Session1 Summary&Presentations

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THE UNIVERSITY OF HONG KONG
Centre for Water Technology and Policy
Inter-disciplinary Forum
“Climate-resilient Urban Water Systems: New Technologies and Policy Challenges”
Report on Plenary Session 1: Technology-Policy Interface
Date: 29 May 2018
Time: 9:15 – 10:45 a.m.
Plenary Session 1: Technology-Policy Interface
Chaired by:
Professor Xiao-yan Li
Co-Director, Centre for Water Technology and Policy
The University of Hong Kong
“Overcoming the Barriers to a New Paradigm for Urban Water Infrastructure”
Professor David Sedlak
Co-Director of Berkeley Water Center
UC Berkeley
“Decision Support Tools for Assessing the Climate Change Impacts on Design and
Management of Urban Water Systems”
Professor Van-Thanh-Van Nguyen
Chair of Department of Civil Engineering and Applied Mechanics
McGill University
“Technology-Policy Interface Issues in Hong Kong: Legacies and Impacts”
Dr. Frederick Lee
Executive Director, Centre for Water Technology and Policy
The University of Hong Kong
“Overcoming the Barriers to a New Paradigm for Urban Water Infrastructure”
by Professor David Sedlak
Co-Director of Berkeley Water Center,
UC Berkeley
Background: Fourth Water Revolution (Water 4.0)
- Water can be reused for many times before releasing back to the environment.
- In general decision-makers agree that the idea and technology of water recycling and
water re-use are good, but new water technology is not always adopted immediately.
- Adoption of new water technology usually is not impeded by technological availability
but policy.
- It is therefore essential for researchers to collaborate with social scientists to overcome
impediments in policy decision-making.
Adventure 1: A regional vision
- Water infrastructure planning usually involves multiple decisions makers, such as local
leaders from the mayor’s office, legislators, and other city managers.
- To help them see the challenges and risks pertaining to water security and water
sustainability, it is important to outline and visualize the (expected) changes in longerterm.
- For example, the Berkeley Water Center outlined the possible scenarios or portfolio of
water diversification for San Francisco Bay Area in 30 years and showed how San
Francisco Bay Area can live up to the challenges of population growth by diversifying
water supply portfolios (changing/ increasing use of multiple sources of water, like
imported water, exploiting local source, water reuse and desalination).
- The longer-term, regional perspective can help align the vision of planners and
government officials with regard to water resources planning.
- It is also important to find out what the most “economically feasible” solution is:
o From the Bay Area regional partnerships experience regarding “portable water
re-use”, researchers can help steer decision-making by showing that new water
technology would be feasible under two conditions. First, regional
collaborations have to be in place. Second, decision-makers have to start
experimenting with the least expensive solution.
o Driving technology diffusion is the most challenging part. Technology diffusion
refers to a gradual adoption of new technology, as in the case of switching from
the use of fossil fuels to the use of renewable energy sources.
o To encourage technology diffusion, the first challenge is to create an
“innovation ecosystem”, which comprises different stakeholders like
consultants, policy makers, enterprises, intermediaries, the financial sector, and
academia. Researchers have to bring together these people.
o The second challenge is about identifying the early adopters. It takes time to
leap from a pilot project to second attempts and eventually future generation
technology. Successful pilot project can play an important role in demonstrating
the technology at the local scale and encouraging changes.
o It is important for HKU’s water center to convene an innovation ecosystem.
Adventure 2: Overcoming the yuck factor
- Yuck factor refers to the negative stigma of portable water re-use that policy makers
and the public have.
- Researchers have to find out how to make people accept and understand the new
technology and promote “technology adoption”.
- Technology adoption and technology transition can be facilitated by use of three
dimensions of legitimacy
o Type 1: Pragmatic legitimatization (This project serves my own interests)
 Talk about the community’s need and direct benefits
o Type 2: Moral (This innovation meets external quality standards. I trust the
institution responsible for my safety)
 Public acceptance for a technological solution is higher when the
technological solution is transparent.
 Public trust is also higher when having an external, independent review
panel in place.
o Type 3: Cognitive (This innovation meshes with my cultural belief system and
daily life experience)
- We made the different dimensions of legitimacy regarding portable water re-use
problem evident to the community by convening a meeting, writing of a report1, and
disseminating it to the community. Policy change then began by spreading the word of
mouth beyond the academic circle.
o The workshop and reports helped municipalities and utilities that are
considering potable reuse develop their approach. They also helped advance the
efforts of those who are ready to implement projects.
o The idea of “portable water re-use” is gaining popularity with an increasing
number of press appearances.
o When key stakeholders, including the general public, decision-makers and
politicians, started to register the idea of “portable water re-use”, changes in
policy decision-making are more likely.
Adventure 3: Multi-benefit projects
- Most new water technology solutions are more expensive than conventional water
treatment plants.
- Researchers can facilitate technology transition by encouraging and facilitating project
evaluation beyond the financial aspect. Other considerations, like reduced energy use,
landscaping benefits, and reduced discharge of nutrients, should be taken into account
in policy decision-making.
1
“Mainstreaming Portable Water Reuse in the United States” Available at:
https://www.epa.gov/sites/production/files/201804/documents/mainstreaming_potable_water_reuse_april_2018_final_for_web.pdf
-
-
Multicriteria decision analysis is necessary to help compare and contrast the benefits of
different complex water solutions (e.g. wetland levee, recycling irrigation water,
traditional approaches).
