1. 1 OPTIMA INCO-MPC Project kick-off Meeting, October 28/29 Malta 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. 2 WP03: Modelling MODELS provide a Formal Structured Quantitative description of the problems and possible solutions. MODELS provide a Formal Structured Quantitative description of the problems and possible solutions.

3. 3 WP03: Modelling WP1: identifies problem issues, develops a structure for the description of the cases, identifies data needs and availability, constraints; WP2 analyzes perceptions and preferences, institutional or regulatory frameworks, plausible socio-economic developments; WP4 compiles the set of ALTERNATIVE WATER TECHNOLOGIES that can be used; WP5 looks into LAND USE change as one of the major driving forces, consistent with WP 2. WP1: identifies problem issues, develops a structure for the description of the cases, identifies data needs and availability, constraints; WP2 analyzes perceptions and preferences, institutional or regulatory frameworks, plausible socio-economic developments; WP4 compiles the set of ALTERNATIVE WATER TECHNOLOGIES that can be used; WP5 looks into LAND USE change as one of the major driving forces, consistent with WP 2.

4. 4 WP03: Modelling WP1, 2, 4 and 5 develop the boundary conditions and specifications for Complete Consistent Plausible Set of SCENARIOS for simulation modelling and optimization. WP1, 2, 4 and 5 develop the boundary conditions and specifications for Complete Consistent Plausible Set of SCENARIOS for simulation modelling and optimization.

5. 5 WP03: Modelling WaterWare dynamic water resources model (daily, annual)  optimization Embedded models: RRM rainfall-runoff model Automatic RRM calibration IRWDM irrigation water demand model Related model: LUC dynamic land use change model WaterWare dynamic water resources model (daily, annual)  optimization Embedded models: RRM rainfall-runoff model Automatic RRM calibration IRWDM irrigation water demand model Related model: LUC dynamic land use change model

6. 6 WP 3: Modelling Models provide estimates for 1.Economic efficiency 2.Environmental compatibility 3.Equity (intra- and intergenerational) Models provide estimates for 1.Economic efficiency 2.Environmental compatibility 3.Equity (intra- and intergenerational)

7. 7 WP03: Modelling LUC: land use change model Discrete state (LUC) transition model Markov chain with stochastic transition probabilities Rule-based constraints and TP adjustments Temporal resolution: year, scope: decades (20- 50 years) Spatial resolution: ha to km 2 Resource use and pollution as land-use specific output; Possibility for external, global driving forces LUC: land use change model Discrete state (LUC) transition model Markov chain with stochastic transition probabilities Rule-based constraints and TP adjustments Temporal resolution: year, scope: decades (20- 50 years) Spatial resolution: ha to km 2 Resource use and pollution as land-use specific output; Possibility for external, global driving forces

8. 8 WP03: LUC Modelling Global/local adjustments of the transition probabilities expressed as First-order logic RULES in relative terms (INCREASE, DECREASE in %). http://www.ess.co.at/SMART/luc.html Global/local adjustments of the transition probabilities expressed as First-order logic RULES in relative terms (INCREASE, DECREASE in %). http://www.ess.co.at/SMART/luc.html

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12. 12 WP03: LUC Modelling Interactive editors for 1.Land use classes 2.Transition probabilities 3.Modifying rules 4.Class specific resource needs/outputs are available on-line together with the viewer (player for animated results) Links from http://www.ess.co.at/SMART will be moved to http://ww.ess.co.at/OPTIMA Interactive editors for 1.Land use classes 2.Transition probabilities 3.Modifying rules 4.Class specific resource needs/outputs are available on-line together with the viewer (player for animated results) Links from http://www.ess.co.at/SMART will be moved to http://ww.ess.co.at/OPTIMA

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14. 14 WP03: LUC Modelling Derived values per unit area, class specific: 1.Water consumption 2.Waste water generated 3.Energy use 4.Solid waste production OTHERS ?? Derived values per unit area, class specific: 1.Water consumption 2.Waste water generated 3.Energy use 4.Solid waste production OTHERS ??

