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Approaches for assessing risk and losses
on fisheries and agriculture
Geospatial analyses based on the 2015/2016 El Niño and other climate variabilities in Pacific SIDS
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?
• Thermal remote sensing for chlorophyll identifying fishing grounds
• Higher catches reported for high chlorophyll areas (track 1-9)
2015/2016 El Niño Impacts on fisheries
Hokkaido, S.S, Chasso, E. et.al. (2009). Remote sensing applications to fish harvesting.
2015/2016 El Niño Impacts on fisheries
• Directly related to agriculture productivity including fisheries
• Amplifications of potential weather impacts
• Huge masses of warm water travel east across the Pacific Ocean and warm water changes storm systems in the atmosphere
• El Niño impacts chlorophyll concentration and phytoplankton bloom
Source: National Ocean and Atmospheric Administration, http://sos.noaa.gov/Datasets/dataset.php?id=141
Determining global risk for fisheries during the 2015/2016 El Niño year
2005
2013
2015
Determining global risk for fisheries during the 2015/2016 El Niño year
Determining global risk for fisheries during the El Niño year
2005 2013
2015
2005
2013
Determining regional risk for fisheries in Pacific Islands during the 2015/2016 El Niño year
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
Determining regional risk for fisheries in Pacific Islands during an El Niño year
2005 2013
2015
Probabilistic Approach- Average Annual Loss (AAL): AAL in Pacific SIDS in agriculture sector (Vanuatu case study)
AAL data downloaded from the Pacific Catastrophe Risk Assessment and Financing Initiative (http://pcrafi.sopac.org/layers/), 2013
• Agriculture is the backbone of the Pacific Island economies.
• It is the main source of livelihood for the
population as well as a major export earner.
• The proportion of crop loss as a percentage of total AAL is significant as in the Pacific SIDS.
Climate variability and AAL in Pacific SIDS in agriculture sector
Source: GDACS data, 2015, http://www.gdacs.org/resources.aspx
El Niño Loss Assessment [AAL*Amplification Factor]
• An El Niño year can have additional impacts on agriculture. • The El Niño amplification factor is a rough-and-ready way of
calculating the potential additional losses in the case of El Niño event in a country
• It is found using the ratio of country-level climatological parameters in the no-El Niño case and El Niño case
• For example, if 3 storms are expected in no-El Niño event and 5 storms are expected in El Niño event scenario, the amplification factor will be 5/3 = 1.67
Average Annual Loss (AAL): Probable Maximum Loss (PML)
Loss
(U
S$)
Source: http://www.eea.europa.eu/data-and-maps/figures/example-of-the-adjustment-of-lossdistribution-as-a-consequence-of-changing-risk
Average Annual Losses (AAL) El Niño amplifies AAL by a factor related to specific hazards – cyclone, floods, drought, heat waves…
Country AAL (Million USD)
El Niño associated amplification factor (Cyclone)
Potential losses (Million USD)
Micronesia 9.8
Marshall Islands 3.7 2.7 10.1
Palau 2.8
Kiribati - - -
Papua New Guinea 27.9 1 27.9
Tuvalu 0.1 1.43 0.14
Cook Islands 6 1.46 8.76
Fiji 94.1 1.04 97.86
Niue 1.1 1.33 1.46
Samoa 8.5 1.41 11.99
Tonga 11.7 1.14 13.34
Vanuatu 44.3 1 44.3
North Pacific
z Central Pacific
z Southern Pacific
2015-16 El Niño potential economic impacts in Pacific Island Countries
Impact-based Forecasting: El Niño as an example
Source: Baode Chen and Xu Tang (2014) Translating weather forecasts into impact-relevant information
An illustration on how El Niño information can be translated to response actions
Relevant information from El Niño information is extracted and placed into the situation
context to produce impact estimations;
With potential impact information available, response scenarios can be set-up
Thank you
Madhurima Sarkar-Swaisgood
Economic Affairs Officer, Information Communication
Technology and Disaster Risk Reduction Division
UNESCAP
