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KORE - Knowledge Sharing Platform on Resilience
KORE@fao.org
INFORMED
INFORMATION FOR NUTRITION FOOD SECURITY AND RESILIENCE FOR DECISION MAKING
3
#ks4resilience
#resilience
#UNFAO
THE IMPACT OF DISASTERS ON AGRICULTURE
Assessing risks and impacts from extreme events/natural
hazards on the agriculture ...
ASSESSING DISASTER IMPACTS FROM NATURAL
HAZARDS IN AGRICULTURE
 natural hazard–induced disasters occur more often and wit...
THE INFORMATION GAP
 Actual cost of disasters to
the agriculture sector?
 Drawing
conclusions
available data? and
inform...
FAO’S WORK IN ADDRESSING THE INFORMATION GAP
THE IMPACT OF DISASTERS ON AGRICULTURE
Recent data and the new
SFDRR monitoring mechanism - indicator C-2
Speaker I
Niccol...
0
50
100
150
200
250
300
350
400
450
500
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2...
Source: FAO (2017), based on EM-DAT CRED
Economic damage of disasters triggered by natural hazards worldwide, 1980 - 2016
...
 Review of 74 PDNAs of disaster events in 53
developing countries b/n 2006-2016
 Analysis of crop and livestock producti...
23%16% 31%
D&LLossesDamage
IMPACT OF DISASTERS ON AGRICULTURE
Share of D&L absorbed by agriculture (2006-2016)
Source: FAO...
Source: FAO (2017), based on 74 PDNAs conducted between 2006 and 2016
IMPACT OF DISASTERS ON AGRICULTURE
Varying sectoral ...
IMPACT OF DISASTERS ON AGRICULTURE
Focus on drought
oMore than 80% of the impact of drought is on
agriculture
oDrought cau...
IMPACT OF DISASTERS ON AGRICULTURE
Focus on drought in Africa
Drought losses as a percentage of potential production
oBetw...
Standardiz
ed
procedures
and
methodolog
ies
Strengthene
d country
capacities
Better
informed
DRR
planning
Comprehens
ive D...
FAO’s METHODOLOGY TO MEASURE DISASTER
IMPACT
SFDRR Global Targets
Target C
Indicator C-2: Direct agricultural loss attributed to disasters will be computed
using the FAO methodology, as requested b...
FAO’s METHODOLOGY TO MEASURE DISASTER
IMPACT
SDG targets
Target 1.5: By 2030, build the resilience of
the poor and those i...
Damage Losses
Crops
Livestock
Fisheries
Aquaculture
Forestry
Production
Value of destroyed
stored production and
inputs, d...
• FAO’s technical guidance and
formulas to compute Indicator C2
are included in the draft Collection
of Technical Notes on...
o Typhoon Haiyan (Yolanda) hit central Philippines in Nov 2013
o Winds registered at over 300 km per hour (Figure) - stron...
$ 522 M
$ 902 M
Key Results
o Total D&L in Agriculture: USD 1.4
billion – in line with government
assessment
o Most affect...
DATA
COLLECTION
Harmonize and
systematize
assessment methods
FAO’s support to
countries
DATA SOURCES
WAY FORWARD
Towards a...
o Cooperation/coordination with UNISDR on monitoring indicator C-2
o Further testing and validation of the methodology on ...
THANK YOU!
THE IMPACT OF DISASTERS ON AGRICULTURE
From the Global Agriculture Drought Monitoring to Country
Level using Geospatial In...
Agricultur
a
58%
Industria
8%
Electricid
ad
25%
Agua
potable
2%
Emergen
cia
7%
CENTROAMÉRICA: PERDIDAS
OCASIONADAS POR LA ...
OBJECTIVE
Limitation using rainfall data:
o Currently weather stations are sparse and provide discontinuous data
o Rainfal...
ELECTROMAGNETIC ENERGY RECORDED BY THE
SENSOR
RE
D
NI
R
Water
stress
Source: Kogan, F. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting
satellite data...
Vegetation Health Index (VHI)
VHI = a*VCI + (1-a)* TCI
low VHI high VHI
Temperature condition index (TCI)Vegetation condit...
VHI temporal
average value
agric Crop
area
Administrative
unit
ASIS ASSESS THE SEVERITY (INTENSITY, DURATION
AND SPATIAL E...
GLOBAL CROPLAND MASK
SOS and EOS of the first season, as derived from the long term NDVI averages
of SPOT-VGT (roi GLD, 21 km resolution).
TEMP...
