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SYLVATROP
Editorial Staff
Antonio M. Daño
Editor-in-Chief
Adreana Santos-Remo
Veronica O. Sinohin
Liberato A. Bacod
Managing EditorLayout Artists
Liberty E. Asis
Gliceria B. De Guzman
Adreana Santos-Remo
Eduardo M. Tolentino
EditorsCirculation Assistant
Marilou C. Villones
Liberato A. Bacod
Editorial Assistant Printing Coordinator
January - December 2015 Vol. 25 Nos. 1&2
SYLVATROP, The Technical Journal of Philippine Ecosystems and Natural Resources is
published by the Department of Environment and Natural Resources (DENR) through
the Ecosystems Research and Development Bureau (ERDB), College, Laguna.
Subscription rates: P75 for single issue copy (local); P150 for combined issues and
US$15 per single issue copy (foreign) including airmail cost; US$30 for combined
issues. Re-entered as Second Class Mail CY 2012 at the College, Laguna Post Office
on 14 May 2013. Permit No. 2013-14.
Address checks to The Circulation Officer and contributions or inquiries to
The Editor-in-Chief at the following address:
SYLVATROP, The Technical Journal of Philippine Ecosystems and Natural Resources
Ecosystems Research and Development Bureau, DENR
Tel. No. (049) 536-2229, 2269 Fax: (049) 536-2850
E-mail: [email protected] or [email protected]
Cover Photo: Photo shows the watersheds of La Mesa and San Cristobal in Metro Manila and Sta. Rosa, Laguna, respectively.
Cover Layout: Adreana Santos-Remo
PREFACE
Climate change has taken a huge impact in the research world. Several
studies have been conducted to determine its effects in the environment. In
the Philippines, while we have lower carbon emissions compared to other
developing countries, we are considered to be one of the most vulnerable to
the impacts of climate change.
Because of the risks associated to climate change, Philippines has to
push for developing strategies that could help us to either mitigate or to adapt
to these impacts. Assessing the ecosystem’s vulnerability to hazards due to
climate change forms an important decision tool towards better management
of natural resources as well as minimized risk to environmental disasters. With
the projected impacts of climate change, the streamflow and groundwater
recharge in many water-stressed areas could further be decreased. This will
vary depending on the vulnerability of the watershed.
Thus, on 2009, the Ecosystems Research and Development Bureau
implemented the study entitled “Vulnerability assessment (VA) of priority
watersheds with coastal areas in the Philippines to climate change” to assess the
vulnerability of different watersheds. This study employed an interdisciplinary
approach in addressing a complex problem in aid of identifying the natural
and social factors that magnify or intensify the effects of natural hazards.
The study followed the framework formulated by ERDB in assessing the
vulnerability of watersheds to hazards. In 2011, ERDB shifted its framework
to include climate change as an important contributory to the vulnerability of
watersheds.
Four of the watersheds included in this VA project are featured in this
Special Issue of Sylvatrop. This special issue contains results on the vulnerability
of the watersheds, namely: 1) La Mesa Watershed in Metro Manila, 2) San
Cristobal Watershed in Sta. Rosa, Laguna, 3) Kisloyan Watershed in Mindoro,
Vulnerability framework used by ERDB in its vulnerability assessment
studies to identify natural and anthropogenic hazards
Vulnerability framework used by ERDB for vulnerability assessment
studies from 2011 onwards (Adapted from the framework of
Intergovernmental Panel on Climate Change (2011))
and the 4) Matutinao Watershed in Cebu. These VA results can therefore serve
as input in the planning and preparation of watershed managers in managing
the complexities of the watersheds under their jurisdiction. Emerging issues
related to climate change are also incorporated in the discussion of the
research results.
Results of the four vulnerability assessment studies can be utilized
in planning the sustainable development of a watershed and conserving
its natural resources. With this, more responsive and integrated watershed
management plans can be expected from our policy makers.
We hope that this issue will inspire more researchers to conduct indepth studies on vulnerability assessment using up-to-date and science based
information.
ANTONIO M. DAÑO
Lead Author, VA Special Issue
Sylvatrop, The Technical Journal of Philippine Ecosystems and Natural Resources 25 (1 & 2) 1-26
Vulnerability assessment of the La Mesa
Watershed Reservation, Quezon City,
Philippines
Esmeralda P. Andres
Supervising Science Research Specialist
Ecosystems Research and Development Service
DENR-National Capital Region
North Avenue, Quezon City, Philippines
Email address: [email protected]
Manuel S. Sabater
Community Enviroment and Natural
Resources Officer
Rudolfo Espada, Jr.
Planning Officer I
Eduardo C. Calzeta
Forester II
Riza C. Arjona
Science Aide
The vulnerability assessment of the La Mesa Watershed Reservation in Novaliches,
Quezon City was conducted to provide the basis for the formulation of a
sustainable watershed development and management plan. The guidelines on
vulnerability assessment prepared by Daño (2006) of the Ecosystems Research and
Development Bureau (ERDB) was also tested in the identification of vulnerable
areas in the La Mesa Watershed.
Four priority environmental hazards were assessed in the study area using a spatial
analysis tool, the ArcGIS Model Builder. The composite map identified a total
of 10.285 ha of very highly vulnerable areas distributed as follows: soil erosion
(0.285 ha), landslide (0.014 ha), biodiversity loss (8.685 ha), and fire (1.141 ha).
Keywords: Vulnerability assessment, La Mesa Watershed Reservation, ecological profiling
and characterization, soil erosion vulnerability, landslide vulnerability, fire
vulnerability, biodiversity loss vulnerability, Metro Manila, Philippines
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E. Andres et al.
THE SUSTAINABLE DEVELOPMENT AND MANAGEMENT OF ANY WATERSHED IS
primarily dependent on the existing socioeconomic, political, institutional, ecological
and physical resources. The status of these resources, both quantitatively and
qualitatively, can be determined through a comprehensive ecological profiling and
characterization of the watershed. These results of characterization are valuable inputs
in assessing the area in terms of its vulnerability to geohazards. Knowing the geological
hazards will help environmental managers in pinpointing priority conservation areas
and formulating intervening actions to reduce environmental degradation and enhance
the coping capacity of the watershed to potential environmental hazards.
In Metro Manila, the remaining recharge is La Mesa Watershed Reservation
(LMWR). The dam serves as a holding reservoir for the water coming from the transbasins
of Umiray, Angat and Ipo Watersheds which are all declared as watershed reservations.
The Proclamation of the La Mesa Watershed under Presidential Proclamation 1336 on
July 25, 2007 completed the water system, thus, providing water security for Metro
Manila.
To date, LMWR is still considered as Unclassified Land of Public Domain,
otherwise referred to as Public Forest per L. C. Map No. 639 issued on March 11,
1927. It is a titled property under the name of the Metropolitan Waterworks and
Sewerage System (MWSS). Politically, it is under the jurisdiction of the Quezon City
local government.
This vulnerability assessment was conducted to determine the degree of
vulnerability of the watershed in terms of soil erosion, landslide, fire and loss of
biodiversity. The results of the study would provide baseline information for the
preparation and implementation of the La Mesa Development and Management Plan.
Review of literature
Watersheds, considering the role they play, should always be given due
conservation efforts. Vulnerability assessment of the watershed is one important tool to
determine its risks and hazards.
Different approaches can be used in the conduct of vulnerability assessment.
One earlier study on vulnerability of watershed to climate change was conducted by
Tiburan, Jr., et al. The author developed an approach that integrates geospatial-based
model involving 21 indicators, classified into three major components: exposure,
sensitivity and adaptive capacity. Each indicator was given a scale of 1 to 5 to signify
the degree of vulnerability. Threshold level for each scale was determined using
statistics and existing geospatial-based techniques. Subindices in the model was used
to evaluate the extent of damage brought about by other pertinent issues associated
Vulnerability assessment of the La Mesa Watershed Reservation
3
with climate change such as flood, drought, erosion, landslide, and biodiversity loss. It
was emphasized in the paper that all these information played a significant role in the
effective and efficient management of watersheds in the country, as well as in targeting
policy interventions associated with climate change (Tiburan et al. 2010).
Susceptibility assessment of areas prone to landslide remains one of the most
useful approaches in landslide hazard analysis. The key point of such analysis is the
correlation between the physical phenomenon and its triggering factors based on past
observations. Many methods have been developed to capture and model this correlation,
usually within a geographic information system (GIS) framework. Among these, the
use of neural networks, particularly the multilayer perceptron (MLP) networks, has
provided successful results. A successful application of the MLP method to a basin area
requires the definition of different model strategies, such as the sample selection for
the training phase or the design of the network structure. Investigation of the effects of
the different strategies on the development of landslide susceptibility maps by applying
different model configurations was done to a small basin located in northeastern Sicily,
Italy. A number of historical slope failure events have been documented in the study
area over the years. Model performances and their comparison were evaluated using
specific metrics (Arnone et al. 2014).
In Naguilian River Watershed in Benguet, Philippines, the simple overlaying
technique in GIS was used to evaluate the vulnerability to landslide and forest/grass fire.
The watershed attributes contributing to the two mentioned hazards were analyzed.
Slope was the most important factor. Other factors considered were rainfall, landuse/
land cover, faultline, geology, and soil attributes. Results showed that the watershed
has high vulnerability to landslide and a moderate vulnerability to forest/grass fire
(Lopez et al. 2014).