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16. 16 WP03: Modelling LUC EXTENSIONS : Include transportation network in rules (connectivity) Other external variables (specified as time series) More LUC specific coefficients and processes (employment, value added, etc) LUC EXTENSIONS : Include transportation network in rules (connectivity) Other external variables (specified as time series) More LUC specific coefficients and processes (employment, value added, etc)

17. 17 WP03: Modelling LUC OBJECTIVES: 1.Hypothesis testing 2.Developing CONSISTENT scenarios with high explanatory value that can also be used directly in the rainfall-runoff basin water budget model LUC OBJECTIVES: 1.Hypothesis testing 2.Developing CONSISTENT scenarios with high explanatory value that can also be used directly in the rainfall-runoff basin water budget model

18. 18 WP03: Modelling RRM: rainfall-runoff model Dynamic, daily time step Uses daily rainfall and temperature Major basin characteristic: LAND USE (summarized from LUC scenarios ??) Estimates runoff and dynamic water budget for ungaged basins, provides input for WRM start nodes (catchment) RRM: rainfall-runoff model Dynamic, daily time step Uses daily rainfall and temperature Major basin characteristic: LAND USE (summarized from LUC scenarios ??) Estimates runoff and dynamic water budget for ungaged basins, provides input for WRM start nodes (catchment)

19. 19 WP03: RRM Modelling Includes automatic calibration with runoff observation data Method: Monte Carlo, evolutionary programming; Extract reliable features (Gestalt) from observations, define as constraints on model behavior, FROM  TO (period) CMIN < FEATURE < CMAX FEATURES: min, max, avg, total, values Includes automatic calibration with runoff observation data Method: Monte Carlo, evolutionary programming; Extract reliable features (Gestalt) from observations, define as constraints on model behavior, FROM  TO (period) CMIN < FEATURE < CMAX FEATURES: min, max, avg, total, values

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23. 23 WP03: WR Modelling WRM: water resources model Dynamic, daily time step Topology of NODES and REACHES Demand nodes (cities, irrigation, industry, tourism) Estimates dynamic water budget, supply/demand, reliability of supply Complete on-line implementation with editors WRM: water resources model Dynamic, daily time step Topology of NODES and REACHES Demand nodes (cities, irrigation, industry, tourism) Estimates dynamic water budget, supply/demand, reliability of supply Complete on-line implementation with editors

24. 24 WP03: Modelling User/scenario management: User authentication by name and password (monitored … ) User can see and copy ALL scenarios, edit/delete only their own ! TEST scenarios installed as EXAMPLES to demonstrate features implemented On-line manual pages User/scenario management: User authentication by name and password (monitored … ) User can see and copy ALL scenarios, edit/delete only their own ! TEST scenarios installed as EXAMPLES to demonstrate features implemented On-line manual pages

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26. 26 WP03: Modelling Model structure: Topology (network) of NODES, connected by REACHES; NODES represent functional OBJECTS in the basin: Sub-catchments, well(s) fields, springs Reservoirs, structures Water demand: cities, irrigation districts, industries, environmental uses (wetlands, minimum flow) Model structure: Topology (network) of NODES, connected by REACHES; NODES represent functional OBJECTS in the basin: Sub-catchments, well(s) fields, springs Reservoirs, structures Water demand: cities, irrigation districts, industries, environmental uses (wetlands, minimum flow)

27. 27 WP03: Modelling Model structure: Topology (network) of NODES, connected by REACHES: Represent natural and man-made channels, canals, pipelines that transfer (route) water between NODES. Networks include: Diversions (splitting the flow) Confluences (merging flow) Model structure: Topology (network) of NODES, connected by REACHES: Represent natural and man-made channels, canals, pipelines that transfer (route) water between NODES. Networks include: Diversions (splitting the flow) Confluences (merging flow)

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30. 30 Water demand NODES Intake quality constraint, conveyance loss Consumptive use recycling return flow (pollution) Water demand and use: 1.domestic, 2. agricultural, 3. industrial Costs of supply Benefits of use Costs of supply Benefits of use losses

31. 31 WP03: Modelling DEMAND NODE is defined by Its type (domestic, industrial, agricultural) Its connectivity (upstream, downstream, aquifer) Its water demand (time series) Conveiance losses (evaporation, seepage) Consumptive use fraction, resulting in return flow, and its losses Quality changes (pollution) Costs of supply – Benefits of use DEMAND NODE is defined by Its type (domestic, industrial, agricultural) Its connectivity (upstream, downstream, aquifer) Its water demand (time series) Conveiance losses (evaporation, seepage) Consumptive use fraction, resulting in return flow, and its losses Quality changes (pollution) Costs of supply – Benefits of use

