1. GIS-based Climate Change Vulnerability Assessment
of the Municipality of Del Carmen, Surigao del
Norte Province, Philippines
Diomedes A. Racelis, Elenita L. Racelis and Angela A. Limpiada
University of the Philippines Los Banos
2. PRESENTATION OUTLINE
• Country Background: Philippines
• Study Site: Del Carmen, Siargao Island
Surigao del Norte, Mindanao
• Objectives of the Study
• Methodology
• Results of Vulnerability Mapping
• Lessons Learned
16. Top 17 Megadiverse Countries
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17. Study Site: Del Carmen, Siargao Island , Surigao del Sur
MANILA
MINDANAO
SIARGAO
24. Sugba Lagoon Del Carmen, Siargao Island
Photo Credit: http://www.mindanaotours.com
25. OBJECTIVES OF THE STUDY
Determine using GIS the vulnerability to the
impacts of climate change and climate risks of
ecosystems, communities, and infrastructure in
the municipalities of Del Carmen in Surigao del
Norte
26. METHODOLOGY
A. Site Reconnaissance
B. Coordination with Stakeholders
C. Gathering of Secondary Data
D. Community Mapping
E. Map Consolidation
F. Scanning
G. Georeferencing
H. Digitizing
I. Generation of Vulnerability Maps
29. C. Gathering of Secondary Data
METHODOLOGY
• Thematic maps prepared by the GIS team of
CENERGY – topographic, vegetation, land use,
political, etc.
• Natural resources data – forest resources,
agriculture, fisheries
• Socio-economic data – settlement,
population, infrastructure
30. D. Community Mapping
METHODOLOGY
• Stakeholders were invited to annotate
existing maps as to their first-hand
knowledge on the information shown on the
thematic maps
• Map sheets, transparencies, colored pens,
colored papers, sticky notes were used to
mark details on the draft maps
31. E. Map Consolidation
METHODOLOGY
• Involved compiling all analog (paper-based)
spatial information prior to GIS mapping.
• In this particular activity, a total of twenty (20)
draft thematic maps for the four (4) selected
municipalities of Siargao Island were received
for conversion to GIS-based digital maps
32. F. Scanning
METHODOLOGY
• transferred analog (paper) maps into a digital
format the computer can read
• the maps were scanned individually in
preparation for on-screen digitizing.
• each map was placed carefully on ordinary
printer-scanner and scanned to produce a
digital image for subsequent digitizing
33. F. Georefencing
METHODOLOGY
• involves aligning geographic data to a known
coordinate system so it can be viewed, queried,
and analyzed with other geographic data
• to georeference an image, one first needs to
establish control points, input the known
geographic coordinates of these control points,
choose the coordinate system and other
projection parameters and then minimize
residuals
• Residuals are the difference between the actual
coordinates of the control points and the
coordinates predicted by the geographic model
created using the control points
34. F. Georefencing
METHODOLOGY
In this project, the scanned maps were georeferenced
individually by clicking the “add data” button in the
ArcMap including the boundary per municipality that
was previously used in earlier maps
35. F. Georefencing
METHODOLOGY
…georeferencing tool was activated to allow adding of the
control point on the image; … the four (4) control points
were chosen preferably along the corners of the map;
…“update” button was clicked to update the georeferenced
of the image…
36. F. Digitizing
METHODOLOGY
• analog (or paper) map is converted into a
digital format
• in this particular set of maps, the scanned
images were individually digitized on-screen
by encoding all the relevant information
shown on each image
37. F. Digitizing
METHODOLOGY
…the specific map features were digitized by clicking in sequence
the ArcMap catalog and add new shape files buttons; …spatial
information such as plants, crops, buildings, and schools were
digitized as “points” while roads, streams, partial boundary were
digitized as “polylines” with the same coordinates as “points”;
Municipal and barangay boundary was digitized as “polygons”…
39. F. Digitizing
METHODOLOGY
…The finished map is then added as a new shapefile; the GIS
mapping process is completed by editing the map by clicking
“editor” button; this is done by drawing each item per point,
polyline and polygon…
40. G. Preparation of Vulnerability Maps
1. Storm surge hazard map analysis
The digital elevation model (DEM) of the municipality
was used to derive the storm surge map. Five-meter
elevation interval was generated through interpolation.
METHODOLOGY
41. G. Preparation of Vulnerability Maps
1. Storm surge hazard map analysis
METHODOLOGY
42. G. Preparation of Vulnerability Maps
1. Storm surge hazard map analysis
METHODOLOGY
43. G. Preparation of Vulnerability Maps
2. Drought hazard map analysis
METHODOLOGY
• Drought risk map was generated based on land use map/data
by identifying the agricultural lands (rice, coconut, orchard,
etc.) to be affected and label them as high (red), populated
areas medium (yellow), others low (green).