The multicriteria decision analysis model can help decision makers visualize the
cumulative/ overall benefits of new water technologies, so that they are more
comfortable when making plans for the next phase water technology, despite the high
capital costs.
Concluding remarks
- Researchers must couple engineering and social sciences research with genuine
engagement with decision makers.
- A regional vision of a water infrastructure transition informed by research can catalyze
investment in new approaches.
- The creation of legitimacy of new water technologies requires effective communication,
transparency, trust building and technological excellence.
- Inter-disciplinary collaboration at the technology-policy interface is essential.
“Decision Support Tools for Assessing the Climate Change Impacts on Design and
Management of Urban Water Systems”
By Professor Van-Thanh-Van Nguyen
Chair of Department of Civil Engineering and Applied Mechanics,
McGill University
An introduction of the Center for Water Resources Management of McGill University
- Formed by members from the Faculty of Agricultural and Environmental Sciences,
Faculty of Engineering and Faculty of Science.
- Major research areas are food security and water security.
Key challenges and issues:
- Population growth & urbanization
- Too much or too less water (uneven and extreme spatial and temporal distributions of
water)
- Canada’s main climate change threats: flooding and storms
How to develop climate change scenarios for impact studies in hydrology
- Conventional climate change modellings show scenarios at a macro scale. Spatially
speaking, scientists from IPCC developed models to forecast climate change risks at
the global scale (>1000 km2) over decades.
- Such a macro scale is not effective to inform decision making at the local scale.
- Therefore, resolving the spatial and temporal scale issue is important, so that local
decision makers can see what the likely climate change impacts are, such as maximum
rainfalls, at the urban or local scale.
- Solution/ Challenge: Downscaling methods
- The new model should also address the “time scale” issues (local infrastructures have
to adapt to/ cope with fluctuations at shorter time intervals).
- It is also important to learn about geography, climate change etc., by bringing in
researchers in these fields, before building an effective model.
Key research outputs
- The Floodnet-Nserc Canadian Strategic Network (2014-2019) was established. New
guidelines and procedures for updating intensity, duration and frequency (IDF) curves
were developed.
- Research outputs on modelling of extreme rainfall processes in sub-daily extreme
precipitation were also used to update the extreme rainfall map at the local scale in
Canada.
- A software (SMExRain: A decision-support tool for estimating extreme design rainfalls)
was developed to consolidate the weather extreme data and to help visualize descriptive
and predictive scenarios. This software is also capable of making projection of climate
change (to see smaller regional impacts of climate change) with and without empirical
data.
“Technology-Policy Interface Issues in Hong Kong: Legacies and Impacts”
By Dr. Frederick Lee
Executive Director, Centre for Water Technology and Policy,
The University of Hong Kong
Background
- Hong Kong’s water supply came from three sources: Local yield, imported water from
Dongjiang and seawater for flushing.
- Water demand in HK comprises domestic use, non-domestic use and flushing.
- Currently, water issues in Hong Kong has been sensationalized and politicized. For
example, the drying up of Lau Shui Heung Reservoir, which is no longer in use, was
used by local media to draw public’s attention to the record-breaking heatwave recently
and the need of maintaining sufficient water reserve locally.
- Our thinking about water technology in Hong Kong is constrained by the colonial
legacies. To promote a paradigm shift that can produce long-term benefits and enhance
water security for Hong Kong, it is important to understand the rationales and premises
for planning the “cutting-edge” technologies (i.e. seawater flushing, reservoirs
reclaimed from the sea, desalinization plant) back then.
Why High Island and Plover Cove Reservoirs were built?
- The three biggest reservoirs in Hong Kong, namely, High Island, Plover Cove and Tai
Lam Chung, account for over 90% of freshwater storage in Hong Kong.
- High Island Reservoir and Plover Cove Reservoir were not built for collecting rainwater.
Even there was not much rain in Hong Kong in the past several weeks, the average
storage level of our impounding reservoirs remained at around 60%, which is quite a
safe level.
- The rather high storage level is a result of using Dongjiang water before exploiting local
yields. Any leftover water would then be stored in High Island and Plover Cove
Reservoirs.
Water distribution in Hong Kong
- High Island and Plover Cove Reservoirs bring us a false sense of water security. We
forgot that long-term water security has to be evaluated at the regional scale. Water
sustainability and security of Hong Kong are contingent upon the health and sound
functioning of major reservoirs in the East River Basin (i.e. the Baipenzhu reservoir,
Xinfengjiang Reservoir and Fengshuiba Reservoir), instead of local reservoirs.
- By situating HK in a regional context, it would make better sense for HK to invest
money to ensure water sustainability in the East River Basin.
- According to the official discourse, the Plover Cove and High Island reservoirs are
essential for ensuring a reliable local water supply. In case of any disruption in the
supply of Dongjiang water, the two reservoirs can provide water for HK for 6 months.
- However, other international cities, like Paris, London and New York do not have such
a concept or practice of maintaining “6-month reserve” water.
-
Instead of using reliability, we need to use “sustainability” as a guide for planning of
water infrastructure in Hong Kong
Seawater for flushing
- Hong Kong is the only city using seawater for flushing.
- Amount of water used for flushing in Hong Kong is out-of-proportion, as compared to
other cities.
