Modelling cities and water infrastructure dynamics

Transcription

1 infrastructure dynamics ice proceedings Proceedings of the Institution of Civil Engineers Engineering Sustainability 166 October 2013 Issue ES5 Pages Paper Received 31/10/2012 Accepted 25/04/2013 Keywords: infrastructure planning/sewers & drains/town and city planning ICE Publishing: All rights reserved Christian Urich PhD student, Unit of Environmental Engineering, University of Innsbruck, Innsbruck, Austria Peter M. Bach PhD student, Monash Water for Liveability, Civil Engineering Department, Monash University, Melbourne, Australia Robert Sitzenfrei Research Fellow, Unit of Environmental Engineering, University of Innsbruck, Innsbruck, Austria Manfred Kleidorfer Assistant Professor, Unit of Environmental Engineering, University of Innsbruck, Innsbruck, Austria David T. McCarthy Senior Lecturer, Monash Water for Liveability, Civil Engineering Department, Monash University, Melbourne, Australia Ana Deletic Professor, Monash Water for Liveability, Civil Engineering Department, Monash University, Melbourne, Australia Wolfgang Rauch Professor, Unit of Environmental Engineering, University of Innsbruck, Innsbruck, Austria Urban water systems are under increasing pressure due to the impact of climate change, population growth and urbanisation. In order to make our urban water systems more adaptable to these challenges new water management strategies must be developed. During the last 20 years many new decentralised technologies have emerged and their integration with existing centralised technologies, in particular, creates complex interactions. To deepen our understanding of these interactions at the city scale and to identify possible transition strategies the development of a potential strategic planning tool is thus proposed. This paper focuses on the evolution of the urban environment and water system, in space and time, in the tool. The dynamics of the model is shown for alpine cities. Numerous test cases are stochastically generated by means of the virtual infrastructure benchmarking approach and evolved over time. Different scenarios for the development of the urban environment and water system are statistically evaluated. An increase of rainfall intensities of more than 10% was identified as critical for the performance of the combined sewer systems investigated. By using DAnCE4Water such critical points in the time line of system performance can be identified. 1. Introduction Urban water systems are under increasing pressure due to the impact of climate and urban change. Conventional centralised water infrastructures, especially network systems, which have a long life span, are frequently classified as highly unsuited to address future challenges (Ashley et al., 2005). In order to adjust our urban water systems to future challenges to mitigate future negative impact, development of new water management strategies is vital. During the last 20 years many new decentralised technologies in stormwater harvesting and management have emerged. These technologies known as low-impact development in the USA or water sensitive urban design in Australia integrate stormwater management into urban design. Additionally, many new technologies for water saving and reuse have been developed. But, as noted by Wong and Brown (2009), not only is a technical overhaul of conventional urban water systems required but an overhaul of the existing socio-political environment is also necessary to enable sustainable and water-sensitive decision-making and behaviour. On a small scale, numerous projects have been successfully realised, but the transition from a conventional (centralised) urban water system to an adaptive and sustainable system at the city scale is still unknown. The mix of existing central and novel decentralised technologies creates complex interactions within the urban water system. To deepen our understanding of such interactions at a city scale and to identify possible transition strategies, new analysis tools are required. The dynamic adaptation for enabling city evolution for water is a strategic planning tool for urban planners, government, watershed managers and local councils (Rauch et al., 2012), developed in the EU framework programme 7 Prepared: enabling change by the University of Innsbruck and Monash University in collaboration with Melbourne Water. The model is based on the concept of virtual infrastructure benchmarking (VIBe) (Sitzenfrei et al., 2010a; 2010b) that enables the evaluation of different technologies and strategies within a dynamic urban environment (in both space and time) by means of a stochastic modelling approach. The model simulates an entire urban water system (see Figure 1) through three modules, linked to one another by means of complex interactions. This paper focuses on the design of the dynamics and interactions in the urban development module and the biophysical module of 301