• Crops and agricultural areas were selected and reclassified as
high while forests and water bodies were reclassified as low
LAND USE DROUGHT
'Arable land, crops mainly cereals and sugar' High
'Crop land mixed with coconut plantation' High
'Cultivated Area mixed with
brushland/grassland'
High
'Mangrove vegetation' nd/grassland' Low
'Unclassified' Low
‘Built-up’ Medium
44. G. Preparation of Vulnerability Maps
2. Drought hazard map analysis
METHODOLOGY
45. G. Preparation of Vulnerability Maps
2. Flood and Landslide map analysis
METHODOLOGY
Based on
existing flood
and landslide
map of the
areas and
rainfall data
from PAGASA
MONTH
OBSERVED CHANGE PROJECTED
(1971-2000) (2006-2035)
(2036-
2065)
(2006-2035)
(2036-
2065)
JAN 603.4 22.1 1.9 736.8 615.1
FEB 428.6 12.3 -0.5 481.2 426.3
MAR 369.8 18.7 -23.8 438.9 281.7
APR 203.1 -16.4 -30.2 169.8 141.8
MAY 132.6 -5.5 -9.8 125.3 119.6
JUN 148.6 31.7 -0.4 195.8 148.0
JUL 170.7 10.0 -15.1 187.8 144.9
AUG 136.9 17.5 6.5 160.8 145.8
SEP 165.7 21.7 2.9 201.7 170.5
OCT 267.9 -12.1 26.0 235.5 337.6
NOV 510.8 18.6 18.3 606.0 604.4
DEC 510.8 10.2 41.2 562.7 721.1
Average 304.1 10.7 1.4 341.8 321.4
Average
/30 10.1359906 0.35748333 0.0472675 11.3947482 10.713652
(Averag
e/30)/3
65 0.02776984 0.00097941 0.0001295 0.03121849 0.02935247
46. G. Preparation of Vulnerability Maps
2. Flood and Landslide map analysis
METHODOLOGY
The average/30 means that the result of the average per year is
divided into 30 which is the number of years that it is projected
(1971-2000, 2000-2020, 2020-2050)
Topo-
corrected
Observed
Hazard
Map
Observed Mean Frequency of Daily Rainfall 100 mm
and above (2020, 2050)
Low (1)
Moderately
Low (2)
Moderate
(3)
Moderately
High (4)
High (5)
Very High
(6)
Low (2) 2 4 6 8 10 12
Moderate
(4) 4 8 12 16 20 24
High (6) 6 12 18 24 30 36
47. G. Preparation of Vulnerability Maps
2. Flood and Landslide map analysis
METHODOLOGY
…the result of this is divided again into 365 which is the number
of days per year…
0-6 Low
7-12 Moderately Low
13-18 Moderate
19-24 Moderately High
25-30 High
>30 Very High
Average number of daily rainfall (100 mm above)
0-1 Low
1-2 Moderately Low
2-3 Moderate
3-4 Moderately High
4-5 High
>5 Very High
48. G. Preparation of Vulnerability Maps
2. Flood and Landslide map analysis
METHODOLOGY
49. G. Preparation of Vulnerability Maps
2. Flood and Landslide map analysis
METHODOLOGY
50. Areas affected by storm surge (Population)
Areas affected by rain-induced landslide (Population)
Areas affected by drought (Agriculture sector)
Vulnerability Maps for Del Carmen
51. OTHER GIS-RELATED ACTIVITIES
1. Preparation of hazard map in jpeg format:
–Flood map overlayed with topo map. Map
showing barangay boundaries and other
principal/key features that should be included
in the map.
–Landslide map overlayed with topo map.
Map showing barangay boundaries and other
principal/key features that should be included
in the map.
52. OTHER GIS-RELATED ACTIVITIES
2. Preparation of overlay/thematic maps and
attribute tables:
• Spatial Extent for Rainfall-induced Landslides
• Exposed Population for Rainfall-induced
Landslides
• Spatial Extent for Flooding
• Exposed Population for Flooding
58. Table 25. Populated areas at risk to storm surge in Del Carmen.
High
Populated Unpopulated Populated Unpopulated None Populated Unpopulated
Antipolo 46.5 83.5 7.8 298.0 277.1 713.0
Bagakay(Alburo) 8.5 28.2 4.7 485.7 410.4 937.5
Bitoon 27.8 19.1 5.0 60.8 233.5 346.1
Cabugao 16.5 31.5 5.6 99.9 272.9 426.3
Cancohoy 0.0 205.7 399.1 604.8
Caub 20.4 17.8 52.7 60.4 209.4 360.5
Del Carmen 30.0 4.1 49.3 0.0 0.4 53.6 79.9 217.3
Domoyog 38.9 121.6 5.3 530.4 696.2
Esperanza 23.6 70.3 1.4 148.0 438.4 681.8
Jamoyaon 41.7 95.3 5.5 323.1 1,139.2 1,604.7
Katipunan 0.6 0.4 195.5 362.0 558.5
Lobogon 401.4 157.4 558.8
Mabuhay 13.7 51.4 0.9 44.8 224.0 334.7
Mahayahay 37.9 127.0 164.9
n.a. ( 30199) 13.3 9.7 18.0 23.5 64.5
Quezon 24.3 226.3 250.5
San Fernando 315.0 1,024.1 147.1 21.5 1,426.7 2,934.4
San Jose 12.0 3.8 28.3 3.9 1.0 151.9 74.0 275.0
Sayak 1.3 0.8 407.6 91.1 500.9
Tuboran 8.7 143.3 152.0
Low Medium None
Barangay
Populated Areas at Risk to Storm Surge (ha)
Total
59. LESSONS LEARNED
• Local Government Units (LGUs) play an
important role in mainstreaming Climate Change
Adaptation (CCA) strategies
• Attitude, commitment and competence of Local
Chief Executives (LCEs) determine the level of
success in Climate Change Adaptation strategies
• Local awareness and appreciation of the value of
CCA determine the extent of support afforded
by communities
• Local and indigenous knowledge complement
science-based solutions to CC impacts
60. ACKNOWLEDGEMENT
The authors wish to thank the following:
• Philippine Climate Change Commission and
the Climate Change Office
• Global Green Growth Initiatives
• Local Government of Del Carmen, Surigao del
Norte
• University of the Philippines Los Banos
• Philippine Department of Science and
Technology