Another way to study vulnerability assessment (VA) includes the use of a GIS
model that integrates the Universal Soil Loss Equation (USLE) (Lanuza 2014). This
method was used by Lanuza in geospatial modeling of soil erosion of the Buhisan
Watershed Forest Reserve in Cebu City, Philippines. It was predicted that about 60.20%
or 369.22 ha of the watershed forest reserve have high potential for soil erosion. On the
average, predicted soil erosion is about 160.23 t/ha/yr.
The USLE, remote sensing satellite data, digital elevation model (DEM) and GISbased geospatial approach were utilized to study the soil erosion of some sections of the
Upper Subarnarekha River Basin, Jharkhand, India. Raster grids of topography acquired
from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global
DEM data were analyzed to determine vulnerability. LANDSAT TM and ETM+ satellite
data of March 2001 and March 2011 were used to infer the land use cover of the watershed.
USLE was integrated within the GIS framework to derive the annual soil erosion rates and
also the areas with varying degrees of erosion vulnerability, from very low (0-5 t/ha/yr) to
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E. Andres et al.
very severe (>40 t/ha/yr.) Results indicated an increase of erosion rates in 2011 compared
to 2001. Factors for the increase in the overall erosion could be attributed to variation in
rainfall, decrease in vegetation cover or protective land covers, and the increase in built-up
or impervious areas (Chatterjee et al. 2014).
In a study conducted at a watershed in Taiwan, the watershed’s ecoenvironmental vulnerability was analyzed using three watershed-based environmental
indicators with multiple-criteria decision-making techniques (i.e., Analytical Hierarchy
Process, and the Preference Ranking Organization Method for Enrichment Evaluations).
The study was conducted at Ari-Jia-Wan Stream Watershed, an area famous for
slopeland agriculture and land-locked salmon. The composite evaluation index system
was set up including sediment, runoff, and nutrient factors. Using GIS and K-means
clustering, vulnerability of the watershed was classified into four levels: potential, low,
moderate, and high. Evaluation results showed that 8.82% of the six subwatersheds are
in the moderately and highly vulnerable zones (Pi-Hui Huang et al. 2010).
In Italy, a vulnerability assessment to validate the landslide hazard
susceptibility, using GPS monitoring technique, was undertaken in the high Cordevole
river basin (Eastern Dolomites, Italy). Hazard map was prepared adopting the Swiss
Confederation semi-determinalistic approach taking into account parameters such as
velocity, geometry, and frequency of landslides. The work illustrated some progress of
the approach by refining the parameters for more reliable results on landslide hazard
assessment (Tagliavini et al. 2007).
In Bartin Province of Western Black Sea Region, Turkey, the effects of mapping
unit on different susceptibility mapping methods was investigated. GIS and remote
sensing techniques were used to create the landslide factor maps, obtain susceptibility
maps and compare results. Use of the Logical Regression (LR) and Spatial Regression
(SR) were also compared. The Relative Operating Characteristics (ROC) curve was
used to compare the predictive abilities of each model and mapping unit. Accuracy was
also evaluated based on observations made during the field surveys. Analyzing the area
under the ROC curve for grid-based and slope-unit-based mapping units showed that
SR model provided better predictive performance as compared to the LR model. The
result was also supported by the accuracy analysis. Better performance of the SR model
was derived from the incorporation of the spatial correlation between the mapping
units into the model while it was not considered in the LR model (Erener et al. 2012).
In the Philippines, Tiburan et al. assessed LMWR using a geospatial-based
environmental vulnerability index called the Geospatial-based Regional Environmental
Vulnerability Index for Ecosystems and Watersheds (GeoREVIEW). The LMWR is a
vital carbon sink and an important source of domestic water supply in Metro Manila.
Based on the assessment, LMWR received an overall vulnerability point of 62.52 that
classifies it as “at risk” level. A vulnerability map ranging from 2.86 to 3.52 was also
5
generated from the process. Around 69.7% of the watershed have vulnerability scales
of >3.0. In addition, priority areas were determined using an evaluation matrix and
results showed that around 8.4% (193.4 ha) of LMW have high to very high priority
levels. All these information were considered as very indispensable and can be used
to address management issues, such as resource prioritization and optimization. In
addition, these can be utilized to sustainably manage the watershed, particularly, on
the provision of quality water for domestic use of several cities in the National Capital
Region, as well as its neighboring provinces (Tiburan et al. 2012).
Methodology
Study area
La Mesa Watershed Reservation (LMWR) is located at 14o75” N, 121o10” E
in Novaliches, Quezon City, Philippines. It is bounded by Caloocan City on the
northwestern side; Quezon City on the southeastern and southwestern side; and San
Mateo and Rodriguez, Rizal on the northeastern and southeastern side. It has a total
area of 2,659.59 ha (Fig. 1).
The vulnerability assessment of the LMWR was conducted in 2008. The
Vulnerability Assessment guidelines by Daño (2006) of the Ecosystems Research and
Development Bureau (ERDB) were applied in the conduct of this project. The Technical
Working Group (TWG) from the Ecosystems Research and Development Service
(ERDS), Planning Office and the Forest Management Service (FMS) of the DENR-NCR,
together with a GIS specialist, reviewed the Profiling and Characterization Report of the
La Mesa Watershed. Four environmental hazards were identified for the watershed: soil
erosion, landslide, fire hazard, and biodiversity loss (Table 1).
Table 1 Critical factors used in the vulnerability assessment
Hazard
Soil erosion
Landslide
Critical factors
Agro-climatic type, soil type, slope, vegetative
cover/landuse, conservation practices
Slope, soil genesis/morphology, proximity to faultline, climate, typhoon risk, vegetative cover/land
use, road/river cut, geologic
Fire
Vegetation, slope, aspect/wind exposure, proximity
to fire sources, accessibility, infrastructure
Biodiversity loss
Slope, road and river, natural disturbances, market,
encroachment
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E. Andres et al.
Figure 1 Location map of the La Mesa Watershed per Presidential
Proclamation No. 1335
Spatial analysis using the ArcView/ArcGIS Model Builder was used to identify
vulnerable areas within the watershed. Researchers utilized two spatial analysis tools: the
arithmetic overlay and the weighted overlay. The former was applied to soil erosion while
the latter was applied to landslide, fire hazard, and biodiversity loss.
Arithmetic overlay involved the use of specified calculations to come up with
the desired map. Figure 2 presents the schematic diagram for the operation of arithmetic
overlay, process of ArcView/ArcGIS Model Builder to assess the soil erosion potential of
LMWR using the USLE.
7
Figure 2 Schematic diagram showing the operation of arithmetic overlay process to
estimate soil erosion using Universal Soil Loss Equation.
The schematic diagram presents input factors (i.e., agroclimatic, soil, slope,
vegetative cover/landuse, and conservation practices) that are converted into vector/raster
format and subsequently reclassified prior to the operation of arithmetic overlay process.
Reclassification was done to assign values to reclassified critical factors. Applying the USLE
in the arithmetic overlay, soil erosion map was generated and further reclassified to assign
vulnerability class value for a certain range of soil erosion estimate.
Weighted overlay technique was used in this study. It combines multiple rasters
by applying a common measurement value or percentages on each raster to create an
integrated analysis. Input factors critical for each hazard were converted into vector/raster
format and subsequently reclassified and/or buffered prior to the operation of the weighted
overlay process. Reclassification was done to assign degree of influence and vulnerability
classification value for each critical factor and reclassified critical factor.
For each hazard, the critical factors were identified. Table 1 presents the critical
factors utilized in the assessment. Table 2 presents the qualitative classification of areas
vulnerable to identified hazards, with corresponding classification value.
The degree of influence assigned to each critical factor, as used during the
overlay process for landslide, fire and biodiversity loss, is presented in Table 3. Soil
erosion (t/ha/yr) was estimated by applying the Universal Soil Loss Equation (USLE) and
spatial analysis using the Model Builder of ArcView/ArcGIS to estimate the soil erosion
potential.
E. Andres et al.
8
Table 2 Qualitative classification of areas vulnerable to identified
hazards, with corresponding classification value
Vulnerability classification value
1
2
3
4
5
Degree of vulnerability
Very low
Low
Moderate
High
Very high
Table 3 Critical factor’s degree of influence
Hazard
Landslide
Fire
Biodiversity
loss
Critical factor
Degree of influence (%)
Slope
30.00
Soil genesis/morphology
10.00
Proximity to faultline
15.00
Climate
10.00
Typhoon risk
10.00
Vegetative cover/landuse
10.00
Road/river cut
5.00
Geologic
10.00
Vegetation
16.67
Slope
16.67
Aspect/wind exposure
16.67
Proximity to fire sources
16.67
Accessibility
16.67
Infrastructure
16.67
Slope
20.00
Road and river
20.00
Natural disturbances
20.00
Market
20.00
Encroachment
20.00
9
Using the ArcView/ArcGIS Model Builder, the following models for LMWR were
formulated: a) Vulnerability to soil erosion; b) Vulnerability to landslide; c) Vulnerability to
fire; and d) Vulnerability to biodiversity loss.
Results and discussion
Background information
Information from the Profiling and Characterization of the LMWR Report on
the physical and natural attributes of the watershed.
Geomorphological features
The surface elevation of the area ranges from 40 to 260 masl. The watershed
is characterized as having a gently sloping to rolling topography with most of the area
having slopes of 18% and below. There are no flood-prone areas in the watershed
since the two major creeks, namely, the Sapang Krudo Kamatis and Sapang Kawayan,
adequately drain into the reservoir. The elevation and slope of the watershed is
presented in Table 4.
Geology and soils
Table 4 Elevation and slope of La Mesa Watershed
Features
Value
Elevation
40-260 meters
above sea level
(masl)
Slope
0-50% = 834.64
Description
With gentle slopes and relatively flat areas around
the watershed indicate low sediment loss or
surface runoff.
