1. © K.Fedra 2007 1 DSS for Integrated Water Resources Management (IWRM) IWRM model representation, scenarios, optimization 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 Main topics: model representation of river basin and water resources: conservation laws, hydrological cycle, precipitation, EVT, Basin topology: cascading reservoirs, routing, GW water quality; model representation of river basin and water resources: conservation laws, hydrological cycle, precipitation, EVT, Basin topology: cascading reservoirs, routing, GW water quality;

3. © K.Fedra 2007 3 IWRM: what to decide ? Water allocation (sectoral: agriculture, domestic, industrial, recreational, environmental (dilution ?), hydropower, shipping, or geographic: upstream/downstream)Water allocation (sectoral: agriculture, domestic, industrial, recreational, environmental (dilution ?), hydropower, shipping, or geographic: upstream/downstream) Waste allocation: permitting, emission standards, treatmentWaste allocation: permitting, emission standards, treatment Development projects (investment)Development projects (investment) Strategic planning: regional/national development, security, sustainability (climate change)Strategic planning: regional/national development, security, sustainability (climate change)

4. © K.Fedra 2007 4 Decision support paradigms Information systems (menu of options) Scenario analysis (and comparison) Scenario analysis (and comparison) WHAT IF WHAT IF Rational maximization Rational maximization HOW TO (reach objectives), HOW TO (reach objectives),  optimization  optimization Information systems (menu of options) Scenario analysis (and comparison) Scenario analysis (and comparison) WHAT IF WHAT IF Rational maximization Rational maximization HOW TO (reach objectives), HOW TO (reach objectives),  optimization  optimization

5. © K.Fedra 2007 5 DSS structure: Analytical core: Design of alternativesDesign of alternatives Assessment and evaluation, alternativesAssessment and evaluation, alternatives WHY Model based analysis: Impossible to experiment in the real world (costs)Impossible to experiment in the real world (costs) Impossible to try enough alternatives (time)Impossible to try enough alternatives (time) Analytical core: Design of alternativesDesign of alternatives Assessment and evaluation, alternativesAssessment and evaluation, alternatives WHY Model based analysis: Impossible to experiment in the real world (costs)Impossible to experiment in the real world (costs) Impossible to try enough alternatives (time)Impossible to try enough alternatives (time)

6. © K.Fedra 2007 6 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).

7. © K.Fedra 2007 7 Model representation Hydrological Cycle: Water evaporates from land and sea, precipitates, evaporates, forms runoff, gets stored, diverted and/or used (consumptive use), percolates into groundwater. Hydrological Cycle: Water evaporates from land and sea, precipitates, evaporates, forms runoff, gets stored, diverted and/or used (consumptive use), percolates into groundwater.

8. © K.Fedra 2007 8

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11. © K.Fedra 2007 11 Dynamic water budget Rainfall-runoff model

12. © K.Fedra 2007 12 Model representation Precipitation: THE key variable == input High variability in time and space (synoptic observation: weather radar) High measurement error  large uncertainties Precipitation: THE key variable == input High variability in time and space (synoptic observation: weather radar) High measurement error  large uncertainties

13. © K.Fedra 2007 13 Model representation Evapotranspiration Evaporation: phase change from liquid to gaseous, function of temperature and vapour pressure Transpiration: physiological vapour production by plants (evaporation from stomata, and animals in respiration ) Evapotranspiration Evaporation: phase change from liquid to gaseous, function of temperature and vapour pressure Transpiration: physiological vapour production by plants (evaporation from stomata, and animals in respiration )

14. © K.Fedra 2007 14 Model representation Evaporation: Penman-Monteith

15. © K.Fedra 2007 15 Penman-MonteithPenman-Monteith where: R n is the net radiation, G is the soil heat flux, (e s - e a ) represents the vapour pressure deficit of the air, r a is the mean air density at constant pressure, c p is the specific heat of the air, Δ represents the slope of the saturation vapour pressure temperature relationship, γ is the psychrometric constant, r s and r a are the (bulk) surface and aerodynamic resistances. where: R n is the net radiation, G is the soil heat flux, (e s - e a ) represents the vapour pressure deficit of the air, r a is the mean air density at constant pressure, c p is the specific heat of the air, Δ represents the slope of the saturation vapour pressure temperature relationship, γ is the psychrometric constant, r s and r a are the (bulk) surface and aerodynamic resistances.

16. © K.Fedra 2007 16 EvapotranspirationEvapotranspiration Simple practical method: Degree day method: EVTP = a * avg.air Temperature a is in the order of 0.1 mm/ o K varies with land cover/vegetation and humidity, wind exposure Simple practical method: Degree day method: EVTP = a * avg.air Temperature a is in the order of 0.1 mm/ o K varies with land cover/vegetation and humidity, wind exposure

17. © K.Fedra 2007 17 Model representation Cascading non-linear reservoirs:

18. © K.Fedra 2007 18 Model representation “reservoir” water budget: Infiltration percolation Interflow Surface runoff precipitationEVTP Outflow is a non-linear function of storage

19. © K.Fedra 2007 19 Water demand Intake (quality constr., conveyance loss Consumptive use recycling return flow (pollution) Demand node (production process)

