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Decision Support for Urban Environmental Planning

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Decision Support for Urban Environmental Planning

  1. 1. Decision Support for Urban Environmental Planning Vishal K. Mehta, Ph.D. Stockholm Environment Institute Dec 29, 2011 6th International Public Policy and Management Conference IIM-Bangalore, India Acknowledgements: M.Sekhar, D.Malghan, Arghyam
  2. 2. OUTLINE I. Decision Support in Planning Need for Decision Support Examples II. Barriers to effective decision Support Knowledge Gaps III. Ongoing Research
  3. 3. 1. Need for Decision Support in Urban Planning THE CHALLENGE Transport Decisions made Security Energy across multiple sectors Comprehensive In multiple dimensions and Integrated Urban Planning by multiple actors/agencies Landuse Water Infrastructure
  4. 4. 1. Need for Decision Support in Urban Planning Drivers LARGE UNCERTAINTIES Impact Critical uncertainties Uncertainty
  5. 5. 1. Need for Decision Support in Urban Planning RAPID URBANIZATION -> CHANGING CITY Bangalore, India: • In 60 yrs, India’s urban Population Density Built-up area % Year (per sq population growth rate (m) km) urban footprint twice that of overall 1971 1.65 9,465 20% population 1981 2.92 7,990 26% • Urban poor ~2 5% of urban 1991 4.13 9,997 39% population 2001 5.1 11,545 69% • 20 m/100m lack safe 2011 ~9 na na water/sanitation Sources: Census; Iyer et al (2007)
  6. 6. I. Role of Scenario-based Risk assessment Scenario-Based Risk Assessment considers: System performance over all plausible conditions, moves away from traditional “design event” approach Explicitly recognizes that uncertainty (lack of quantified probabilities) exists in the process and must be addressed through scenario analysis Relies upon two way communication with stakeholders to select the level of risk they can tolerate with consideration of tradeoffs of multidimensional costs vs safety Results in Robust Decisions – adaptation strategies that are least likely to fail
  7. 7. I Examples of DST: Urban Air pollution (Meerfert, Denmark) Motivation: Larsen et al (1997) found that mortality from traffic-related air pollution as high as that from accidents Jensen et al., 2001. A Danish decision-support GIS tool for management of urban air quality and human exposures. Transportation Research Part D: Transport and Environment 6, 229-241.
  8. 8. I Examples of DST: Urban Air pollution (Meerfert, Denmark)
  9. 9. I Examples of DST: Urban Air pollution (Meerfert, Denmark) Air Quality Monitoring Forecasts Emissions inventory Elements of Air Pollution Information DSS Air Quality &Exposure to the public mapping Assessment of abatement measures Linking Environmental Quality to Public Health is key to public awareness & behavioral change, and should be an urban governance mandate
  10. 10. I Examples of DST: Broad St Cholera Outbreak, London 1854 Linking Environmental Quality to Public Health : the beginnings of epidemiology Dr. John Snow mapped Cholera outbreak to a single contaminated pump
  11. 11. I Examples of DST: Low-carbon development EUREAPA Consumption-based footprint of 45 countries, 57 sectors
  12. 12. I Examples of DST: Low-carbon development pathways
  13. 13. I Examples of DST: Low-carbon development
  14. 14. I. Examples of DST: Water Supply/Water Resources Management • Focus on increasing extraction and supply -> No comparative cost-benefit analysis of various options (scenarios) Installe -> Examples: d Present Projects Year Supply Bangalore Capacit y (MLD) (MLD) Chennai Delhi Arkavathy (TG Halli) 1933 149 60 UTILITY PERFORMANCE Cauvery Stage I 1974 135 135 • No city has 24/7 water supply Cauvery Stage II 1983 135 135 • Poor often pay more for water Cauvery Stage III 1993 270 300 • High leakage rates (20-60%) Cauvery Stage IV, 2002 270 270 Phase – I • Big cities: surface water supply from afar Total Supply 959 900 • Small towns: groundwater • Electricity is >30% of costs • Inability to recover costs