1989
http://www.fao.org/giews/earthobservation
NEAR REAL TIME MONITORING AT GLOBAL LEVEL
STANDALONE ASIS
funded by:
Global ASIS
FAO HQ
Input
data
Country/regional
ASIS
External
inputs
a and b
VHI= a VCI + b TCI
Weighted VHI from SOS to
EO...
VHI temporal
average value
agricCrop
area
Administrative
unit
% of crop area
affected by drought
0- 10
10-20
20-25
25-35
>...
0
0.2
0.4
0.6
0.8
1
1.2
0 25 35 45 55 65 75 85 100
Indice
VHI
Drought categories
Abnormal dry
No drought
Extreme drought
S...
PROBABILISTIC FORECAST OF AGRICULTURAL
DROUGHT
CALIBRATED ASIS FOR NICARAGUA
First crop
season
(Primera)
Second crop
season (Postrera)
Third crop
season
(Apante)
Land us...
PERCENTAGE OF AREA STAPLE CROPS AFFECTED BY
DROUGHT AT COUNTRY LEVEL
0
10
20
30
40
50
60
70
80
90
100
ASI
Estelí, Nicaragua
0
10
20
30
40
50
60
70
80
90
100
ASI
Granada, Nicaragua
0
10
20
30
...
TRIGGER FOR A INDEXED CROP INSURANCES BASED
ON GEOSPATIAL DATA (1985-2014)
40% 60%
Fuente: INETER, 2017Fuente: INETER, 2017
HISTORICAL PROBABILITY OF OCCURRENCE OF >50% OF GRAIN
AREA AFFECTED BY DROUGHT DURING PRIMERA, POSTRERA
AND APANTE
First c...
UNDERSTANDING THE DROUGHT IMPACT OF EL NIÑO
ON THE GLOBAL AGRICULTURAL AREAS
An assessment using FAO’s Agricultural Stress...
ASIS´S CONTRIBUTION
1.
Automatic-system fed by pre-
processed imagery from VITO that
guarantee the sustainability of the
s...
THANK YOU!
Assessing risks and impacts from extreme
events/natural hazards on the agriculture sector
Comments ?
Questions?
Please wri...
THANK YOU!
Give us your feedback
Click on the link
in the chat box
KORE - Knowledge Sharing Platform on Resilience
KORE@fa...
Stay tuned for the final
DRM webinar:
Returns from investments in disaster risk
reduction technologies and practices in
ag...
DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme ev...
DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme ev...
DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme ev...
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DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme events/natural hazards on the agriculture sector

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Over the past decade, economic damages resulting from natural hazards have amounted to USD 1.5 trillion caused by geophysical hazards such as earthquakes, tsunamis and landslides, as well as hydro-meteorological hazards, including storms, floods, droughts and wild fires. Climate-related disasters, in particular, are increasing worldwide and expected to intensify with climate change. They disproportionately affect food insecure, poor people – over 75 percent of whom derive their livelihoods from agriculture. Agricultural livelihoods can only be protected from multiple hazards if adequate disaster risk reduction and management efforts are strengthened within and across sectors, anchored in the context-specific needs of local livelihoods systems.
This series of three webinars on Disaster Risk Reduction and Management (DRR/M) in agriculture is organized to:
1. Discuss the new opportunities and pressing challenges in reducing and managing disaster risk in agriculture;
2. Learn and share experiences about disaster risk reduction and management good practices based on concrete examples from the field; discuss how to create evidence and conditions for upscaling of good practices; and
3. Exchange experiences and knowledge with partners around resilience to natural hazards and climate-related disasters.
This webinar covered:
• Monitoring risk in agriculture - the Agriculture Stress Index System
• Damage and loss from disasters on agriculture and food security - recent data and the new SFDRR monitoring mechanism - indicator C2

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DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme events/natural hazards on the agriculture sector

  1. 1. KORE - Knowledge Sharing Platform on Resilience KORE@fao.org
  2. 2. INFORMED INFORMATION FOR NUTRITION FOOD SECURITY AND RESILIENCE FOR DECISION MAKING
  3. 3. 3 #ks4resilience #resilience #UNFAO
  4. 4. THE IMPACT OF DISASTERS ON AGRICULTURE Assessing risks and impacts from extreme events/natural hazards on the agriculture sector with focus on drought Moderator Stephan Baas, Strategic Advisor on Resilience, FAO Tuesday, 30 May 2016: 15.00 – 16.30 CEST
  5. 5. ASSESSING DISASTER IMPACTS FROM NATURAL HAZARDS IN AGRICULTURE  natural hazard–induced disasters occur more often and with higher magnitude  90 % of these disasters are weather-related  high dependence of the agriculture sector on climate.