2 User Reporting and presentation Scenario input Dance Cycle Scenario Biophysical module Urban form Water infrastructure System performance Water system performance Conductor Information storage & management Urban envrionment Constellations facets Societal transition module Urban development module Focus of this paper Figure 1. Overview of the Dance4Water model Dance4Water. The social transition module is therefore not discussed here. This paper addresses the question of how we can connect newly developed areas and adapt the existing water infrastructure. It also presents the current state of the model development. The dynamics of the module are illustrated using test cases based on virtual case studies generated with a parameter set for an alpine city. This includes the development of the urban environment and combined sewer system. An adaption strategy is tested in which new or redeveloped buildings are required to install on-site infiltration systems. The aim is to investigate the effects of different renewal rates (RR) to compensate for the impacts of different climate change and urban development scenarios. In contrast to similar previous studies (e.g. Kleidorfer et al., 2009), the new approach proposed in this paper considers the temporal dynamics of adaptation strategies. 2. Materials and methods 2.1 Dance4Water model Dance4Water is designed as a software tool that enables a wide variety of stakeholders to explore possible future scenarios and consequences of policies and action strategies. Therefore, what if scenarios for the urban water system can be investigated in a dynamically evolving environment, which take into account the interactions between urban water infrastructure, the urban environment and the societal system in space and time. Users can thus identify sustainable and reliable adaptation strategies for the urban water system. Dance4Water has three modules (Figure 1) that can be run independently of one another. The central unit of the tool is the conductor. The conductor drives the entire simulation by calling individual modules and storing, managing and providing them with the required data. Within the scenario input, the modules required are selected and external drivers (e.g. climate change, urban development or master plan for infrastructural development) defined. To run a simulation the conductor executes each required module sequentially. Every module evolves its subsystem one time step into the future. Initially, the societal transition module (STM) projects the preferences of different decentralised and centralised technologies and the societal needs of the population (de Haan et al., 2012). Subsequently, the urban development module (UDM) evolves the spatially explicit urban environment by 1 year. Finally, the biophysical module (BPM) is executed. It adapts the existing water infrastructure to connect newly built-up areas. The decision on which technology is implemented is based on the preferred technologies suggested by the STM (Bach et al., 2012). A more detailed description of Dance4Water is given in Rauch et al. (2012). After one Dance cycle is finished (the execution of all required modules for the current time step) the results for that time period can be reviewed. To evolve the urban water system 20 years in the future the Dance cycle is repeatedly executed at a yearly time step. For this paper a simulation is set up using the UDM and BPM to show the interdependencies of the modules using a simple scenario. 2.2 UDM The UDM spatially translates the population projections and the master plan for the urban development (e.g. growth 302

3 corridors) in time steps of 1 year. The well-known and widely applied UrbanSim package (Waddell, 2002) is integrated in Dance4Water and enables a detailed projection of the urban environment, including not only future land use and population projections but also such features as the number of households or housing types. UrbanSim is a software tool that is designed to reflect the interdependencies in dynamic urban systems, focusing on the real estate market and the transportation system. The model reflects the key decision-makers households, businesses and developers and the choices impacting on urban development (UrbanSim Project, 2011). The detailed model enables a linkage with the STM and BPM and, hence, analyses of the multiple benefits of water infrastructure that is, the change in land value following the installation of a new stormwater treatment system (e.g. a wetland), which provides an additional amenity benefit and is within walking distance (Joke, 2000). Currently, a simple UrbanSim model has been integrated into Dance4Water. It is derived from the Eugene Springfield model delivered with UrbanSim. The model is based on a grid representation (200 m m) of the urban environment. Based on future projections of households, the model evolves the urban environment by means of sub-models for household transition and the household location choice. To set up the UrbanSim model a detailed description of the urban environment is required. This includes information on households (e.g. their size, number of children and income), buildings (e.g. the number of residential units and available commercial area) and jobs (e.g. location). Based on available land use and population maps as the input, the missing data (e.g. household size or income) are stochastically sampled from distributions extracted from real-world case studies. 2.3 BPM The BPM has three sub-components: urban form generator, water infrastructure generator and performance assessment (see Figure 1). To place new decentralised water infrastructure and assess its performance, detailed information on the urban environment is required. This includes the percentage of the impervious areas split up in their origin (such as the roof or the street), the available space in backyards for an infiltration system or rainwater harvesting tanks and much more. The necessary parameters are calculated for typical land uses like low, medium andhighresidentialandmixedtypes,asshownbybachet al. (2012). The so-called building blocks can range in size from 200 m m up to 1000 m m. Based on the land use and population map results from the UDM the BPM s urban form generator builds a map of building blocks, each containing the necessary information for designing suitable technologies. Based on these characteristics the water infrastructure generator then identifies feasible locations within the city and places on them a corresponding decentralised water infrastructure. At present, on-site stormwater systems for residential buildings and combined sewer system facilities (combined sewer overflows and pipes) are included in the BPM. Next, the centralised water infrastructure is linked with the building blocks and adapted. To connect newly populated building blocks to the existing infrastructure the agent-based approach for generating the combined sewer system presented in Urich et al. (2010) has been enhanced. The algorithm is based on an agent-based modelling approach where inlets placed in the centre of the grid cells (in this case study the centres of newly populated building blocks) become starting points for agents that try to find an existing combined sewer system or wastewater treatment plant. The path an agent takes is driven by gravity and by the attraction of existing conduits. This process is repeated over several generations of agents and produces a possible sewer network layout extracted out of the agents paths. The enhanced approach enables the newly populated building blocks to be connected by placing new agents that connect themselves to existing conduits at any time. After the layout generation of the combined sewer network, combined sewer overflows and storage units are placed and the newly added elements are designed according to Austrian national guidelines (ÖWAV-RB11, 1982; ÖWAV-RB19, 1987). On-site stormwater infiltration systems are designed according to German guidelines (German Association for Water, Wastewater and Waste, 2006). The performance of the generated combined sewer system is assessed using the stormwater management model (SWMM) software package (Rossman, 2010). SWMM input files are automatically generated and also include decentralised stormwater technologies. For this study, two hydraulic performance indicators were evaluated, a detailed description of which can be found in Möderl (2009). PI CSO assesses the combined sewer overflow (CSO) efficiency and is calculated as ratio between volume of surface runoff (V R ) (without stormwater) and volume of surface runoff treated at the wastewater treatment plant (WWTP) (V WWTP ) 1. PI CSO ~ V WWTP ð{ Þ½01 j Š V R PI FLOOD is a performance indicator for surface flooding. It is calculated as unity minus the volume of the ponded water (V P ) over V R 2. PI FLOOD ~1{ V P ð{ Þ½01 j Š V R 303

