Transcript CGE TRAINING MATERIALS VULNERABILITY AND ADAPTATION ASSESSMENT CHAPTER 5 Coastal Resources Expectation from the Training Material • Having read this presentation, in conjunction with.

CGE TRAINING MATERIALS VULNERABILITY AND ADAPTATION
ASSESSMENT
CHAPTER 5
Coastal Resources
Expectation from the Training Material
• Having read this presentation, in conjunction with the
related handbook, the reader should:
a) Be able to identify the drivers and potential
impacts of climate change on coastal zones
b) Have an overview of the methodological
approaches, tools and data available to assess the
impact of climate change on coastal zones
c) Be able to identify appropriate adaptation
measures.
Outline
• Overview of drivers and potential impacts of climate
change on coastal zone;
• Methods, tools and data requirements on coastal
zone integrated assessment methods and models,
including an overview of ENSO and sea level data;
• Adaptation planning in the coastal sector
Climate Change and Coastal Resources
• Coastal resources will be affected by a number of consequences of
climate change, including:
a) Higher sea levels
b) Higher sea temperatures, sea-surface temperature,
•
El Niño/La Niña-Southern Oscillation (ENSO)
events/climate cycle
c) Changes in precipitation patterns and coastal run-off
d) Changes in storm tracks, frequencies, and intensities, and
e) Other factors such as wave climate, storminess, and land
subsidence.
Coastal Climate Change Drivers
Primary drivers of coastal climate change impacts, secondary drivers
and processes (adapted from NCCOE, 2004)
Primary driver
Secondary or process variable
Mean sea level
• Local sea level
Ocean currents, temperature and
acidification
• Local currents
• Local winds
Wind climate
• Local waves
Rainfall/run-off
• Groundwater
Some Climate Change Factors
Net extreme
event
hazards
Net regional
mean sea
level rise
(SLR)
Timeframe
Cause
Predictability
Recurring
extremes (storm
surge/tide)
Hour–days
Wave, wind,
storms
Moderate to
uncertain
Tide ranges
Daily–yearly Gravitational
cycle
Predictable
Regional sea level
variability
Seasonal–
decadal
Wave climate,
ENSO, PDO
Moderate;
Not well known
Regional net land
movement
Decades–
millennia
Tectonic
Predictable once
measured
Regional SLR
Months–decades
Ocean warm/
current/climate
Observable;
future uncertain
Global mean SLR
Decades –
centuries
Climate
change (temp,
ice melt)
Short term
understandable;
future uncertain
Potential Impacts
Climate Change: Global context
1900-2000: Global mean surface air temp increased
by 0.6 0C
Projected increase (1990-2100): 1.4 – 5.80C
(Based on greenhouse gas emission)
2030: + 0.7 in monsoon,+ 1.3 in winter
2050: + 1.1, + 1.8 in 2050.
(Source: IPCC report)
Current Global Predictions of Sea Level Rise
• Conclusions about future SLR in the IPCC’s Third
Assessment Report (TAR, 2001) and Fourth
Assessment Report (AR4, 2007) were broadly similar.
• The IPCC AR4 projections estimated global sea-level
rise of up to 79 cm by 2100, noting the risk that the
contribution of ice sheets to sea level this century could
be higher.
Post AR4
• Research since AR4 has suggested that dynamic
processes, particularly the loss of shelf ice that
buttresses outlet glaciers, can lead to more rapid
loss of ice than melting of the top surface ice
alone.
• There is a growing consensus in the science
community that SLR at the upper end of the IPCC
estimates is plausible by the end of this century,
and that a rise of more than 1.0 metre and as high
as 1.5 metres cannot be ruled out.
Post AR4
(Source: Church et al., 2008)
Projected Global Average Surface and Sea Level Rise at the end of 21st Century
Temperature change
(0C at 2090-99 relative to 1980-99)a
Sea level rise
(m at 2090-99 relative to 1980-99)
Case
Best
estimate
Likely
range
Model-based range excluding future rapid
dynamical changes in ice flow
Year 2000
concentration b
0.6
0.3-0.9
NA
B1 scenario
1.8
1.1-2.9
0.18-0.38
B2 scenario
2.4
1.4-3.8
0.20-0.45
A1T scenario
2.4
1.4-3.8
0.20-0.43
A1B scenario
2.8
1.7-4.4
0.21-0.48
A2 scenario
3.4
2.0-5.4
0.23-0.51
A1F1 scenario
4.0
2.4-6.4
0.26-0.59
Notes: aThese estimates are assessed from a hierarchy of models that encompass a simple climate
model, several Earth System Models of Intermediate Complexity, and a large number of
Atmosphere-Ocean General Circulation Models (AOGCMs).
bYear 2000 constant composition is derived from AOGCMs only.
(Source: IPCC, 2007a)
IPCC AR4 is missing the rapid
ice flow changes….
“…an improved estimate of the range of SLR to 2100 including
increased ice dynamics lies between 0.8 and 2.0 m.”