20. © K.Fedra 2007 20 Model representation Runoff of excess storage that exceeds the “reservoir” capacity: from canopy (interception storage) soil surface (exceeding infiltration capacity  Hortonian sheet flow, flash floods) Unsaturated zone: –horizontal interflow –vertical percolation (> field capacity) Saturated zone: Darcy flow of groundwater, f of head difference and conductivity Runoff of excess storage that exceeds the “reservoir” capacity: from canopy (interception storage) soil surface (exceeding infiltration capacity  Hortonian sheet flow, flash floods) Unsaturated zone: –horizontal interflow –vertical percolation (> field capacity) Saturated zone: Darcy flow of groundwater, f of head difference and conductivity

21. © K.Fedra 2007 21 Model representation Navier-Stokes equations:

22. © K.Fedra 2007 22 Navier-Stokes Equations Kronecker Delta Divergence:

23. © K.Fedra 2007 23 Model representation Open channel flow: The empirical Manning formula states: where: –V is the cross-sectional average velocity (m/s) –n is the Manning coefficient of roughness (0.01 – 0.075) –R h is the hydraulic radius (m) –S is the slope of the water surface or the linear hydraulic head loss (m/m) (S = hf / L) Open channel flow: The empirical Manning formula states: where: –V is the cross-sectional average velocity (m/s) –n is the Manning coefficient of roughness (0.01 – 0.075) –R h is the hydraulic radius (m) –S is the slope of the water surface or the linear hydraulic head loss (m/m) (S = hf / L)

24. © K.Fedra 2007 24 Model representation Hydraulic radius: R h part of the channels resistance that controls speed of flow: A: cross section P: wetted perimeter Hydraulic radius: R h part of the channels resistance that controls speed of flow: A: cross section P: wetted perimeter P = b + c + d

25. © K.Fedra 2007 25 ReachesReaches

26. © K.Fedra 2007 26 Channel Flow Routing Muskingum routing: S = K [ xI + ( 1 - x ) O ] where S = reach storage I = inflow rate O = outflow rate K= storage parameter (~ travel time) X= storage parameter (0 - 0.5, describes attenuation) Muskingum routing: S = K [ xI + ( 1 - x ) O ] where S = reach storage I = inflow rate O = outflow rate K= storage parameter (~ travel time) X= storage parameter (0 - 0.5, describes attenuation)

27. © K.Fedra 2007 27 Model representation Groundwater Laminar flow (Darcy) depends on elevation difference (gravity) conductivity (resistance) cross-sectional area Groundwater Laminar flow (Darcy) depends on elevation difference (gravity) conductivity (resistance) cross-sectional area

28. © K.Fedra 2007 28 Model representation Water quality: BOD/DO (Streeter-Phelps) Nutrients (fertilizer, NO 3 in GW ) Agrochemicals (toxic, persistent, bioaccumulating) Heavy metals (industrial waste) Turbidity, sediments, erosion, siltation Water borne diseases Water quality: BOD/DO (Streeter-Phelps) Nutrients (fertilizer, NO 3 in GW ) Agrochemicals (toxic, persistent, bioaccumulating) Heavy metals (industrial waste) Turbidity, sediments, erosion, siltation Water borne diseases

29. © K.Fedra 2007 29 Streeter-Phelps (DO, BOD)

30. © K.Fedra 2007 30 Streeter-Phelps (DO, BOD)

31. © K.Fedra 2007 31 Streeter-Phelps (DO, BOD)

32. © K.Fedra 2007 32 Streeter-Phelps (DO, BOD)

33. © K.Fedra 2007 33 Model representation Data requirements Physiography Hydro-meteorology Drainage network, structures Demand areas (nodes) Pollution sources Techno-economics Data requirements Physiography Hydro-meteorology Drainage network, structures Demand areas (nodes) Pollution sources Techno-economics

34. © K.Fedra 2007 34 Multi criteria optimization

35. © K.Fedra 2007 35

36. © K.Fedra 2007 36 ReachesReaches

37. © K.Fedra 2007 37

38. © K.Fedra 2007 38 Model purpose Structure the problem: WHAT FOR (purpose) Questions to be answered ? Identify gaps in understanding Define data requirements Define validation strategy Structure the problem: WHAT FOR (purpose) Questions to be answered ? Identify gaps in understanding Define data requirements Define validation strategy

39. © K.Fedra 2007 39 Model purpose WHAT FOR, WHY (not how) Model is a TOOL for purpose No BEST model (or hammer …) Choice of model and data requirements depend on the QUESTION to be answered WHAT FOR, WHY (not how) Model is a TOOL for purpose No BEST model (or hammer …) Choice of model and data requirements depend on the QUESTION to be answered

40. © K.Fedra 2007 40 Modeling and DSS MOST IMPORTANT: ask good questions (that can be answered to support decisions ) Model application is an experiment, hypothesis testing: does it make sense, does it add up ? Multiple models (agreement ?) MOST IMPORTANT: ask good questions (that can be answered to support decisions ) Model application is an experiment, hypothesis testing: does it make sense, does it add up ? Multiple models (agreement ?)