Transcript Final-Coastal-Resources-2012-07-12

COASTAL RESOURCES
CGE TRAINING MATERIALS FOR VULNERABILITY AND ADAPTATION
ASSESSMENT
Expectation from the Training Material
• After having read this Presentation, in combination with the related
handbook, the reader should:
•
Identify the drivers and potential impacts of climate change
on coastal zones;
•
Have an overview of the methodological approaches, tools
and data available to assess the impact of climate change on
coastal zones;
•
Identify appropriate adaptation measures.
2
Presentation Summary
•
The PPT presentations covers the following topics:
I. Overview of drivers and potential impacts of climate
change on coastal zone;
II. Methods, tools and data requirements covering an
overview on coastal zone integrated assessment methods
and models;
III. Hands-on exercise (not included in this PPT file)
IV. Adaptation planning in the coastal Sector.
I (a). Climate Change and Coastal Resources
 Coastal resources will be affected by a number of consequences of climate change,
including:
 Higher sea levels
 Higher sea temperatures, sea-surface temperature,
 El Niño/La Niña-Southern Oscillation (ENSO) events/climate cycle
 Changes in precipitation patterns and coastal runoff
 Changes in storm tracks, frequencies, and intensities, and
 Other factors like Wave climate, Storminess, Land subsidence etc.
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/runoff
 Groundwater
Some Climate Change Factors
Net extreme
event
hazards
Net regional
mean sea
level rise
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
Seasonaldecadal
Wave climate,
ENSO, PDO
Moderate;
Not well known
Regional net land
movement
DecadesMillennia
Tectonic
Predictable once
measured
Regional SLR
Monthsdecades
Ocean warm/
current/climate
Observable;
future uncertain
Global mean SLR
Decadescentauries
Climate
change (temp,
ice melt)
Short term
understandable;
future uncertain
Potential Impacts
Climate Change: Global context
---IPCC report
1900-2000: Global mean- surface air temp
increased by 0.6 0C
Source: IPCC
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.
Current Global Predictions of Sea Level Rise
 Conclusions about future sea-level rise 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 centimeters 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 growing consensus in the science community that sea-level rise 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 (Source: IPCC, 2007a) (Summary)
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.
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
Develops in JAS, strengthen through OND, and
weakens in JFM
(Warm SST)
lowP
 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

H(Cold SST)
low

(Source: IRI Web Portal)
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;
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).
14
Sea Level Change during EL Niño Year
+ 24”
- 12”
SA
H
NINO 3.4
B
A
G
NWP
Nino 4
M
Nino 3
SP
A
S
W
E
16
El Niño/ La Niña Years (1950-2012)
The numbers of El Niño/
La Niña years have
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;
12
*2008-09
13
*2009-11
So,
more
frequent
extreme
events
are
likely in the future—
17
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 differing 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 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.
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 subsistence 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
 Land uplift will counter any global sea level rise


Land subsidence will exacerbate any global sea level rise
Other dynamic oceanic and climatic effects cause regional differences (oceanic
circulation, wind and pressure, and ocean-water density differences add additional
component)
Science Summary
 Under a high-emissions scenario, a sea-level rise of up to a meter 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 sea-level rise scenario by mid-century,
inundations that previously occurred once every hundred years could happen
several times a year
 Sea-level rise will not stabilise by 2100. Regardless of reductions in greenhouse
gas emissions, sea level will continue to rise for centuries; an eventual rise of
several meters is possible.
I (b). 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 (2)
Effect category
Example effects on the coastal Environment
Secondary impacts
of accelerated sea
level rise
 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 sea
level rise 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 fertilization, decreases ocean acidification
negatively impacting coral reefs and other pH
SST (I, R)
 Increased stratification/changes circulation; reduced incidence
of sea ice at higher latitudes; increased coral bleaching and
mortality; poleward 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 Coastal Environment (1)
Threats to Coastal Environment (2)
Threats to Coastal Environment (3)
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 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
New 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 sea-level rise in a century.
There are still major uncertainties in sea-level science, but these latest
results are significant in that:
1.
2.
3.
They do not point in the direction of smaller rates of rise,
They are consistent with the worse case of longstanding predictions,
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
 Rapid assessment to highlight possible impacts of a sea level rise scenario and
identify information/data gaps
 Qualitative or semi quantitative
 Steps
1.
Collation of existing coastal data
2.
Assessment of the possible impacts of a 1-m sea level rise
3.
Implications of future development
4.
Possible responses to the problems caused by sea level rise
Step 1: Collation of Existing Data
 Topographic surveys
 Aerial/remote sensing images – topography/ land cover
 Coastal geomorphology classification
 Evidence of subsidence
 Long-term relative sea level rise
 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

