• Techno-economics
• Physiography• Hydro-meteorology• Drainage network, structures• Demand areas (nodes)• Pollution sources• Socio-economics (demography)
• Techno-economics
© K.Fedra 20077
Data requirementsData requirements• Never enough data• Never the “right” data• Never sufficient quality, coverage1. Start with the QUESTIONS2. Then, collect the data needed
(hypothetico-deductive)3. Consider alternative sources (RS,
modeling)
• Never enough data• Never the “right” data• Never sufficient quality, coverage1. Start with the QUESTIONS2. Then, collect the data needed
(hypothetico-deductive)3. Consider alternative sources (RS,
modeling)
© K.Fedra 20078
Data requirementsData requirementsStart with the QUESTIONS:• Data collection is NOT an end in
itself (always new questions)
• Data are used to test hypotheses, models. Explicit collection strategy !
• Data should serve the DM process (what for ?)
Start with the QUESTIONS:• Data collection is NOT an end in
itself (always new questions)
• Data are used to test hypotheses, models. Explicit collection strategy !
• Data should serve the DM process (what for ?)
© K.Fedra 20079
Models and Data needs:Models and Data needs:There will never be “enough” data in a fractal
and stochastic world ! (e.g., 30-50 years of hydrometeorology !!!)
Challenge: to make the best use of the information/knowledge available.
Models can be used to: • IDENTIFY critical data needs• QUANTIFY the importance of data (sensitivity
analysis)• REPRESENT uncertainty, exploit it !
There will never be “enough” data in a fractal and stochastic world ! (e.g., 30-50 years of hydrometeorology !!!)
Challenge: to make the best use of the information/knowledge available.
Models can be used to: • IDENTIFY critical data needs• QUANTIFY the importance of data (sensitivity
analysis)• REPRESENT uncertainty, exploit it !
© K.Fedra 200710
Models and Data:Models and Data:1. Use models to TEST assumptions:
Data sets represent the best available knowledge, estimates, “educated guess”
• Complete• Consistent• Plausible
2. Include UNCERTAINTY explicitly:1. Probabilistic model results 2. Adaptive decisions/planning
1. Use models to TEST assumptions: Data sets represent the best available knowledge, estimates, “educated guess”
• Complete• Consistent• Plausible
2. Include UNCERTAINTY explicitly:1. Probabilistic model results 2. Adaptive decisions/planning
© K.Fedra 200711
Models and Data:Models and Data:1. All data contain some error,
uncertainty: make it explicit2. Determine effect on decision
(robustness ?) by sensitivity analysis: does it matter, make a difference ?
3. Balance uncertainty considering1. Feasibility and cost of data collection2. Alternative sources of information (RS,
modeling)
1. All data contain some error, uncertainty: make it explicit
2. Determine effect on decision (robustness ?) by sensitivity analysis: does it matter, make a difference ?
3. Balance uncertainty considering1. Feasibility and cost of data collection2. Alternative sources of information (RS,
modeling)
© K.Fedra 200712
Models and Data:Models and Data:Always remember:
• The product of • A double precision number• A random number
• is a RANDOM NUMBER !• The product of
• A very large precise number• A small, uncertain number
• Is a large, very uncertain number
Always remember:• The product of
• A double precision number• A random number
• is a RANDOM NUMBER !• The product of
• A very large precise number• A small, uncertain number
• Is a large, very uncertain number
© K.Fedra 200713
Model representationModel representationMETA data• Description (variable, classification,
unit, methods, quality)
• Source (author/institution, ownership, IPR, use/restrictions, cost)
• Date (time-stamp, validity)
• Geo-reference (location, projection, coordinate system …)
META data• Description (variable, classification,
unit, methods, quality)
• Source (author/institution, ownership, IPR, use/restrictions, cost)
• Date (time-stamp, validity)
• Geo-reference (location, projection, coordinate system …)
© K.Fedra 200714
Meta Data: what for ?Meta Data: what for ?• Several standards: ISO/IEC JTC1 SC32 WG2 ,
ISO Standard 15836-2003 (February 2003), NISO Standard Z39.85-2007 (May 2007), Dublin Core, …)
• Search and retrieval: INDEXING, classification, keywords (ontology, thesaurus, taxonomy, folksonomy – Wikipedia)
• Interpretation: background, context, technical and
methodological description
• Several standards: ISO/IEC JTC1 SC32 WG2 , ISO Standard 15836-2003 (February 2003), NISO Standard Z39.85-2007 (May 2007), Dublin Core, …)
• Search and retrieval: INDEXING, classification, keywords (ontology, thesaurus, taxonomy, folksonomy – Wikipedia)
• Interpretation: background, context, technical and
methodological description
© K.Fedra 200715
Design of alternatives:Design of alternatives:Decision variables:• Structural change• Allocation rules• Water technologies (use)• Policy (law, regulations) • Economic instruments (pricing,
subsidies, taxes, penalties …..)
