Coping with global change – flexible design for sustainable urban water systems by jochen eckart, seneshaw tsegaye, kala vairavamoorthy
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Coping with global change – flexible design for sustainable urban water systems by jochen eckart, seneshaw tsegaye, kala vairavamoorthy
- 1. Coping with Global Change – Flexible Design for Sustainable Urban Water Systems Jochen Eckart, Seneshaw Tsegaye, Kala Vairavamoorthy
- 2. Global Change Pressures are Associated with Huge Uncertainties Uncertainty Uncertainty in population urbanization Uncertainty in carrying capacity/breakage rate Uncertainty in Uncertainty in quantity & quality demand
- 3. Uncertainties are Increasing during Life Span of Urban Water Systems 2000 2010 2020 2030 2040 2050
- 4. Flexibility as Approach to Cope with Uncertainties 2000 2010 2020 2030 2040 2050
- 5. Flexible Urban Water Systems
- 6. Definition of Flexibility as Basis for Theory of Flexible Design Flexibility is the ability of urban water systems to use their active capacity to act to respond on relevant alterations during operation in a performance efficient, timely and cost effective way. • Change the system during operation • Deal with future uncertainties • Characteristics of the change process
- 7. 3 Metrics for the Metrics Measurement of Flexibility of Flexibility Range of change: Range of future states which could be managed Performance: Homogeneity system performance for life span Effort of change: Cost of change and duration of change
- 8. Flexible Water Distribution Systems
- 9. Clustered & Flexible WDS Clustered Design T0 T1 T2 Time
- 10. Clustered & Flexible WDS Case Study Arua ARUA • Small emerging town in Eastern Uganda • Current Population:107,000 • Population 2032: 160,000 - 220,000
- 11. Identify Optimal Clusters for WDS Emerging Area Existing Central System New Development plan (Forest Area)
- 12. Minimization of Source-demand Distance - Demand Assignment River Enyau 1 16 S1 2 15 5 S7 S2 3 4 6 7 Current source (S9) S3 11 12 S6 14 8 S8 S5 10 9 S4 13 (a) (b)
- 13. Minimization of Source-demand Distance - Demand Assignment Legend Closer Far 22010110.27 8535803.521 1346868.662 9048904.414 9714152.365 4089875.217 930 41.268 850138.1101 797106.1611 249424.1131 473482.1641 869961.1161 340570.1671 610599.0191 536629.0602 135768.0122 639518.0632 405077.0152 591037.0662 781496.0182 728166.0692 885236.0113 Membership M9 M8 Closer to source center Far from source center M1 M2 M3 M4 M5 M6 M7 2 20 10 1 10. 2 7 8 5 3 5 80 3. 5 2 1 1 3 4 6 8 6 8. 6 6 2 90 4 8 90 4. 4 1 4 9 7 1 4 1 5 2. 3 6 5 40 8 9 8 7 5. 2 1 7 9 300 4 1. 2 6 8 8 50 1 3 8. 1 10 1 7 9 7 10 6. 1 6 1 1 2 4 9 4 2 4. 1 1 3 1 4 7 3 4 8 2. 1 6 4 1 8 6 9 9 6 1. 1 1 6 1 3 40 5 70. 1 6 7 1 6 10 5 9 9.0 1 9 1 5 3 6 6 2 9.0 60 2 1 3 5 7 6 8.0 1 2 2 6 3 9 5 1 8.0 6 3 2 40 50 7 7.0 1 5 2 5 9 10 3 7.0 6 6 2 7 8 1 4 9 6.0 1 8 2 7 2 8 1 6 6.0 6 9 2 8 8 5 2 3 6.0 1 1 3 Parcel assignment Membership M of parcels based on (Minimized Euclidean norm) source-demand distance
- 14. Maximizing Inter-Cluster Homogeneity C1 C7 C2 C9 C4 C6 C8 C4 C5 Cluster using K-means Algorithm Final cluster boundaries
- 15. Future Demand 17217 (m3/d) A5 14909 14909 A4 12809 12809 12809 A3 8860 8860 8860 8860 A2 7694 7694 7694 7694 7694 A1 T0 T1 T2 T3 T4 Time (2012) (2017) (2022) (2027) (2032) (year)
- 16. Clustered WDS Offers Higher Flexibility Than Centralized WDS Regret(m3/US$) Scenario no. Central Cluster 1 17.7 0.0 2 17.5 0.0 3 17.6 0.0 4 17.7 0.0 5 17.7 0.0 6 14.5 0.0 7 14.2 0.0 8 13.6 0.0 9 12.4 0.0 10 12.0 0.0 11 8.0 0.0 Maximum regret 17.7 0.0 Minimax regret Cluster
- 18. Flexible Urban Drainage Systems - Case Study ‘Hamburg-Wilhelmsburg’ Key Facts • residential area with 400 living units • total area 17 ha • 60% impervious area • High uncertainties - consequences climate change
- 19. Comparison Flexibility Provided by SUDS and Separate Sewer System Alt 2 SUDS Alt 1 Sewer
- 20. Adaptation of Sewer System During Operational Life Span Alt 1 SEWERS +90 +60 +30 Runoff 0 -30 EAC: EUR 246.011 Performance: 51 Performance UV Time