Recent findings
~1 m
Considering the dynamic effect of ice-melt contribution to global sea level rise,
Vermeer and Rahmstorf (2009) estimated that by 2100 the sea level rise would
be approximately three times as much as projected (excluding rapid ice flow
dynamics) by the IPCC-AR4 assessment.
Even for the lowest emission scenario (B1), sea level rise is then likely to be
about 1 m and may even come closer to 2 m.
Also see http://www.msnbc.msn.com/id/42878011/ns/us_news-environment
El Niño/ La Niña -Southern Oscillation (ENSO)-Another Major Driver of Climate Change
(Warm SST)
lowP
Develops in JulAugSept, strengthen through OctNovDec,
and weakens in JanFebMar
•
El Niño - major warming of the equatorial waters
in the Pacific Ocean
• The anomaly of the SST in the tropical Pacific
increases (+0.5 to +1.5 deg. C in NINO 3.4 area)
from its long-term average;
• A high pressure region is formed in the western
Pacific and low-pressure region is formed in the
eastern Pacific —this produces a negative ENSO
index (SOI negative).
•
La Niña—major cooling of the equatorial waters in
the Pacific Ocean
• The anomaly of the SST in the tropical Pacific
decreases (-0.5 to -1.5 deg. C in NINO 3.4 area)
from its long-term average;
H(Cold SST)
low
• A high pressure region is formed in the eastern
Pacific and low-pressure region is formed in the
western Pacific—this produces a positive ENSO
index (SOI positive).
(Source: IRI Web Portal)
SA
H
NINO 3.4
B
A
G
NWP
Nino 4
M
Nino 3
SP
A
S
W
E
El Niño/ La Niña Years (1950-2012)
The number of El Niño/ La
Niña
years
has
considerably increased in
the recent years. Scientists
argue that this is the result
of climate variability and
change (instability)
and…
This trend is likely to
continue in future as we are
in a stage of changing
climate…
So, more frequent extreme
events are likely in the
future.
12
13
*2008-09
*2009-11
Impacts of ENSO: Venezuela
• Venezuela is in the midst of a genuine power and water crisis. There may not be a
clear cut answer to this question “What is causing Venezuela's energy crisis”, and
different people provide different interpretations.
•
Among others, pointing the finger at weather changes, President Chávez said “It's
El Niño,” partly to be blamed for this recent crunch;
•
The El Niño is blamed for a lack of rainfall and the cause of water shortages, which
in turn have starved Venezuela's hydroelectric dams which provide approximately
three quarters of the nation's electricity.
Other Climate Change (Hurricane Katrina) - Global to Local context
Land Subsidence
Subsidence on the coast of Turkey
following an earthquake in 1999
Non-Climate Drivers
• Port/harbour construction
• Coastal protection works
• Upstream damming for freshwater supply
• Hydroelectric power
• Deforestation
• Coastal subsidence due to ground water abstraction — particularly
significant in delta region
• Socio-economic scenario changes in coastal regions including
urbanization
• Geological natural hazards — earthquake.
Uncertainty in Local Predictions
• Relative sea level rise: global and regional components
plus land movement:
a) Land uplift will counter any global sea level rise
b) Land subsidence will exacerbate any global sea level
rise
c) Other dynamic oceanic and climatic effects cause
regional differences (oceanic circulation, wind and
pressure, and ocean-water density differences add
additional components).
Science Summary
• Under a high-emissions scenario, an SLR of up to a metre or more
by the end of the century is plausible.
• Changes in the frequency and magnitude of extreme sea level
events, such as storm surges combined with higher mean sea
level, will lead to escalating risks of coastal inundation. Under the
highest SLR scenario, by mid-century, inundations that previously
occurred once every hundred years could happen several times a
year.
• SLR will not stabilize by 2100. Regardless of reductions in
greenhouse gas emissions, sea level will continue to rise for
centuries; an eventual rise of several metres is possible.
Potential Impacts
Effect category
Example effects on the coastal Environment
Bio-geophysical




Displacement of coastal lowlands and wetlands
Increased coastal erosion
Increased flooding
Salinization of surface groundwater
Socio-economic






Loss of property and lands
Increased flood risk/loss of life
Damage to coastal infrastructures
Loss of renewable and subsistence resources
Loss of tourism and coastal habitants
Impacts of agriculture/aquiculture and decline soil and
water quality
Example Effects of Climate Change on the Coastal Zone (continued)
Effect category
Example effects on the coastal Environment
Secondary impacts
of accelerated SLR
 Impact on livelihoods and human health
 Decline in healthy/living standards as a result of decline
in drinking water quality
 Threat to housing quality
Infrastructure and
economic activity
 Diversion of resources to adaptation responses to SLR
impacts
 Increasing protection costs
 Increasing insurance premiums
 Political and institutional instability, and social unrest
 Threats to particular cultures and ways of life
Biophysical Impacts
Climate driver (trend)
Main physical/ecosystem effects on coastal
ecosystems
(CO2) concentration
Increased CO2 concentration, decreases ocean acidification
negatively impacting coral reefs and other pH
Surface sea temperature
(SST) (I, R)
Increased stratification/changes circulation; reduced incidence of
sea ice at higher latitudes; increased coral bleaching and mortality;
pole-ward species migration; increased algal blooms.
