CRVA a tool to spatially assess vulnerability to support communities at high risk to climate variability and change thru the provision of climate resilient agriculture (CRA). The project in the Philippines is funded by the Department of Agriculture - System Wide Climate Change Office (DA-SWCCO) to enhance farmer resilience in vulnerable areas. Watch out for the Landscape-CRVA that we are currently piloting in Isabela. This is also funded by DA-SWCCO.

1. Climate Risk Vulnerability
Assessment (CRVA) for CRA
Geospatial Targeting and
Prioritisation
Date: 22 February 2018
Venue: DA-BAR Quezon City
LEO KRIS MARIANO PALAO
Senior Research Associate – Geospatial Specialist
CIAT Data Intelligence Hub
l.palao@cgiar.org

2. Vulnerability
Global
Regional – Cross Country
Country
Regional – Sub national
Landscapes
Watershed
Municipality
Barangay
Purok/Sitio
Vulnerability assessment studies
VAs are conducted at different scales – based on target
users and information required
Many methods exist for different sectors
Components may vary (Sens, Haz, AC)
Different stakeholders have different needs
VAs gives the overall picture of risk and adaptive capacity
It gives a sense of where to target and why

3.  Exposure: The nature and degree to which a system is exposed to significant climate variations
(IPCC 2014).
 Sensitivity: The increase or decrease of climatic suitability of selected crops to changes in
temperature and precipitation.
 Adaptive Capacity: The ability of a system to adjust to climate change (including climate
variability and extremes) to moderate potential damages, to take advantage of opportunities, or
to cope with the consequences. (IPCC 2014)
Three (3) Key Dimensions of Vulnerability

4. Changes in
Temperature
Exposure I: changes in temp. and prec.
Changes
Precipitation
Sensitivity Index
“Changes in Climatic Suitability to Grow Crops”
Future Conditions – Baseline Conditions
Exposure II: Biophysical Indicators (climate-related pressures)
Flooding Landslide Drought
Salt Water
Intrusion
Sea Level
Rise
Tropical
Cyclones
Hazard Index
Storm
Surge
Erosion
“Exposure from hazards”
Pot. Impact
“Climate-Risk Vulnerability”External Inputs Spatial AnalysisDerived Data
Legend:
Climate-Risk Vulnerability Assessment (CRVA) Framework
“Capacity to Resist and Adapt
to Pressures”
Economic
Natural
Human
Physical
Institutional
Adaptive
Capacity
Index
Adaptive Capacity
CapitalsofAdaptiveCapacity
Anticipatory

5. 3. Model validation
2.
Species/Crop
Distribution
Models
1. Climate
Data/Models
Exposure 1: Sensitivity: The increase or decrease of climatic suitability of selected crops to changes in
temperature and precipitation

6. IPCC AR5 (2013)
Representative Concentration Pathways (RCPs)
Machine Learning
Crop Distribution Models
Climatic Suitability
EcoCrop
GCMs
BioclimaticVariables
Statistical/
Spatial
Downscaling
 Bio1 = Annual mean temperature
 Bio2 = Mean diurnal range
 Bio3 = Isothermality
 Bio4 = Temperature seasonality
 Bio5 = Maximum temperature of warmest month
 Bio6 = Minimum temperature of coldest month
 Bio7 = Temperature annual range
 Bio8 = Mean temperature of wettest quarter
 Bio9 = Mean temperature of driest quarter
 Bio10 = Mean temperature of warmest quarter
 Bio11 = Mean temperature of coldest quarter
 Bio12 = Annual precipitation
 Bio13 = Precipitation of wettest month
 Bio14 = Precipitation of driest month
 Bio15 = Precipitation seasonality
 Bio16 = Precipitation of wettest quarter
 Bio17 = Precipitation of driest quarter
 Bio18 = Precipitation of warmest quarter
 Bio19 = Precipitation of coldest quarter
 Bio 20 = No. of consecutive dry days
Impact of Climate Change to
Crop Suitability
Adaptation Options

7. Climate Data Portal www.ccafs-climate.org | www.ccafs-climate.org/data_spatial_downscaling
http://www.ccafs-climate.org/citations/

8. Exposure 1: Sensitivity: Uncertainty analysis for Precipitation (Proportion of GCMs saying the same thing)
January-AprilMay-AugustSeptember-December