- The very high amount of water used for flushing is attributed to the high leakage rate
in the flushing system.
Concluding remarks
- Advancement in water technology can open up new policy debate. Such policytechnology interaction is true otherwise. Use of seawater for flushing and other colonial
water infrastructures are examples of how existing “technology” prevented search of
latest technology from taking place.
- During the colonial times, colonial government’s aspiration for “political autonomy”
functioned as a strong stimulus for driving policy change. New technological solutions
were therefore developed to meet the colonial government’s aspiration.
- Even after the handover of Hong Kong, “political autonomy” remained an important
guiding principle. Water technology developed in the 1970s, which is apparently an
example of colonial legacy, was not challenged or scrutinized objectively.
- Water technology should be evaluated by using a wider range of policy objectives,
including reliability, cost-effectiveness, equity and sustainability. After taken into
account new policy objectives like cost-effectiveness, we can’t help but question why
ocean desalination, which has the highest per unit production cost, was first adopted as
part of the 6-pronged water supply structure of WSD.
Panel Discussion
Q1: What are the major barriers to adopting new water technology?
Sedlak:
Each city’s situation is different. In the United States, new water technology planning
is often hindered by short-sightedness of decision makers. Decision makers, like
mayors, are most concerned about policy problems in the coming 2 to 5 years or focused
on problems pertaining to day-to-day, project-to-project implementation. It is
academics’ job to ask, “what we want for our next generation”. Policy-making for
achieving sustainability requires a longer-term planning and vision.
Q2: How to make robust decisions regarding climate change threats?
Sedlak:
Water portfolio diversification is very important. By diversification of water supply
sources, we diverse risks. New water technologies ensure drought-proof water supplies,
which can justify higher per-unit costs. This should become a strong motivation for
policy change. In contrast, Australian’s response to Millilumen Drought by building
only desalination plants is not diverse enough to address a changing problem.
Nguyen:
Even though different models show different forecast scenarios, the risks of climate
change remain. Therefore, cities should still build new infrastructure that are more
flexible to adapt to different climate change risks, in spite of disagreements regarding
the magnitude and frequency of risks.
Q3: How to overcome policy inertia or to facilitate policy change?
Sedlak:
Policy deliberations regarding new water technology usually begin with the usual
suspects from water agencies and local authorities. When we bring in new
representatives from the community and the business sector, like the insurance
companies, suddenly we have a new set of players who can tell officials another side of
the story or provide a new perspective. Increasing attention from elected officials and
public also create greater political force, which would be beneficial for creating
pressures for change of mentality of decision makers.
Lee:
The center is envisioning to create a platform to bring in the community. It’s also
important to keep thinking “what is best for HK” and to dismantle the colonial legacy
and adopt a regional perspective in water sustainability research.
Q4: Is drought a pressing issue for Canada?
Nguyen:
Uncertainty modellings of climate change look at both drought and flooding issues, but
drought is normally not a concern of Quebec government.
Q5:
What are the differences between portable water re-use and non-portable reuse. What
are the different challenges pertaining to the taking up of these two technologies?
Sedlak:
Non-portable reuse refers to use of water for purposes other than drinking. Non-portable
reuse is not a popular policy option in the US because it requires construction of another
set of pipe system. Popularity of direct portable reuse (use water for purposes including
drinking) is picking up when uncertainties about contaminating the environment,
particularly groundwater storages, are removed. It also has the advantage of saving
energy for pumping.
Q6:
Are your models on extreme weather events applicable to Canada only or they can be
applied to predict extreme climatic events in other parts of the world?
Nguyen:
Local variations are more important. Even though scientists agreed that climate change
is going to have impacts in other parts of the world, climate change impact at the global
scale is not my primary concern. In order to make the best use of modelling to inform
policy decisions, it is more important to get data about a particular area, than to
generalize findings to the global scale.
Overcoming the Barriers to a New Paradigm for Urban Water Infrastructure
David L. Sedlak
Department of Civil & Environmental Engineering & Berkeley Water Center
University of California, Berkeley
Climate‐Resilient Urban Water Systems: New Technologies and Policy Challenges
Hong Kong University
May 29, 2018 Fourth Water Revolution
non‐potable
potable
Water
In
Thayer (2013)
Water potable
Treatment
sewage
runoff
Resource
Recovery
Managed
Aquifer
Source: Wikipedia
Managed
Surface Water
Water
Out
Adventure 1: A Regional Vision
Source: Wikipedia
Thayer (2013)
Gradual Portfolio Diversification
Imported Water
2015
2025
2030
2040
2050
Local Supplies
Source: Wikipedia
Demand Management
Water Reuse
Thayer (2013)
Desalination
Filling in the Details
Source: Wikipedia
Thayer (2013)
Gonzales and Ajami in review Supporting Technology Diffusion
Final Generation
(mature technology)
Source: Wikipedia
Second Generation
(building on lessons learned from first applications)
Demonstration
Modified from
Parker (2011)
Thayer (2013)
Innovators
Early
Adopters
Early
Majority
Late
Majority
Laggards
Regional Partnerships Support Innovation
Source: Wikipedia
Thayer (2013)
Adventure 2: Overcoming The Yuck Factor
Source: Wikipedia
Harris‐Lovett S.R., Binz C., Sedlak D.L., Kiparsky M. and Truffer B. (2015) Beyond user acceptance: a legitimacy framework for potable water reuse in California. Environ. Sci. Technol. 49: 7552‐7561.