(I: increasing, R:Regional
variability)
Sea level (I, R)
Inundation, flood and storm damage; erosion; saltwater intrusion;
rising water tables/impeded drainage; wetland loss (and change)
Storm intensity (I, R)
Increased extreme water levels and wave heights; increased
episodic erosion, storm damage, risk of flooding and defence
failure
Altered surges and storm waves, and hence risk of storm damage
and flooding
Storm frequency (?, R);
Storm track (?, R)
Wave climate
Run-off (R)
Altered wave conditions, including swell; altered patterns of erosion
and accretion; re-orientation of beach plan form
Altered flood risk in coastal lowlands; altered water quality/salinity;
altered fluvial sediment supply; altered circulation and nutrient
supply.
Threats to the Coastal Environment
Threats to Coastal Environment (continued)
Threats to Coastal Environment (continued)
Vulnerable Regions Mid-estimate (45 cm) by the 2080s
Caribbean
Pacific
Oc ean
SMALL
ISLANDS
A
C
PEOPLE ATRISK
(millions per region)
A
> 50 million
B
10 - 50 million
C
< 10 million
region boundary
vulnerable island region
C
Indian
Oc ean
SMALL
ISLANDS
B
Atolls
Impacts of Climate Change: Antigua and Barbuda
• Damage to critical habitats (beaches,
mangroves, sea grass beds, coral
reefs)
• Loss of wetlands, lands due to sea
level change
• Increased coral bleaching as a result of
a 2°C increase in SST by 2099
• Destruction to coastal infrastructure,
loss of lives and property
• Changes in coastal pollutants will occur
with changes in precipitation and runoff
• General economic losses to the
country.
Source: http://unfccc.int/resource/docs/natc/antnc2.pdf
Also see: http://unfccc.int/national_reports/non-annex_i_natcom/items/2979.php
Coastal Megacities (>8 million people)
Tianjin
Dhaka
Seoul
Osaka
Istanbul
Tokyo
New York
Shanghai
Manila
Los Angeles
Bangkok
Lagos
Mumbai
Lima
Karachi
Buenos Aires
Rio de Janeiro
Madras
Jakarta
Calcutta
Elevation and Population Density Maps for Southeast Asia
Indo-China Peninsula
Sea-Level Rise: Summary
Research indicates:
1.
2.
3.
4.
5.
6.
Doubled melting rate of Greenland ice sheet
Net melting of the Antarctic ice sheet
Global rise approaching 3.0 mm/yr, twice the rate last century,
Continued heating of atmosphere – heating of water column,
More than 1 m rise is now expected during this century.
30C temperature rise suggests 3-6 m SLR in a century.
There are still major uncertainties in sea-level science, but these
latest results are significant in that:
1. They do not point in the direction of smaller rates of rise,
2. They are consistent with the worse case of long-standing
predictions,
3. Counter arguments grow fewer and fewer…
II (a). Overview of Coastal Vulnerability Assessment
Level of
assessment
Timescale Precision
required
Prior
Other scenarios in
knowledge addition to SLR
Strategic level
(screening
assessment)
2-3 months
Lowest
Low
Direction of change
Vulnerability
assessment
1-2 years
Medium
Medium
Likely socio-economic
scenarios and key
scenarios of key
climate drivers
Site-specific
level (planning
assessment)
Ongoing
Highest
High
All climate change
drivers (often with
multiple scenarios)
Level of Assessment: Screening Assessment
• This is a rapid assessment to highlight possible impacts
of a sea level rise scenario and identify information/data
gaps.
• Qualitative or semi-quantitative.
• Steps
a) Collation of existing coastal data
b) Assessment of the possible impacts of a 1-m sea
level rise
c) Implications of future development
d) Possible responses to the problems caused by
SLR
Step 1: Collation of Existing Data
• Topographic surveys
• Aerial/remote sensing images – topography/ land cover
• Coastal geomorphology classification
• Evidence of subsidence
• Long-term relative SLR
• Magnitude and damage caused by flooding
• Coastal erosion
• Population density
• Activities located on the coast (cities, ports, resort areas
and tourist beaches, industrial and agricultural areas).
Step 2: Assessment of Possible Impacts of 1m Sea Level Rise
• Four impacts are considered:
a) Increased storm flooding
b) Beach/bluff erosion
c) Wetland and mangrove inundation and loss
d) Salt water intrusion
(i) Increased Storm Flooding
• Describe what is located in flood-prone areas.
• Describe historical floods, including location,
magnitude and damage, the response of the local
people, and the response of government.
• How have policies toward flooding evolved?
(ii) Beach/bluff Erosion
•
Describe what is located within 300 m of the ocean coast.
•
Describe beach types.