9. Exposure 1: Sensitivity: Uncertainty analysis for temperature (Proportion of GCMs saying the same thing)
January-AprilMay-AugustSeptember-December

10. Exposure 1: Sensitivity (EcoCrop)
EcoCrop parameter requirements:
Growing Season Minimum
Growing Season Maximum
Killing Temperature
Temperature Minimum
Temperature Optimum Minimum
Temperature Optimum Maximum
Temperature Maximum
Rainfall Minimum
Rainfall Optimum Minimum
Rainfall Optimum Maximum
Rainfall Maximum

11. Exposure 1: Sensitivity
Model Training
GLM
GBM
CTA
ANN
FDA
MARS
RF
SVM
MaxEnt
Model Prediction
EnvironmentalVariables/SpatialLayersSamples/Observations
Green color = high
probability (suitability)
Blue color = low probability
(suitability)

12. Exposure 2: Hazards: The nature and degree to which a system is exposed to significant climate variations
(IPCC, 2014)
Frequency analysis
Noisy Data Organized Data

13. Exposure 2: Hazards: Spatially weighted hazard index map (shows municipality with have high hazard risk)
Flooding Landslide Drought
Salt Water
Intrusion
Sea Level
Rise
Tropical
Cyclones
Storm
Surge
Erosion
“Exposure from hazards”

14. Adaptive Capacity: the ability of the system to adjust to climate change (including climate variability and extremes)
to moderate potential damages, to take advantage of opportunities, or to cope with the consequences (IPCC, 2014)
Measured by Capitals (Assets)
• Economic Capital
• Natural Capital
• Human Capital
• Physical Capital
• Institutional Capital
• Anticipatory Capital

15. Adaptive Capacity
• Comprehensive data/information of AC indicators developed
• Statistical processes and stakeholder consultation were used to select and process
relevant indicators
• Adaptive capacity was given highest weights and was set to 70%

16. Adaptive Capacity
Presence of climate/weather information facilities
(anticipatory capital) – Private sector

17. Adaptive Capacity
Economic
Natural
Human
Physical
Institutional
CapitalsofAdaptiveCapacity
Anticipatory

18. Climate-Risk Vulnerability Assessment (CRVA) Maps

19. Camarines Sur Bukidnon Negros Occidental
Climate Risk Vulnerability Assessment: What’s the story behind the maps
Very high vulnerability
High vulnerability

20. Climate Risk Vulnerability Assessment: What’s the story behind the maps (Negros Occidental)
Adaptive Capacity: Assessment of Adaptive Capacity per Capital
Hazard Risks in Pontevedra
• Flooding
• Storm surges
• Drought
• Erosion
Pontevedra, Negros Occidental:
Classification: High Vulnerability
Target for AMIA Village
Some historical accounts:
• Decreasing yields in Rice and
Maize
• Lack of water and irrigation
system/infrastructure
• Drought prone area
Exposure 1: Sensitivity (Change in climatic suitability to grow crops – Maize, Rice)
Exposure 2: Exposure from climate related
natural hazards
Flood Susceptibility

21. Climate-Risk Vulnerability Assessment (CRVA) – Sensitivity Analysis of Weights
Sensitivity Analysis

22. Conclusion
• CRVA was done using modeling and a series of consultative workshops with experts
• CRVA can be used to inform and guide decision makers (DA), extension staff, and private sectors on: where? are
geographical areas that are in most need of interventions; and what? Package of interventions are needed for each
geographical areas
• It opens the door for cross sectoral collaboration from government agencies and private sectors.
• Combine wealth of previous expertise from various CRVAs conducted globally.
• Quantify the current and future suitability (climate domains) of key agri-systems – Result of sensitivity analysis can be
used to target areas to do more detailed crop modeling work
• Results at the municipality level (fine resolution) – and option to scale up to landscape level vulnerability

23. The International Center for Tropical Agriculture (CIAT), a CGIAR center
and leader of the CGIAR Research Program on Climate Change,
Agriculture and Food Security, performs scientific research enabling
smallholder farmers to make agriculture eco-efficient, meaning,
competitive and profitable as well as sustainable and resilient.
Headquartered in Colombia and working across Latin America, Africa and
Asia, CIAT has a mission to reduce hunger and poverty, and improve
human nutrition, through eco-efficient agriculture and towards a
sustainable food future. ciat.cgiar.org/