- 21. Adaptation of SUDS During Operational Life Span Alt 2 SUDS +90 +60 +30 Runoff 0 -30 EAC: EUR 32.694 Performance: 64 Performance UV Time
- 22. SUDS Provide Lowest Minimax Regret Range of change (R in m3) Scenario 1 Scenario 2 Scenario 3 Max Regret (Rr) Alt 1 SUDS 123 -25 31 0 Alt 2 Sewer 123 0 31 25 Performance (U95) Scenario 1 Scenario 2 Scenario 3 Max Regret (Ur95) Alt 2 SUDS 64 80 74 4 Alt 1 Sewer 51 83 78 13 Max Regret Effort of change (EAC in EUR) (EACr) Alt 2 SUDS 32,694 16,983 20,463 0 Alt 1 Sewer 246,011 20,109 20,900 213,317 SUDS provides a higher flexibility than sewers
- 23. It’s already happening – Emscher Region, Germany Both projects are implemented at the same time – how to consider the uncertainties for the design of the sewer system Planning to decouple areas from Building a new main sewer sewer system using SUDS – system – if decoupling is extend of decoupling is uncertain successful dimensions of the sewer can be reduced
- 24. Flexibility Options Used to Save Investment Costs Solution: • Need for flexibility option which can be implemented if decoupling is not achieving planned goal • Flexibility option – land reserved to build central retention basins required if decoupling goals are not achieved • The new sewer can be dimensioned smaller even if the decoupling is not implemented now
- 25. Take home message Move away from a deterministic, path- dependent approach to a more flexible & adaptive approach
- 26. Thank You
Notas do Editor
- Good MorningThank you for the possibility the present at the Asian Water WeekMy name is Jochen Eckart I am working as a senior research fellow at the Patel CollegeI would like to present some result of our research group on flexible design of UWSAs it is the work of our research group I would also like to acknowledge Seneshaw and Kala
- If we have a closer look how these uncertainties look like thisThis is for change in precipitation due to climate change the predictions of different models but other global change pressures will look similarWe see that range of uncertainties increases in future - first it is relatively small than the range increases
- We can describe this with an uncertainty envelopeConventional design strategy would be that we predict a certain development and build for this fixed trajectory Depending what we predicted the system could be over or under designedTo cope with the uncertainties the idea is that we design in stages, for short stage the uncertainties are much smaller. When we are at the end of this time step we make a new short term forecast based on the new information now available and with smaller uncertainty envelopAs result we track the future development much closer - in addition we are able to cover the whole uncertainty envelopWe call this a flexible design approach – Will explain what this means for UWS
- First some general definition and metrics for flexible design which applies to all urban water systems
- We have to identify which design approaches can provide us flexibility when we design urban water systemsDiscussed approaches are …While the definition and the metrics are generic for all urban water systemsThe flexibility options are specific for different urban water systemsWhat provides flexibility for urban drainage system do not necessarly provide flexibility for water distribution system This is why I now will have a closer look on flexibility options for water distribution system and urban drainage systems
- Looking in particular on clustered systems – different from one central system for whole city – city is divided in different clusters which can be developed independently from each other
- An important option which is discussed for the flexible design of WDS is the design ofclustered systemsDivide WDS in independent clusters which can be developed independently without affecting other clusters so that it is easier to follow different trajectoriesWe developed and tested concept of clustered and flexible systems for a case study of a town in Uganda as part of a WB projectThe town is quickly growing but growth rate and spatial growth patterns a uncertain – just look on the uncertainty range for future population and associated water demand