•
Describe the various livelihoods of the people living in coastal
areas such as commercial fishers, international-based coastal
tourism, or subsistence lifestyles.
•
Describe any existing problems of beach erosion including
quantitative data. These areas will experience more rapid erosion
given accelerated sea level rise.
•
For important beach areas, conduct a Bruun rule analysis
(Nicholls, 1998) to assess the potential for shoreline recession
given a 1-m rise in sea level.
•
What existing coastal infrastructure might be impacted by such
recession?
(iii) Wetland and Mangrove Inundation
• Describe the wetland areas, including human
activities and resources that depend on the wetlands.
For instance, are mangroves being cut and used, or
do fisheries depend on wetlands?
• Have wetlands or mangroves been reclaimed for
other uses, and is this likely to continue?
• Are these wetlands viewed as a valuable resource for
coastal fisheries and hunting or merely thought of as
wastelands?
(iv) Salt Water Intrusion
• Is there any existing problem with water supply for
drinking purposes?
• Does it seem likely that salinization due to sea level
rise will be a problem for surface and/or subsurface
water?
Step 3: Implications of Future Developments
• New and existing river dams and impacts on
downstream deltas
• New coastal settlements
• Expansion of coastal tourism
• Possibility of transmigration
Step 4: Responses to the Sea Level Rise Impacts
• Protect (i.e. hard and soft defences, seawalls,
beach nourishment).
• Planned retreat (i.e. setback of defenses)
• Accommodate (i.e. raise buildings above flood
levels)
Screening Assessment Matrix (Biophysical vs. Socioeconomic Impacts)
Biophysical
impact of SLR
Socio-economic impacts
Gender
Human
Health
Financial
Services
Others?
Fisheries
Salinization
Water
Supply
Flooding
Agriculture
Erosion
Human
Settlements
Tourism
Inundation
Bruun Rule
R = shoreline recession due to a sea-level rise S
h* = depth at the offshore boundary
B = appropriate land elevation
L = active profile width between boundaries
G = inverse of the overfill ratio
R = G(L/H)S; where H=B + h*
Beach Profile in Equilibrium with Sea Level
Y
Eroded profile
X
Accreted profile
Y/X = 50 to 200….say, 100
1 m sea level rise = 100 m (~400 ft) shoreline recession
Depth of
closure
Limitations of the Bruun Rule
• Only describes one of the processes affecting sandy
beaches
• Indirect effect of mean SLR:
a) Estuaries and inlets maintain equilibrium
b) Act as major sinks
c) Sand eroded from adjacent coast
d) Increased erosion rates.
• Response time – best applied over long timescales.
Level of Assessment: Vulnerability Assessment
Coastal Vulnerability Assessment
• Vulnerability assessment (1-2 years):
i.
Erosion
ii. Flooding
iii. Coastal wetland/ecosystem loss.
• The aim of screening and vulnerability assessment is
to scale prioritization of concern and to target future
studies, rather than to provide detailed predictions.
(i) Vulnerability Assessment: Beach Erosion
(ii) Vulnerability Assessment: Flooding
• Increase in flood levels due to rise in sea level
• Increase in flood risk
• Increase in populations in coastal floodplain
• Adaptation:
a) Increase in flood protection
b) Management and planning in floodplain.
Coastal Flood Plain
Flood Methodology
Global Sea-level
Rise Scenarios
Subsidence
Storm Surge
Flood Curves
Coastal
Topography
Relative Sea-Level
Rise Scenarios
Raised Flood Levels
Population
Density
Size of Flood
Hazard Zones
Protection Status
People in the
Hazard Zone
(“EXPOSURE”)
Average Annual
People Flooded,
People to Respond
(“RISK”)
(1in 10, 1 in100, etc.)
(iii) Vulnerability Assessment: Wetland/Ecosystem Loss
• Inundation and displacement of wetlands e.g.,
mangroves, saltmarsh, intertidal areas:
a) Wetland areas provide:
• Flood protection
• Nursery areas for fisheries
• Important areas for nature conservation.
• Loss of valuable resources, tourism.
Areas Most Vulnerable to Coastal Wetland Loss
Coastal wetland Loss (Mangrove Swamp)
Coastal Squeeze (of coastal wetlands)
Coastal squeeze under SLR: impact of development (Image: DCCEE, 2009)
Coastal Ecosystems at Risk
KEY:
mangroves, o saltmarsh, x coral reefs
Planning Assessment
• On-going investigation of an specific area and
formulation of policy.
a) Requires information on:
• Role of major processes in sediment budget
• Including human influences
• Other climate change impacts
• Combined flood hazard and erosion assessment.
How do beaches respond to sea level rise?
…they erode…
(Source: http://www.soest.hawaii.edu/coasts/presentations/)
How do people respond to eroding beaches?
…they armour…
(Source: http://www.soest.hawaii.edu/coasts/presentations/)
…and how do beaches respond to armoring?