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34. 34 WP03: Modelling WRM EXTENSIONS: 1.Full groundwater coupling, single or multi-cell aquifers with Darcy-flow coupling, in/exfiltration for reaches 2.Quality integration (return flow) 3.Economic analysis: 1.Water efficiency; added value/unit water 2.Cost-benefit analysis, requires, per node: Investment, lifetime, OMR, discount rate WRM EXTENSIONS: 1.Full groundwater coupling, single or multi-cell aquifers with Darcy-flow coupling, in/exfiltration for reaches 2.Quality integration (return flow) 3.Economic analysis: 1.Water efficiency; added value/unit water 2.Cost-benefit analysis, requires, per node: Investment, lifetime, OMR, discount rate

35. 35 WP03: Modelling Full groundwater coupling, single or multi- cell aquifers with Darcy-flow coupling, in/exfiltration for reaches Every node is optionally connected to an AQUIFER OBJECT: 1.Extracting water from it (wells, infiltration (lateral inflow, baseflow contribution) into reaches, depending on relative levels 2.Returning water to it: seepage losses, explicit recharge Full groundwater coupling, single or multi- cell aquifers with Darcy-flow coupling, in/exfiltration for reaches Every node is optionally connected to an AQUIFER OBJECT: 1.Extracting water from it (wells, infiltration (lateral inflow, baseflow contribution) into reaches, depending on relative levels 2.Returning water to it: seepage losses, explicit recharge

36. 36 WP5-9: Modelling REMEMBER: Model applications are THE central part of the case studies !!! All data compilation in view of model input data requirements REMEMBER: Model applications are THE central part of the case studies !!! All data compilation in view of model input data requirements

37. 37 WP03: Model steps 1.Define the domain or system boundaries (river basin including any transfers !) 2.Describe all important OBJECTS: Inputs = sub-catchments, wells, springs, transfers, desalination, Aquifers Demands: cities, tourist resorts, industries, agriculture (irrigated) Structures: reservoirs 2.Define NETWORK: link nodes through reaches (connectivity) 1.Define the domain or system boundaries (river basin including any transfers !) 2.Describe all important OBJECTS: Inputs = sub-catchments, wells, springs, transfers, desalination, Aquifers Demands: cities, tourist resorts, industries, agriculture (irrigated) Structures: reservoirs 2.Define NETWORK: link nodes through reaches (connectivity)

38. 38 WP03: Model steps 1.Compile and edit the DATA for the NODES and REACHES: –Time series of flow, pumping, water demand, diversion, reservoir release as rules or explicit time series, –Loss coefficients –Consumptive use fractions, –Costs (investment, OMR, and benefits per units water supplied/used; 2.Edit one or more scenarios, document 3.RUN the model, evaluate runs. 1.Compile and edit the DATA for the NODES and REACHES: –Time series of flow, pumping, water demand, diversion, reservoir release as rules or explicit time series, –Loss coefficients –Consumptive use fractions, –Costs (investment, OMR, and benefits per units water supplied/used; 2.Edit one or more scenarios, document 3.RUN the model, evaluate runs.

39. 39 WP03: OPTMIZATION steps 1.Define CRITERIA, sort into 1.OBJECTIVES (min/max) and 2.CONSTRAINTS (inequalities), set numerical values, symbolic targets; 2.RUN the optimization model on-line (that may take a while …) 3.ANALYZE results as input to WP 14, 15 1.Define CRITERIA, sort into 1.OBJECTIVES (min/max) and 2.CONSTRAINTS (inequalities), set numerical values, symbolic targets; 2.RUN the optimization model on-line (that may take a while …) 3.ANALYZE results as input to WP 14, 15

40. 40 WP03: OPTMIZATION steps OPTIMIZATION generates sets of feasible alternatives, each optimal in some (well defined) sense; Discrete multi-criteria methodology SELECTS a single preferred solution from that set by defining preferences and trade-offs (multi-criteria) interactively: Users explore the decision space to learn what can be obtained, and for what price (the trade-offs) and how to approach their UTOPIA solutions. OPTIMIZATION generates sets of feasible alternatives, each optimal in some (well defined) sense; Discrete multi-criteria methodology SELECTS a single preferred solution from that set by defining preferences and trade-offs (multi-criteria) interactively: Users explore the decision space to learn what can be obtained, and for what price (the trade-offs) and how to approach their UTOPIA solutions.