Transcript Impact of climate change on road infrastructure

Impact of Climate Change on Road
Infrastructure
Mark Harvey
[email protected]
www.bitre.gov.au
Austroads project published in 2004
• Austroads AP-R243/04 : Impact of Climate change on road
infrastucture
• Downloadable for free from Austroads website, with volume
of appendices AP-R243/04A
– http://www.austroads.com.au
• Also downloadable for free from BITRE website (main
volume only) http:/www.bitre.gov.au
– http://www.bitre.gov.au/publications/92/Files/climate_change.pdf
Project aims
• assess likely local effects of climate change for Australia for
the next 100 years, based on the best scientific assessment
currently available
• assess the likely impacts on patterns of demography and
industry, and hence on the demand for road infrastructure
• identify the likely effects on existing road infrastructure and
potential adaptation measures in road construction and
maintenance, and
• report on policy implications arising from the findings.
• Note: The project was not concerned with impacts of
transport emissions on climate change.
Project structure
• BITRE coordinated the project and prepared the Executive
Summary, Introduction and the Policy Implications chapters.
• The CSIRO Division of Atmospheric Research ran its global
climate change models to produce forecasts of climate on a
grid of about 50 kilometres up to 2100.
– The resultant data was passed on to three consultants to assess
its implications.
Project structure
IPCC
CSIRO
Emissions
A2 scenario developed from
population, energy and economic
models
Global climate change
Temperature, rainfall, etc.
(200 – 400 km horizontal resolution)
from Atmospheric-Ocean Global
Climate Change Model Mark 2
ARRB, Monash
University,
ABARE, BITRE
Concentrations
CO2, methane, sulphates, etc. from
carbon cycle and chemistry models
Regional climate change
Mountain & coastal effects, islands,
extreme weather, surface properties,
etc. (50 km horizontal resolution) from
Conformal-Cubic model
Impacts
Population, industry, road pavements,
salinity (various consultants)
Project structure continued
• The Monash University Centre for Population and Urban
Research investigated the likely effects on population
settlement patterns and demographics.
• ARRB Group used these population projections to forecast
changes in road transport demand.
– calculated changes to an index of climate from the CSIRO data
– road demand and climatic indexes were together used in
pavement deterioration models to predict the implications for
pavement deterioration and maintenance expenditure needs.
Project structure continued
• Australian Bureau of Agricultural and Resource Economics
(ABARE) employed its hydrological–economic model of the
Murray-Darling basin to forecast implications of climate
change for salinity and agricultural production in the region,
and related this to road infrastructure.
• Multi-disciplinary project involving experts from a range of
fields.
Project structure continued
CSIRO: Regional climate
change forecasts to 2100
Monash University Centre
for Population and Urban
Research:
Impacts on population
projections
ARRB Group:
Impacts on
demand for roads
ABARE: Impacts on
salinity and
agriculture in MurrayDarling basin
ARRB Group: Impacts on
road pavements
BITRE: report and summary
Not covered in the study
• Local flooding implications
– requires a catchment hydrological model to predict flooding
heights, durations and water velocities, and
– an area topology model to relate flood heights to local road
infrastructure.
• Salinity and impacts of agricultural industries outside the
Murray-Darling Basin
• The CSIRO models do not forecast sea level rises or the
likelihood of changes in storm activity.
Emissions forecasts
• International Panel on Climate Change (IPCC) emissions
scenario selected
• ‘A2’ scenario
– high scenario chosen to provide strong contrast with current
conditions
– predicated on global population of 15 billion in 2100
– rate of CO2 release grows, increasing to nearly fourfold by 2100.
IPCC emission scenarios
A2 scenario (red line) used in this study.