Binz C., Harris‐Lovett S.R., Kiparsky M., Sedlak D.L., and Truffer B. (2016) The thorny road to technology legitimation ‐ institutional work for potable water reuse in California. Tech. Forecasting Social Change 103: 249‐263.
Three Dimensions of Legitimacy
Type I: Pragmatic
This project serves my self interest.
Source: Wikipedia
Thayer (2013)
Three Dimensions of Legitimacy
Type I: Pragmatic
Serves my self interest.
Type II: Moral
This innovation meets external quality standards. I trust the institutions responsible for my safety.
Source: Wikipedia
Three Dimensions of Legitimacy
Type I: Pragmatic
Type II: Moral
Serves my self interest. I trust the institutions
Type III: Cognitive
This innovation meshes with my cultural belief system and daily life experience.
Spreading the Word Beyond the Academy
“For me, this report shows that, by sharing what we have done and by working together to
create real project plans, the water industry has the power to create new water supplies that
are drought‐proof, that are sustainable and that can be implemented today.”
Adventure 3: Multi‐Benefit Projects
Source: Wikipedia
Gikas and Tchobanoglous (2014)
Multi‐Criteria Decision Analysis
Source: Wikipedia
Harris‐Lovett et al., in preparation
Making Plans for the Next Phase
Source: Wikipedia
Observations
• To advance new approaches for urban water management researchers must couple engineering and social science research with genuine engagement with decision makers.
Source: Wikipedia
Observations
• To advance new approaches for urban water management researchers must couple engineering and social science research with genuine engagement with decision makers.
• A regional vision of a water infrastructure transition informed by rigorous research can catalyze investments in new approaches.
Source: Wikipedia
Observations
• To advance new approaches for urban water management researchers must couple engineering and social science research with genuine engagement with decision makers.
• A regional vision of a water infrastructure transition informed by rigorous research can catalyze investments in new approaches.
Source: Wikipedia
• The creation of legitimacy for new water technologies requires effective communication, transparency and technological excellence.
Observations
• To advance new approaches for urban water management researchers must couple engineering and social science research with genuine engagement with decision makers.
• A regional vision of a water infrastructure transition informed by rigorous research can catalyze investments in new approaches.
Source: Wikipedia
• The creation of legitimacy for new water technologies requires effective communication, transparency and technological excellence.
• There are many opportunities for collaboration at the technology‐policy interface. Acknowledgments
Newsha Ajami (Stanford)
Christian Binz (Eawag)
Sasha Harris‐Lovett
Michael Kiparsky (Berkeley Law)
Judit Lienert (Eawag)
Bernhard Truffer (Eawag)
Source: Wikipedia
Van-Thanh-Van Nguyen
Endowed Brace Chair Professor of Civil Engineering
Chair of Department of Civil Engineering and Applied Mechanics
McGill University (http://www.mcgill.ca/)
• Established in 1821
• Number of students: 40,000 (25% International Students)
• Nobel Prize Winners: 2 Professors and 10 Graduates
• PhD students: the highest percentage of PhD students of any
Canadian research university
• Student Awards: 142 Rhodes Scholars - the highest among
Canadian universities
• More than 300 programs of study
• 250,000 alumni live and work in 180 countries - 3 Canadian PMs
1
Who are we in the Brace Centre for
Water Resources Management?
Research Staff:
Faculty of Agricultural and Environmental Sciences
 Dr. Jan Adamowski – Bioresource Engineering: Integrated water resources management.
 Dr. Caroline Begg – Plant Science: Crop, soil, and water management systems.
 Dr. Martin Chénier – Food Science and Animal Science: Bacterial ecology.
 Dr. Chandra A. Madramootoo – Bioresource Engineering: Irrigation and drainage.
 Dr. Shiv Prasher – Bioresource Engineering: Soil and water quality, bioremediation.
 Dr. Zhiming Qi - Bioresource Engineering: Irrigation and drainage engineering
 Dr. Don Smith – Plant Science: Biofuels, greenhouse gas management.
 Dr. Joann K. Whalen – Natural Resource Sciences: Soil ecology and nutrient management..
Faculty of Engineering










Dr. Vincent H. Chu – Civil Engineering: Hydraulics and fluid mechanics.
Dr. Dominic Frigon – Civil Engineering: Environmental biotechnology.
Dr. Ronald Gehr – Civil Engineering: Water and wastewater treatment.
Dr. Susan Gaskin – Civil Engineering: Environmental hydraulics.
Dr. Subhasis Ghoshal – Civil Engineering: Geo-environmental engineering.
Dr. Jinxia Liu– Civil Engineering: Environmental engineering.
Dr. Van-Thanh-Van Nguyen – Civil Engineering: Hydrology and water management
Dr. James A. Nicell – Civil Engineering: Enzymatic treatment processes
Dr. Patrick Selvadurai – Civil Engineering: Geomechanics.
Dr. Nathalie Tufenkji – Chemical Engineering: Transport and fate of microorganism.
Faculty of Science


Dr. Peter Brown – Geography: Ethics, governance, and the environment.
Dr. Jeffrey McKenzie – Earth and Planetary Sciences: Hydrogeology and climate change.