…they disappear…
(Source: http://www.soest.hawaii.edu/coasts/presentations/)
Goals for Planning Assessment
• For future climate and protection scenarios, explore
interactions between cliff management and flood risk
within sediment sub-cell (in Northeast Norfolk):
• In particular, quantify:
a) Cliff retreat and associated impacts
b) Longshore sediment supply/beach size
c) Flood risk
d) Integrated flood and erosion assessment.
Method for Planning Assessment
Scenarios
Climate Change,
Sea-Level Rise
Scenarios
Protection,
Socio-economic
Scenarios
Overall
Assessment
Analysis
Regional
Wave/Surge
Models
SCAPE
Regional
Morphological
Model
Flood
Risk Analysis
(LISTFLOOD-FP)
SCAPE GIS
Data Storage
Cliff Erosion
Analysis
Integrated
Cell-scale
Assessment
Overview of ENSO and Sea Level Variability
Sea Level Change during EL Niño Year
+ 24”
- 12”
ERS: European Remote Sensing
ENSO—Major driver of CC
La Niña (strengthened trade winds)
(Cold SST) high
pressure
system
(Warm SST) low
pressure
system
…….shifts
Temp
Rainfall
Run-off
Sea level
across the
globe….
El Niño (weakened trade winds)
67
The recent water and power crisis: Is El Niño to be blamed partly?
•
Venezuela is in the midst of a genuine power and water
crisis.;
•
The El Niño is blamed to have resulted in a lack of
rainfall and the cause of water shortages, which in turn
have starved Venezuela's hydroelectric dams which
provide approximately three quarters of the nation's
electricity.
‘Hot Spots’ of Climate Hazards : USAPI Case Study, Operational Sea Level Forecasts
• Pacific Island communities
are among the most
vulnerable
to
climate
variability/change—
• Economic
plans
are
dependent on climatesensitive sectors—
• ENSO
has
significant
impact on the overall
development of the US
Asia-Pacific
Islands
region—
• There
is
increasing
concern
that
extreme
events is changing in
frequency and intensity.
USAPI –
Climate Counts in the Pacific!!

USAPI
(09º0´N; 168º0´E)
(13º48´N; 144º45´E)
(14º20´S; 170º0´W)
ENSO Impact on the Caribbean Islands
• Caribbean response to ENSO depends very much on WHICH part of the
Caribbean we are talking about.
a) For example, like southern Florida, Cuba is expected to have below
average precipitation during La Nina winters.
b) Haiti and Dominican Republic are often also included in that response,
but less reliably.
c) Puerto Rico also does, but to a still lesser degree.
• The Lesser Antilles are in a transition zone, where the northern ones have
a slightly greater chance to be dry during La Nina (and wet during El Niña),
while the southern ones (such as Grenada) share the effect of northern
South America, which is the opposite (wet tendency during La Niña).
• So the place where the dryness can be most confidently attributed to the
La Niña is Cuba, and the opposite effect is expected in the islands just
north of South America.
Sea Level Data (hourly/daily/monthly; max/mean/anomaly/deviations)
University of Hawaii Sea Level Center
http://ilikai.soest.hawaii.edu/uhslc/data.html
Sea Level Data: Tide Gauge

2007

Palau
Majuro
Source: http://uhslc.soest.hawaii.edu/
S El Niño: 1951, 58, 72, 82, & 97/ (Yr,0)
S La Niña: 1964, 73, 75, 88, 98 (Yr, 0)
M El Niño: 1963, 65, 69, 74, & 87
M La Niña: 1956, 70, 71, 84, 99
Guam
10
S _ E l Ni no
5
M_ E l Ni no
S _ LaNi na
M_ LaNi na
0
Jun
May
Apr
Mar
(+1)
Jan
D ec
Nov
Oct
Aug
S ep
Year (0)
-10
Feb
-5
Jul
S L devi at i ons ( i nches)
ENSO and Sea Level Variability
Mont h
Year (0)
Marshalls (Kwajalein)
(+1)
S _ E l Ni no
5
M_ E l Ni no
S _ LaNi no
M_ LaNi no
Composites of monthly sea-level
deviations in El Niño /La Niño years
0
Mont h
Jun
May
Apr
Mar
(+1)
Jan
D ec
Nov
Oct
Aug
S ep
Year (0)
-10
Feb
-5
Jul
S L devi at i ons
( i nches)
10
(Source: Chowdhury et al., 2007a)
SST Composites for Low and High Sea Level Years - Predictability
Guam
Grid Analysis and Display System (GrADS)
El Niño signal
La Niña signal
Probabilistic forecasts for sea level variability is possible well ahead of time….
Composites of Strong El Niño and Strong La Niña Years
(e)
(SE-SL)
+2
(j)
C-100S
(d)
+1
(i)
C-E
Niño3.4
(c)
(h)
EqW-DL
EqC –E
(b)
0
(g)
–
1
(f)
–3
(a)
(Source: Chowdhury et al., 2007a)
Correlations between SST and sea level—Predictability
Sea-level variability is correlated to SSTs in the Pacific on seasonal time scales….