- This is the existing system and the emerging areaQuestion is how to identify the optimal size and shape of clusters
- We optimize the clusters for two conditionsFirst the minimize the distance between the resource centers and the water demand centers We identify all potential water sourcesThen we identify the center of the group of water sources
- Last steps to minimize the distance between the resource centers and the water demand centers Allocating each pixel two a resource balancing demand and supply using algorithm to minimize euclidean norm
- Second criteria is maximizing the homogeneity within the clusters – in particular minimizing elevation differences in order to reduce pressure differences and combine pixels with a comparable demandAs result we get optimal cluster boundaries
- Now we want to measure the flexibility provided by clustered design and compare it with conventional centralized system First we optimize the flexible design using a decision treeOn X axis we have different time stepsOn Y axis we have different future demands so each quadrant represent a certain time step and future demandUsing this decision tree we can create different future scenariosWe optimize the design so that it reduces the costs for all possible scenarios not for a single scenarioWe see in the picture how the clustered design can evolve over time for different scenariosSecond we measure the flexibility focusing on the metrics effort of change the costs (as the performance and the range of change should be the same for all systems)
- Compare both the clustered system and centralized system both optimized Focus on the costs per m3 additional capacityUsing minimax regret strategy - Looking on how much we would regret if certain scenario happensAs we do not know which scenario will happen we pick for each system design the highest regret - 17.7 for central and 0 for cluster – we save 24% to 34% of live cycle cost depending on scenarioCluster system has the lowest maximal regret and hence provide higher flexibility – as cluster can be developed independently and better can trace different future trajectories
- Now look which flexibility options are available for urban drainage systems
- The concept of flexible design for urban drainage systems is already applied in the Emscher regionPlanning to decouple 15% of area from sewer system in the next 7 years using SUDS – as it is necessary to deal with many stakeholders the extend of decoupling which can be really achieved is uncertain – in the picture you can see a new SUDS just before the decouplingAt the same time they are building a new main sewer system – if decoupling is successful it is possible to reduce the dimensions and costs of the new sewerThere is a dilema – It is possible to save costs if extend of decoupling would be known before the start of the construction of the new sewer system but extend of decoupling is uncertainAs both projects are implemented at the same time there is the danger that the sewer is dimensioned to small if the decoupling could not be achieved to the planned extendThe approving authority was not willing to give permit to smaller sewer system
- Solution: In order to dimension the new main sewer smaller, there is a need for flexibility option which can be implemented if decoupling is not successful in futureThe option is to reserve land for the construction of central retention basins which can guarantee the performance of the sewer if decoupling goals are not achieved in futureAs result the new sewer can be dimensioned smaller even if decoupling is not implemented nowThis solution was accepted by the approving agency
- Considering the uncertainties we face we should move away from conventional deterministic approaches and apply more flexible design approaches We can improve the cost and performance of urban water systems in the face of future uncertaintiesThe scientific approaches and frameworks required to do this are now available such as metrics to measure flexibility, approaches to optimize flexible design and identified flexibility optionsNow it si the time to apply the flexible design approach to case studies