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
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FOOD
SECURITY
WATER
SECURITY
IRRIGATION AND DRAINAGE
INFRASTRUCTURE
CLIMATE CHANGE
ENVIRONMENT
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
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DECISION-SUPPORT TOOLS FOR ASSESSING THE
CLIMATE CHANGE IMPACTS ON DESIGN AND
MANAGEMENT OF URBAN WATER SYSTEMS
Van-Thanh-Van Nguyen
and
Others (Students and Collaborators)
4
OUTLINE
Urban Water Management – Challenges and
Issues?
The “SCALE” Issues in Hydrologic Modeling?
Extreme Rainfall Events - IDF Relations - Issues?
Objectives
Modeling of Extreme Rainfall Processes in the
Climate Change Context - Downscaling Methods:
Advances and Limitations?
 Decision-Support Tools
Conclusions
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
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Water Management Challenges and Issues:
Population Growth – Water Stress
Land-use Change (Urbanization)
Climate Change Impacts:
 Water Quantity Issues: Floods,
Droughts, Water Supply, etc.
 Water Quality Issues: Water Pollution,
Water Treatment, etc.
HOW TO ASSESS THESE IMPACTS ON
HYDROLOGIC PROCESSES AT THE
CATCHMENT SCALE (e.g., “SMALL”
URBAN AREAS)?
The SPATIAL and TIME Scale Issues?
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Toronto July 2013
$1 billion
Montreal May 2017
Montreal May 2012
Calgary, June 2013
> $6 billion
Montreal Roads
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Frequency of Natural Disasters in Canada (1900-2005)
160
Wildfires
Avalanches
Cold Waves/Heat Waves
140
Number of Natural Disasters
Droughts
Earthquakes/Landslides
120
Floods
Freezing Rain
100
Hail/Thunderstorms
Hurricane/Typhoon
Storms
80
6 years
data!
Tornados
Tsunamis/Storm Surges
60
40
20
0
1900-09
1910-19
1920-29
1930-39
1940-49
1950-59
1960-69
1970-79
1980-89
1990-99
2000-05
10 Year Period
Environment Canada (H. Auld)
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
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Climate Trends and Variability
1950-1998


Maximum and minimum temperatures have increased at similar rate
Warming in the south and west, and cooling in the northeast (winter & spring)
Trends in
Winter
Mean Temp
(° C / 49 years)
Trends in
Spring
Mean Temp
(° C / 49
years)
Trends in
Summer
Mean Temp
(° C / 49 years)
Trends in
Fall
Mean Temp
(° C / 49
years)
From X. Zhang, L. Vincent, B. Hogg and A. Niitsoo, Atmosphere-Ocean, 2000
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
How to develop Climate Change scenarios for impacts studies in
hydrology?
 Spatial scale ~ a few km2 to several 1000 km2
 Temporal scale ~ minutes to years
A scale mismatch between the information that GCM can
confidently provide and scales required by impacts studies.
“Downscaling methods” are necessary!!!
300km
Impact models require ...
10km
50km
GCM Climate
Simulations
Point
1m
GCMs or RCMs supply...
Precipitation
(Extremes) at a
Local Site
(P. Gachon)
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The “Time Scale” Issue:
The “scale” problem?
The properties of a variable depend on the scale of
measurement or observation:
I (mm/hr)
True image
time (hr)
I (mm/hr)
time (hr)
Source: Holman-Dodds et al., IIHR Project, Univ. of Iowa
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The “Spatial Scale” Issue:
 Scale of topographic map?
UNKNOWN TRUE
IMAGE
A
A1
1 ≠ 2 ≠ 
 = 0 
1
1

A2
2
2
…
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GEC3 Research Themes
Global climate system variability and
change
Regional climate modeling and analysis
Climate and environmental change
impacts on ecosystems
Response of hydrological systems to
environmental changes
Impacts, adaptation and vulnerability
assessment: An integrated approach to
sustainable water management
GEC3 (2004-2010): 42 Professors from 7 universities; 3
Researchers from EC and HQ; 200 Graduate Students; and 30
Postdoctoral Fellows.
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FLOODNET - NSERC Canadian Strategic Network
(2014-2019)
(P. Coulibaly, McMaster University)
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PROJECT OBJECTIVE AND KEY CHALLENGES
OBJECTIVE:
Evaluate climate change impacts on
Intensity-Duration-Frequency (IDF) curves
and develop new regional IDF curves for
selected cities in Canada.
KEY CHALLENGES:
• Climate Change Impacts:
 Downscaling Approaches
 Non-stationarity Process
• Single-Site and Regional Rainfall Modeling:
 Multi-site Modeling Methods
 Regionalization Methods (Ungaged Sites)
FloodNet
NSERC
15
Observed Rainfall Data – Complete Time Series
IDF RELATIONS
Rainfall
Frequency
Analysis
Intensity (in/hr)
Extreme
Rainfall
Series
Frequency
Xo
(Chow, 1988)
Duration (min)
IDF
Curves
ISSUES:
 To analyze a large amount of data for
different time scales and for different
stations.
 To select a suitable probability
distribution for a given site or region.
 To develop IDF relations for the
current climate.
 To assess the climate change impacts
on IDF relations.
Intensity
Probability of extreme rainfall
occurrence & amount ???
