Presentation on theme: "CGE TRAINING MATERIALS - VULNERABILITY AND ADAPTATION ASSESSMENT CHAPTER 5 Coastal Resources."— Presentation transcript:
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 driverSecondary or process variable Mean sea levelLocal sea level Ocean currents, temperature and acidification Local currents Local winds Wind climateLocal waves Rainfall/run-offGroundwater
Some Climate Change Factors TimeframeCausePredictability Net extreme event hazards Recurring extremes (storm surge/tide) Hour–daysWave, wind, storms Moderate to uncertain Tide rangesDaily–yearlyGravitational cycle Predictable Regional sea level variability Seasonal– decadal Wave climate, ENSO, PDO Moderate; Not well known Net regional mean sea level rise (SLR) Regional net land movement Decades– millennia TectonicPredictable once measured Regional SLRMonths–- decades Ocean warm/ current/climate Observable; future uncertain Global mean SLRDecades – centuries Climate change (temp, ice melt) Short term understandable; future uncertain
Climate Change: Global context : Global mean surface air temp increased by 0.6 0C Projected increase ( ): 1.4 – 5.80C (Based on greenhouse gas emission) 2030: in monsoon,+ 1.3 in winter 2050: + 1.1, in (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 ( 0 C at relative to ) a Sea level rise (m at relative to ) CaseBest estimate Likely range Model-based range excluding future rapid dynamical changes in ice flow Year 2000 concentration b NA B1 scenario B2 scenario A1T scenario A1B scenario A2 scenario A1F1 scenario Notes: a These 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). b Year 2000 constant composition is derived from AOGCMs only. (Source: IPCC, 2007a)
“…an improved estimate of the range of SLR to 2100 including increased ice dynamics lies between 0.8 and 2.0 m.” IPCC AR4 is missing the rapid ice flow changes…. 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
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; 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). H (Cold SST) low (Warm SST) lowP El Niño/ La Niña -Southern Oscillation (ENSO)-Another Major Driver of Climate Change Develops in JulAugSept, strengthen through OctNovDec, and weakens in JanFebMar (Source: IRI Web Portal)
B ASAS M G Nino 3 NINO 3.4 Nino 4 H NWP SA SP E W A
* * 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. El Niño/ La Niña Years ( )
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 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 categoryExample 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 categoryExample 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 (CO 2) concentrationIncreased CO 2 concentration, decreases ocean acidification negatively impacting coral reefs and other pH Surface sea temperature (SST) (I, R) (I: increasing, R:Regional variability) Increased stratification/changes circulation; reduced incidence of sea ice at higher latitudes; increased coral bleaching and mortality; pole-ward species migration; increased algal blooms. 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 Storm frequency (?, R); Storm track (?, R) Altered surges and storm waves, and hence risk of storm damage and flooding Wave climate Altered wave conditions, including swell; altered patterns of erosion and accretion; re-orientation of beach plan form Run-off (R) 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)
Vulnerable Regions Mid-estimate (45 cm) by the 2080s
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 run- off General economic losses to the country. Source: Also see:
Coastal Megacities (>8 million people)
Elevation and Population Density Maps for Southeast Asia Indo-China Peninsula
Research indicates: 1.Doubled melting rate of Greenland ice sheet 2.Net melting of the Antarctic ice sheet 3.Global rise approaching 3.0 mm/yr, twice the rate last century, 4.Continued heating of atmosphere – heating of water column, 5.More than 1 m rise is now expected during this century C 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… Sea-Level Rise: Summary
Level of assessment Timescale required PrecisionPrior knowledge Other scenarios in addition to SLR Strategic level (screening assessment) 2-3 monthsLowestLowDirection of change Vulnerability assessment 1-2 yearsMedium Likely socio-economic scenarios and key scenarios of key climate drivers Site-specific level (planning assessment) OngoingHighestHighAll climate change drivers (often with multiple scenarios) II (a). Overview of Coastal Vulnerability Assessment
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)
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 X Y/X = 50 to 200….say, m sea level rise = 100 m (~400 ft) shoreline recession Eroded profile Accreted profile 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
(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)
KEY: mangroves, o saltmarsh, x coral reefs Coastal Ecosystems at Risk
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: /)
How do people respond to eroding beaches? …they armour… (Source:
…and how do beaches respond to armoring? …they disappear… (Source:
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
- 12” + 24” Sea Level Change during EL Niño Year Overview of ENSO and Sea Level Variability ERS: European Remote Sensing
67 La Niña (strengthened trade winds) El Niño (weakened trade winds) (Cold SST) high pressure system pressure system (Warm SST) low pressure system pressure system …….shifts Temp Rainfall Run-off Sea level across the globe…. ENSO—Major driver of CC
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.