CSIRO Atmospheric-Ocean Global Climate
Change Model
• global circulation model with atmospheric, oceanic, sea-ice
and biospheric submodels
• globe divided up into a grid comprised of 300 km squares
– 9 layers of atmosphere, each block having parameters such as
temperature, air pressure, wind velocity, water vapour content
– 12 layers of ocean
• time step of 30 minutes
• run from 1870 to 2100
• suite of properties (temperature, moisture) saved for
6-hourly intervals for the 230 years
• took three months on a supercomputer
CSIRO Conformal-Cubic General
Circulation Model
• Results from global model used to ‘nudge’ more detailed
model for Australia
– wind speeds outside Australia adjusted to make consistent
between models
• grid of about 50 km squares for Australia and lower
resolution for rest of the world (up to about 800 km for the
other side of the globe)
Method of deriving detailed forecasts
• outputs: monthly means of average, maximum and minimum
temperatures, precipitation, solar radiation, potential and
actual evaporation for each grid point
• converted to
– local temperature change per degree of global warming for
temperature
– percent for rainfall, radiation evaporation change per degree of
global warming
• used to derive forecast for any IPCC scenario for any grid
point over the next 100 years.
Key findings: temperatures
• average annual temperatures increase by 2⁰ to 6⁰C by 2100
• Tasmania coastal zones least affected, inland areas most
affected
• more extremely hot days and fewer cold days, for example
– average number of summer days over 35⁰C in Melbourne to
increase from 8 at present to 10-20 by 2070
– average number of winter days below 0⁰C in Canberra to drop
from 44 at present to 6-38 by 2070
Average annual temperature: base (2000)
and 2100 climate
5
15
17
19
21
23
Base (2000) climate
24
25
26
27
28
29
30
32
34
40
2100 climate
Temperature changes: year 2100 relative
to base climate
Key findings: rainfall and evaporation
• general reduction in rainfall except for the far north where
there will be significant increases
• where average rainfall decreases, more droughts
• where average rainfall increases, more extremely wet years
• in the north, more intense tropical cyclones, more severe
oceanic storm surges, more frequent and heavier
downpours
• evaporation to increase over most of the country adding to
moisture stress on plants and drought
Average annual rainfall: base (2000) and
2100 climate
10
15
20
25
30
Base (2000) climate
35
40
50
60
70
100
125
200
>500
2100 climate
% change in average annual rainfall 20002010
134
-9
-12
-14
-25
Sea level rise
• Not predicted in CSIRO modelling.
• IPCC projects rise of 9 to 88 cm by 2100
– 0.8 to 8.0 cm per decade
Impact on population and settlement
patterns: methodology
• undertaken by Monash University Centre for Population and
Urban Research (Dr Bob Birrell)
• population projections developed for Australia as a whole,
States and major metropolises (based on ABS mid-range
projections supplemented by ANU demographic projection
software).
• adjustments made to the projections for the eight major
metropolitan regions for climate change
– using expert judgement supported by a comfort index (function of
temperature and humidity). A comfortable climate is a major driver
of internal migration.
Population base case: without climate
change
• total fertility rate will fall to 1.6 and net overseas migration
90,000 per year over the 21st century
• total population 19.1m in 2000 to 27.3m in 2100
• greater concentration in four major metropolises: Sydney,
Melbourne, Brisbane and Perth
• other growth outside the four cities is in non-metropolitan
Queensland and WA.
• significant increase in share of population in Queensland
• for planning purposes, need to take base case, then adjust
for climate change
Population: climate change impacts
• of the eight metropolitan regions assessed, only Darwin and
Melbourne gain population from climate change
– even though hotter, wetter Darwin less attractive, higher rainfall
should promote agricultural production
o but note the contrary view from the recent Northern Australia Land
and Water Taskforce report: lack of suitable soils; high evaporation
and lack of dam sites limits water storage
• Losers: Adelaide (water supply), Cairns (less attractive
climate) and Perth (water and climate)
• coastal areas of NSW and Victoria more attractive climate
• hotter, drier climate in inland areas will may have adverse
impact on agriculture
2100 population without and with climate
effects
Selected
Statistical
Division
% of 2000
population without
climate change
Adjustment factor
Climate change factors
with climate change driving population change
Sydney
159%
1.00
Melbourne
125%
1.15
Brisbane
211%
0.96
Moreton
305%
0.98
Adelaide
63%
0.79
Perth
195%
0.88
Darwin
275%
1.34
ACT
93%
1.00
Cairns
279%
0.83
temps higher but not expected to
affect population growth
temperatures higher resulting in
more attractive climate
temperatures higher resulting in
less attractive climate
temperatures higher resulting in
less attractive climate
restricted water supply,
especially in spring
less attractive climate; restricted
water supply
temps high but heavy rainfall
drives increased agriculture
temps higher but not expected to
affect population growth
temperatures higher resulting in
less attractive climate
Note: Climate change is not the most
important influence on population
patterns.