  15. 15. Will retail Will the customers Will recreation remain Will groundwater Will hydropower management hydrology How will practice compatible with future remain viable? change in response to shifts in the change? climate conservation? operations? Groundwater flow and market? Hydrology change? Demand side Recreational use surveys transport models Energy policy analysis with energy models with land Climate models with future projections sector forecast models use projections models 324643 Note: Image adapted with permission from the City of Portland, Oregon Water Bureau How much will new Will industrial Will this fish be listed Can we tap into a new Will agriculturecompete for shared residential discharges change? for protection? water supplies or become a potential supply? construction Regulatory and Habitat and species source? emerging lifecycle models with River hydraulic and Agricultural production models with increase demand? Regional economic technology analysis Ecosystem contaminant transport water rights database
  16. 16. I Examples of DST: Integrated Water Resources Management Water Evaluation and Planning (WEAP) System ( ) A generalized water resources software that provides flexible user- friendly interface to build custom applications A Decision Support Tool for Integrated, Comprehensive, Cross- Scale Water Management Planning Integrated : Hydrology with Priority-based Demand Allocation Comprehensive: Can include Equity, Environmental constraints, Financial Aspects, Water quality, Groundwater Cross-Scale: From a single house to a city to a riverbasin Ideal for ”What-if” scenario investigations for PLANNING and POLICY Analysis Management scenarios Climate change impacts
  17. 17. I. Example: Water Supply - Lake Victoria towns Lake Victoria region Masaka Bukoba Kisii Population 70,000 69,000 200,000 Streamflow 6.9 - - (106 m3) Water produced 2.35 0.9 (106 m3) Demand 80% 60% <50% coverage Operating Costs 496,000 USD 465,000 USD 726,000 USD Revenues 768,000 USD 470,000 USD 383,000 USD Key issues Waterworks Unaccounted Water Revenue<<Costs capacity, (UAW)~50%, high Very low population electricity costs coverage growth UAW~50%, high (1) To examine how climate, electriciity costs demography and infrastructure Scenarios Investigated impacts water utility performance Infrastructure Increased Increased capacity, Increased capacity reduced EAW capacity, reduced in 3 east African towns EAW Demand 2% population 4% population 4% population growth and growth growth (2) To develop water resources climate-related management tools that integrate demand model above aspects in a single platform Climate CCSM, Reduced rainfall None None
  18. 18. I. Example: Water Supply - Lake Victoria towns NABAJUZI watershed, Masaka
  19. 19. I. Example: Water Supply - Lake Victoria towns Results from Masaka, Uganda “…Hydrologic integration is necessary to evaluate the water availability and impacts side of the water supply problem. Collection of the hydroclimatic data needed in order to do the same, should be a priority for utilities and agencies in the LV region…”
  20. 20. II Barriers to Effective Decision Support Knowledge Gaps Communication Institutional Barriers Inclusion Financial Technical
  21. 21. II Barriers to Effective Decision Support Crucial Knowledge Gaps • Hydrology is rarely understood -> biophysical limits to water availability What is the natural water balance ? • Of both far-off source waters, of local water sources Ex: The resolution of groundwater monitoring (1 per 40-50 km2) is not enough for highly variable urban landscapes
  22. 22. II Barriers to Effective Decision Support Crucial Knowledge Gaps • Changed hydrology of urban environments -> (biophysical impacts) What is the impacted water balance? • E.g. Elevated, and contaminated water tables (Seoul, Mulbagal, Bangalore) BWSSB supplies 900 MLD into the city from surface water that is not local to Bangalore Sekhar, M. and Kumar, M.S.M. 2009. “Geo-hydrological studies along the Metro Rail Alignment in Bangalore
  23. 23. II Barriers to Effective Decision Support Crucial Knowledge Gaps • Extraction and Demand from each source remains unknown • Demand drivers for above across the social-economic spectrum • E.g. tankers, pvt borewells, local water bodies • How many wells? How much being pumped out? How much returning and where? • E.g. Chennai: 22-66% of water demand met by private wells