Decision variables:• Structural change• Allocation rules• Water technologies (use)• Policy (law, regulations) • Economic instruments (pricing,
subsidies, taxes, penalties …..)
© K.Fedra 200716
Model representationModel representationAlternatives: defined by policies,
technologies, instruments, affecting:• Demand (reduced, behavioural change)• Efficiency (lower demand, higher benefits • Losses (reduced, increase efficiency)• Supply (increase, alternative sources)• Storage (increased)• Allocation (changed)• Quality (improved)
Alternatives: defined by policies, technologies, instruments, affecting:
• Demand (reduced, behavioural change)• Efficiency (lower demand, higher benefits • Losses (reduced, increase efficiency)• Supply (increase, alternative sources)• Storage (increased)• Allocation (changed)• Quality (improved)
© K.Fedra 200717
Instruments, measuresInstruments, measuresBasic parameters:• Effects, efficiency• Investment costs (EAC)• Life time of instrument/components• Operating costs (fixed or activity based)
• Compatibility, possible combinations of instruments (side effects ?)
• Ranges of application (min, max)
Basic parameters:• Effects, efficiency• Investment costs (EAC)• Life time of instrument/components• Operating costs (fixed or activity based)
• Compatibility, possible combinations of instruments (side effects ?)
• Ranges of application (min, max)
© K.Fedra 200718
Instruments, measuresInstruments, measures
© K.Fedra 200719
Instruments, measuresInstruments, measures
© K.Fedra 200720
Instruments and measuresInstruments and measures• Structures (storage: dams, recharge,
distribution: canals, pipelines)
• Alternative supply (desalination, inter-basin transfer, water harvesting)
• Demand reduction (education, increased efficiency: alternative technologies (irrigation), recycling, reuse, pricing)
• Loss reduction (pipe repair, lining, …)
• Quality (treatment, landuse and watershed management)
• Economic instruments (incentives, penalties)
• Structures (storage: dams, recharge, distribution: canals, pipelines)
• Alternative supply (desalination, inter-basin transfer, water harvesting)
• Demand reduction (education, increased efficiency: alternative technologies (irrigation), recycling, reuse, pricing)
• Loss reduction (pipe repair, lining, …)
• Quality (treatment, landuse and watershed management)
• Economic instruments (incentives, penalties)
© K.Fedra 200721
Instruments and measuresInstruments and measuresImportant attributes:
• Scalability, economies of scale, minimum % ?
• Possible market penetration• Operational costs,
sustainability• Adaptability, upgrades ?
Important attributes:
• Scalability, economies of scale, minimum % ?
• Possible market penetration• Operational costs,
sustainability• Adaptability, upgrades ?
© K.Fedra 200722
Economies of scale:Economies of scale:
Economies of Scale
0
5
10
15
project size
proj
ect c
ost
size 1 2 3 4 5 6 7 8 9 10
cost 1.00 1.80 2.50 3.00 4.00 4.50 5.00 5.20 5.90 6.50
1 2 3 4 5 6 7 8 9 10
Economies of Scale
0
5
10
15
project size
proj
ect c
ost
size 1 2 3 4 5 6 7 8 9 10
cost 1.00 1.80 2.50 3.00 4.00 4.50 5.00 5.20 5.90 6.50
1 2 3 4 5 6 7 8 9 10
© K.Fedra 200723
Instruments and measuresInstruments and measures
Distributional effects:• Who pays (including
social costs, externalities)
• Who benefits
Distributional effects:• Who pays (including
social costs, externalities)
• Who benefits
© K.Fedra 200724
Instruments and measuresInstruments and measures
Basic idea:
• INCREASE EFFICIENCY:– generate MORE benefits
– with LESS inputs (costs)
– equitably, sustainably …
Basic idea:
• INCREASE EFFICIENCY:– generate MORE benefits
– with LESS inputs (costs)
– equitably, sustainably …
© K.Fedra 200725
Instruments and measuresInstruments and measuresInstruments will affect:
• Efficiency of allocation, use:• Supply, Demand, Losses
• Water qualityCOST (investment, operation EAC):
find the best combination of measures with optimization/DSS
Instruments will affect:
• Efficiency of allocation, use:• Supply, Demand, Losses
• Water qualityCOST (investment, operation EAC):
find the best combination of measures with optimization/DSS