• range of projections for 2100 compared with 2000
– without climate change: -63% Adelaide to 305% Darwin
– with climate change: -50% Adelaide to 369% Darwin
Impact on road demand: Methodology
• passenger and freight tasks considered separately
• base-case forecasts developed
– cars a function of population, per capita car ownership
– freight a function of population, per capita freight, average payload
(trend to larger vehicles)
o converted to equivalent standard axel loads for pavement impacts
• ARRB used a gravity model to estimate impacts of climate
change on traffic.
– If population at A increases by 100a% and population at B by
100b% due to climate change, then traffic between them increases
by 100[(1+a)(1+b)-1]%.
Impact on road demand 2100: conclusions
• 60% additional traffic (total vehicles passengers and freight)
– dramatic increase in Queensland, moderate in Syd-Mel corridor,
decline around Adelaide, increase in Perth urban only slight rise in
Perth intercapital traffic
•
•
•
•
proportion heavy freight vehicles will rise from 12.1 to 13.9%
total road freight to rise by 112% from 2000 to 2100
average payload to increase by 25%, most in next decade
equivalent standard axles per articulated truck to double due
to higher mass limits
• total ESA-kms on National Highway to rise by 230%
– due to freight growth, higher mass limits and payloads
Impact on pavement performance:
methodology
• climate represented by ‘Thornthwaite moisture index’
– a function of precipitation, temperature and potential evapotranspiration. Index varies from +100 on Cape Yorke Peninsula to
-50 in central Australia.
• used a National Highway System road database
• pavement models estimate present value of life-cycle road
agency costs (maintenance and rehabilitation) and roaduser costs (travel time and vehicle operation)
– select treatment options and timings to minimise present value of
costs subject to specified constraints on maximum roughness and
annual agency budgets.
Pavement Life Cycle Costing (PLCC) model
• 60 year analysis period, 7% real discount rate
• National Highway System split into 60 sections with similar
climate characteristics, traffic levels, vehicle mixes and
pavement characteristics
• pavement deterioration a function of pavement age,
cumulative equivalent standard axle loads, Thornthwaite
index, and average annual maintenance expenditure.
HDM4 model
• much more detailed pavement deterioration algorithm
covering roughness, rutting, cracking, potholing, ravelling,
strength etc and consequently much more detailed data
requirements
• case studies of 8 road segments analysed in detail
– one segment from each state and territory located in or near a
metropolitan area
• data inputs that vary with climate are site-specific changes in
Thornthwaite index, traffic levels and per cent heavy
vehicles
Other points to note
• Note: only trucks cause pavement wear, not cars
– but cars impact on the models because increased roughness adds
to road user costs for cars
• Limitations
– effects of floods, severe storms and sea-level rise not taken into
account
– no allowance for expansion of lane-kilometres
– design pavement strengths assumed to remain unchanged
– road agencies may not minimise present value of costs due to
budget constraints causing maintenance to be deferred and higher
than economically warranted maintenance standards in some
areas for social and equity reasons
Thornthwaite moisture index: base (2000)
and 2100 climates
-45
-30
Base (2000) climate
-15
0
20
40
60
80
>100
2100 climate
Changes in Thornthwaite moisture index:
2000 to 2100
Changes to Thornthwaite index
• tendency to a drier climate overall (negative change in
Thornthwaite Index)
• central area of Australia relatively unchanged
• localised areas where the changes are greatest include
–
–
–
–
south-west of Western Australia
north-east Victoria and southern NSW
south-west Tasmania
top-end Queensland.