(b)
(a)
–1
(13º48´N;
144º45´E)
0.5-0.6
0.6-0.7
Nino 3.4
NW-SW
(c)
(d)
(09º0´N;
168º0´E)
SC
(e)
(f)
(14º2´S;
170º0´W)
SC
Source: Chowdhury et al., 2007b
Climate Predictability Tool (CPT)
International Research Institute
for Climate and Society
Sources:
• http://www.google.com/#hl=en&sclient=psyab&q=Climate+predictability+tools&oq=Climate+predictability+tools&aq=f&aqi=gK1&aql=&gs_l=hp.3..0i30.1246.11109.0.13040.28.14.0.13.13.1.746.3392.0j4j6j0j1j0j1.12.0...0.0.JAOJ
XOziHRE&pbx=1&bav=on.2,or.r_gc.r_pw.r_qf.,cf.osb&fp=a8708267f6810afa&biw=1280&bih=685 (By
Ousmane Ndiaye and Simon J. Mason)
• http://portal.iri.columbia.edu/portal/server.pt?open=512&objID=697&PageID=7264&mode=2
What is the CPT?
• The Climate Predictability Tool (CPT) provides a Windows package for :
a) Seasonal climate forecasting
b) Forecast model validation (skill scores)
c) Actual forecasts given updated data.
• Uses ASCII input files
• Options :
a) Principal components regression (PCR)
b) Canonical correlation analysis (CCA)
• Help pages on a range of topics in HTML format
• Options to save outputs in ASCII format and graphics as JPEG files
• Program source code is available for those using other systems (e.g.,
UNIX).
Selecting the Analysis
Choose the analysis to perform: PCR or CCA
Input Datasets
Both analysis methods require two datasets:
• “X variables” or “X Predictors” dataset; (SST, monthly anomaly)
• “Y variables” or “Y Predictands” dataset; (SL, monthly deviations)
SST Data (NCEP monthly SST field)
(Source: http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version3b/.sst/X/100/260/RANGE/Y/35/35/RANGE/T/%28Jan%201975%29%28Mar%202012%29RANGE/T/3/0.0/boxAverage/T/12/STEP/dup%5BT%5Daver
age/sub/-999.0/setmissing_value)
Multiple Linear Regression via Canonical Correlation Analysis (CCA)
• Regress seasonal average observed rainfall fields y onto GCM forecast
fields x,
y = Ax + ε
• Expand x and y in truncated Principal Component time series Vx and Vy,
and standardize the PCs
• The singular value decomposition VyTVx = RMST identifies linear
combinations of the observation and predictor PCs with maximum
correlation and uncorrelated time series
(Barnett and Preisendorfer, 1987)
• These new pattern-variables give a diagonal regression matrix whose
coefficients are correlations: (VyR) = M (VxS)
• The CCA modes with low correlation should be neglected.
a)
JFM_SST
(30.5%)
1998
1997
b)
AMJ_SST
(26.2%)
c)
JAS_SST
(29.0%)
d)
OND_SST
(31.5%)
83
Source: Chowdhury et al., 2007b
a)
JFM_SST
(15.5%)
b)
AMJ_SST
(17.1%)
c) JAS_SST
(17.5%)
d) OND_SST
(17.5%)
84
CCA Cross-validated Hindcast Skills
Cross Validation skill
Sea-level Forecasts –CCA Cross-validation Skill
Guam
0.9
Marshalls
A Samoa
0.7
0.5
0.3
0
1
2
JFM
3
0
1
2
AMJ
3
0
1
2
JAS
3
0
1
2
3
OND
Target Season
EOF (%)
X:75.8
X:75.5
X:76.0
X:73.1
Y:91.0
Y:83.0
Y:84.0
Y:96.0
With a lead time of one or two seasons, the forecasts for all the seasons are accurate
(Source: Chowdhury et al., 2007b)
Summary and Conclusions
• Climate variability in the USAPI region are sensitive
to ENSO;
• ENSO-based seasonal forecasts are successful in
the USAPI region: other countries can also benefit
from it;
• Some immediate responses - adaptations and
mitigations - are necessary;
• As an adaptation strategy, ENSO-based forecasts
can play an important role in facing some of these
challenges.
Tide Predictions (high/low water level)
http://tidesandcurrents.noaa.gov/station_retrieve.shtml?type=Tide+Predictions
Extremes of Sea-level at 20- and 100-yr RP
• There is increasing concern that extreme events are changing
in frequency and intensity as a result of changing climate.
• The occurrence of dangerously high water levels and the
associated erosion and inundation problems are extremely
important issues.
Methodology:
• Hourly max/min SL data
• Generalized Extreme Value (GEV) Distribution
• L-moments
• Bootstrap method
(Source: http://ilikai.soest.hawaii.edu/uhslc/woce.html)
Generalized Extreme Value (GEV) distribution
• Probability distribution function (PDF) of GEV
11/ 
1   (x   ) 
f ( x)  1 

 
 (x   )
 ( x   ) 1/ 
 0,
exp{[1 
] }, 1 


Here there are three parameters:
A location (or shift) parameter  , a scale parameter  and a shape parameter  .