  24. 24. II Barriers to Effective Decision Support What can we do the in the meantime? AET Rain ~ 80% 100% Streamfl Surface watershed Example: Mulbagal ow ~ 10% Percolation (Rainfall Recharge) ~ 10% Net Groundwater Aquifer Groundwater discharge ~ 10% With Arghyam, IISc Aim: Impacts of population growth on GW depths; RWH, WWT, investment decisions
  25. 25. II Barriers to Effective Decision Support What else can we do in the meantime? Room for innovation? • Public participation in data collection (e.g. OpenStreetMaps) • Crowdsourcing (e.g. Thailand flood) • Sensor Networks
  26. 26. III. Current Research Activity in Bangalore Key research questions: 1. What is the city-wide pattern of (water) resource availability? 2. What is the geographic distribution of (water) consumption? 3. What are the drivers that explain the pattern of water consumption observed? 4. What projections can we make for water demand and supply, as well as feedbacks to sources into the future? 5. What are the links and feedbacks to the biophysical system
  27. 27. III. Current Research Activity in Bangalore Research Activities and Methods .. 1. Household Water consumption survey Mental model for drivers of Quantity, source-mix
  28. 28. III. Current Research Activity in Bangalore Research Activities and Methods… 2. Understanding the biophysical resource: groundwater models, mass balances Groundwater –surface water models, 3. Optimal monitoring density in urban mass balances environments Adaptive sampling, Bayesian data fusion
  29. 29. III. Current Research Activity in Bangalore Research Activities and Methods … 4. Formal participatory planning exercises 5. Urban Metabolic Mapping Geospatial web-based planning platform Geospatial web tools An open-source application for - Information Communication - Web-based scenario-planning
  30. 30. Summary 1. Decision Support Tools can be very valuable for comprehensive urban and regional planning 2. These tools already exist; or can be built with scientific input 3. Knowledge gaps limit the full potential of DST to be achieved – but progress can be made in parallel 4. Intensive data-driven approaches will be necessary to fill knowledge gaps 5. Urgent need for • Intensive environmental quality monitoring • Linkage between environmental quality and public health • Effective public participation and communication • Formal scenario-based planning for the future THANK YOU !
  31. 31. Extras
  32. 32. I. Some WEAP examples Water Systems Planning Small Reservoirs Project, Ghana/Brazil California Water Plan, California, USA Guadiana River, Spain Transboundary Water Policy Okavango River, Angola/Namibia/Botswana Lower Rio Grande, USA/Mexico Mekong River, Thailand/Cambodia/Vietnam/Laos Jordan River, Syria/Israel/Jordan Climate Change Studies Sacramento and San Joaquin River Basins, California, USA Massachusetts Water Resources Authority, Massachusetts, USA Yemen Second National Communication Mali Second National Communication Ecological Flows Connecticut Department of Environmental Protection Town of Scituate, Massachusetts, USA Water Utility DSS Application Portland, Oregon; Austin, Texas; Philadelphia, Pennsylvania Towns in East Africa; Mulbagal, India.
  33. 33. WEAP Network Schematic GIS Tool Model Intuitive GIS-based graphical interface Building Scenari o Building Graphs & Maps
  34. 34. Urban Water examples > Austin, Texas Aim: Cost-effectiveness of conservation and reuse strategies vs increasing water treatment capacity
  35. 35. Urban water examples>Portland, Oregon Aim: conjuctive use of SW and GW vs. building new reservoir

Editor's Notes

  • Urban and regional planners and decision makers have to make decisions across multiple sectors, in many dimensions, and through a multitude of actors and agencies. At the same time
  • There are large uncertainties regarding the possible growth of a city – some of which are critical uncertainties, those uncertainties that also have a big impact.