The major geological feature in the area is the West Marikina Valley Fault that
runs from Angat Dam from Pasig to Tagaytay. On the northeastern part of the La Mesa
reservoir is the Guadalupe Formation, a major geologic formation which is made up
of clastic and volcanic rocks. Guadalupe Formation overlies pre-Quaternary Basement
Rock Formations, namely, the Madlum, Angat, Maybangan and Kinabuan Formation
which serve as basement rocks for the watershed and its adjacent areas.
The LMWR exhibits three types of soil, namely, loamy-sand, sandy-clay loam,
and sandy-loam. Sandy-clay loam is the dominant soil type.
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E. Andres et al.
Climate
The nearest climatological station of the Philippine Atmospheric, Geophysical
and Astronomical Services Administration (PAGASA) is at the Science Garden in
Quezon City. Parameters such as rainfall, air temperature, wind speed and humidity
were regularly measured and recorded. The climate at the Science Garden can be
considered as very similar to La Mesa watershed. During the assessment, PAGASA has
not yet issued data scenario, thus, the old system was used.
The PAGASA classifies the climate in the Philippines on the basis of temporal
rainfall distribution (Coronas Scheme). Under this classification, the area has a Type 1
climate: two pronounced seasons, dry from November to April and wet from May to
October.
The LMWR derives its rainfall for the most part from the warm, moist southwest
monsoon, as well as, the convergent storm cells associated with the intertropical
intensification and strong winds due to the frequent passage of tropical typhoons during
the rainy season. The cooler and drier northeast monsoon occurs from October to
January, occasionally producing light rainfall.
The mean annual rainfall over the study area is around 2,000 mm. Temperature
ranges from a minimum of 20 ºC around January and February to a maximum of 35 ºC
around April and May. Mean monthly temperature varies from 25 ºC to 30 ºC. Mean
annual temperature is at 27 ºC. Monthly relative humidity ranges from the maximum
of 95% in August and September to a minimum of 55% in March and April. Mean
annual relative humidity is 76%.
Flora
Flora characteristics of the LMWR is a product of various reforestation efforts to
include those undertaken by the Manila Seedling Bank Foundation (1978-1983), Alpha
Omega Foundation (1984-1999), DENR-NCR, ERDS (1998-2000), and the ABS-CBN
Bantay Kalikasan. Prominent in the area are stands of different species of dipterocarps,
teak and molave.
ABS-CBN Bantay Kalikasan uses 86 species of indigenous and endemic
species for their enrichment activities. Of these, five are critically endangered, three are
endangered and four are vulnerable under the International Union for the Conservation
of Nature (IUCN) category.
A total of 520 plant species are now located in the area to include those
enumerated during the inventories as well as planted species during reforestation and
enrichment efforts. Out of the listed species, 10 are vulnerable, seven are endangered,
and four are critically endangered according to the IUCN category (Table 5).
11
Table 5 Conservation status of some plant species at the LMWR
Common name
Scientific name
Conservation status
Tanglin
Antipolo
Pili-liitan
Dao
Hamindang
Narra
Almon
White lauan
Tanguile
Molave
Palosapis
Hingiw
Dapong kahoy
Nito vine
Anchoan-dilau
Payong-payong
Ayo
Adenanthera intermedia
Artocarpus blancoi
Canarium luzonicum
Dracontomelon dao
Macaranga bicolor
Pterocarpus indicus
Shorea almon
Shorea contorta
Shorea polysperma
Vitex parviflora
Anisoptera thurifera
Ichnocarpus volubilis
Loranthus philippinensis
Lygodium flexuosum
Senna spectabilis
Tacca palmate
Tetrastigma harmandii
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Kamagong
Diospyros discolor
Critically endangered
Dalingdingan
Hopea foxworthyi
Baguilumbang
Reutealis trisperma
Philippine teak
Tectona philippinensis
Fauna
General assessment of all the fauna studies conducted by Pampolina, the
Wildbird Club of the Philippines, and the DENR revealed that at least 90 bird species
exist in the watershed. The highlight species of the area is osprey (Pandion haliaetus),
which is an uncommon migrant species listed under Convention on the International
Trade of Endangered Species (CITES) Appendix II. Of the identified species, 24 are
endemic, 53 are residents, 11 are migrants, and one is migrant/resident species. Five
species are listed under CITES Appendix II which means that they are vulnerable species
affected by wildlife trade. These species are presented in Table 6.
E. Andres et al.
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Table 6 Avifaunal species at the LMWR which are listed under CITES Appendix II
Local name
Scientific name
Guiabero
Brahminy kite
Bolbopsittacus lunutatus
Haliastur indus
Colasisi or hanging parakeet
Loriculus philippensis
Philippine scops owl
Otus megalotris
Crested serpent eagle
Spilormis cheela
Land use
GIS mapping of the LMWR shows that it is a mixture of closed forest, open
forest, other wooded land, built-up areas, barren land, and inland water. More
specifically, the watershed has a forested area of 2166.90 ha, natural barren land of
9.30 ha, built-up area of 116.20 ha, and inland water of 367.18 ha.
The watershed is now bordered by expansive urban development. Situated at
the upper portion of the watershed is a nature park for recreational activities such as
guided trekking, hiking, biking, among others. In a certification of Barangay Macabud
to Samahang Magsasaka sa Seedling dated May 12, 2006, about 56 families of informal
settlers have established their residences in the Montalban area of the watershed.
Communities within the watershed
According to the database provided by Barangay Macabud Council, there are 64
families utilizing some areas within the watershed located in Sitio Calumpit, Barangay
Macabud in Rodriguez (formerly known as Montalban), Rizal side for agriculture and
housing. The socioeconomic data gathered by the DENR-NCR ERDS team during the
conduct of the PASA in July 2006 were utilized. The group interviewed a total of 21
respondents. Some residents were not present in the area during the interview, while
most of them refused to be interviewed due to the intermittent conduct of demolition
efforts. They were all members of the Samahang Magsasaka ng Seedling, a people’s
organization established in 1998 with 56 members.
Vulnerability assessment
1. Soil erosion
Soil erosion is defined as the movement of soil particles either by water or wind
usually expressed in tons per hectare per year (t/ha/yr). Utilizing the critical factors, the
rate of soil erosion (t/ha/yr) was estimated by applying the Universal Soil Loss Equation
(USLE) as expressed in the following formula:
USLE A = RKLSCP
where:A
R
K
L
S
C
P
=
=
=
=
=
=
=
13
annual soil erosion in t/ha/yr
rainfall erositivity factor
soil erodibility factor
slope length factor
slope gradient factor
cover and management factor
conservation and practice factor
Spatial analysis using the Model Builder of ArcView/ArcGIS was used to estimate
the soil erosion potential of the La Mesa Watershed. Input factors like agroclimatic, soil,
slope, vegetative cover/landuse, and conservation practices are converted into vector/
raster format and subsequently reclassified prior to the operation of arithmetic overlay
process. Reclassification was done to assign values to reclassified critical factors as
shown in Table 7. Applying the USLE in the arithmetic overlay, a soil erosion map was
generated and further reclassified to assign vulnerability class value for a certain range
of soil erosion estimate (t/ha/yr) as shown in Table 8.
Using the annual soil loss map, soil erosion index map was generated and the
result thereof is presented in Table 8. Erosion index was regrouped and its value of
>1.5 is classified as highly vulnerable area (Table 9).
Table 10 shows that 1.96 ha of the LMWR is very severely eroded with a soil
erosion estimate of ≥2 t/ha/yr. Annual soil loss map of the LMWR is presented in Fig. 3.
From the erosion vulnerability map (EVM), 0.28 ha of the watershed is very
highly vulnerable to erosion (Figure 3 ). These are the areas located within a 600-m
distance away from streambanks and the reservoir.
Figure 4 shows the erosion hazard map of the LMWR. The slope factor greatly
contributed to the identified areas of the watershed with high risk to soil erosion.
Agricultural production in sloping areas located below the water treatment plants
further enhanced the risk.
2. Landslide
Landslide is defined as the downward movement of rocks/soil due to gravity.
Biophysical factors included in the assessment are slope, soil morphology or genesis,
proximity to faultline, typhoon risk, climate, vegetative cover or landuse, road and river
cut, and geology.
areas vulnerable to landslide within the La Mesa Watershed. Input factors (i.e., slope,
soil morphology or genesis, proximity to faultline, typhoon risk, climate, vegetative cover
E. Andres et al.
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Table 7 Reclassification of critical factors and corresponding values
Critical factor
Assigned
value
Reclassified critical factor
Agro-climatic
Type 1
390
Soil type
Loamy sand
Sandy clay loam
Sandy loam
0.11
0.20
0.10
Agro-climatic
Type 1
390
Loamy sand
Sandy clay loam
Sandy loam
0-3%
3-8
8-18
18-30
30-50
> 50
Closed forest broad-leaf
Open forest broad-leaf
Other build-up
Other land, Natural barren land
Other land, natural grassland
Other wooded land, shrub
Other wooded land, wooded grassland
0.11
0.20
0.10
0.009
0.024
0.069
0.083
0.182
0.520
0.002
0.003
0.300
0.500
0.500
0.100
0.100
Soil type
Slope
Landuse
Conservation
management
practice
1.000
Table 8 Soil erosion estimate (t/ha/yr) with corresponding class rating
Soil loss (t/ha/yr)
0.0001-0.5
0.5-1.0
1.0-2.0
2.0-4.0
>4.0
Vulnerability class value
1
2
3
4
5
15
Figure 3 Annual soil loss map of the La Mesa
Watershed Reservation
or land use, road and river cut, and geology) were converted into vector/raster format
and subsequently reclassified and/or buffered prior to the operation of the weighted
overlay process. Reclassification was done to assign degree of influence and vulnerability
classification value for each critical factor and reclassified critical factor, respectively.