(i) Increased storm flooding

(ii) Beach/bluff erosion

(iii) Wetland and mangrove inundation and loss

(iv) 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 being 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
 Planned retreat (i.e. setback of defenses)
 Accommodate (i.e. raise buildings above flood levels)
 Protect (i.e. hard and soft defenses, seawalls, beach nourishment)
Screening Assessment Matrix (Biophysical vs. Socioeconomic Impacts)
Biophysical Impact
of Sea
Level Rise Tourism
Inundation
Erosion
Flooding
Salinization
Others?
Socioeconomic impacts
Human
Settlements
Agriculture
Water
Supply
Fisheries
Financial
Services
Human
Health
Gender
Bruun Rule
R = G(L/H)S; where H=B + h*
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
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 sea level rise

Estuaries and inlets maintain equilibrium

Act as major sinks

Sand eroded from adjacent coast

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

Increase in flood protection

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

Wetland areas provide
 Flood protection
 Nursery areas for fisheries
 Important 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 sea-level rise: 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

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 armor…
(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

Cliff retreat and associated impacts

Longshore sediment supply/beach size

Flood risk

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
II (b). ‘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 USAPI 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)
Guam, Palau, CNMI, Marshalls
Islands, FSM, and American Samoa
67
ENSO Impact on Caribbean Island
•
Caribbean response to ENSO depends very much on WHICH part of the Caribbean we
are talking about—For example, like southern Florida, Cuba is expected to have below
average precipitation during La Nina winters, and that definitely happened this last winter,
•
Haiti and DR are often also included in that response, but less reliably,
•
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 Nino), while the southern ones
(like Grenada) share the effect of northern South America, which is the opposite (wet
tendency during La Nina),
•
So the place where the dryness can be most confidently attributed to the La Nina 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
http://uhslc.soest.hawaii.edu/

Majuro
Source: Personal Photo Album., 2007

Palau
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
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
Composites of monthly Sea-level
deviations in El Niño /La Niño years
71
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….
72
Composites of Strong El Niño and Strong La Niña Years
(e)
(SE-SL)
(j)
+2
C-100S
(d)
(i)
(c)
(h)
+1
C-E
Niño3.4
EqC –E
0
EqW-DL
(b)
(g)
–
1
(f)
–3
73
(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….
–1
0.5-0.6
(b)
(a)
(13º48´N;
144º45´E)
NW-SW
(c)
0.6-0.7
Nino 3.4
(d)
(09º0´N;
168º0´E)
SC
(f)
(e)
(14º2´S;
170º0´W)
SC
74
Source: Chowdhury et al., 2007b
Climate Predictability Tool (CPT)
http://portal.iri.columbia.edu/portal/server.pt?ope
n=512&objID=697&PageID=7264&mode=2
International Research Institute
for Climate and Society
Source: 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.
JAOJXOziHRE&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)
What is Climate Predictability Tool (CPT)?
The Climate Predictability Tool (CPT) provides a Windows package for :
 seasonal climate forecasting
 forecast model validation (skill scores)
 actual forecasts given updated data
Uses ASCII input files
Options :
 principal components regression (PCR)
 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)
Sea Surface Temperature Data (NCEP monthly SST field)
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%5
Daverage/sub/-999.0/setmissing_value
Multiple linear Regression via Canonical Correlation Analysis (CCA)
 • Regress seasonal average observed rainfall fields y onto GCM f’cast 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 are neglected
a)
JFM_SST
(30.5%)
1998
1997
b)
AMJ_SST
(26.2%)
c)
JAS_SST
(29.0%)
d)
OND_SST
(31.5%)
81
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%)
82
CCA Cross Validated Hindcast Skills
Cross Validation skill
Sea-level Forecasts –CCA Cross-Validation Skill
A Samoa
Marshalls
Guam
0.9
0.7
0.5
0.3
0
1
2
3
0
JFM
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 skillful
84
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 to face some of these challenges.
85
II (b) Tide Predictions (H/L WL)
http://tidesandcurrents.noaa.gov/station_retrieve.shtml?type=Tide+Predictions
II (b). Extremes of sea level at 20- and 100-yr RP
 There is increasing concern that extreme events is
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:
http://ilikai.soest.hawaii.edu/uhslc/woce.html
Generalized Extreme Value Distribution
L-moments
Bootstrap method
Generalized Extreme Value (GEV) distribution

PDF of GEV
f ( x) 
11/ 
1   (x   ) 
1


 
exp{[1 
 ( x   ) 1/ 
 (x   )
] }, 1 
 0,


Here there are three parameters: a location (or shift) parameter
a scale parameter  , and a shape parameter
.



