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