Meanwhile, buffer operation was performed to create buffers such that areas near faultlines,
for example, have higher vulnerability class value.
Table 11 presents the tabulated result of the analysis showing areas with
corresponding qualitative degree of vulnerability to landslide. The landslide hazard map
of the area is shown in Figure 5.
E. Andres et al.
16
Extracting areas with reclassification values greater than 4 (Table 12) from the
landslide vulnerability map, landslide hazard areas were identified with a total area of
0.014 ha. The presence of gully within the identified landslide hazard areas confirmed
such observation during validation. Although the said gully is dominantly vegetated
with vines and shrubs, may not suffice to control landslide phenomenon.
3. Fire
Identified critical factors for the vulnerability assessment to fire include
vegetation, slope, wind exposure aspect, proximity to possible sources of fire,
accessibility and nature of existing infrastructures.
areas vulnerable to fire within the La Mesa Watershed. Input factors (i.e., vegetation,
slope, wind exposure aspect, proximity to possible sources of fire and accessibility)
Table 9 Soil erosion index with corresponding class rating
Erosion index
Vulnerability class value
0.0001-0.1
0.1-0.5
0.5-1.0
1.0-1.5
>1.5
1
2
3
4
5
Table 10 Soil loss per hectare per year in the LMWR
Soil erosion
classification
Soil erosion
estimates
(t/ha/yr)
Qualitative
classification
Erosion class 1
0.0001-0.5
None to slightly eroded
Class 2
0.5-1.0
Moderately eroded
Class 3
1.0-2.0
Severely eroded
Class 4
2.0-4.0
Very severe eroded
1.86
Class 5
>4.0
Very severe eroded
0.09
Reservoir
Total
NA
-
Area (ha)
2098.15
2098.15
172.81
172.81
19.26
19.26
367.18
1.95
367.18
17
were converted into vector/raster format and subsequently reclassified and/or buffered
prior to the operation of the weighted overlay process.
Vulnerability analysis for fire for the LMWR was undertaken using GIS. The
resulting fire hazard map is presented in Figure 6. Table 11 presents the tabulated result
of the analysis showing areas with corresponding qualitative degree of vulnerability.
Fire-prone area of the LMWR based on spatial analysis is 8.685 ha. Field validation
revealed that the identified areas are sparsely planted with trees and are still dominated by
grasses, which usually dry up during summer, thus, increasing the fire vulnerability of the
area.
4. Biodiversity loss
Critical factors contributing to the continuing loss of biodiversity at the LMWR
include slope, roads, presence of natural disturbances such as landslides, availability of
markets especially near the area, and encroachment within the watershed itself.
areas vulnerable to biodiversity loss within the watershed. Input factors were converted
into vector/raster format and subsequently reclassified and/or buffered prior to the
operation of the weighted overlay process.
Extracting areas (Figure 7) with reclassification values greater than 4 from
the resulting biodiversity loss vulnerability map, biodiversity loss hazard areas were
identified with a total area of 0.01 ha (Table 12). The presence of illegal entry and exit
points within the identified biodiversity loss hazard areas confirmed such observation
Table 11 Landslide vulnerability table for the LMWR
Qualitative
classification
Reclassification value
Area (ha.)
Slightly vulnerable
<2.5
1050.39
Fairly vulnerable
2.51 – 2.99
1333.90
Moderately vulnerable
3.0 – 3.50
271.29
Highly vulnerable
3.51 – 3.99
3.74
Very highly vulnerable
>4.0 (Landslide hazard)
0.01
18
Figure 4 Erosion hazard map of the La Mesa Watershed Reservation
E. Andres et al.
Figure 5 Landslide hazard map of the La Mesa Watershed Reservation
Figure 8. Landslide hazard map of the LMWR. 19
E. Andres et al.
20
Figure 6 Fire hazard map of the La Mesa Watershed Reservation
Figure 9. Fire hazard map of the LMWR. Vulnerability assessment of the La Mesa Watershed Reservation
Figure 7 Biodiversity loss hazard map of the La Mesa Watershed Reservation
21
Figure 8 Ecological hazards map of the La Mesa Watershed Reservation.
23
during validation.
Conclusion and recommendation
The identified ecological hazards of the watershed were observed to be very
minimal with respect to its total area of 2,659 ha. Table 13 summarizes the identified
ecological hazards of the watershed with corresponding extent/areas of coverage.
It can be deduced from the table that only 0.38% of the entire watershed area is
highly vulnerable to soil erosion, landslide, fire and biodiversity loss. Figure 8 shows
the ecological hazards of the LMWR. This indicates that these hazards have a very
minimal negative environmental effect in the area. The very minimal occurrence of
soil erosion and landslide may be due to the continuous reforestation being done in
the area focusing on indigenous and native species. Nonetheless, proper mitigation
is recommended to retard escalation of these environmental problems especially the
biodiversity loss vulnerability of the area.
The formulation and implementation of La Mesa Watershed Management and
Development Plan is necessary to ensure the sustainable protection, management and
conservation of the area. Considering that the study was undertaken in 2008, the
Table 12 Biodiversity loss table for the La Mesa Watershed Reserve
Biodiversity loss
Rating
covered vulnerability class
Area
(ha)
Slightly vulnerable
1.0 and below
Fairly vulnerable
1.1 – 2.0
2.1 – 3.0
Highly vulnerable
3.1 – 4.0
> 4.0 (vulnerable to
biodiversity loss)
Reservoir
199.13
1555.81
440.32
95.41
1.14
367.18
Table 13 Summary of identified ecological hazards with corresponding area
Factors
Area (ha)
Soil erosion
0.28
Landslide
0.01
Biodivesity loss
8.68
Fire
1.14
Total area
10.11
24
E. Andres et al.
scenario within the LMWR might have already changed by now, thus, an updated
vulnerability study is recommended.
The applicability of the prepared ERDB Vulnerability Assessment Manual for
the La Mesa Watershed Reservation was confirmed with the positive results obtained
during the ground validation process.
Acknowledgment
We would like to thank the other members of the team who helped in the
profiling and characterization as follows: Forester Angelito O. Arjona, Forester Rolando
Acosta and Forester Rodelina de Villa; Acknowledgment is also due the following offices: the River Basin Coordinating
Office of the DENR; Metropolitan Waterworks and Sewerage System (MWSS); ABSCBN Bantay Kalikasan; and the Ecosystems Research and Development Bureau (ERDB).
Considering that this is a multisectoral study of the DENR-NCR, our heartfelt
appreciation for all the support, encouragement and appreciation of Regional Executive
Director Corazon C. Davis, Regional Executive Director Jose Andres L. Diaz, Regional
Technical Director Ali Bari, Regional Technical Director Carlos Gubat I, Regional
Technical Director Perfecta B. Hinojosa, Regional Technical Director Cesar Orallo, OIC
Regional Technical Director Ma. Consolacion Capino, and CENRO Ibarra Calderon.
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basin, Jharkhand, India. Environmental Earth Sciences. January 2014 (71):1, p357374. 18p.
[DENR] Department of Environment and Natural Resources . 2007. Profiling and
characterization of the La Mesa Watershed Reservation. Terminal Report. ERDS
NCR, Quezon City.
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Daño A. 2006. Guidelines on vulnerability assessment of watersheds. Ecosystems
Research and Development Bureau. Department of Environment and Natural
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Lanuza RL. 2014. Geospatial modeling of soil erosion in Buhisan Watershed Forest
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of the Naguilian River Watershed to landslide and forest/grass fire. Sylvatrop. Tech.
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Sabater M, Andres E, Espada R, Calzeta E, Arjona R. 2007. Profiling and characterization
of the La Mesa Watershed Reservation, Quezon City, Philippines. Terminal Report.
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System Sciences. 7(1):185-193.
Tiburan CL Jr, et al. 2012. Geospatial-based vulnerability assessment of an urban
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November 2012, Clock Tower Centennial Hall, Kyoto University, Japan.
Sylvatrop, The Technical Journal of Philippine Ecosystems and Natural Resources 25 (1 & 2) 27 - 50
San Cristobal Watershed vulnerability
assessment to soil erosion and water
pollution
Antonio M. Daño, Ph. D.
Ecosystems Research and Development Bureau (ERDB),
College, Laguna, 4031
Email address: tonydano093
Karen Rae M. Fortus
Science Research Specialist I
The study reviewed the characterization report of San Cristobal Watershed
located in Laguna, Cavite, and Batangas. Its vulnerability to soil erosion and water
pollution was assessed and mitigation and adaptive measures were recommended
to address erosion and pollution hazards. Hazards and their contributory factors
were determined through analysis of biophysical and socio-economic data and
conduct of focus group discussion (FGD). Locations where hazards have been
observed were recorded and inputted in the maps generated using geographic
information system (GIS) software.
The watershed provides various functions aside from contributing an estimated 5%
of the total freshwater discharge to Laguna Lake. The study revealed that out of the
total area of the watershed (14,162 ha), 1,173 ha located mainly in the upstream
portion was zoned as highly vulnerable to soil erosion. Vulnerability of the water
resource was attributed to the water quality problem brought about by the fastpaced conversion of agricultural lands into subdivisions and factory areas. Three
vulnerability levels (very high, high and moderate) were developed for specific
stretches of the river system. The upstream portion of the river was classified as
moderate due to lesser level of development in the area compared to the other
portions of the watershed. In the formulation of a Watershed Management Plan,
interventions should focus on minimizing soil erosion and improving the water
quality of the river.
