Pascal Peduzzi, Bruno Chatenoux and Adonis Velegrakis UNEP/GRID-Geneva Risk and Vulnerability...

Pascal Peduzzi, Bruno Chatenoux and Adonis VelegrakisUNEP/GRID-Geneva

Risk and Vulnerability Assessment Methodology development Project (RiVAMP)

Kingston, 5-8 December 2011

www.grid.unep.chRiVAMP training sessionQuantifying the role of ecosytems

Work plan

Dates Morning Afternoon

5 Dec. 2011 Presentation of RiVAMP methodology

Presentation of OpenSource GIS

QGIS practices (creation of population grid & bathymetric grid)

6 Dec. 2011 Estimation of exposure; data extraction for statistical analysis

data extraction for statistical analysis (practice)

Work plan

Dates Morning Afternoon

7 Dec. 2011 Identification of the role of ecosystems using statistical regressions

Beach erosion: Occurrence & Impacts

8 Dec. 2011 Beach Morphodynamic Models I & II

Beach Morphodynamic Models III & IV

A. Velegrakis

UNEP

DEWA DEPI DRC DGEFDCPIDTIEDEWA

UNEP/GRID-Geneva

Global Change & Vulnerability

Unit(ex Early Warning)

P.Peduzzi C.Herold G. Giuliani

Global Change & Vulnerability Unit

R. Harding

Swiss Env. Agency

University of Geneva

B. Chatenoux

Global Change & Vulnerability: a unit of the

UNEP/GRID-Europe

Field data collection

Image analysisStatistical analysisSpatial analysis (GIS)

Global Change & Vulnerability Unit

Maps & Info

1999 - 2010

PREVIEW

Data (SDI)

1. Introduction: why RiVAMP ?

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

World Population1975: 4.1 billion1990: 5.3 billion2011:7.0 billion

Population distribution change between 1975 and 2011

What are the drivers of global change?

More than 50% of world population is now urban…… and about a third of urban population lives in slums

Glaciers:Precipitations’buffers and watertanks.

19792007

Rise faster than expected

Future projections

Future sea level (rel. to 1990) basedon IPCC AR4 global temperature projections

Full range: 75 – 190 cm by 2100

Vermeer & Rahmstorf, PNAS 2009

constant rate 3.2 mm/year

Fig.1 Beach erosion (defined as irreversible coastline retreat) due to increased sea level

S, coastal retreat, α, sea level rise

Driver 1: Sea level rise (ASLR), process

Driver 2: Tropical cyclones & SST

North Atlantic tropical cyclones by temperatures

3. RiVAMP in Negril

Aims of RiVAMP

1 Env’ factors, climate change & Tropical Cyclones Disaster Risk in SIDS

2 Associate scientific quantitative analysis together with local expertise and community consultations

3 Quantifying the role of ecosystems and climate change on risk

Identifying local perspectives for developments4

Provide capacity building, to multiply these assessments.5Targeting audience at governments (both local and national levels) to

achieve better land planning.6

Study area: Negril

Erosion rate between 1968-2006 : 0.5 and 1 m/yr(large temporal and spatial variability; Smith Warner International, 2007)

Observation 2006-2008, shows that beach erosion continues (UNEP, 2010)

Aims of the spatial analysis

1. Evaluate the scale of beach erosion and coastal ecosystem degradation;

2. Identify the role of geomorphology (bathymetry, elevation) and natural features (coral, sea- grass) in the observed erosion patterns;

3. Prognosis under different Sea Level Rise scenarios and under increases in the frequency/intensity of tropical cyclones.

4. Identify population and infrastructures exposed to tropical cyclones storm surges.

Current beach erosion rate

Erosion rate between 1968-2006 : 0.5 and 1 m/yr(large temporal and spatial variability; Smith Warner International, 2007)

Observation 2006-2008, shows that beach erosion continues (UNEP, 2010)

Beach erosion: the drivers

1. Sea level rise

2. Storm surges from tropical cyclones.

3. Waves from storms

4. Decline of ecosystems

5. Presence of infrastructures

Hypothesis (not comprehensive)

Driver 1: See level rise

Sea levelrise (m)

Minimum beach retreat (m)

Maximum beach retreat (m)

0.35 3.6 12.3

0.52 5.3 17.3

0.65 6.3 20.9

0.75 7.5 24.0

1.05 10.0 32.9

2.02 20.6 61.9

2.55 25 78.7

Beach erosionScenarios

Beach erosionScenarios

Intensity equivalent to

return period

(years)

Hs

(m)

Period

(s)

Max. combined sea level elevation

(m)

Probability

for a period of 50 years

5 4.7 8.7 0.45 99.9

10 6.4 10.7 0.65 99.5

25 8.2 12.4 0.74 87.0

50 9.2 13.4 0.93 63.6

100 10.2 14.3 1.25 39.5

150 10.7 14.7 1.29 28.4

Sources: SWI, 2007. Key: Hs = significant wave height; Period = significant wave period; Max. combined sea level elevation, sum of inverse barometric pressure effects, long-term sea level rise (assumed at 5 mm/yr) and tidal effects (0.3 m above Mean Sea Level on springs).

