© K.Fedra 2007 1 DSS for Integrated Water Resources Management (IWRM) Problems, data, instruments DDr. Kurt Fedra ESS GmbH, Austria [email protected] .

© 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

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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

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Instruments, measuresInstruments, measures

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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