Step1-CDRA.pptx

Ministry of Human Settlements and Development
he Housing Arm of the BARMM
Assessing risks and vulnerabilities,
determining priority decision areas and risk
management and adaptation options

6-Step Process
2
Step 1 Step 2 Step 3
Step 4
Step 5
Step 6
Collect and
organize
climate change
and hazard
information
Scope the
potential
impacts of
hazards and
climate change
Develop the
Exposure
Database
Conduct a
Climate Change
Vulnerability
Assessment
Conduct a Disaster Risk
Assessment
Summarize findings

3

COLLECT AND ANALYZE CLIMATE
AND HAZARD INFORMATION
Objectives:
• Understand the various future climate
scenario/s by analyzing climate change
scenarios
• Characterize the natural hazards that may
potentially affect the locality/barangay
• Understand previous disasters and severely
affected elements
4

COLLECT AND ANALYZE CLIMATE
AND HAZARD INFORMATION
Outputs:
• Local Climate Change Projections
• Inventory of natural hazards and their
characteristics
• Tabular compilation of historical disaster
damage/loss data
• Summary of barangay-level hazard inventory
matrix
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Task 1.1
• Collect and analyze climate change information
Task 1.2
• Collect and organize hazard information
• Sub-Task 1.2.1: Gather hazard maps and characterize hazards
• Sub-Task 1.2.2: Prepare a summary hazard inventory matrix
• Sub-Task 1.2.3: Analyze Previous Disasters
• Sub-Task 1.2.4: Prepare a Hazard Susceptibility Inventory Matrix

Collect and analyze climate
change information
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 This step involves collecting and reviewing important climate change information relevant to
the local government unit.
 Key climate variables to collect are:
 Temperature
 Precipitation
 Extreme events (i.e. number of dry days, number of days with temperature exceeding 35OC, and number of extreme
rainfall events)
 The basic source for climate information is the Climate Change in the Philippines publication of PAGASA. It
includes the baseline climate trends from 1971-2000 where the projected changes in 2020 (2006-2035) and
2050 (2036-2065) can be compared. The climate projections are available for each region and province of the
country. The municipality or city, at first pass, may consider the provincial data, and consult PAGASA on the
applicability.

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• Seasonal Temperature:
• In computing for the 2020 and 2050 projected seasonal temperature, the projected changes per season
were added to the observed baseline (refer to table 3.1.1)

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• In the Table 3.1.1 example, the data suggest that the area will experience relatively warmer conditions by
2020 and 2050 compared to the observed seasonal temperatures. There will be 1.2℃ warming during the MAM
and JJA while a 1.0℃ warming during the DJF and SON seasons in 2020. In 2050, temperature may increase
by as much as 2.3 to 2.4℃ during the MAM and JJA seasons respectively while the projected increase during
the DJF and SON season will be 1.9 ℃ and 2.0 ℃ C respectively

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2020 Projected Seasonal Temperature DJF = Baseline DJF + 2020 DJF
2020 Projected Seasonal Temperature DJF = 27.6+1.0
2020 Projected Seasonal Temperature DJF = 28.6
2020 Projected Seasonal Temperature MAM = Baseline MAM + 2020 MAM
2020 Projected Seasonal Temperature MAM = 28.3+1.2
2020 Projected Seasonal Temperature MAM= 29.5

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Period
Season
DJF MAM JJA SON
Observed 1971-2000 27.6 28.3 27.5 27.6
Change in 2020 ( 2006-2035) 28.6 29.5
Change in 2050 ( 2036-2065)
• In this example, the data suggest that the area will experience relatively warmer conditions by 2020 and
2050 compared to the observed seasonal temperatures. There will be 1.2℃ warming during the MAM and JJA
while a 1.0℃ warming during the DJF and SON seasons in 2020. In 2050, temperature may increase by as
much as 2.3 to 2.4℃ during the MAM and JJA seasons respectively while the projected increase during the DJF
and SON season will be 1.9 ℃ and 2.0 ℃ C respectively

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• For seasonal rainfall, projected data are expressed as percentage change from the baseline values. The
percentage change are multiplied to the baseline values to get the rate of change in mm and added to the
baseline values to derive the projected seasonal rainfall values (Table 3.1.3).