Optimal road agency costs (PLCC model)
Optimal Agency Cost ($million)
Change
State
Base Climate
2100 Climate
NSW
72.3
90.1
25%
VIC
32
37.6
18%
QLD
82
124.2
51%
WA
48.3
56.1
16%
SA
27.6
23.4
-15%
TAS
6.5
6.8
5%
NT
17.9
37.3
108%
ACT
0.6
0.7
17%
Total
287.3
376.1
31%
Comparing optimal road agency costs
• comparison is not with and without climate change
– but 2000 traffic volumes and climate
– with 2100 climate-adjusted traffic volumes and climate
• Northern Territory and Queensland experience large
increases
– primarily due to population growth but wetter climate contributes.
• South Australia declines due to smaller population and drier
climate.
Maintenance: rehabilitation funding split
• maintenance = routine and periodic maintenance (pothole
patching, kerb and channel cleaning, surface correction,
resealing)
• rehabilitation = chipseal resheeting, asphalt overlays,
stabilisation, pavement reconstruction
• for Australia as a whole, no predicted change in 35:65 split
• rehabilitation proportion to rise (maintenance proportion to
fall) significantly for Tasmania
– converse for WA
• reflects differences in pavement age distributions and life
times
HDM4 results: road agency costs
Base climate
2100 traffic changes only
2100 climate & traffic changes
Cost ($'000)
Cost ($'000)
Change (3/1)
Cost ($'000)
Change (5/1)
1
2
3
4
5
ACT
97.8
97.8
0%
97.8
0%
NSW
46.2
72.7
57%
72.8
58%
NT
176.6
176.9
0%
177.1
0%
QLD
83.6
103.1
23%
106.1
27%
SA
99
96.8
-2%
96.8
-2%
TAS
140.2
159.5
14%
159.4
14%
VIC
128.7
175.5
36%
177.3
38%
WA
205.9
244.5
19%
244.1
19%
Col number
• undiscounted total costs per kilometre over 20-year period
• Virtually all the changes are from population growth leading
to traffic increases, not climate change.
Impact on salinity in the Murray-Darling
Basin: methodology
• ABARE Salinity and Land-use Simulation Analysis (SALSA)
model
• network of land management units linked through overland
and ground water flows
• hydrological: rainfall, evapo-transpiration, surface water
runoff, irrigation, ground water recharge/discharge rates, salt
accumulation in streams and soil
– climate projections incorporated by changing rainfall and evapotranspiration
• rate of flow of groundwater depends on ‘hydrolic gradients’
– very flat in lower parts of the catchment
Impact on salinity in the Murray-Darling
Basin: methodology continued
• land-use allocated to maximise economic return from use of
agricultural land and irrigation water
• relationship between yield loss and salinity for each
agricultural activity
• land-use can shift with changes in salinity and water
availability
• costs of salinity measured as reduction in economic returns
Catchments in the Murray Darling Basin
covered by the SALSA model
Impact on salinity in the Murray-Darling
Basin: Key findings
Without climate
Units Base scenario
change
Year
With climate
change
2000
2100
2100
Net production revenue
$m, npv
3827
3718
3400
Area of high water tables
‘000 ha
1137
5341
4404
Darling – below the Macquarie
mg/L
152
277
483
Murray – below the Murrumbidgee
mg/L
141
181
198
Murray – below the Darling
mg/L
226
301
343
Murray – at Morgan
mg/L
313
445
548
Darling – below the Macquarie confluence
GL
7345
7784
6060
Murray – below the Murrumbidgee confluence
GL
8128
9040
5259
Murray – below the Darling
GL
6789
7720
4435
Murray – at Morgan
GL
3827
3718
3400
SALT CONCENTRATION
SURFACE WATER FLOWS
Impact on salinity in the Murray-Darling
Basin: comparisons
• area affected by high water tables
– base case rise: from 1.1m hectares in 2000 to 5.3m hectares in
2100
– climate change: rise to 4.4m hectares in 2100
– climate change mitigates salinity problems but nowhere near
sufficient to reverse the rising trend
• net production revenue
– base case: falls by 3% due to high water tables, shift from pasture
to cropping
– with climate change: falls by 11% due to reduced surface water
flows, switching from irrigated to dryland activities
– less demand for road transport
Impact on salinity in the Murray-Darling
Basin: comparisons
• higher water tables are bad for road pavements, but this is a
problem in both the base and climate change scenarios
– slightly less with climate change
• reduced surface water flows make salt concentrations higher
in rivers which reduces yields from irrigated production
– and rusts steel reinforcing in concrete structures in riverine
environments such as bridges and culverts.