Pacific Island communities are among the most vulnerable to climate variability/change— Economic plans are dependent on climate- sensitive 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. (13º48´N; 144º45´E) (14º20´S; 170º0´W) (09º0´N; 168º0´E) USAPI USAPI – Climate Counts in the Pacific!! ‘Hot Spots’ of Climate Hazards : USAPI Case Study, Operational Sea Level Forecasts
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.
University of Hawaii Sea Level Center Sea Level Data (hourly/daily/monthly; max/mean/anomaly/deviations)
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 Year (0)(+1) Year (0)(+1) Year (0)(+1) Composites of monthly sea-level deviations in El Niño /La Niño years ENSO and Sea Level Variability (Source: Chowdhury et al., 2007a)
Probabilistic forecasts for sea level variability is possible well ahead of time…. Guam El Niño signal La Niña signal SST Composites for Low and High Sea Level Years - Predictability Grid Analysis and Display System (GrADS)
(SE-SL) –3 –1– EqC –E EqW-DL C-10 0 S (e) (a) (c) (d) (f) (g) (h) (i) (b) (j) C-E Nio3.4 Ni ñ o3.4 Composites of Strong El Niño and Strong La Niña Years (Source: Chowdhury et al., 2007a)
– 1– 1 Nino 3.4 NW-SW (13º48´N; 144º45´E) (09º0´N; 168º0´E) (14º2´S; 170º0´W) SC SC (a) (e) (c) (d) (b) (f) Sea-level variability is correlated to SSTs in the Pacific on seasonal time scales… Correlations between SST and sea level—Predictability Source: Chowdhury et al., 2007b
International Research Institute for Climate and Society Climate Predictability Tool (CPT) Sources: ab&q=Climate+predictability+tools&oq=Climate+predictability+tools&aq=f&aqi=g- K1&aql=&gs_l=hp.3..0i j4j6j0j1j0j JAOJ XOziHRE&pbx=1&bav=on.2,or.r_gc.r_pw.r_qf.,cf.osb&fp=a f6810afa&biw=1280&bih=685 (By Ousmane Ndiaye and Simon J. Mason)http://www.google.com/#hl=en&sclient=psy- ab&q=Climate+predictability+tools&oq=Climate+predictability+tools&aq=f&aqi=g- K1&aql=&gs_l=hp.3..0i j4j6j0j1j0j JAOJ XOziHRE&pbx=1&bav=on.2,or.r_gc.r_pw.r_qf.,cf.osb&fp=a f6810afa&biw=1280&bih=685
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). What is the CPT?
Choose the analysis to perform: PCR or CCA Selecting the Analysis
Both analysis methods require two datasets: “X variables” or “X Predictors”dataset; (SST, monthly anomaly) “Y variables” or “Y Predictands”dataset; (SL, monthly deviations) Input Datasets
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.