  • Add to this the fact that India is rapidly urbanizing; take Bangalore as an example; it grew by about 1 million per people upto 2001; but in the last decade by almost 4 million.
  • It is informative to take a look at the elements of this DSS developed in Denmark.
  • My next example is from the world of GHG emissions, in particular with an on-line Decision Support System built by SEI called EUREAPA. I am going to quickly go through a few slides in which I focus on India, what our CO2 current footprint is, where it comes from, and how I can build an alternate policy scenario online to assess what
  • You can build scenarios for the future online: in this case I’m building one where coal powerplants are reduced by half and replaced by renewables. So for the purpose of this demonstration, I’m asking this DSS to tell me, what might be the reduction in GHG emissions if I replace half of current coal-fired electricity production by renewables
  • This graphic shows how that particular scenario reduces our Carbon footprint by 100 ktons of CO2 eq. How does this relate to urban context? Well, most of India’s footprint comes from urban consumption – and not only is our urban population growing, but consumption lifestyles are changing rapidly.
  • Now what does a comprehensive DST look like when adopting an Integrated Water Resources Management approach? As this slide shows, such a systems approach tackles several dimensions of water: including water availability driven by climate-driven hydrology, that is integrated with multi-sectoral demands for water that are met through infrastructure. Using scenarios, it moves away from utility-centric augmentation-only affects, and is able to comparatively assess plans and strategies for the future.
  • One such DSS is the Water Evaluation and Planning System (WEAP) developed by SEI over the last 20 years. It is a generalized software platform that lets you build specific models – you can buil a model for a house, up to a riverbasin. Two key aspects of it are that it has in-built capacity to integrate climate-driven simulation of the water balance, to allocation of water demands; and ii, any number of scenarios can be created in it.
  • In only Masaka, were we able to link the hydrology of the water source (the wetland) to the extraction and supply of water by the utility.
  • So far I’ve described -with examples- how various Decision Support Tools can be very useful in urban and regional planning. Now there are many barriers to effective and comprehensive decision making: from knowledge gaps to institutional inertia, technical, financial, lack of public participation. I will focus on knowledge gaps in the urban water sector, based on some work we are doing that is Bangalore focused.
  • First, the natural system is rarely understood well. In this case the hydrology or the water balance, which determines the biophysical limits to water availability.
  • I will focus on knowledge gaps in the urban water sector, based on some work we are doing that is Bangalore focused.
  • I will focus on knowledge gaps in the urban water sector, based on some work we are doing that is Bangalore focused.
  • What can we do when so much of the puzzle is missing. Let me give you an example from Mulbagal, supported by Arghyam and IISc. As part of Arghyam’s IUWM, some 400 wells were sampled by Dr. Sekhar’s team, which led to a very good understanding of the groundwater regime. However at the time, no similar sampling of the surface water balance was performed. Dr. Sekhar and I anyway used our expereince from other similar sites to build an integrated surface water and groundwater model in WEAP.
  • Given the current state of ICT, there is plenty of opportunity for innovation. For example, the public can become active participants in filling in knowledge gaps. Take for example the impressive Open Street Maps project: in which street level mapping for the entire world has been generated that is freely accessible and usable by volunteers around the world.
  • I’m going to end with a few slides on what we are currently doing on urban sustainability in Bangalore
  • Here is a screenshot of the WEAP GUI for a model for Mulbagal that we created with Arghyam and Dr. Sekhar. Building the model consists of dragging and dropping objects onto the screen – objects like catchments to simulated the hydrology, groundwater for recharge and extraction, and demand nodes for simulating various sectoral demands. Very complex models have been built – for example for the mountains in Sierra Nevada, I huilt a model which has 325 catchments and 25 reservoirs, 33 hydropowerplants plus municipal supply, but I’m going to show you only a few urban network examples, which tend to be simpler.