Imax
Design
Storm
Tp
Time
FloodNet
NSERC
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Extreme Rainfall Estimation Issues:
At-site Frequency Analysis of Precipitation
Current practice: Annual maximum series (AMS) using
2-parameter Gumbel/Ordinary moments method, or
using 3-parameter GEV/ L-moments method.
Problem: Uncertainties in Data, Model and Estimation
Method
The Time Scale Issue?

Regional Frequency Analysis of Precipitation
Current practice: GEV/Index-flood method.
Problem: How to define similarity (or homogeneity) of
sites?

(WMO Guides to
Hydrological
Practices: 1st
Edition 1965 →
6th Edition:
Section 5.7, 2009)
1
2
3
4
Geographically
Geographically
Hydrologic
contiguous fixed non contiguous
neighborhood
regions
fixed regions
type regions
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Extreme rainfall estimation
Design Rainfall = to estimate maximum amount of rainfall at a given
site for a specific duration and return period ⇒ Intensity-DurationFrequency (IDF) curves
Traditional IDF estimation methods
Time scale problem: no consideration of rainfall
properties at different time scales;
Space scale problem: results limited to data available at
a local site;
Climate change problem: no consideration.
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
18

How to develop Climate Change scenarios for impacts studies in
hydrology?
 Spatial scale ~ a few km2 to several 1000 km2
 Temporal scale ~ minutes to years
A scale mismatch between the information that GCM can
confidently provide and scales required by impacts studies.
“Downscaling methods” are necessary!!!
300km
Impact models require ...
10km
50km
GCM Climate
Simulations
Point
1m
GCMs or RCMs supply...
Precipitation
(Extremes) at a
Local Site
(P. Gachon)
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
19
DOWNSCALING METHODS
RCM or LAM
(Dynamic
Downscaling)
Stochastic
Weather
Generators
GCM
Statistical
Models
(Statistical
Downscaling)
Weather Typing or
Classification
Impact
Models
(Hydrologic
Models)
Regression
Models
low resolution
~ 300 km
month, season, year
high resolution
1 km
day, hour, minute
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
20
20
DYNAMIC DOWNSCALING METHODS
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
21
Single-Site Downscaling Approaches:
GCM Climate Predictors
Is it feasible?
Local Daily Precipitation Series
Is it feasible?
Daily Extreme Precipitations
Is it feasible?
Sub-Daily Extreme Precipitations
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
22
Spatial-Temporal SD of Sub-Daily Rainfall
Extremes at a Local Site
A combination of
A spatial downscaling method: the
statistical downscaling model such
as SDSM (Wilby et al., 2002) or
SDRain (Yeo and Nguyen, 2011)
A temporal downscaling method:
the scaling GEV model (Nguyen et al.
2002).
GCM Climate Predictors
Local Daily Precipitation Series
Daily Extreme Precipitations
Sub-Daily Extreme Precipitations
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
23
Software Description
Data
screening
&
Preliminary
analysis
IDF
Relations
Current
Climate
2020s
Selection of a best
distribution
Projected
Climate
Change
2080s
2050s
Model Test and Application
84 stations in Ontario
x 3 durations (5m, 1h, 24h)
0
85 170
340
510
680
Km
25
= 252 datasets
Introduction
 Probability distributions
Gamma family
 (2) GUM || (3) GEV, GPA, PE3, LP3, GNO, GLO || (5) WAK …
Normal family
Extreme values family
Descriptive
Predictive
(Goodness-of-fit)
26
Observed & Estimated Rainfall (mm)
Estimated Rainfall (mm)
SMExRain Descriptions
Descriptive
Ability
Observed Rainfall (mm)
Up to
12
Distributions
Predictive
Ability
Probability
Test Results
Ranking
Top distributions
27
Largest values
Rainfall
(mm)
5-min AMS
391
354.5
248
243
313
242.5
298
297
323
225.5
166.5
366
318
241
246
332.5
231
340
356.5
289.5
227
154.5
671
594.5
428
511
537
420
518.5
611
545
410
298
632.5
541.5
417
494
555
411.5
604.5
670
504
423.5
290.5
BEK
BEP
GEV
GEV*
GLO
GNO
GPA
GUM
LP3
PE3
WAK
BEK
BEP
GEV
GEV*
GLO
GNO
GPA
GUM
LP3
PE3
WAK
30-40 year records
20-30 year records
MAE
MAE
28 Distribution
154
147
120
108
149
105
151
174
129
102
48.5
≥ 40 year records
RRMSE
RRMSE
161
152
121
102
129
107
144
138
161
100
71
151
147
94.5
134
156
92
177
165
74.5
106
90.5
155
155
113
115
150
102
148
166
130
97.5
56
RMSE
RMSE
CC
CC
Overall score
Overall rank
334
328
224
317.5
352
214
373
328
170
230.5
231
563
560
393.5
571.5
558
398
663.5
593
335
449.5
459
BEK
BEP
GEV
GEV*
GLO
GNO
GPA
GUM
LP3
PE3
WAK
≥20yr
375.5
331.5
240
254.5
331
232.5
332
333
291.5
210
170.5
≥30yr
644.5
575
408.5
515.5
565.5
405.5
585.5
623
495.5
390.5
335
≥40yr
BEK
BEP
GEV
GEV*
GLO
GNO
GPA
GUM
LP3
PE3
WAK