Keywords: Vulnerability assessment, watershed characterization, Geographic Information
Systems, hazards, soil erosion
28
A.M. Daño and K.R.M. Fortus
IN THE PHILIPPINES, SAN CRISTOBAL WATERSHED IS ONE OF THE 142 PRIORITY
watersheds that suport irrigation structures as identified by the Forest Management
Bureau of the Department of Environment and Natural Resources (DENR). San Cristobal
watershed provides several amenities and performs various functions to benefit nearby
provinces of Cavite, Batangas, and Laguna. It is the source of domestic potable water
(upper stream only) to more than 200,000 inhabitants in the communities of Cabuyao,
Sta. Rosa City, and Biñan in the province of Laguna. It is also a source of industrial
water for commercial establishments and factories located inside the Light Industrial
Park in Calamba City, Laguna. San Cristobal River also contributes about five percent
(5%) of the total freshwater discharge to Laguna Lake. The quality of water from the
river, however, continues to deteriorate and its function as source of irrigation water is
becoming less feasible (Lasco and Espaldon 2005). Such situation is not unique to San
Cristobal watershed; it is also true to many watersheds in the country.
Watersheds not only serve as vital habitat for plants and wildlife but also
perform a critical water quality function and provide natural aesthetics and various
environmental benefits. However, watersheds in the Philippines are vulnerable to
various hazards because of the country’s steep topography, poor vegetation cover,
earthquake faults and effects of climate change. Adverse changes in seasonal river
flows, floods, droughts and loss of biodiversity are among the major vulnerabilities
and concerns in Asia-Pacific region. The greatest vulnerabilities are likely to occur in
watersheds that are currently subjected to stress, or are being unsustainably managed.
In unmanaged watersheds, there are few or no structures in place to absorb the effects
of hydrologic variability, population pressures and natural hazards.
The conduct of vulnerability assessment to watersheds is now required prior
to the formulation of an integrated watershed management plan to address hazards
in the watersheds (DENR MC 2008-05). Vulnerability assessment does not have a
straightforward definition. There is no universally accepted concept of vulnerability
assessment. Thywissen (2006) lists 35 definitions of the term. The plurality of its
definition leads to very diverse assessment frameworks and methods. Some authors
even argue that by principle, vulnerability cannot be measured as it does not denote
observable phenomena. Fussel and Klein (2006) define vulnerability as the degree to
which a system is susceptible to, unable to cope with adverse effects of natural or manmade hazards. It identifies strengths and weaknesses of the recipient subject in relation
to the identified hazard. The vulnerability of human societies and natural systems to
natural and man-made hazards is demonstrated by the damage, hardship and death
caused by events such as droughts, floods, landslides, typhoons, and wildfires.
Considering the relative importance of San Cristobal watershed in providing
suitable water, this study was conducted to assess the vulnerability of the watershed
to soil erosion and water pollution. The study assesses the current vulnerability as a
Vulnerability to soil erosion and water pollution assessment
29
basis for mitigation and adaptive measures. The study results are expected to be useful
to the concerned authorities in formulating suitable integrated watershed management
policies and strategies and in prioritizing the actions needed to protect the water
resources and the environment of the river.
Watersheds may undergo significant changes due to natural and anthropogenic
hazards. Adverse effects to watershed resources can be mostly due to human activities
like improper land use and agricultural practices. The degree of watershed stress
can be detrimental to a large extent with the impact of climate change interplaying
with anthropogenic effects (Ahmadi et al. 2014). Among of the major indicators of
watershed's health are its soil and water quality. Assessment of soil and water quality,
through biological and physico-chemical parameters, has always been an urgent process
of determining the extent of effects of natural forces and anthropological impacts.
Soil erosion has been identified as one of the problems of both rural and urban
landscapes all over the world. Developed as well as developing countries like the
Philippines, face problems of soil erosion of varying intensity and nature. A number of
parametric models have been developed to assess soil erosion vulnerability of drainage
basins. Universal Soil Loss Equation (USLE) is a largely used empirical method for
quantifying soil erosion taking into account various contributing factors. For watershedbased computation of soil erosion, remote sensing and GIS are widely used, especially
employing USLE method (Chen Tao et al. 2010; Bez 2011). Qualitative and quantitative
models provide appropriate information about the spatial distribution of erosion-risk
areas in the watershed where suitable and urgent measures and treatments will be
required (Kefi et al. 2011). USLE predicts soil loss for a given site as a product of six
major erosion factors – soil, rainfall, topography, cropping and management. The values
at a particular location can be expressed numerically and is suitable for predicting longterm averages. Spatial patterns of soil erosion play an important role in studying sources
of erosion, sinks as well as soil and water conservation (Shinde et al. 2011). Prediction
of soil loss is important for assessing soil erosion hazard and determining suitable land
use and soil conservation measures for the watershed (Baskan et al. 2010).
Rapid increase in population, urbanization, and industrialization contribute to
the reduction of the quality of Philippine waters, especially in densely populated areas
and regions of industrial and agricultural activities. Discharge of domestic and industrial
wastewater, and agricultural runoff have caused extensive pollution of the receiving
water bodies. This effluent is in the form of raw sewage, detergents, fertilizers, heavy
metals, chemical products, oils, and even solid wastes. Each of these pollutants has a
different noxious effect that influences human livelihood and translates into economic
costs (State of Water Environmental Issues: Phil., WEPA).
30
Pollution of groundwater is an issue because aquifers and the contained
groundwater are susceptible to contamination from wastewater and agricultural
activities (Alwathfa and Mansouri 2011). The increasing and widening disposal of
household solid wastes and industrial hazardous waste in the environment is a growing
threat to the quality of water, air, and land.
Many have emphasized the importance of vulnerability assessments and
presented useful frameworks (Metzger et al. 2005; Polsky et al. 2003; Turner et al.
2003; Schröter et al. 2004; Yohe and Tol 2002), yet relatively few have presented
methods to assess vulnerability empirically. As the literatures illustrate, ambiguity
surrounds not only the components of vulnerability, but also the operationalization and
measurement of those components. Some empirical studies use indicators to characterize
vulnerability, although indicator values may not adequately reflect impacts, especially
at the local level, and may not be relevant across multiple regions and sectors. Further,
even in empirical studies, data often focus on the hazard itself (e.g., magnitude of a
water shortage rather than overall vulnerability), which would also consider impacts
like losses due to water shortages and the ability to reduce and mitigate those impacts,
both short-term and long-term (e.g., water reallocation, water conservation). Studies
needed are empirical assessments to understand how vulnerability is experienced “on
the ground”, by those who are vulnerable, to elucidate the causes and effects of that
vulnerability, and to provide database guidance to decision makers (Brooks et al. 2005;
Cutter et al. 2003; Metzger et al. 2005).
ERDB (2011) presented a conceptual framework on the vulnerability assessment
on watershed such as assessment of the biophysical and socioeconomic, hazard
identification and analysis, critical factor analysis, GIS-based analysis and mitigation
opportunities. The ERDB Manual on Vulnerability Assessment stressed that quantitative
and qualitative description of a watershed are basic to the understanding of and control
on the various biophysical and socioeconomic processes in a watershed. An adequate
knowledge on the characteristics of watershed will help immensely in the prediction
of the behavioral response of a watershed to diverse environmental conditions and
management activities.
Methodology
Characterization of the watershed
Gathering/updating of secondary and primary data on various watershed
characteristics was conducted in 2009 under the guidelines defined in DENR
Memorandum Circular 2008-05. The activity also involved the review of available
documents/reports to determine data gaps that should be augmented through field visits
and other means. Assessments included:
31
a. Biophysical assessment
Soil (soil physical and chemical properties)
Climate (annual/monthly rainfall, evaporation, typhoon occurrence and
frequency)
Hydrology (monthly streamflow pattern)
Water Quality. Water samples were taken at different times of the year and
analyzed for various water quality parameters (temperature, pH, dissolved
oxygen, BOD, coliforms). Water samples were brought to DENR-Region 4A
laboratory for analysis.
b. Community perceptions on hazards
Assessment included the determination of attitude, awareness and perceptions
of watershed occupants including existing programs in the area that may aggravate or
reduce the vulnerability of the watershed to natural and anthropogenic hazards. A total
of 11 barangays from Sta. Rosa, Calamba and Cabuyao, Laguna and Silang, Cavite were
visited. Community responses were translated into Rating Classes 1 to 5. Location of
critical facilities like schools, roads and bridges, hospitals, floodplain/riverbank houses
and other critical structures were also mapped. Data on socio-demography (sex, age,
income, education, etc.) were also gathered during the interview.
Hazard identification, critical factor analysis, and mapping
Hazards occurring in the watershed both in the upstream and downstream
portions were identified from characterization data and site visits. Hazard identification
focused on the soil and water resources particularly on soil erosion and water pollution.
Hazard information from other agencies/institutions were also sourced out.
Hazards and their contributory factors were also verified through analysis of
watershed characterization data and through the conduct of focus group discussion
(FGD) with occupants of the watershed and other key informants. Specific locations
where the hazards occurred or were observed were recorded during the field surveys
and inputted to maps generated using geographic information system (GIS). A crucial
element in reducing vulnerability to natural hazards was the analysis of human
settlements and infrastructures gathered during field validation and Focus Group
Discussion (FGD).