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Computation can be
summarized and presented
as follows:
ADD A FOOTER
In this example, the data suggest that there will be a reduction in rainfall during the summer and
Habagat seasons in 2020 and 2050. Also, there will be a slight increase in rainfall during
Amihan season, but the amount of rain is expected to be lesser than the Habagat and transition
seasons. Summer months are expected to be drier and Amihan months will be slightly wetter
compared to observed trends.

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BARMM
(Maguindanao Province)
2020 Projected Rainfall DJF =
Baseline DJF + ((Baseline DJF)*(2020
DJF))
2020 Projected Rainfall DJF = 225.3+225.3(6.3%)
2020 Projected Rainfall DJF = 239.5
2020 Projected Rainfall MAM =
Baseline MAM + ((Baseline MAM)*2020
MAM))
2020 Projected Rainfall MAM = 399.1+399.1(1.4%)
2020 Projected Rainfall MAM = 404.687

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Projections shall be
presented in this format:
Period
Season
DJF MAM JJA SON
Observed 1971-2000 225.3 399.1 635.3 553.6
Change in 2020 ( 2006-
2035) 239.5 404.68
Change in 2050 ( 2036-
2065)
In this example, the data suggest that there will be a reduction in
rainfall during the summer and Habagat seasons in 2020 and
2050. Also, there will be a slight increase in rainfall during Amihan
season, but the amount of rain is expected to be lesser than the
Habagat and transition seasons. Summer months are expected
to be drier and Amihan months will be slightly wetter compared to
observed trends.

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• EXTREME EVENTS
Provincial-level projections provide three climate variables to cover extreme events namely:
• Number of days with temperature exceeding 35℃;
• Number of days (defined as days with rainfall less than 2.5mm); and
• Number of extreme daily rainfall.
Projected data are expressed in frequency and can be compared to observed trends to establish the
projected changes in 2020 and 2050.

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Based on the data, there will be a
significant increase in the number of days
exceeding 35℃ in 2020 and 2050 based
on observed trends. In terms of extreme
rainfall, the number of dry days will
decrease in 2020 and 2050 but the
number of extreme daily rainfall event will
increase in 2020 and a slight decrease in
2050 compared to observed trends.

21
Representative Concentration Pathways (RCPs) are scenarios that
include time series of emissions and concentrations of the full suite of
greenhouse gases and aerosols and chemically active gases, as well as
land use/land cover (Moss et al., 2008).
Four RCPs produced from Integrated Assessment Models were selected
from the published literature and are used in the present IPCC
Assessment as basis for the climate predictions and projections.
The Global mean sea level rise for 2081–2100 relative to 1986–2005 will
likely be in the ranges of 0.26 to 0.55m for RCP2.6, 0.32 to 0.63 m for
RCP4.5, 0.33 to 0.63 m for RCP6.0, and 0.45 to 0.82m for RCP8.5
(medium confidence).
It is important to note that regional rates of sea level rise can vary. This is
the result of regionally differing rates of thermal expansion of the oceans
as well as regional differences in atmospheric circulation, which can
affect relative sea levels. In addition, many coastal areas are either
subsiding or being uplifted

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• The LGU may also consider international or local published studies which provide climate
and climate change information applicable for their locality.
• Local or indigenous knowledge are also important sources of information.
Indigenous peoples, particularly have a way of interpreting meteorological phenomena
which have guided their responses to climate variation particularly in their livelihood
practices.
• Downscaling of climate projections at the municipal level, as demonstrated in Siligao,
Southern Leyte, can also be pursued by LGUs to provide site specific climate change
parameters

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ADD A FOOTER
Prepare a summary of projected changes in the climate variables. Computed values can be
further summarized and organized using the recommended summary table. This table shall
facilitate the identification of the expected changes in climate variables and the comparison
between the observed and projected changes. This output shall be used for the initial
scoping of impacts in Step 2.