Driver 2: Tropical cyclones, generated waves

Numerical model results for wave heights (a) and wave-induced currents (b) at the Negril coast. Conditions: Offshore wave height (Hrms) = 2.8 m, Tp=8.7 s. Wave approach from the northwest. Note the diminishing wave heights and changed nearshore flow patterns at the lee of the shallow coral reefs.

Driver 3: High waves

Driver 4: Decline of ecosystems

Does the ecosystems (coral, seagrass) provide protection

against beach erosion ?

Sea grass and coral reef are declining (see later)

Site ID 55

Erosion 20.5124

Seagrass width 424

Dense Seagrass width 240

Width Reef 110

Deep Reef width 344

Total Seagrass Width 664

Depth at 500m 3.987434

Depth at 1000m 1.8887

Depth at 1500 m 6.466622

Depth at 2000m 20.57736

Depth at 2500 m 50.82628

Depth at 3000m 77.97657

Distance to 4 m depth 1168

Distance to 5 m depth 1316

Distance to 6 m depth 1420

Distance to 10 m depth 1738

6m depth

Erosion

Profiles

Data extraction

Driver 4: Multiple regression analysis

The model explains 41% of the erosion (correlation r=0.64).

Sea grass, slopes and waves were selected as significant parameters in the model. Sea grass plays the main role (47%): the wider the sea grass the less the erosion.

Erosion rate behind sea grass

Driver 4: Multiple regression analysis

Erosion rate behind coral

The model explains 83% of the erosion (correlation r=0.91).

Coral width and slopes were selected as significant parameters in the model. Width of coral plays the main role (59%): the wider the coral the less the erosion.

Waves H=2.8 m, T= 8.7s

Sea level rise 1m

Coastal ecosystem importance: beach protection by coral reefs

Fig. 9b. Modelled bed shear stress (force per unit area) (SBEACH model) induced by waves in the Negril coastal zone, showing the protection effects of inshore coral reefs

Coastal ecosystem importance: beach protection by seagrass meadows

Fig. 11. Modelled bed shear stress (force per unit area) (SBEACH mode (wave height 1 m, period 6s) in the Negril coastal zone

The seagrass meadows spread the wave force on wider area and dissipate wave energy

Without meadows

With meadows

Flooded areaReclassification of elevation grid

Wave splash on cliff: 7.5 m 8.0 m

Return period (years) 10 50

Tide (m) 0.3 0.3

Inverse Barometric Pressure Rise (m)

0.22 0.38

Global Sea Level rise (m) 0.1 0.5

Storm surge (m) 0.3 1.5

Wave run-up (m) 2.7 4

Storm wave elevation: 3.6 m 6.7 m

Exposure to tropical cyclones

Population exposure1 km grid10 yrp: 478 people exposed50 yrp: 2 487 people exposed

Population on the littoralarea: 18 500

Exposure to tropical cyclones

Assets exposure10 yrp

Asset type Beach Cliff

Hotels   2

Markets 1  

Nwc priority facilities 1  

Wastewater facilities   2

Wells 1  

50 yrpAsset type Beach Cliff

Emergency shelters   1

Health centres 3  

Health facilities 2  

Hotels 61 2

Markets 1  

Nwc priority facilities 2  

Public schools   1

Touristic facilities 3  

Waste water facilities 6 2

Wells 9  

licj airport 1  

Exposure to tropical cyclones

Stakeholder consultations

Quatre Bornes, September 2010

2. Stakeholder consultations

Study Area: Negril

• National level

• Negril Parish level

• CommunitiesWhitehall

Little Bay

Major Types of Ecosystems in Jamaica

• Coral reefs• Coastal vegetation: sea

grasses, mangroves and other types of beach vegetation

• Wetlands / peatlands (the “morass”)

• Forests

Critical services provided by ecosystems

Provisioning services Regulating / Buffering services

Social-cultural benefits

• Supports local livelihoods

• Supports other important economic activities

• Coastal ecosystems supply beach sediments

• Supports biodiversity

• Coastal ecosystems contribute to coastal stability and shoreline protection against storm surges

• Trees (including mangroves) act as a wind breaker and protective barrier

• Wetlands serve as a

natural filter and flood control system

• Forests protect watersheds and stabilize soils

• Educational / research opportunities

• Recreation / leisure / sports activities

• Contributes to local cultural identity

Drivers of ecosystem degradation

External drivers Local human-induced drivers

• Impacts from tropical storms or cyclones (hurricanes)

• Pathogens / diseases (e.g. CWBS, black sea urchin die offs)

• Climate change and variability: increased sea surface temperatures and more frequent and intense storms

• Invasive species (e.g. lionfish)

• Fires / drought

• Beach erosion

• Land-based sources of pollution: agricultural runoff, sewage and freshwater drainage, road and building construction runoff, solid waste