Generation of thematic and hazard maps
Relevant secondary information needed in the GIS-assisted approach to
vulnerability assessment were gathered from various sources. These include the
topographic map of the study area (scale of 1:10,000), political boundary, land cover,
32
land classification map, soil and geology, and climate. The topographic map was used
to digitize the contours at 10-m interval which served as reference to generate the
digital elevation model (DEM).
All thematic maps were transformed to hazard class rating maps based on the
procedure contained in the ERDB Manual for Vulnerability Assessment (ERDB 2008).
Rating Classes 1 to 5 rate the thematic maps’ features from very low (1) to very high (5)
susceptibility to the occurrence of the hazard.
For assessing vulnerability to soil erosion, the thematic maps were assigned
class and weights according to their relative importance in influencing erosion and
mass movement. These are briefly discussed below.
1. Slope. To make the assessment more systematic, all slopes from 0-8% (level
to gently sloping) were categorized as areas with low susceptibility to erosion.
Steep slopes (>50%) were considered to be areas that are very highly
susceptible to landslide.
2. Soil. Soil characteristic contributes to occurrence of erosion and mass
movement. A general soil map based on soil classification was used in this
study.
3. Rainfall and typhoon occurrence. Rainfall is considered as the triggering factor
to the occurrence of any hazard. The rainfall isohyets and historical monthly
average rainfall were used in assessing the susceptibility of the watershed to
soil erosion.
4. Land use. Land use map derived from LANDSAT satellite images were
analyzed and validated in the field. Rating was based on the presence and
type of vegetation cover in the watershed.
Table 1 Rating class for vulnerability to water pollution
Water discoloration due to pollutants
Level of chemical and
biological contaminants
Negligible sediments
None. Class A/AA water
Slight discoloration after Low. Class B water
heavy rainfall
Moderate discoloration Low level contaminants
after heavy rainfall
which are still within DENR
limits
Severe discoloration Levels of contaminants exceeded DENR standards
Class
1
2
3
4
33
Table 1 Rating class for vulnerability to water pollution (Continued)
Water discoloration due to pollutants
Level of chemical and
biological contaminants
Class
Very severe discoloration
Levels of contaminants are very high, very high levels
of biological contamination
that could lead to widespread
incidence of water-borne
diseases
Land use impact
Agricultural impact
Very large agricultural activity
High agricultural activity
Moderate agricultural activity
Minimal agricultural activity
No agricultural activity
5
Industrial and household
impact
5
Very large discharge or very heavy impact on the
surrounding
Large discharge or heavy impact on the surrounding
Moderate discharge or moderate impact on the
surrounding
Minimal discharge or minimal impact on the
surrounding
No industry or households
Transportation avenue
National and provincial road system
Provincial/municipal road system
Paved roads in most of the watershed
Unimproved road or dirt road
throughout the watershed
area
No road system
5
4
3
2
1
4
3
2
1
5
4
3
2
1
34
Vulnerability of water resource to pollution was determined in terms of the
alteration of the physical, chemical, biological or radiological properties of the water
body and land use impact that may result in the impairment of its purity or quality. The
assessment involved two major activities: 1) review and comparison of the gathered
water quality data with the DENR water quality standards as contained in Department
Administrative Order (DAO) 35; and 2) survey of land use and sources of pollutants.
GIS spatial analysis and output validation
Overlay and index method which involved combination of various watershed
attributes (e.g., geology, soils, slope, climate, land use, anthropogenic factors) was
used. In this approach, watershed attributes were assigned class (Class 1-5) and weights
(1-100%). Results of this activity include location of vulnerable areas including the
classification (from high to low) of various hazards (degree of vulnerability). Results
were validated by simple comparison with recorded occurrence of the hazard and the
degree/class reflected in the vulnerability map.
Description of the area
San Cristobal Watershed is located at the southwestern side of Laguna de Bay. It lies
within four provinces, with the largest area located in Laguna (10,645.70 ha). Most of the
watershed’s upstream area is in Silang (1,967.30 ha) and Tagaytay City (1,493 ha) in Cavite
while a very small portion towards the headwater is part of Tanauan City, Batangas (56 ha).
The watershed is shaped like a fish with its tail along the Laguna Lake (Fig. 1).
The whole watershed encompasses an area of 14,162 ha. Hydrologically, the
watershed’s area affects the peak flow and the time it takes for the total floodflow to
reach the outlet. As the area of the watershed increases, runoff takes longer time to
reach a given station. The watershed has a perimeter of 63 km.
The average length of stream is highest for the fourth and fifth orders for the
entire watershed which is 8.62 and 7.5 km, respectively. The watershed has a total of
219 streams with a total length of 269 km. On the other hand, the subwatersheds have
higher average length only in second and third orders except for Diezmo which is a
fourth order stream with a total length of 9 km.
Channel cross-section and profile varied from a width of about 2 m in the
upstream to about 20 m near the bridge along the Calamba-Cabuyao highway. In most
35
Figure 1 Administrative map of San Cristobal Watershed
parts of the river system, the channel has a deep ravine, indicating less problem of
channel overflow or flooding.
Slope
The slope distribution ranges from 0% to more than 50%. About 70% of the
area (9,842.5 ha) is level to nearly level to undulating. This area occupies near the lower
to middle portion of the watershed. Close to 18% (2,525 ha) of the area is within the
slope range of 8-18%, described to be undulating to rolling. This slope range is situated
in Calamba City, Sta. Rosa City, and Cabuyao. About 1,243.8 ha (8.8%) belongs to
Table 2 Slope distribution of San Cristobal Watershed
Slope range Description
(%)
Area (ha)
Percent
(%)
0 - 8
Level to undulating
8 - 18
Undulating to rolling
18 - 30
Rolling to moderately steep
30 - 50
Steep
>50
Very steep
Total
9,842.5
2,525.2
1,243.8
485.4
66.1
14,162.0
69.5
17.8
8.8
3.4
0.47
100.00
36
rolling to moderately steep (18-30% slope). Steep slope (30-50% slope) occupies 485.4
ha (3.4% ) of the total land area of the micro-watershed. The very steep slope (>50%)
shares less than 1% of the total land area.
Geology and soil
The portion of the watershed within Calamba City is generally underlain by
quarternary pyroclastic deposits, which may have originated from Taal occurrences
of mudstone agglomerate. Two types of rock formations are found in Sta. Rosa City,
namely, clastic and alluvium rocks. Clastic rocks consist of interbedded shale and
sandstone with occasional thin lenses of limestone, as well as tuff and reworked sandy
tuffs and partly tuffaceous shale. Alluvium rocks are found in the remainder of the
municipality. These rocks consist of an unconsolidated mixture of gravel, sand, silt, and
clay. The clastic and alluvium type of rocks found in the city are both known for good
water bearing abilities (Fig. 3).
Soil greatly influences the infiltration capacity of watersheds, hence, affects the
nature of subsequent surface runoff, groundwater recharge, and other related processes.
Soil properties also influenced the susceptibility of the area to soil erosion and suitability
to crops. The common soil types of the San Cristobal Watershed are presented in Table
3. The most dominant soil type in the area is Lipa loam which occupies 6,990 ha.
Table 3 Common soil types in San Cristobal Watershed
Soil type
Area (ha)
Percent (%)
Quingua fine sandy loam
Tagaytay sandy loam
Tagaytay loam
Carmona sandy clay loam
Mountain soil (undifferentiated)
Taal fine sandy loam
Lipa loam
582.8
1,102.4
389.3
3,875.4
1,091.2
130.8
6,990.0
4.11
7.78
2.74
27.70
7.70
0.92
49.35
This is followed by Carmona sandy clay loam (3,835 ha) and the rest are Tagaytay
sandy loam (1,072 ha), mountain soil, undifferentiated (1,062 ha), Quingua fine sandy
loam (575 ha), Tagaytay loam (381 ha), and the Taal fine sandy loam (125 ha).
Land classification and use
The watershed is basically an Alienable and Disposable land where most of
the areas are part of the sugarcane plantations of Yulo Estate. The elevated portion of
the watershed is agricultural land devoted to coconut and annual crops (Fig. 4).
37
Barangay Casile in Cabuyao is the drainage area of the Matang Tubig Spring,
which is the source of water for the municipality of Cabuyao and corresponds to an
area of about 318 ha. In Silang, forest areas are devoted primarily for forest purposes.
These cover an aggregate area of 208.0 ha or 1.3 % of the municipality’s total land area.
Agricultural area, comprised 41.4% of the total land area. Grasslands/
shrublands comprised about 39.7% of the area while about 17.6% are built up areas
(Table 4). Comparison of the data taken from 2007 imagery showed the doubling of
built-up areas (from 17.6 to 35.9%) and the reduction of grasslands and agricultural
lands. This was observed in the area wherein grasslands and agricultural lands had been
converted to subdivisions and industrial parks. The increase in open canopy areas can
be attributed to classification of some brushlands as open canopy areas. With the rapid
development of high-class subdivisions and industrial parks in the area, it is expected
that built-up areas will continue to increase with subsequent reduction of agricultural
and grassland areas.
Table 4 General land uses within San Cristobal Watershed (1996 and 2007 imagery)
Land use
category
1996 Imagery
Area
(ha)
Percent
total (%)
2007 Imagery
Area
(ha)
Percentage Percent change
total (%)
from 1997 imagery
Agricultural
areas
5,865
41.4
4,926
34.7
-6.7
Grassland/
shrubland
areas
5,621
39.7
2,926
20.7
-19.0
Built-up areas
2,488
17.6
5,084
35.9
+18.3
Open canopy
188
1.3
1,226
8.7
+6.4
14,162
100.0
14,162
100.0
Total
Climate
The most dominant climatic type of the watershed is Type 3 where the seasons
are not very pronounced and relatively dry from November to April and wet during the
rest of the year. The maximum rainy period is from June to October and on the average,
the area is visited by five cyclones every three years.