Station
 Descriptive Ability Comparison
Model Test and Application
5m-Boxplot
Rank
1
2
3
4
5
6
7
8
9
10
11
IDF Relations
Top 03 distributions based on both
descriptive and predictive performances
GEV, GNO, PE3
Frequency curves and 90% Confidence Intervals
 GEV tends to produce more conservative results
29
IDF . Projected Climate Change
At-site AMS
2
Temporal SMExRain
downscaling
GCMs or RCMs supply…
low resolution
IDF - PCC
high resolution
~ 1 km
~ 300 km
Regional Clim. Models
a
Wilby et al (2002)
Yeo and Nguyen (2011)
c Wang X. (2015)
b
(Dynamic Downscaling)
GCM
Statistical Models
(Statistical Downscaling)
30
Point
(Source: P. Gachon)
1 km
Spatial
downscaling
SDSM a
SDRain b
CCDP c
NASA
10 km
1
Impact models
require…
25 km
GCM/RCM
Step 1: Spatial downscaling
250 km
2 steps:
Impact
Models
(Hydrologic
Models)
IDF . Projected Climate Change
Montreal Int. Airport station
After BC
Before BC
31
Calibration
1961-1990
Validation
1991-2005
(daily AM)
IDF . Projected Climate Change
Winsor station
Toronto Int. A. station
Calibration
1961-1990
Validation
1991-2005
(daily AM)
32
IDF . Projected Climate Change
Step 2: Temporal downscaling
2 steps:
GCM/RCM
1
Spatial
downscaling
SDSM a
SDRain b
CCDP c
NASA
At-site AMS
2
Temporal SMExRain
downscaling
IDF - PCC
a
Wilby et al (2002)
Yeo and Nguyen (2011)
c Wang X. (2015)
b
CGCM3 – A1B
33
Note: Statistical downscaling using SDRain (Yeo, 2016)
FloodNet
NSERC
IDF . Projected Climate Change
Step 2: Temporal downscaling
2 steps:
GCM/RCM
1
Spatial
downscaling
SDSM a
SDRain b
CCDP c
NASA
At-site AMS
2
Temporal SMExRain
downscaling
IDF - PCC
a
Wilby et al (2002)
Yeo and Nguyen (2011)
c Wang X. (2015)
b
CMIP5 models – RCP 4.5
34
Note: Dynamic downscaling using RCMs
FloodNet
NSERC
Baseline (1961-1990) & Projected (2020s, 2050s, 2080s)
5-min Annual Maxima (mm)
(Montreal Airport)
35
SUMMARY OF RESEARCH PROGRESS:
Climate Change Impacts on Extreme Rainfalls SDExRain
At-site Rainfall Estimation
for a Gaged Site
Regional Rainfall Estimation
for an Ungaged Site
(Spatial) SD model: SDRain
Regionalization method
to identify homogenous daily
rainfall regions: OFA
(Temporal) SD
model: Scaling-GEV
Construction of
IDF relations in
the context of
climate change
Stochastic estimation method
for estimating missing data
Regional Rainfall
Estimation
for Multi-sites
Multi-site SD method
for daily precipitations
(Spatial) SD SDRain
for an ungaged site
Climate change
impacts on
daily
precipitation at
a gaged site
Construction of
missing daily
precipitation at an
ungaged site
Climate change
impacts on daily
precipitation at
an ungaged site
Climate change impacts on
daily precipitations over
many sites concurrently
FloodNet
NSERC
36
CONCLUSIONS





Significant advances have been achieved regarding
the global climate modeling. However, GCM outputs
are still not appropriate for assessing climate change
impacts at the regional or local scales.
Downscaling methods provide useful tools for this
assessment.
In general, statistical downscaling models could
provide “good” but “biased” estimates of the
observed statistical properties of the daily
precipitation and extreme temperature processes at a
local site. Hence, bias-correction methods are
required.
It is feasible to assess the impacts of climate change
on runoff at small watershed scales using the
proposed precipitation downscaling methods for
gaged and ungaged sites.
FURTHER STUDIES
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
37
FURTHER WORKS
 Modeling of Rainfall Processes in
Consideration of Nonstationarity (Journal of
Hydrology, 2016a and 2016b for GEV)
 Stochastic Modeling of Extreme Rainfall
Processes in the Context of Climate Change
 Regional Rainfall Maps for Selected Cities
 Guidelines for Developing IDF Relations
FloodNet
NSERC
38
PUBLICATIONS
1. Herath, S.M., Sarukkalige, P.R., and Nguyen, V-T-V. (2016), A spatial temporal
downscaling approach to development of IDF relations for Perth airport region in
the context of climate change, Hydrological Sciences Journal, 61:11, 2061-2070,
DOI:10.1080/02626667.2015.1083103.
2. Gado, T.A., and Nguyen (2016a), An at-site flood estimation method in the context
of nonstationarity. I. A simulation study, Journal of Hydrology, DOI:
http://dx.doi.org/10.1016/j.jhydrol.2015.12.063
3. Gado, T.A., and Nguyen (2016b), An at-site flood estimation method in the context
of nonstationarity. II. Statistical analysis of floods in Quebec, Journal of Hydrology,
DOI: http://dx.doi.org/10.1016/j.jhydrol.2015.12.064
4. Yeo, M, and Nguyen, V-T-V. (2016), Downscaling of daily rainfall process at an
ungaged site, Chapter 20 in Advances in Hydroinformatics, Gourbesville, P. et al.