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: It is chosen because this method is
computationally simpler than the method of ML and because Lmoment 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., 2009
Summary


20-RP: while the SL deviations of the Hawaiian Islands are moderate
(< 200 mm), the deviations in the U.S.-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 &/or (ii) human interferences
to physical environment;
II (b). Downscaling
 The first stage to develop 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




Data/Model/Tools:
(UHSLC);
and
http://tidesandcurrents.noaa.gov/data_menu.shtml?stn=1630000%20Guam,%20MA
RIANAS%20ISLANDS&type=Tide+Predictions
Extremes of SL @ 20, 100-RP




SL
Tide predictions (hour-to-yearly time scales)


Data:
SST
(NCEP,
IRI
Library),
http://www.esrl.noaa.gov/psd/data/correlation/
Model: Composite, Correlations, and CCA;
Tools: CPT, GrAds
Data: Hourly SL (UHSLC)
Model: GEV, Bootstrap method, L-moment
Tools: Excel, Mat lab
Downscaling of GCMS



Data: SL (UHSLC), SST or SLH (IPCC-AR4, GCMs)
Model: CCA
Tools: CPT, GrAds,
IV. Adaptation
Adaptation Methods
 Retreat

Managed retreat

Relocation from high risk zones
 Accommodation

Public awareness

Natural disaster management planning
 Protect

Hard options
 Revetments, breakwaters, groins
 Floodgates, tidal barriers

Soft options
 Beach/wetland nourishment
 Dune restoration
Responding to Coastal Change (including sea level rise)
 Retreat
 Accommodation
 Protect

Soft

Hard
Adaptation to Saltwater Intrusion
•
Reclaiming land in front of the coast to allow new freshwater lenses to develop;
•
Extracting saline groundwater to reduce inflow and seepage;
•
Infiltrating fresh surface water;
•
Inundating low-lying areas;
•
Widening existing dune areas where natural groundwater recharge occurs;
•
Creating physical barriers.
Shoreline Management and Adaptation
Proactive
Adaptation
Coastal Adaptation
(IPCC)
Shoreline
Management (Defra)
Increasing
robustness
Protect
Hold the line
Increasing
flexibility
Accommodate
Advance the line
Enhancing
adaptability
Retreat
Managed realignment
No active intervention
Reversing
maladaptive trends
(Project appraisal
methods)
Improving
awareness and
preparedness
(Flood plain
mapping and flood
warnings)
Adaptations Case Study: USAPI
PEAC’s forecasts and Outreach



Monthly Teleconference—
 PEAC-forecasts (i.e., sea-level, rainfall, tropical
cyclone etc.) are placed for discussion within a PEACsponsored teleconference;
 The WSO 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
101
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 9-month
Source: Schroeder TA, Chowdhury MR., and other Co-authors (2012)
drought
102
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, Chowdhury MR., and other Co-authors (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, Chowdhury MR., and other Co-authors (2012)
Example Approach to Adaptation Measures
 Caribbean small island developing country
 Climate change predictions

Rise in sea level

Increase in number and intensity of tropical weather systems

Increase in severity of storm surges

Changes in rainfall

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 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 a
range of criteria in deciding the best adaptation option within a given
local context. Criteria include:
•
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?
•
•
Does taking action avoid damages to human health, property, or livelihoods?
•
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.
2.
3.
4.
Willing Seller Purchase
Sand Replenishment
Do not armor public lands
Set back new development
4. Mainstreaming: Set Back New Development
Waaaaaaaaayyyyyy back
300 to 500 feet…
This means new lot dimensions,
new building codes, new designs,
new types of subdivisions –
the end of R-5 zoning
References
o
o
o
o
o
Schroeder T A., Chowdhury M. R., Lander M. A., Guard C., Felkley C., and Gifford D.: (2012): The Role
of the Pacific ENSO Applications Climate Center in Reducing Vulnerability to Climate Hazards. Bulletin
of Am. Met. Soc. (In Press)
Chowdhury M. R., P-S Chu, Xin Zhao, Schroeder T, and Marra J (2009): Sea-level extremes in the U.S.Affiliated Pacific Islands—a coastal hazards scenario to aid in decision analysis, Journal of Coastal
Conservation, 14:53-62, Springer.
Chowdhury M. R., P-S Chu, Schroeder T, and Xin Zhao (2008): Variability and predictability of sealevel extremes in the Hawaiian and U.S.-Trust Islands—a knowledge base for coastal hazards
management, Journal of Coastal Conservation, 12:93-104, Springer.
Chowdhury M. R., P-S Chu, Schroeder T, and Colasacco N (2007): Seasonal Sea-level Forecasts by
Canonical Correlation Analysis – An Operational Scheme for the U.S-Affiliated Pacific Islands (USAPI),
International Journal of Climatology, 27:1389-1402.
Chowdhury M. R , P-S Chu, and Schroeder T (2007): ENSO and Seasonal Sea-level Variability – A
Diagnostic Discussion for the U.S-Affiliated Pacific Islands, Theoretical and Applied Climatology, 88:
213-224, Springer-Verlag, Wien.