Rainfall in the watershed usually occurs as short high-density storms rather
than as a long-lasting moderate intensity rainfall. Four synoptic stations enveloping
the watershed were taken to determine the rainfall distribution in the area. The mean
monthly rainfall (mm) is presented in Figure 6.
38
Figure 2 Drainage map of San Cristobal Watershed
Figure 3 Soil map of San Cristobal Watershed
Figure 4 Land cover map of San Cristobal Watershed
Figure 5 Isohytal map of San Cristobal Watershed
39
40
Hydrology and water quality
San Cristobal River is one of the major tributaries draining into the Laguna de
Bay (Fig. 2). It is also one of the most polluted rivers affecting the lake’s productivity and
survival. It contributes about 5% of the total freshwater discharge into the lake. Figure
7 shows the mean monthly discharge (m3/sec) of San Cristobal Watershed at a point
along the National Highway. The watershed has an estimated mean discharge of 0.694
Rainfall (mm)
Figure 6 Monthly rainfall from four synoptic stations around San Cristobal
Watershed
monthly flow
Figure 7 Monthly streamflow behavior (m3/sec) of San Cristobal Watershed
41
m3/sec. Maximum peak discharge recorded in the watershed was 411.9 m3/sec during
the September 1, 1956 flood event.
Water samples collected from three samples sites showed high level of BOD
and coliforms. Water quality status based on different parameters is summarized in
Table 5.
Other parameters were measured in-situ using portable equipment. Sampling
conducted did not differ much from the findings of LLDA monitoring team which
showed high BOD level of the river system (Table 5) (Espaldon 2005).
Table 5 Water quality analysis result of San Cristobal River Basin
Results
Parameter
Diezmo River San Cristobal River San Cristobal (upstream) (midstream) mouth
Temperature (⁰C)
pH
BOD5 (mg/l)
DO (mg/l)
Total P (mg/l)
Total N (mg/l)
Sulfate (mg/l)
Conductivity
(mS/cm)
TDS (mg/l)
Total Coliforms MPN/100ml
Fecal Coliforms MPN/100ml
29.50
8.29
2.20
6.70
0.55
2.51
5.30
0.22
30.532.4
8.17
12.280
1.30
0.24
1.58
0.07
0.292
7.13
19.20
12.8
0.54
0.61
16.0
0.592
114.0
>1.6 x 106
145.0294.0
4.9 x 105
>1.6 x 107
>1.6 x 106
7.8 x 104
>1.6 x 107
The DO level during field visits varies from 6.7 to 1.3 mg/l. The permissible
limit for DO concentration is 5.0 mg/l. Diezmo River and the mouth of San Cristobal
have an average of 6.7 mg/l and 2.8 mg/l, respectively. The DO concentration is the
primary parameter on determining the suitability of water for fish and wildlife. Reduced
DO indicates depletion as a result of organic pollutants coming from the industrial and
residential area along the river.
The biological oxygen demand (BOD)5 (5 days at 20°C) was 12.8 from the
midtsream to 19.2 mg/l to the mouth of the river system. The value provides information
on the quantity of oxygen needed by the river for biochemical degradation of organic
compounds. High BOD 5 level indicates a polluted water body. The prescribed limit of
BOD5 for “Class C” water body is 5.0 mg/l. Upstream tributary (Diezmo River) has BOD
level of 2.2mg/l. High levels of BOD were observed from samples collected from the
42
middle to the mouth of San Cristobal River. The high BOD5 value was attributed to the
organic pollutants coming from the industrial establishments and domestic households
that abound in the area.
Data indicated that water pollution from the river systems was brought about
by discharges from residential septic tanks, domestic liquid wastes, and industrial
discharges. The elevated BOD and coliform counts plus depleted DO level also
indicate that contamination of water largely comes from fecal matter and unmitigated
discharges from households.
Socioeconomic
The level of employment seems to follow the development of different
economic sectors in each municipality. Industrialized cities and municipalities like
Calamba City, Cabuyao and Sta. Rosa City have more people employed in the services
sector, followed by those in industries, and lastly, in agriculture. As the local economies
move towards an industrialized state, agriculture appears to attract lesser investments
and consequently, employment.
The major sources of family income can be grouped into two: employment
and entrepreneurial activities. Household incomes are derived mainly from nonagricultural activities and only a few from farming and fishing. In Calamba City, 36,225
(68%) of the households mainly derived their income from entrepreneurial activities
of non-agricultural nature. In Sta Rosa City, only 7,969 (13%) of the households still
derive their income from farming or fishing. Similarly, in Cabuyao, only 304 families
depend on fishing for their income. While actual data are unavailable, it can also be
inferred that for highly commercialized localities, majority of the households derive
their income from non-agricultural endeavors.
Table 6 Major sources of income of households in Laguna
Source of income
Sta. Rosa
Entrepreneurial activities
n.d.
Farming and Fishing
7,969
Oversees Remittances
n.d.
Municipality
Cabuyao
n.d.
304
n.d.
Calamba
36,225
n.d.
4,255
*n.d. - no data
Overseas remittances also contribute significantly to household incomes.
In Calamba City, about 4,255 (8%) of the households derive their income from
remittances of family members who are working overseas. The same is true in Tagaytay
City. Most of the residents in San Cristobal Watershed are employed in non-agricultural
activities other than farming and fishing. Migration of employment from agriculture to
non-agricultural activities are driven by wage differential across sectors (Habito and
Briones 2005); increasing land conversion from agricultural to non-agricultural areas;
43
lowered agricultural productivity and consequently lowered farm incomes (Balicasan
et al. 2006) and declining interest among the younger generation to have this kind of
livelihood.
Perception of residents on water resources
Different perceptions on the importance and benefits derived from San Cristobal
River were noted. The usefulness of the river was positively expressed by respondents
in Sitio Matang Tubig, Canlubang, Laguna; Barangay Casile, Cabuyao, Laguna; Pasong
Langka, Silang, Cavite; and Sto. Domingo, Sta. Rosa City. Accordingly, the importance
of the river is as follows: fishing, irrigation/agricultural uses, domestic and aesthetic
values. It also generates electricity in Sitio Matang Tubig according to some residents.
In Barangay Gulod, San Isidro, Baclaran, Mamatid, and Marinig in Cabuyao,
Laguna, the farmers use the water provided by National Irrigation Administration (NIA)
to irrigate their farm lots. According to one of the respondents during the interview,
the polluted state of the river for irrigation, has no effect on the yield and growth of the
crops, instead, it served as fertilizer. Some of the farmers no longer buy fertilizers, thus,
minimizing their expenses.
Farming is undertaken both in the lowlands and in the uplands. In Cabuyao,
Laguna, agricultural activities are predominantly practiced in the lowlands. In Tagaytay
City and Silang, Cavite, it is predominantly done in the uplands. Multi-storey cropping
is the common practice in the uplands. This system usually involves intercropping
coconut, corn, pineapple, coffee, sugarcane, banana, papaya, root crops and assorted
vegetables.
On the other hand, residents in Barangay Pittland and Barangay Diezmo in
Cabuyao, Laguna found the river to be useless. Pollution of the rivers is attributed to
the presence of industrial plants. Industrial wastes are also being dumped in the river.
Residents derive no benefit from these waterbodies.
According to the farmers interviewed, there are three types of water used in
irrigating their farm lots. These are: Class A which comes directly from NIA irrigation;
Class B, water from NIA irrigation and collected rainwater (rainfed); and Class C which is
non-NIA water or spilled water coming from deep wells of industrial plants in Cabuyao,
Laguna. Barangay San Isidro and Barangay Gulod in Cabuyao use class B in their farm
lots. Barangay Baclaran and Barangay Mamatid use the excess water coming from the
NIA irrigation system in watering their farm lots. Accordingly, their crops are healthy
and do not require fertilizer application. The common farm problems include presence
of black bugs and snail on crops.
44
Hazard identification and critical factor analysis
Analysis of watershed attributes, as well as, findings from FGD revealed two
major hazards affecting the soil and water resources of San Cristobal watershed – soil
erosion and water pollution.
Soil erosion was the dominant hazard in the watershed due to community
cropping practices particularly in the planting of pineapple and vegetables in moderately
steep slopes, mostly in the Silang-Tagaytay portion of the watershed.
Water pollution is the biggest problem threatening the usefulness of the water
resources of the river. Water quality assessment revealed heavy pollution from the
middle to downstream portion of the river system. The water from San Cristobal river
had fecal coliform greater than 1.6 x 107 MPN/100ml and BOD level reaching about
20 mg/l. The high BOD level and coliform count particularly fecal coliforms can
be attributed to households and industries that discharge their waste directly to the
river system resulting in foul smell and black color of the water. Most of the houses
located near the river discharge their waste directly to the river. Furthermore, results
of interviews with residents revealed that water pollution is the main problem of
communities living near the river system. Accordingly, the river has a very foul smell.
The situation is worst particularly during rainy season, wherein at a certain time of the
day, the smell is almost intolerable. The communities suspected that some industries
discharge their waste during heavy rainfall to avoid being noticed by the communities.
Surface water bodies are progressively subjected to stress as a result of
anthropogenic activities. The rivers, strongly influenced by household wastewater,
have the highest concentrations of nutrients (Wang et al. 2006).
Interview with the NIA personnel revealed the sad state of the river system.
Accordingly, it is difficult to conduct maintenance operations on the irrigation structures
due to the intolerable smell of water. The structures are difficult to visit due to proliferation
of houses. Permit need to be secured first from private landowners before NIA personnel
can pass through the private properties to reach the site of the infrastructures.