4 2.4 Scenarios To show the dynamics of UDM and the BPM, VIBe cities are generated with the characteristics of alpine cities (Sitzenfrei et al., 2010a) and subsequently evolved 20 years into the future. VIBe cities are algorithmically generated cities, including features such as topography, land use and population. The generation process is based on a certain parameter set or parameter ranges that statically mimic a real-world city. For example, the city of Innsbruck is located in a U-shaped valley with a population of people. For urban development, population and demographic projections of an alpine city are used (Hanika, 2010). The impact of implementing an on-site stormwater infiltration system is evaluated as a possible climate change adaptation strategy. For all newly built or redeveloped buildings in areas with discontinuous urban fabric, an infiltration system targeting roof runoff is installed on the lot. The aim is to test different RRs to compensate for the effects of climate change and urban growth. In the evaluation of system performance, a decrease of 5% in the described performance indicators is tolerated in the period 2010 to As a climate change scenario, a shift in rainfall intensities is considered. Depending on the duration, return period and anticipated technical lifetime of drainage systems, Arnbjerg- Nielsen (2012) suggests an intensity increase of 10 50%. In that range, four climate change scenarios are investigated. A linear function of time is assumed to model the increase in rainfall intensities from 2010 to Four end-points were chosen for this linear approximation: 100% (i.e. rainfall intensities do not change with time), 110, 130 and 150% (i.e. the rainfall intensity in 2030 was 50% higher than that in 2010). The integration of more sophisticated climate change projections is possible when such data are available; however, this would not change the approach described. In our case, a design storm Euler Type II (described by de Toffol (2006)) for an alpine region, with a duration of 2 h and a return period of 5 years is used. Building stock RRs of 0, 1, 3 and 5% are investigated. As each RR is combined with each climate change scenario, a total of 16 scenarios were investigated. For each scenario 100 VIBe test cases were created and evolved 20 years into the future, resulting in 1600 simulations. 3. Results Figure 2 shows the development of the impervious area and the combined sewer network between 2010 and 2030 with an assumed building stock RR of 3% for a single simulation. It Population 2010 Population 2030 Impervious 2010 Impervious 2030 Flooding Pipe diameter (mm) Impervious (%) Population > Figure 2. Combined sewer system 2010 and 2030 with impervious area and flooded nodes. Diameters are in mm, imperviousness values are in proportion of catchment area and population is per building block 304

