1. © K.Fedra 2007 1 DSS for Integrated Water Resources Management (IWRM) Problems, data, instruments DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen

2. © K.Fedra 2007 2 IWRM: what to decide ? Water allocation (sectoral: agriculture, domestic, industrial, recreational, environmental, hydropower, shipping, or geographic: upstream/downstream)Water allocation (sectoral: agriculture, domestic, industrial, recreational, environmental, hydropower, shipping, or geographic: upstream/downstream) Development projects (investment)Development projects (investment) –Structures, supply, demand, quality, land use ….. Strategic planning: regional/national development, security, sustainability (climate change)Strategic planning: regional/national development, security, sustainability (climate change)

3. © K.Fedra 2007 3 IWRM: which scope ? Bounding the system, what toBounding the system, what to INCLUDE (part of the system state)INCLUDE (part of the system state) EXCLUDE (treat as boundary conditions, initial conditions, dynamic inputs)EXCLUDE (treat as boundary conditions, initial conditions, dynamic inputs)Examples: Fisheries managementFisheries management Watershed management, land use, erosion controlWatershed management, land use, erosion control Public health, sanitationPublic health, sanitation

4. © K.Fedra 2007 4 Generating alternatives Explore 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

5. © K.Fedra 2007 5 Model representation Conservation laws: Mass conservation, mass budget inputs - output - storage change = 0 Water 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 = 0 Water is neither generated nor lost within the system, but can change state (evaporation, ice) or be incorporated into products (crops, beverages).

6. © K.Fedra 2007 6 Model 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

7. © K.Fedra 2007 7 Data requirements Never enough data Never the “right” data Never sufficient quality, coverage 1.Start with the QUESTIONS 2.Then, collect the data needed (hypothetico-deductive) 3.Consider alternative sources (RS, modeling) Never enough data Never the “right” data Never sufficient quality, coverage 1.Start with the QUESTIONS 2.Then, collect the data needed (hypothetico-deductive) 3.Consider alternative sources (RS, modeling)

8. © K.Fedra 2007 8 Data requirements 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 ?) 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 ?)

9. © K.Fedra 2007 9 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 !

10. © K.Fedra 2007 10 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

11. © K.Fedra 2007 11 Models and Data: 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 considering 1.Feasibility and cost of data collection 2.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 considering 1.Feasibility and cost of data collection 2.Alternative sources of information (RS, modeling)

12. © K.Fedra 2007 12 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

13. © K.Fedra 2007 13 Model representation 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 …) 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 …)

14. © K.Fedra 2007 14 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

15. © K.Fedra 2007 15 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 …..)

16. © K.Fedra 2007 16 Model representation 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) 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)

17. © K.Fedra 2007 17 Instruments, measures 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) 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)

18. © K.Fedra 2007 18 Instruments, measures

19. © K.Fedra 2007 19 Instruments, measures

20. © K.Fedra 2007 20 Instruments 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)

21. © K.Fedra 2007 21 Instruments and measures Important 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 ?

22. © K.Fedra 2007 22 Economies of scale:

23. © K.Fedra 2007 23 Instruments and measures Distributional effects: Who pays (including social costs, externalities) Who benefits Distributional effects: Who pays (including social costs, externalities) Who benefits

24. © K.Fedra 2007 24 Instruments 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 …

25. © K.Fedra 2007 25 Instruments and measures Instruments will affect: Efficiency of allocation, use: Supply, Demand, Losses Water quality COST (investment, operation  EAC): find the best combination of measures with optimization/DSS Instruments will affect: Efficiency of allocation, use: Supply, Demand, Losses Water quality COST (investment, operation  EAC): find the best combination of measures with optimization/DSS