83 a) JFM_SST (30.5%) b) AMJ_SST (26.2%) c) JAS_SST (29.0%) d) OND_SST (31.5%) Source: Chowdhury et al., 2007b
84 a) JFM_SST (15.5%) b) AMJ_SST (17.1%) c) JAS_SST (17.5%) d) OND_SST (17.5%)
CCA Cross-validated Hindcast Skills
X:75.8 Y:91.0 X:75.5 Y:83.0 X:76.0 Y:84.0 X:73.1 Y:96.0 EOF (%) With a lead time of one or two seasons, the forecasts for all the seasons are accurate Sea-level Forecasts –CCA Cross-validation Skill (Source: Chowdhury et al., 2007b)
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. Summary and Conclusions
Tide Predictions (high/low water level)
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:
Here there are three parameters: A location (or shift) parameter, a scale parameter and a shape parameter. GEV products define the thresholds beyond the seasonal tidal range that have low but finite probabilities of being exceeded on a seasonal scale. Cumulative distribution function (CDF) Probability distribution function (PDF) of GEV Generalized Extreme Value (GEV) distribution (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: 100-year RP Deviations: 20-year RP (Source: Chowdhury et al., 2008)
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. Summary
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. Downscaling
Four methods: ENSO-based seasonal sea level forecasts: a)Data: SST (NCEP, IRI Library), SL (UHSLC); and b)Model: Composite, Correlations and CCA c)Tools: CPT, GrAds Tide predictions (hour-to-yearly time scales): a)Data/Model/Tools: ANAS%20ISLANDS&type=Tide+Predictions ANAS%20ISLANDS&type=Tide+Predictions Extremes of 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. Summary (Methods, Tools, and Data requirements: Case Study)
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.
Monthly Teleconference a)PEAC-forecasts (i.e., sea-level, rainfall, tropical cyclone etc.) are placed for discussion within a PEAC-sponsored teleconference; b)The Weather Service Office from each of the island communities is invited to attend this conference; c)Representatives from the forecasting centers are also invited - past, present, and future climatic conditions are brought up; d)A consensus forecast is achieved; Warning messages are developed. PEAC’s forecasts and Outreach Adaptations Case Study: USAPI
India Discharges in the Ganges, Brahmaputra Meghna region ‘Current situation’ Bangladesh Discharge Rainfall and runoff variability in GBM basin Himalayan melting risk Ganges 907,000 sq. km Brahmaputra 583,000 sq. km Meghna 65,000 sq. km
‘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 ( ) 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’
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 , 1997: 224-km/hr Strong El Niño April , 1991: 225-km/hr (killed 150,000) El Niño May , 1985: Severe cyclone hit Chittagong causing ft surge (killed 12,000 people) La Niña to El Niño November 12, 1970: 222-km per hour causing ft high tidal surge (killed 0.5 M/1.2 M people) La Niña May May, 1963: Cyclonic storm hit Chittagong (killed 12,000 people) El Niño Chronology of TC and El Niño/La Niña events Number of major cyclones have drastically increased in recent years (BBS)Number of major cyclones have drastically increased in recent years (BBS) i.e., and : 3, : 18, : 51
Rainfall : Wet/dryBrahmaputra-flow: High/low Ganges-flow : High/low SST Composites of wet and dry years—Predictability The rainfall and stream-flows in Bangladesh is connected to variation in SST in the Pacific…. La Niña signal El Niño signal
106 SST and JAS flood- Correlation map OND (-3) FMA NDJ DJF JFM (-2) MAM MJJ AMJ (-1) JJA OND (+1) JAS (0) Flooding is correlated to SSTs in the Pacific on months-to-seasonal time scales….
People line up for water in Majuro to receive ration once every fourteen days Lessons from El Niño Water rationing in Majuro; Crop losses in FSM, RMI, CNMI Palau experienced 9- month drought Adaptation: Drought in Majuro (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 (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 (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)Rise in sea level b)Increase in number and intensity of tropical weather systems c)Increase in severity of storm surges d)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… … feet… This means new lot dimensions, new building codes, new designs, new types of subdivisions – the end of zoning