(eds.), Springer Water, DOI: 10.1007/978-981-287-615-7_20.
5. Nguyen, T-H, and Nguyen, V-T-V. (2016), Statistical Modeling of Extreme Rainfall
Processes (SMExRain): A Decision Support Tool for Extreme Rainfall Frequency
Analyses, Procedia Engineering, 154, pp. 624 – 630.
FloodNet
NSERC
39
PUBLICATIONS
6. Khalili, M. and Nguyen, V-T-V. (2017), An efficient statistical approach to multi-site
downscaling of daily precipitation series in the context of climate change, Climate
Dynamics, DOI: 10.1007/s00382-016-3443-6.
7. Nguyen, T-H., El Outayek, S.; Lim, S-H., and Nguyen, V-T-V. (2017), A Systematic
Approach to Selecting the Best Probability Models for Annual Maximum Rainfalls –
A Case Study Using Data in Ontario (Canada), Journal of Hydrology,553, pp. 49-58
http://dx.doi.org/10.1016/j.jhydrol.2017.07.052
8. Khalili, M. and Nguyen, V-T-V. (2018), Efficient Statistical Approach to Multisite
Downscaling of Extreme Temperature Series Using Singular Value Decomposition
Technique, ASCE Journal of Hydrologic Engineering. 23(6),
DOI:10.1061/(ASCE)HE.1943-5584.0001662.
9. Nguyen, T-H. and Nguyen, V-T-V. (2018), A Decision Support Tool for Constructing
Robust IDF Relations in Consideration of Model Uncertainty, ASCE Journal of
Hydrologic Engineering (under review).
FloodNet
NSERC
40
Thank you
for
your attention!
Forum on Climate-resilient Urban Water Systems, May 29, 2018, The University of Hong Kong
41
Technology—Policy Interface Issues in Hong Kong:
Legacies and Impacts
Frederick Lee
Centre for Water Technology and Policy
The University of Hong Kong
29 May 2018
Colonial legacies
‐ Seawater flushing (1958)
‐ Reservoirs reclaimed from the sea (1960; 1971)
‐ Desalination (1977‐78)
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
(mcm)
Composition of water supply
1400
1200
1000
Seawater for flushing
800
600
Imported Dongjiang water
400
200
Local yield
0
Copyright © 2018 Frederick Lee
Composition of water consumption
1400
1200
1000
mcm
800
600
400
200
0
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Domestic freshwater
Copyright © 2018 Frederick Lee
Non‐domestic freshwater
Total flushing (Freshwater + Seawater)
27 May 2018
Irrigation reservoirs
Copyright © 2018 Frederick Lee
Impounding reservoirs
Copyright © 2018 Frederick Lee
Impounding reservoirs and rainfall pattern
Copyright © 2018 Frederick Lee
Water gathering grounds and impounding reservoirs
Copyright © 2018 Frederick Lee
Storage level of impounding reservoirs (16 May 2018)
Copyright © 2018 Frederick Lee
Cumulative reservoir capacity and local yield
1400
1400.0
1200
1200.0
Seawater for flushing
(mcm)
1000
1000.0
800
800.0
Cumulative reservoir capacity
600
600.0
400
400.0
Imported Dongjiang water
200.0
200
Local yield
0.0
0
1961 1963
1963 1965
1965 1967
1967 1969
1969 1971
1971 1973
1973 1975
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995
1961
1995 1997
1997 1999
1999 2001
2001 2003
2003 2005
2005 2007
2007 2009
2009 2011
2011 2013
20132015
2015
Copyright © 2018 Frederick Lee
Water distribution network
Hong Kong rivers
#1
Hong Kong’s reservoirs
Dongjiang
#2A
#2B
Water Treatment Works
User
Water Treatment Works
User
Water Treatment Works
User
Shenzhen Reservoir
Plover Cove High Island
6‐month reserve
Sea water supply zones
Per capita domestic water consumption: Many unknowns
Seawater
Freshwater
Flushing
Freshwater
Total production
Domestic
Metered & reported
Metered & reported
Non‐domestic
Govt mains
Unknown
Inside service
Metered &
reported
Estimated;
Estimated; not reported not reported regularly
regularly
Unknown
Unknown
Estimated; not reported regularly
2015/16:
131.6 lpd
Metered & reported
Unaccounted‐for‐water
2015/16:
92.3 lpd
Metered & reported
Copyright © 2018 Frederick Lee
Unknown
Unknown
Unknown
Unknown
Composition of domestic water use in Hong Kong:
Comparison with other jurisdictions
Hong Kong
40% for flushing ?
UK
Germany
Tokyo
Taiwan
Singapore
0%
Flushing
10%
20%
30%
Shower/ bathing
Copyright © 2018 Frederick Lee
40%
50%
Cooking
60%
70%
Laundry
80%
90%
Others
100%
Year
Seawater flushing system leakage rate 2004
34.1
2005
35.8
2006
32.9
2007
31.4
2008
33.8
2009
30.7
2010
30.4
2011
28.7
2012
27.7
2013
28.3
2014
N. A.
2015
N. A.
2016
N. A.
Towards a 6‐pronged water supply structure
Policy objective
Reliability
Technology
Smart meter leakage detection
Desalination of brackish water
Wastewater recycling
Stormwater capture
Reservoirs
Ocean desalination
Cost
effectiveness
Equity
Sustainability
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