• Removal of sea grasses and other types of beach vegetation

• Coastal development

• Deforestation / vegetation clearance

• Destructive and unsustainable fishing practices

• Tourism-related activities

Community profile: Little Bay

• Small, rural community of about 350 residents

• Highly exposed to storm surges and strong winds due to hurricanes

Community profile: Whitehall

• Experienced rapid growth in past 30 years, mainly due to the expansion of tourism

• Frequent flooding due to heavy runoff

Methods used

• Break out group sessions

• Community mapping (past and present)

• Hazard mapping• Seasonal calendars • Ranking exercises

Key livelihoods depend on natural resources but contribute to ecosystem degradation

• Fishing • Farming • Tourism • Fuelwood production

Environmental degradation increases local exposure and vulnerability to hazards

Storm surges in Little Bay • Increased storm surge-related

flooding over the past 20 years

• Attributed to beach erosion, hurricanes, coral reef degradation, removal of coastal vegetation (mangroves, fruit trees and sea grasses) and illegal sand

mining

Local coping strategies stress the importance of ecosystem protection

• Depending on hurricane damage, residents may be forced to rely on unprotected groundwater sources for drinking and wood sources for fuel and housing materials.

• Local reliance on natural resources in post-hazard contexts highlights the importance of protecting ecosystems to ensure continued access to these services.

Priority Issues and Proposed Solutions

Negril faces 3 main options:

• Change nothing,

• Managed retreat

• Coastal engineering

• Improve the protection of coastal ecosystems

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Results from the Project Evaluation A project evaluation was undertaken on 14-15 May 2010 (summary)

• UNEP provided added value as an external actor.

• Very good data collection and generation, including new satellite images at very high resolution.

• Successfully demonstrated the role of ecosystems for mitigating shoreline erosion and climate change scenarios.

• High government buy-in at all levels. National media coverage.

• Needs an economic analysis.

• Should start by defining long-term development needs. Then environment role(s) will follow.

• Limited timeframe; limited space for hands-on learning by local partner(s).

• Missing a third component: running scenarios with local / national land-use planning authorities

5. GAR 2009 presentation

Figure 2 Spatial pattern of terrestrial NPP linear trends from 2000 through 2009 sources: with kind permission of Zhao and Running, 2010.

More CO2 was supposed to increase photosynthesis (Nemani et al., 2003). But recent measurements on the warmest decades (2000-2009) show that the creation of biomass is slower. (Zao & Running, 2010) Photosynthesis also request H2O, which may be the limiting factors

We thought that with more CO2, there would be more photosynthesis, thus more biomass (Nemani et al. 2003). But it is not the case. Water might be the limitation factor (Zao & Runnin, 2010)

Forest: less biomass produced

Forest: drought, more fires

More drought, more forest fires (Van der Werf et al. 2008)

More drought more forest fires.From Van der Werf et al., (2008), reproduced with kind permission from the authors and courtesy of National Academy of Science.

Positive feedbacks

LessPhotosynthesis

LessCarbon sinks

Warmer temperatures

More CO2

More droughts

Less precipitationsHigher temperatures

MoreForest fires

DeforestationClimate change, deforestation, drought and forest firesA triple-loop of positive feedbacks

Peduzzi et al. (in prep.)

1973: Forests

2000: Deforestation in Paraguay

Barrage d’Itaipu et rivière d’Iguazu

Aral sea: coton is drying out the sea

18 June 2009

20042002

8 Jan. 2010

1973 – a town of 400’000 inhabitants

Images courtesy USGS

Las Vegas

2000 – 1 million inhabitants

Las Vegas

Lake Chad

1975 – Natural forest cover

Amazonia: lungs cancer

Rondonia, Brésil

1989 - Patterns showing conversion of forest to crops.

2001 – Crops are supplanting forest

Santa Cruz, Boliva: Land Use Change

Body text 1975: Forests

2000: agriculture fields for larege compagnies

Honduras: shrimps eat mangroves

1987-1999: shrimps farms are replacing mangroves in the golf of Fonseca.

Mine de cuivre en Papua Nouvelle-Guinée

1990-2004: Impacts d’exploitations minières dans le lit de la rivière

Local level

• The interest for the role of ecosystems in mitigating DRR increased sharply after the tsunami… although no scientific evidences were found.

„We used a zero intercept in our statistical analysis where this should not have been the case…“

SUMMARY:

no evidence for a protection function from this study

…lots of new (empty) housing

close to the seafront

(within the tsunami hazard zone?)

for example…

Aid efforts in Aceh: NGO‘s delivering with little acceptance

…without proper Planning and Research

…costly engineered coastal defence measures……and not necessarily very helpful…

…(fast) Action without Proper Surveying

As of August 2006

75% dead 98% dead

100% dead 100% dead

Photo B. McAdoo

Landslides mitigated by dense vegetation

Souces: Peduzzi, P.: Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach, Nat. Hazards Earth Syst. Sci., 10, 623-640, 2010.