Presented by Dr. Vishal K. Mehta, Invited speaker at the 6th International Public Policy and Management Conference held at the Indian Institute of Management, Bangalore, India.
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
Decision Support for Urban Environmental Planning
1. Decision Support for Urban Environmental Planning
Vishal K. Mehta, Ph.D.
Stockholm Environment Institute
vishal.mehta@sei-us.org
www.sei-international.org
www.sei-us.org
Dec 29, 2011
6th International Public Policy and Management Conference
IIM-Bangalore, India
Acknowledgements: M.Sekhar, D.Malghan, Arghyam
2. OUTLINE
I. Decision Support in Planning
Need for Decision Support
Examples
II. Barriers to effective decision Support
Knowledge Gaps
III. Ongoing Research
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. 1. Need for Decision Support in Urban Planning
Drivers
LARGE UNCERTAINTIES
Impact
Critical uncertainties
Uncertainty
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. 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. 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. I Examples of DST: Urban Air pollution (Meerfert, Denmark)
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. 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. I Examples of DST: Low-carbon development
https://www.eureapa.net/ EUREAPA
Consumption-based footprint of 45 countries, 57 sectors
13. I Examples of DST: Low-carbon development
https://eureapa.stage.isotoma.com/explore/
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. 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. I Examples of DST: Integrated Water Resources Management
Water Evaluation and Planning (WEAP) System
( www.weap21.org )
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. 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. I. Example: Water Supply - Lake Victoria towns
NABAJUZI watershed, Masaka
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. II Barriers to Effective Decision Support
Knowledge
Gaps
Communication Institutional
Barriers
Inclusion Financial
Technical
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. 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. 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. 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. 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
http://de21.digitalasia.chubu.ac.jp/floodmap/
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. III. Current Research Activity in Bangalore
Research Activities and Methods ..
1. Household Water consumption survey
Mental model for drivers of Quantity, source-mix
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. 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
http://www.seimapping.org/bump/index.php
http://seilinux.tccs.tufts.edu/~douglas/bump/index.php
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 !
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. WEAP Network Schematic
GIS
Tool
Model
Intuitive GIS-based graphical interface
Building
Scenari
o
Building
Graphs
&
Maps
34. Urban Water examples > Austin, Texas
Aim: Cost-effectiveness of conservation
and reuse strategies vs increasing water
treatment capacity
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.