Summing up: uncertainty
• high level of uncertainty about
– IPCC emissions forecasts
– CSIRO estimates of climate impacts
– consultants’ forecasts
• uncertainties built upon uncertainties
• numerical results are broad indicators that tell a story
Summing up: demand for roads
• Higher car and truck traffic from population growth is the
main driver of investment and maintenance needs for roads.
• Large changes are forecast without climate change.
• strong growth for SE Queensland, Cairns, Darwin, Brisbane,
Sydney, Melbourne, Perth
• decline for Adelaide and inland areas
• Climate change adds to forecasts for Darwin and Melbourne
and reduces forecasts for Adelaide, Perth and Cairns.
Summing up: road design and
maintenance
• less rainfall should slow pavement deterioration
– but effects so small as to have negligible impact on costs
• exception for far northern parts of Australia, which are
forecast to become wetter. Capacities of culverts and
waterways may prove inadequate.
• sea-level rise a concern for low-lying roads in coastal areas
• changed and frequencies of floods in some areas
– requires modelling of individual catchments to forecast impacts
Overall conclusion
• Changes affecting road infrastructure will occur regardless
of climate change.
– Climate change is just another factor in the mix, and usually not
the most important.
• The main impacts on road infrastructure may come from
changes in flood heights and frequencies, and sea-level rise
with storm surges, which were not addressed in detail in the
project.
– impacts vary greatly between locations
Subsequent research: ARRB: Climate
change framework for Queensland
Department of Main Roads in 2008
• report not published, but a summary is available in a
conference paper by Evans, Tsolakis and Naude
http://www.patrec.org/web_docs/atrf/papers/2009/1737_pap
er66-Evans.pdf (ATRF Conference 2009)
• comprehensive list of potential impacts on road
infrastructure and operations
• detailed review of (short- and long-term) climate change
forecasts for Queensland
• framework to assess risks, and to assist in the planning of
climate change mitigation and adaptation responses.
ARRB Framework
• Four impacts relevant to Queensland
–
–
–
–
temperature changes (increases in very hot days)
rainfall changes (reductions and increases) and flooding
rising sea levels with storm surges
increase in cyclone frequency and intensity.
• Phases of framework to identify investment priorities
– identify climate change effects
o geographic scale, certainty, timeframe
– determine impacts on transport
– adaptation strategies
– planning and project evaluation
Other research underway
• Austroads project: ‘Impact of climate change on road
performance’, undertaken by ARRB
– software to provide climate information from 1960 to 2099 by GPS
coordinates based on CSIRO modelling
• minimum and maximum daily temperatures, rainfall, Thornthwaite
moisture index
• pre-2007 based on historical meteorological data
• Climate Futures Tasmania Infrastructure project
• World Road Association (PIARC) Technical Committees:
C.3 (natural disasters), D.2 (road pavements), D.3 (bridges),
D.4 (geotechnics and unpaved roads)
– all have working groups on adaptation to climate change