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Collect and analyze hazard
information
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• This task involves gathering and analyzing hazard information to better understand the various
natural hazards affecting the locality.
• It also involves an inventory of historical disasters to establish patterns of hazards in terms of its
intensity and magnitude, including the scale of damages to property (i.e. agriculture, houses, socio-
economic support infrastructure and utilities) and how it affected local communities (fatalities,
injuries and number of severely affected families).
• At the end of this task, LGUs should be able to compile the necessary hazard maps and describe
the hazard susceptibilities of barangays or specific areas within the city/ municipality

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• Gather available hazard maps from mandated agencies (refer to data sources of hazard maps).
• When analyzing hazards, the following descriptors should be discussed:
a. Spatial Extent - What areas/barangays within the municipality/city are likely to be inundated or affected by a particular
hazard?
b. Magnitude/Intensity - What is the estimated strength of the hazard that will impact an area (i.e. flood can be expressed in
depth, water flow velocity, and/or duration; storm surge expressed in wave heights; earthquake ground shaking
expressed as intensity scale)?
c. Frequency - What is the estimated likelihood or the average recurrence interval (expressed in years) that a hazard event
may happen?
d. Duration – How long will the hazard occur (expressed in seconds, minutes, days, weeks etc.)?
e. Predictability – Can human systems/technologies accurately determine when and where a hazard might occur including
the estimated intensities?
f. Speed of Onset – Is the hazard slow/creeping (i.e. SLR, Drought) or rapid/fast (flashfloods, earthquakes, landslides)?

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• Gather maps from the mandated agencies.
• In all cases, ground validation should be conducted
• Conduct community-based hazard mapping
• Obtain current local studies and materials (e.g. technical reports, maps) initially from the mandated agencies to
build information that identify and characterize the hazards
• Participate in trainings and workshops by sending representatives from the planning group who will later relay
the findings and learnings, and provide inputs into the risk assessment
• Seek assistance from the climate change community of experts to provide an indication, and if possible, a
localized formal assessment of future impact scenarios and if impacts can be more or less severe, relative to
current climate situation.
• Pursue special studies such as hazard analysis, delineation of flood outlines, and distribution of flood depth to
reduce uncertainty in information and improve map accuracies

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Figure 3.1.2 shows an example of a flood hazard map
obtained from MGB, Region 10. It shows the susceptibility
or proneness to floods of barangays in the Municipality of
Opol.

37
• Upon gathering the various hazard maps, prepare a matrix indicating the various information derived from the
hazard maps. These can be complied and summarized using the sample table (refer to Table 3.1.8).

ADD A FOOTER 38
• The analysis of disaster events in the past provides a better understanding of hazards, specifically their pattern
of occurrence, observed magnitude/intensity, and areas often affected.
• Historical disaster/damage data are available at the local Disaster Risk Reduction and Management Ofce and
other provincial and regional sources (Office of Civil Defense, Provincial Disaster Risk Reduction and
Management Office).
• At a minimum, disaster data should contain statistics on:
• The date of occurrences of hazards by type;
• The affected areas indicated on a map;
• Estimated casualties in terms of the number of fatalities, injuries, and individuals missing;
• Number of houses totally and partly damaged; and
• Estimated value of damages to property such as agriculture, private, and commercial buildings and infrastructure

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• Based on the hazard maps, and climate change projections, prepare a hazard inventory matrix in order to
describe the susceptibilities of the municipality/city for sudden and slow onset hazards. Hazard susceptibility
attributed to climate change (i.e. sea-level rise), including past extreme weather events (drought) experienced
by the municipality, can also be included

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1. TABLE: Seasonal temperature increases (in degrees centigrade) in 2020 and 2050 under medium-range
emission scenario in the Province (table provided)
2. TABLE: Seasonal rainfall change (in %) in 2020 and 2050 under medium-range emission scenario in the
Province (table provided)
3. TABLE: Frequency of extreme events in 2020 and 2050 under medium-range emission scenario in the
Province (table provided)
• Medium Emission-Range Projected Seasonal Temperatures, 1971-2000, 2020 and 2050
• Medium Emission-Range Projected Seasonal Rainfall, 1971-2000, 2020 and 2050
• Frequency of Extreme Events in 2020 and 2050 under Medium-Range Emission Scenario
• Summary of Projected Changes in Climate Variables
• Inventory of Hazards and Their Description
• Records of Previous Disasters
• Hazard Susceptibility Inventory Matrix

Mursidier A. Sajili
HHRO IV – Basilan Province
HHRO IV – Special Geographic Area
42
Zuhaira U. Ebrahim

Step1-CDRA.pptx

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