• Cumulative distribution function (CDF)
 ( x   ) 1/ 
F ( x)  exp{[1 
]
},

GEV products define the thresholds beyond the seasonal tidal range that have low but finite
probabilities of being exceeded on a seasonal scale.
(Source: Chowdhury et al., 2008; Chowdhury et al., 2009)
How to Determine Values of the Distribution Parameters?
• The method of maximum likelihood (ML).
• The method of L-moments: chosen because this method is
computationally simpler than the method of ML and because L-moment
estimators have better sampling properties than the method of ML with
small samples (more robust). Hosking & Wallis, 1997; Zwiers & Kharin,
1998
The Seasonal Extreme Values: Honolulu (1-to 100-Years Return Period)
SL in mm
Seasonal Sea-level Deviations: Hawaii - (i) 20 RP and (ii) 100 RP
(Source: Chowdhury et al., 2008)
Seasonal Sea-level Deviations: USAPI - (i) 20 RP and (ii) 100 RP
Deviations: 20-year RP
Deviations: 100-year RP
(Source: Chowdhury et al., 2008)
Summary
• 20-RP: while the SL deviations of the Hawaiian Islands are
moderate (< 200 mm), the deviations in the U-Trust islands are
higher (close to 300 mm rise)
• 100-RP: considerable deviations (329 mm at Nawiliwili and 547
mm at Wake) are visible in JAS;
a) rise more than 300 mm can cause tidal inundations damage to
roads, harbors, unstable sandy beaches, etc.
• Increasing concern that extreme events may be changing in
frequency and intensity as a result of: (i) natural and/or (ii) human
interferences to physical environment.
Downscaling
• The first stage in developing sea level scenarios involves
downscaling of global scenarios to the regional or local level.
• The spatial resolution of climate models is too coarse to render
them directly applicable to local island environments.
• The outputs of large-scale models are used to help develop
statistical models for rainfall and sea level forecasts on seasonal
time scales for each of the main islands and a few of the outer
islands with unique climate responses.
Summary (Methods, Tools, and Data requirements: Case Study)
Four methods:
•ENSO-based seasonal sea level forecasts:
a) Data: SST (NCEP, IRI Library), SL (UHSLC); and
http://www.esrl.noaa.gov/psd/data/correlation/
b) Model: Composite, Correlations and CCA
c) Tools: CPT, GrAds
•Tide predictions (hour-to-yearly time scales):
a) Data/Model/Tools:
http://tidesandcurrents.noaa.gov/data_menu.shtml?stn=1630000%20Guam,%20MARI
ANAS%20ISLANDS&type=Tide+Predictions
•Extremes of SL @ 20, 100-RP:
a) Data: Hourly SL (UHSLC)
b) Model: GEV, Bootstrap method, L-moment
c) Tools: Excel, Mat lab
•Downscaling of GCMS:
a) Data: SL (UHSLC), SST or SLH (IPCC-AR4, GCMs)
b) Model: CCA
c) Tools: CPT, GrAds.
Adaptation
Adaptation
• Mitigation and/ or adaptations?
• Socio-economic systems in coastal zones also have the capacity
to respond autonomously to climate change
• Farmers may switch to more salt-tolerant crops, and people may
move out of areas increasingly susceptible to flooding—
autonomous adaptation
• Because impacts are likely to be great, even taking into account
autonomous adaptation, there is a further need for planned
adaptation.
Adaptations Case Study: USAPI
PEAC’s forecasts and Outreach
•
Monthly Teleconference
a)
b)
c)
d)
•
PEAC-forecasts (i.e., sea-level, rainfall, tropical cyclone etc.) are
placed for discussion within a PEAC-sponsored teleconference;
The Weather Service Office from each of the island communities
is invited to attend this conference;
Representatives from the forecasting centers are also invited past, present, and future climatic conditions are brought up;
A consensus forecast is achieved;
Warning messages are developed.
http://www.prh.noaa.gov/peac/update.php
Rainfall and runoff variability in GBM basin
Discharges in the Ganges, Brahmaputra Meghna region
‘Current situation’
Brahmaputra
583,000 sq. km
Himalayan melting risk
Discharge
Ganges
907,000 sq. km
India
Meghna
65,000 sq. km
Bangladesh
‘Hot Spots’ of Climate Hazards (II): Bangladesh - Floods and livelihood consequences
• Basin-wide rainfall-runoff is the primary cause of flooding;
• Approximately 20 percent of the country is flooded annually;
• Floods of 1954, 1974, 1987 and 1988 inundated about 50 percent of the
country—1998 flood inundated 90 percent….