5 can be seen how newly populated building blocks are automatically connected to the existing combined sewer network. The connected impervious area also decreases due to implementation of on-site stormwater infiltration systems. The results show that the newly connected areas cause local flooding (shown as stars) in the existing combined sewer network and therefore additional measures are required to increase the capacity of the combined sewer network. In Figure 3 (left) the evolution of the population, total impervious area and the directly connected impervious area for a single case study is shown. Figure 3 (right) illustrates the change in PI CSO and PI FLOOD for the base scenario (0% RR) and 3% RR with a climate change factor (CF) of 1?5. As shown in the left figure, urban development leads to an increase in the impervious area of 8%. It can be observed that an RR of 3% cannot sufficiently mitigate the increase in rainfall intensity even though the impervious area connected to the combined sewer system could be significantly reduced (17%). For the statistical analysis in Figure 4 the performance indicators in 2030 for all investigated test cases are evaluated and compared against four CFs: 1?0, 1?1, 1?3, 1?5 (increase of the rainfall intensity). The figure shows the effect of the implementation of on-site stormwater infiltration systems. Regarding the median values, it can be seen that the effect is more significant for PI CSO than for PI FLOOD. For PI CSO an increase of a median of 10% is observed for an RR of 5%, whereas for PI FLOOD an increase of only 5% is observed. For a CF of 1?0 and a reduction of the impervious area of 8% (due to the RR) PI CSO is increased for the existing system. A comparison of the boxes of the two indicators investigated shows that the range for PI CSO is much smaller than the range for PI FLOOD. For the most extreme case (RR 5%; CF 1?3) the 25% and 75% percentile range is 6% (3?5% on average), for PI FLOOD 13% for the worst case (RR 0%; CF 1?3) and 8% on average. This indicates that the performance indicator for flooding of the test cases is more sensitive to the impacts of climate change and urban development than the performance indicator for the CSO efficiency. Figure 5 shows the median values of changes of PI CSO and PI FLOOD in 2030 compared to the base year in 2010 for all test cases investigated. The left figure shows a bilinear decline of the performance for PI CSO. For a CF of 1?3 the rate of decrease changes for all RRs. This is because the CSO structures have reached their capacity (overflow or throttle). Before the upstream junctions are flooded, additional storage volume is activated in the upstream pipes due to the usual overdesign. As the figure on the right shows, the change in CSO performance does not immediately affect the performance of PI FLOOD after a CSO reaches its capacity. In Figure 5 (right) an RR of 3% and 5% shows an abrupt change in the rate of decrease of PI FLOOD after the additional storage volume in the upstream pipes is activated (CF 5 1?1). A CF higher than 1?1 is therefore identified as critical for the performance for the median of all test cases. 4. Conclusion To enable a transition to a more adaptable urban water system, new strategic planning tools that consider the complex interaction of the urban water infrastructure with the urban environment and societal system are required. This has prompted the development of Dance4Water. In this paper the integration of the UDM and the BPM is shown. To demonstrate the capability of this model, an adaptation strategy is tested in which new or redeveloped buildings are required to install on-site infiltration systems. The aim was to 10 Change to base year 2010: % 0 30 Population PI CSO PI FLOOD Impervious area PI CSO (RR 3%) Connected impervious areas (RR 3%) PI FLOOD (RR 3%) Year Year Figure 3. Storyline base scenario and scenario with 3% renewal rate (201030) for a climate change factor of 1?5 305

6 20 PI CSO in year 2030 Change to base year 2010: % Climate change factor (CF) 1. 0 CF 1. 1 CF 1. 3 CF 1. 5 PI FLOOD in year 2030 Change to base year 2010: % Climate change factor (CF) 1. 0 CF 1. 1 CF 1. 3 CF Renewal rate: % Figure 4. Change of performance indicator PI CSO and PI FLOOD in 2030 compared to the base year The box plots show the median, 25 and 75% percentile, the bars show the 1?5 interquartile range and the notches show the 95% confidence interval of the median. The dotted line connects the median values of the simulations. The change in the performance when compared to the base year 2010 (y-axis) for various renewal rates (RR) (x-axis) is shown investigate the effects of different RRs to compensate for the impacts of different climate change and urban development scenarios. A total of 1600 test cases have been evolved into the future and statistically evaluated. The coupling of the UDM with the BPM was shown to be exemplary for one of the test cases. Therefore, new developed areas were connected to the existing combined sewer network. The storyline of that case study showed that the connected impervious area could be Change to base year 2010: % PI CSO in PI FLOOD in RR 5% RR 3% RR 1% RR 0% Climate change factor Climate change factor 5 Figure 5. Median values of the change of performance indicator PI CSO and PI FLOOD in 2030 compared to the base year