  6. 6. THE INFORMATION GAP  Actual cost of disasters to the agriculture sector?  Drawing conclusions available data? and information?  Documentation of disaster impacts on the AG sectors?
  7. 7. FAO’S WORK IN ADDRESSING THE INFORMATION GAP
  8. 8. THE IMPACT OF DISASTERS ON AGRICULTURE Recent data and the new SFDRR monitoring mechanism - indicator C-2 Speaker I Niccolo Lombardi, Expert in Disaster Impacts and DRR, FAO
  9. 9. 0 50 100 150 200 250 300 350 400 450 500 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Geophysical Meteorological Hydrological Climatological Source: FAO (2017), based on EM-DAT CRED Geophysical Number of disasters triggered by natural hazards worldwide, 1980 - 2016 BACKGROUND Increasing disasters, increasing impacts
  10. 10. Source: FAO (2017), based on EM-DAT CRED Economic damage of disasters triggered by natural hazards worldwide, 1980 - 2016 BACKGROUND Increasing disasters, increasing impacts $0 B $50 B $100 B $150 B $200 B $250 B 1980198219841986198819901992199419961998200020022004200620082010201220142016 Damage (weather and climate related) Damage (geophysical)
  11. 11.  Review of 74 PDNAs of disaster events in 53 developing countries b/n 2006-2016  Analysis of crop and livestock production losses caused by natural hazards and disasters affecting at least 100,000 people, or 10% of the population. BACKGROUND Increasing disasters, increasing impacts
  12. 12. 23%16% 31% D&LLossesDamage IMPACT OF DISASTERS ON AGRICULTURE Share of D&L absorbed by agriculture (2006-2016) Source: FAO (2017), based on 74 PDNAs conducted between 2006 and 2016
  13. 13. Source: FAO (2017), based on 74 PDNAs conducted between 2006 and 2016 IMPACT OF DISASTERS ON AGRICULTURE Varying sectoral vulnerabilities to disasters 14% 65% 20% 1% Crops Livestock Fisheries and Aquaculture 86% 9% 4% 1% 6% 44%38% 11% 1% Forestry 5% 64% 31%
  14. 14. IMPACT OF DISASTERS ON AGRICULTURE Focus on drought oMore than 80% of the impact of drought is on agriculture oDrought caused 19% of total crop and livestock losses between 2005 and 2014, in developing countries oUnder-reporting on the impact of drought, especially on small scale events such as dry spells
  15. 15. IMPACT OF DISASTERS ON AGRICULTURE Focus on drought in Africa Drought losses as a percentage of potential production oBetween 2004 and 2014, drought has led on average to a loss of 3 to 4 percent of potential agricultural production in Africa - peaks of 10 and even 20 percent in certain cases oBetween 1980 and 2014, droughts affected over 363 million people in Africa, of whom 203 million in Eastern Africa o2015-2016 El Niño affected more than 60 million people worldwide, with strong impact in Eastern and Southern Africa
  16. 16. Standardiz ed procedures and methodolog ies Strengthene d country capacities Better informed DRR planning Comprehens ive D&L databases Key issues: o Sub-national level data o Disaster thresholds oCausal relationship o Complementarity with existing systems and methods. FILLING THE KNOWLEDGE GAP Steps required to improve data quality and quantity
  17. 17. FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SFDRR Global Targets Target C
  18. 18. Indicator C-2: Direct agricultural loss attributed to disasters will be computed using the FAO methodology, as requested by member countries. C-2 is calculated as the sum of five sub-indicators:  C2(C): Direct crop loss  C2(L): Direct livestock loss (and apiculture)  C2(FO): Direct forestry loss  C2(AQ): Direct aquaculture loss  C2(FI): Direct fisheries loss FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SFDRR Indicator C-2
  19. 19. FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SDG targets Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure to climate- related extreme events and other economic, social and environmental shocks and disasters SFDRR Indicator C-2 will be used, among others, also to monitor SDG Target 1.5