• Bangladesh floods are sensitive to ENSO—El Niño to lower and La Niña
to higher than normal flooding…
ENSO and seasonal flooding (1954-2009)
1998
1988
1955
1974
2007
Bangladesh floods are connected to ENSO - El Niño to lower and La Niña to higher than normal
flooding
>>>1988 and 1998 are two ‘rapid ENSO transition year’
Chronology of TC and El Niño/La Niña events
May 27, 2009: Tropical Cyclone Aila (TC)
La Niña to
ENSO-neutral
April 14-15, 2009: Cyclonic Storm Bijli, 90-km/hr
-do-
November 15, 2007: Cyclone Sidr: 215-km/hr Category 4,
650,000 evacuated (Killed 5-10,000)
La Niña
November 29 -30, 1997: 224-km/hr
Strong El Niño
April 29 -30, 1991: 225-km/hr (killed 150,000)
El Niño
May 24 -25, 1985: Severe cyclone hit Chittagong causing
10-20 ft surge (killed 12,000 people)
La Niña to El
Niño
November 12, 1970: 222-km per hour causing 10-20 ft
high tidal surge (killed 0.5 M/1.2 M people)
La Niña
May 28- 29 May, 1963: Cyclonic storm hit Chittagong
(killed 12,000 people)
El Niño
• Number of major cyclones have drastically increased in recent years (BBS)
• i.e., 1795-1845 and 1846-1896: 3, 1897-1947: 18, 1948-1998: 51
SST Composites of wet and dry years—Predictability
Rainfall : Wet/dry
Ganges-flow : High/low
Brahmaputra-flow: High/low
La Niña signal
El Niño signal
The rainfall and stream-flows in Bangladesh is connected to variation in SST in the Pacific….
SST and JAS floodCorrelation map
JFM
(-2)
MJJ
Flooding is
correlated to SSTs in
the Pacific on
months-to-seasonal
time scales….
OND
(+1)
JAS
(0)
JJA
DJF
AMJ
(-1)
NDJ
MAM
OND
(-3)
FMA
106
Adaptation: Drought in Majuro
Lessons from 1997 - 98 El Niño
People line up for water in Majuro to receive ration
once every fourteen days
• Water rationing in
Majuro;
• Crop losses in FSM,
RMI, CNMI
• Palau experienced 9month drought
(Source: Schroeder TA, et al., 2012)
Coastal Erosion - Case Example (No forecast no adaptation)
Results of coastal erosion at Blue Lagoon Resort (Weno, Chuuk, FSM)
during the La Niña of 2007-08
(Source: Schroeder TA, et al., 2012)
Forecast-based Adaptation - Case Example
Mitigation-adaptation at the Blue Lagoon Resort, Weno, Chuuk, FSM prior to the La
Niña of 2010-11 (Photo courtesy of Chip Guard, WFO, Guam)
(Source: Schroeder TA, et al., 2012)
Example Approach to Adaptation Measures
• Caribbean small island developing country
• Climate change predictions:
a)
b)
c)
d)
Rise in sea level
Increase in number and intensity of tropical weather systems
Increase in severity of storm surges
Changes in rainfall
e) Reclamation of land, sand mining, and lack of
comprehensive natural system engineering approaches to
control flooding and sedimentation have increased the
vulnerability to erosion, coastal flooding and storm damage
in Antigua.
Example Approach to Adaptation Measures (continued)
Coastal impacts:
•
Damage to property/infrastructure – particularly in low-lying
areas, which can affect the employment structure of the
country
•
Damage/loss of coastal/marine ecosystems
•
Destruction of hotels and tourism facilities— create
psychological effects to visitors
•
Increased risk of disease— increased risk of various infectious
diseases, increased mental and physical stress
•
Damage/loss of fisheries infrastructure
•
General loss of biodiversity
•
Submergence/inundation of coastal areas.
Example Approach to Adaptation Measures (continued)
Adaptation (retreat, protect, accommodate):
•
Improved physical planning and development control
•
Strengthening/implementation of Environmental Impact
Assessments (EIA) regulations
•
Formulation of Coastal Zone Management Plan
•
Monitoring of coastal habitats, including beaches
•
Formulation of national climate change policy
•
Public awareness and education.
Adaptation Options Related to Goals
(Source: USEPA, 2008)
Adaptation Planning, Integration and Mainstreaming
Coastal managers, stakeholders and decision -makers can use the following range of
criteria in deciding the best adaptation option within a given local context:
• Technical effectiveness: How effective will the adaptation option be in solving
problems?
• Costs: What is the cost to implement the adaptation option and what are the
benefits?
• Benefits: What are the direct climate change-related benefits?
a) Does taking action avoid damages to human health, property, or livelihoods?
b) Or, does it reduce insurance premiums?
Implementation considerations:
How easy is it to design and implement the option in terms of level of skill required,
information needed, scale of implementation, and other barriers?
Most adaptation measures can help in achieving multiple objectives and benefits.
‘No regrets’ measures should be the priority.
Workable Tools to Save Beaches
1. Willing seller purchase
2. Sand replenishment
3. Do not armour public lands
4. Set back new development
Mainstreaming: Set Back New Development
…Way back…
…300 - 500 feet…
This means new lot dimensions,
new building codes, new designs,
new types of subdivisions –
the end of zoning