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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) 11/ 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.