  20. 20. Damage Losses Crops Livestock Fisheries Aquaculture Forestry Production Value of destroyed stored production and inputs, dead animals, and fully damaged perennial trees Value of lost production Assets Replacement or repair value of destroyed machinery, equipment, tools FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT Components
  21. 21. • FAO’s technical guidance and formulas to compute Indicator C2 are included in the draft Collection of Technical Notes on Data and Methodology for monitoring SFDRR. • The draft was consolidated by UNISDR with inputs from technical agencies. It is now being shared with countries for consultation. Source: UNISDR, draft of 26 April 2017 FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT Technical Guidance and Computation Methods
  22. 22. o Typhoon Haiyan (Yolanda) hit central Philippines in Nov 2013 o Winds registered at over 300 km per hour (Figure) - strongest wind speed recorded in the country for the landfall of a cyclone o Storm surges reached up to 5.3 meters in height, causing devastation and loss of lives in affected coastal provinces o At least 6,300 deaths recorded (Nov ‘13), estimated 16 million people affected, over 1.1 million houses damaged/destroyed, overall damage to public infrastructure and agricultural land across 41 provinces TESTING THE METHODOLOGY Typhoon Haiyan, Philippines 2013 (Case Study Application)
  23. 23. $ 522 M $ 902 M Key Results o Total D&L in Agriculture: USD 1.4 billion – in line with government assessment o Most affected sub-sectors: crops, followed by fisheries and livestock. o Losses are almost 80 percent higher than damages TESTING THE METHODOLOGY Typhoon Haiyan, Philippines 2013 (Case Study Application)
  24. 24. DATA COLLECTION Harmonize and systematize assessment methods FAO’s support to countries DATA SOURCES WAY FORWARD Towards an integrated disaster impact information system D&L ASSESSMENT DATA REPORTING Harmonize and systematize data collection Improve access to data Facilitate data reporting
  25. 25. o Cooperation/coordination with UNISDR on monitoring indicator C-2 o Further testing and validation of the methodology on different hazards and regions – 2011 drought in Ethiopia o Development of a systematic, harmonized data collection and reporting process on D&L in agriculture o Country capacity enhancement to (a) monitor disaster impacts; and (b) make use of data for DRR/M planning o Development of a global information system on damage and loss in agriculture, linked to existing national and international disaster loss databases (e.g. EM-DAT CRED, Desinventar) – Start from questionnaire o How much damage and loss can be avoided/reduced by adequate DRR/M investment? (ongoing FAO work on resilience) WAY FORWARD Towards an integrated disaster impact information system
  26. 26. THANK YOU!
  27. 27. THE IMPACT OF DISASTERS ON AGRICULTURE From the Global Agriculture Drought Monitoring to Country Level using Geospatial Information Speaker II Oscar Rojas, Natural Resources Officer (Agrometeorology), FAO ASI S In collaboration with: http://www.fao.org/climatechange/asis/en/
  28. 28. Agricultur a 58% Industria 8% Electricid ad 25% Agua potable 2% Emergen cia 7% CENTROAMÉRICA: PERDIDAS OCASIONADAS POR LA SEQUÍA 2001 LOSSES DUE TO DROUGHT ARE CONCENTRATED ON THE AGRICULTURAL SECTOR (58%) AGRO RONDA EL 60% EN SEQUÍAS Philippine s 1991
  29. 29. OBJECTIVE Limitation using rainfall data: o Currently weather stations are sparse and provide discontinuous data o Rainfall estimates have a bias and show deviations in different regions of Africa (Dinku et al. 2007, Lim and Ho 2000). What is ASIS? o Is a expert system for agricultural drought monitoring based on 10-day satellite data of vegetation and land surface temperature from METOP-AVHRR sensor at 1 km.
