techniques for assessing damage and loss: tools to support … · 2020. 12. 18. · tafea malampa...

Techniques for assessing damage and loss:

tools to support mainstreaming

Information Communication Technology and Disaster Risk

Reduction Division

UNESCAP

Assessing damage and loss Three key questions

PRE-DISASTER RISK ASSESSMENT:

Hazard, vulnerability, Exposure - Geospatial approach - Probabilistic Approach

DISASTER LOSSES (PAST EVENTS)

Loss Accounting - Recording impacts (damage and loss) - Measuring Trends

DISASTER LOSSES (FUTURE RISK)

- Downscaling climate scenarios using geospatial approaches - Probability of losses / Average Annual Loss

HOW MUCH IS AT RISK? HOW MUCH WAS LOST? HOW MUCH IS LIKELY TO BE LOST IN THE FUTURE?

- How much is at risk? - How much was lost? - How much likely to be lost in the future?

Drivers of risk assessment

Hazard Vulnerability Exposure Impact/Risk

Source: Modified from Francis Ghesquiere, The Word Bank

Cartographic, Geological, Hydro-meteorological .. Geospatial Data – Vector and Raster

GIS/Geospatial– Infrastructure, settlements, land use..

Statistical - census and survey data

Value at Risk

Ex-ante risk assessment Average Annual Loss (AAL)

HAZARD

VULNERABILITY

EXPOSURE

RISK

AAL data downloaded from the Pacific Catastrophe Risk Assessment and Financing Initiative (http://pcrafi.sopac.org/layers/), 2013

Ex-ante risk assessment Average Annual Loss (AAL) – Vanuatu Case Study

Sanma

Tafea

Malampa

Torba

Shefa

Penama

Average annual loss by district

Low

Medium

High

The Pacific Catastrophe Risk Assessment and Financing Initiative

Vanuatu

AAL in the Pacific countries for earthquakes and cyclones

AAL in the Pacific countries for earthquakes and cyclones

.Ex-ante risk assessment Average Annual Loss (AAL) – Vanuatu Case Study

AAL data downloaded from the Pacific Catastrophe Risk Assessment and Financing Initiative (http://pcrafi.sopac.org/layers/), 2013 Source: GDACS data, 2015, http://www.gdacs.org/resources.aspx

Average Annual Loss

Low

Moderate

High

Cyclone wind speed

60 km/h

90 km/h

120 km/h

Malampa

Tafea

Shefa

Tafea

Shefa

Malampa

Penama

Penama

Penama

.

Cyclone wind speed

60 km/h

90 km/h

120 km/h

Total Damage and loss (Pam)

Low damage

Moderate damage

High damage

Ex-ante risk assessment Average Annual Loss (AAL) – Vanuatu Case Study

AAL data downloaded from the Pacific Catastrophe Risk Assessment and Financing Initiative (http://pcrafi.sopac.org/layers/), 2013

Source: GDACS data, 2015, http://www.gdacs.org/resources.aspx

Total Damage and loss (Pam)

Low damage

Moderate damage

High damage

Average Annual Loss

Low

Moderate

High

Ex-ante risk assessment Average Annual Loss (AAL) – Vanuatu Case Study

Source: World Bank, https://www.gfdrr.org/sites/default/files/publication/PDNA_Cyclone_Pam_Vanuatu_Report.pdf

Ex-Post Risk assessment Using new tools for rapid assessment for post disaster needs

• Due to the extensive time and resources post disaster needs assessment have traditionally focused on high impact events

• However, aggregated impacts are more severe in case of high frequency

and low impact events. Therefore, we need rapid, scientific and evidence based assessments with low opportunity cost.

• How to capitalize upon the innovative technologies – space applications, geo-spatial databases and crowdsourcing for making disaster assessment faster, evidence-based and monitorable?

(Photograph courtesy: Kashmir University)

()

Uttarakhand Flash Floods 2013 Case Study

Source: CSSTEAP

• Damage to buildings and infrastructure

1a. Damage to Buildings

1b. Damage to Infrastructure

1b1. Roads

1b2. Bridges and Culverts

1b3. Other Infrastructure

• Landslides

• River Bank Erosion

• Damage to Land-cover and Natural Resources

• Points of Interest

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

1

2 3

4

5

Controlled Crowdsourcing : Mobile application for collection of primary data from the affected areas

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

Mobile interface: Reported Landslide Locations

Source: CSSTEAP

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

Landslide Damaged roads

Damaged infrastructure

Damaged house

(courtesy: Wadia Institute of Himalayan

Geology)

Ground Photos showing Damage in Bhagirathi Valley

Source: CSSTEAP

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

19,559 data points collected

in total

Primary field data-collection points in affected areas

Source: CSSTEAP

Stratified sample Visualization

through geo-portal

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

Damage to Buildings: Data points collected: 2579

Ex-Post Risk assessment Case study: Uttarakhand Flash Floods, 2013

Damage to Roads: Data points collected: 1147

Damage to Bridges: Data points collected: 174

Source: CSSTEAP

ESCAP in collaboration with SAARC developed a manual for using innovative PDNA tools for rapid assessment

Introduces how to capitalize upon the innovative technologies – space applications, geo-spatial databases and crowdsourcing to make disaster assessment faster, evidence-based and monitorable

It also provides guidelines and knowhow on the ways to get access to data and tools

The Manual introduces a new ways for damage and loss assessment.

It’s based on good practices and take into account the experiences of practitioners

Ex-Post Risk assessment

• Thermal remote sensing for chlorophyll identifying fishing grounds

• Higher catches reported for high chlorophyll areas (track 1-9)

Hokkaido, S.S, Chasso, E. et.al. (2009). Remote sensing applications to fish harvesting.

Sector Risk assessment of climate extremes El Nino Case Study

2005

2013

Risk assessment of climate extremes El Nino Case Study

2015

Risk assessment of climate extremes El Nino Case Study

2005 2013

2015

Risk assessment of climate extremes El Nino Case Study

2005

2013

Risk assessment of climate extremes El Nino Case Study

2015

Determining regional risk for fisheries in Pacific Islands during an El Niño year

NASA: http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MY1DMM_CHLORA NASA-SeaWIFS: http://oceancolor.gsfc.nasa.gov/SeaWiFS/BACKGROUND/SEAWIFS_BACKGROUND.html Aqua-Modis: http://oceancolor.gsfc.nasa.gov/cms/data/aqua

2005 2013

2015

Risk assessment of climate extremes El Nino Case Study

Thank you

Madhurima Sarkar-Swaisgood

Economic Affairs Officer, Information Communication

Technology and Disaster Risk Reduction Division

UNESCAP

Sarkar-swaisgood@un.org