7 reduced (e.g. 18% for an RR of 3%) with the adaptation strategy investigated. It was also shown that, as expected, the connection between the newly connected conduits and the existing combined sewer network is critical for flooding. The statistical analysis of all test cases showed that the investigated scenarios for climate change and land use change cannot solely be compensated for by the installation of on-site stormwater infiltration systems. The analysis indicates that the flooding performance of the test cases is more sensitive to the impacts of climate change and urban development than the performance indicator describing the CSO efficiency. A CF higher than 1.1 (i.e., an increase of rainfall intensities of more than 10%) was identified as being critical for the combined sewer system. Dance4Water showed that, for the investigated scenarios, additional measures are required to preserve the status quo in terms of hydraulic and water quality performance. As the paper shows, the tool in the current state of development enables the exploration of possible futures and consequences of policies and strategic action for the urban water system in light of the interactions between urban water infrastructures. Acknowledgements This research is part of a project that is funded by the EU framework programme 7 Prepared: enabling change. This research is also partly funded by the Australian Government s Department of Industry Innovation, Science and Research. REFERENCES Arnbjerg-Nielsen K (2012) Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design. Urban Water Journal 9(2): 57 65, dx.doi.org/ / x Ashley RM, Balmforth DJ, Saul AJ and Blanskby J (2005) Flooding in the future: predicting climate change, risks and responses in urban areas. Water Science & Technology 52(5): Bach PM, McCarthy DT, Urich C et al. (2012) Dance4Water s BPM: A planning algorithm for decentralised water management options. In Proceedings of the Ninth International Conference on Urban Drainage Modelling ((Prodanović D and Plavšić J (eds)). Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia. de Haan J, Ferguson B, Brown R and Deletic A (2012) Exploring scenarios for urban water systems using a socio-technical model. In Proceedings of the Ninth International Conference on Urban Drainage Modelling (Prodanović D and Plavšić J (eds)). Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia. de Toffol S (2006) Sewer System Performance Assessment an Indicator Based Methodology. PhD thesis. University of Innsbruck, Innsbruck, Austria. See umwelttechnik/teaching/phd/diss_detoffol.pdf (accessed 11/ 09/2013). German Association for Water, Wastewater and Waste (2006) DWA-A117E Dimensioning of Stormwater Holding Facilities. Deutsche Vereinigung für Wasserwirtschaft, DWA, Hennef, Germany. Hanika MA (2010) ÖROK-Prognosen Kleinräumige Bevölkerungsprognose für Österreich mit Ausblick bis 2050 (ÖROK-Prognosen, Austria) (in German). Joke L (2000) The value of trees, water and open space as reflected by house prices in the Netherlands. Landscape and Urban Planning 48(3 4): , /S (00) Kleidorfer M, Möderl M, Sitzenfrei R, Urich C and Rauch W (2009) A case independent approach on the impact of climate change effects on combined sewer system performance. Water Science & Technology 60(6): , doi.org/ /wst Möderl M (2009) Modelltechnische Analyse von Netzwerksystemen der Siedlungswasserwirtschaft. Innsbruck University Press, Innsbruck, Austria (in German). ÖWAV-RB11 (1982) Richtlinie für die abwassertechnische Berechnung von Schmutz-, Regen- und Mischwasserkanälen. Österreichisches Normungsinstitut, Vienna, Austria (in German). ÖWAV-RB19 (1987) Richtlinie für die Bemessung und Gestaltung von Regenentlastungen in Mischwasserkanälen. Österreichisches Normungsinstitut, Vienna, Austria (in German). Rauch W, Bach PM, Brown R et al. (2012) Modelling transitions in urban drainage management. Proceedings of the Ninth International Conference on Urban Drainage Modelling, Belgrade, Serbia. Rossman LA (2010) Storm Water Management Model User s Manual Version 5.0. Water Supply and Water Resources Division, National Risk Management Research Laboratory, Cincinnati, OH, USA. Sitzenfrei R, Fach S, Kinzel H and Rauch W (2010a) A multilayer cellular automata approach for algorithmic generation of virtual case studies: VIBe. Water Science & Technology 61(1): 37 45, Sitzenfrei R, Fach S, Kleidorfer M, Urich C and Rauch W (2010b) Dynamic virtual infrastructure benchmarking: DynaVIBe. Water Science & Technology: Water Supply 10(4): , UrbanSim Project (2011) Open Platform for Urban Simulation and UrbanSim Version 4.3: Users Guide and Reference Manual. University of California, Berkeley, CA, USA and University of Washington, Seattle, WA, USA. Urich C, Sitzenfrei R, Möderl M and Rauch W (2010) An 307

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