  30. 30. ELECTROMAGNETIC ENERGY RECORDED BY THE SENSOR RE D NI R Water stress
  31. 31. Source: Kogan, F. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society vol.76, No. 5 655-668 pp. 0 0,5 1 J F M A M J J A S O N D NDVI Hodh El Gharbi, Mauritania Weather Ecosystem
  32. 32. Vegetation Health Index (VHI) VHI = a*VCI + (1-a)* TCI low VHI high VHI Temperature condition index (TCI)Vegetation condition index (VCI) AGRICULTURAL STRESS INDEX SYSTEM IS BASED ON THE VEGETATION HEALTH INDEX (VHI) (Kogan et al. 1995)
  33. 33. VHI temporal average value agric Crop area Administrative unit ASIS ASSESS THE SEVERITY (INTENSITY, DURATION AND SPATIAL EXTENT) OF THE AGRICULTURAL DROUGHT 0- 10 10-20 20-25 25-35 >35 Percentage of the agriculture areas with VHI below 35 % of crop area affected by drought
  34. 34. GLOBAL CROPLAND MASK
  35. 35. SOS and EOS of the first season, as derived from the long term NDVI averages of SPOT-VGT (roi GLD, 21 km resolution). TEMPORAL AGGREGATION Defining SOS (start of growing season) & EOS (end of growing season)
  36. 36. 1989
  37. 37. http://www.fao.org/giews/earthobservation NEAR REAL TIME MONITORING AT GLOBAL LEVEL
  38. 38. STANDALONE ASIS funded by:
  39. 39. Global ASIS FAO HQ Input data Country/regional ASIS External inputs a and b VHI= a VCI + b TCI Weighted VHI from SOS to EOS Calculation % area with wVHI<35 (Quick look map) Calculation wVHI using ASI as a weighted factor for each Drought Category (Quick look map) % area with wVHI in each Drought Category ROI (lat, long) of VCI, TCI, SOS, EOS, POS Quick look maps of each Drought Category Export to Excel % of each Drought Category by administrative unit Cumulative Weighted VHI (cwVHI) from SOS to EOS Introduction of threshold of critical cwVHI and probability calculation Probability of deficit as defined by threshold (Quick look map) National database and National early warning system (NEWS)
  40. 40. VHI temporal average value agricCrop area Administrative unit % of crop area affected by drought 0- 10 10-20 20-25 25-35 >35 Percentage of the agriculture areas with VHI below 35
  41. 41. 0 0.2 0.4 0.6 0.8 1 1.2 0 25 35 45 55 65 75 85 100 Indice VHI Drought categories Abnormal dry No drought Extreme drought Severe drought Moderate drought Exceptional drought Indicator Drought category VHI pixel ASI* 1 Exceptional Drought <35 % 0.75-0.99 Extreme Drought 36-45 % 0.50-0.74 Severe Drought 46-55 % 0.25-0.49 Moderate Drought 56-65 % 0.01-0.24 Abnormal dry 66-75 % 0 No Drought >75 % * Percentage of pixels in each drought categoy DROUGHT CATEGORIES
  42. 42. PROBABILISTIC FORECAST OF AGRICULTURAL DROUGHT
  43. 43. CALIBRATED ASIS FOR NICARAGUA First crop season (Primera) Second crop season (Postrera) Third crop season (Apante) Land used study (rice, maize and beans)
  44. 44. PERCENTAGE OF AREA STAPLE CROPS AFFECTED BY DROUGHT AT COUNTRY LEVEL
  45. 45. 0 10 20 30 40 50 60 70 80 90 100 ASI Estelí, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Granada, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Jinotega, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Nueva Segovia, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Región Autónoma Caribe Sur 0 10 20 30 40 50 60 70 80 90 100 ASI Managua, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Chinandega, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Rivas, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Madriz, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Chontales, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI León, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Región Autónoma Caribe Norte 0 10 20 30 40 50 60 70 80 90 100 ASI Carazo, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Río San Juan, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Masaya, Nicaragua
  46. 46. TRIGGER FOR A INDEXED CROP INSURANCES BASED ON GEOSPATIAL DATA (1985-2014) 40% 60% Fuente: INETER, 2017Fuente: INETER, 2017
  47. 47. HISTORICAL PROBABILITY OF OCCURRENCE OF >50% OF GRAIN AREA AFFECTED BY DROUGHT DURING PRIMERA, POSTRERA AND APANTE First crop season (Primera) Third crop season (Apante) Second crop season (Postrera) Probability
  48. 48. UNDERSTANDING THE DROUGHT IMPACT OF EL NIÑO ON THE GLOBAL AGRICULTURAL AREAS An assessment using FAO’s Agricultural Stress Index (ASI) El Niño observed from sattelite. The red areas of the tropical coasts of South America indicate the pool of warm water. Source: NOAA
  49. 49. ASIS´S CONTRIBUTION 1. Automatic-system fed by pre- processed imagery from VITO that guarantee the sustainability of the system 2. Temporal-spatial integration, normally not take into consideration for most of the systems on agricultural monitoring based on remote sensing data 3. Unique time series (>30 years) a 1 km resolution that guarantee the long term memory of the pixel of having an extreme drought event
  50. 50. THANK YOU!
  51. 51. Assessing risks and impacts from extreme events/natural hazards on the agriculture sector Comments ? Questions? Please write them in the chat box
  52. 52. THANK YOU! Give us your feedback Click on the link in the chat box KORE - Knowledge Sharing Platform on Resilience KORE@fao.org
  53. 53. Stay tuned for the final DRM webinar: Returns from investments in disaster risk reduction technologies and practices in agriculture KORE - Knowledge Sharing Platform on Resilience KORE@fao.org

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