GIS-based vulnerability assessment
Results of the overlay and index methods provided the spatial location of
vulnerable areas including their classification from high to low to various hazards
(degree of vulnerability).
45
Soil erosion
The identified physical factors affecting soil erosion include: slope (S), climate
(which is influenced by rainfall and typhoon frequency), soil type (St), and land cover
(Lc). These erosion factors were converted to hazard index or scale and transformed
into maps; and the data processed were used to create critical factor layers in grid or
raster formats.
Using overlaying approach of slope, rainfall, landuse, soil and crop management
factor, the polygon formed from the intersection of the five maps was analyzed using
the GIS-assisted spatial analysis. Overlaying produced the soil erosion vulnerability
map as shown in Figure 8. Out of the total area of the watershed (14,162 ha), the
1,173 ha in the upstream portion of the watershed was zoned as vulnerable to soil
erosion. Most of the areas belonged to the zone with moderate to low vulnerability to
soil erosion. The distributions of these areas are shown in Table 7.
Figure 8 Soil erosion vulnerability map of San Cristobal Watershed
46
Table 7 Areas in the watershed that are highly vulnerable to erosion
Municipality/City
Highly vulnerable areas (ha)
Cabuyao18
Calamba
114
Silang
653
Tagaytay
373
Tanauan
15
Total (ha)
1173
Surface water vulnerability to pollution
Vulnerability of the water resource was attributed to the surface water’s
quality problem brought about by the fast pace of conversion of agricultural lands
to subdivisions and factories. Based on water quality assessment and land use of the
watershed, three vulnerability levels were developed for the stretch of the river system
(very high, high and moderate). The upstream portion of the river was classified as
moderately vulnerable due to lesser level of development done in the area as compared
to the other portion of the watershed. Construction of high-class subdivisions in the
Tagaytay area also pose danger to the water quality of the entire river system.
As reflected in the land use map, the lower and middle portions of the watershed
are now occupied by houses and industries. Surface water in the river system which is
used to irrigate ricefields and other agricultural areas in Canlubang and Calamba has
become polluted due to wastewater coming from households and industries. Figure
9 shows the vulnerability map of San Cristobal river system to water pollution. Areas
along the river system are the highly vulnerable portions of the river; and communities
living on it are predisposed to water-borne diseases brought about by the high fecal
coliform and BOD of water.
Conclusion and recommendations
The characteristics of the watersheds served as inputs in identifying the factors
that make the San Cristobal watershed vulnerable to natural and anthropogenic hazards.
These factors include: 1) fast conversion of areas to industrial and residential areas affecting
the water quality of the river; 2) favorable soil influencing farmers to practice planting
annual crops in sloping lands; 3) drainage from households and industries; and 4) domestic
household practices of throwing solid waste into the river system.
47
Figure 9 Water pollution vulnerability map of San Cristobal Watershed
A small portion of the San Cristobal Watershed is vulnerable to soil erosion but
the river, from the mouth to almost the entire stretch of the river system, is vulnerable to
water pollution. There is a need to review the land use of San Cristobal Watershed for
irrigation because of the continued conversion of agricultural lands to non-agricultural
use. The water in San Cristobal is no longer suitable for any contact activities.
San Cristobal River as one of the 21 major tributaries draining into the Laguna
Lake represents a typical agro-industrial condition which if properly and scientifically
managed could serve as a model for development to other basins with similar potential
characteristics. The rapid urbanization of Tagaytay City and the migration of landless
farmers in the heartland of the watershed will inevitably increase cultivated farms. As
the area becomes highly urbanized, infrastructures and settlements should be avoided
in steep areas as these are aquifer recharge areas. Areas with steep slopes particularly in
Silang and Tagaytay City, should be declared as environmental zones in the respective
Comprehensive Land Use Plans (CLUPs) of said municipalities.
48
Hazards identified in the vulnerability assessment should be the focus in
developing intervention projects during the formulation of Watershed Management
Plan. The study showed that interventions should focus on minimizing soil erosion and
improving the water quality of the river. Information, Education and Communication
(IEC) program should be intensified focusing on the identified hazards and the
anthropogenic factors affecting it. Groundwater resource assessment should also be
conducted because its usage will dramatically increase with the increase in water
demand by industries and households.
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Sylvatrop, The Technical Journal of Philippine Ecosystems and Natural Resources 25 (1 & 2): 51- 78
Landslide vulnerability assessment of
Kisloyan subwatershed in Mindoro
Island, Philippines
Edgardo E. Vendiola, PhD
OIC-Regional Technical Director for Research (retired)
Ecosystems Research and Development Service
Department of Environment and Natural Resources
Region IV-B, Roxas Blvd., Manila
Forester Marilyn R. Limpiada
Science Research Specialist II
Kisloyan subwatershed is one of the crucial sources of water to the Magasawang Tubig River. Mag-asawang Tubig River is one of the major rivers in
Oriental Mindoro that provides irrigation and domestic water to at least three
of the big towns in the province and serves as a natural habitat to endemic
and endangered flora and fauna. However, it is threatened because of nickel
and cobalt extraction, with deposits considered as one of the largest in the
Far East. A total mining area of 1,435.90 ha is administratively shared by the
municipalities of Victoria, Oriental Mindoro and Sablayan, Occidental Mindoro.
This study determined the landslide vulnerability of the Kisloyan
subwatershed to come up with recommendations on how to mitigate the
impacts of this hazard. Vulnerability assessment was conducted based
on the natural characteristics and the man-induced attributes of the site.
Results of the study indicated that the Sablayan, Occidental Mindoro portion
has the highest vulnerability to landslide, particularly to geological risks.
Keywords: Kisloyan subwatershed, vulnerability assessment, watershed,
geological hazards, landslide, geospatial technology
52
Vendiola E and Limpiada M
This is caused by the convergence of the effects of slope, rainfall, and fault lines.
Meanwhile, portions of Victoria, Oriental Mindoro have similar rainfall
and fault line characteristics except for the aggravating effect of slope.
Immediate rehabilitation of said vulnerable areas is recommended as a priority mitigating
measure in the watershed management plan, especially the 28.40 ha of severely
eroded/landslide areas in the southern portion of Sablayan, Occidental Mindoro.
WATERSHEDS ARE FRAGILE. IN DISASTER-PRONE AREAS OF THE PHILIPPINES,
the paramount importance of watersheds does not rest solely in its supportive role to
agriculture but more on its role in preventing soil erosion. Knowing their condition
and capability to prevent the occurrence of destructive landslides is vital.
The Kisloyan subwatershed of the Mag-asawang Tubig watershed was
chosen for this study because of the conflicts in resource use that are brewing in
this watershed. Multinational mining companies made big investments with the end
view of extracting nickel and cobalt deposits in the area. The estimated volumes are
considered to be one of the largest in the Far East.
Environmentalists became more vigilant because the Kisloyan subwatershed
serves as one of the crucial sources of water to the Mag-asawang Tubig River. It is
also one of the major rivers in Oriental Mindoro that provides irrigation and domestic
water to at least three of the big towns in the province. In many instances, however,
Mag-asawang Tubig River was the cause of disastrous floods that hit the province in
recent history. The area is also regarded as important because it is a natural habitat to
several endemic and endangered flora and fauna.
Therefore, vulnerability assessment can be effectively utilized in planning the
sustainable development of the area and conserving its natural resources. Assessing
the ecosystem’s vulnerability to hazards due to climate change forms an important
decision tool towards better management of natural resources as well as minimized
risk to environmental disasters.
As cited by Daño (2005), Neil Fraser, a Mindanao-based Australian ecologist
stated that for as long as Filipinos regarded the earth and the environment as resources
to be exploited and abused, a flood similar to that in Ormoc City that killed about
8,000 people and rendered about 50,000 residents homeless in 1991, could occur
anytime. Fraser’s forecast came true. In December 2004, heavy rains, illegal logging
Vulnerability assessment of the Kisloyan subwatershed
53
and the mountainous terrain were blamed for the fatal floods and mudslides in InfantaNakar area in Quezon province.
In 2006, Roberto reported that three flashfloods struck Calapan City and the
municipalities in the northern part of Oriental Mindoro. As reported by PAGASA,
this was caused by the three-day rainfall reaching a total of 194 mm and 77 mm on
6 December 2005 and 17 December 2005, respectively. Heavy rainfall resulted in
large discharges in both Mag-asawang Tubig and Bucayao Rivers.The municipalities/
city of Baco, Naujan, Victoria and Calapan were severely affected with 141 barangays
stricken by the typhoon, leaving 23,364 families affected. Calamity victims reached
9,551 families or 39,006 persons. One death was registered in the municipality of
Naujan.
Former DENR Secretary Elisea G. Gozun said that much of the landslide
tragedy had to do with the improper use of forest land for agricultural purposes;
noting that farmers opted to plant cash crops instead of trees (DENR 2003 as cited by
Daño 2005).
In view of this, the urgency of vulnerability assessment, mapping, and
sustainable ENR management has become imperative to save people whose lives are
affected by watersheds that are highly vulnerable to hazards.
Geospatial technology is now being used in natural resources management
where wildfire, floods, and landslides can be mapped as polygon areas. All these
information can be of great value when surveyed and represented digitally in
computer systems like Geographic Information Systems (GIS) (Godilano 2004 as cited
by Daño 2005).
The paper by van Westen (2008) discussed a number of issues related to the use
of spatial information for landslide susceptibility, hazard, and vulnerability assessment
with focus on the types of spatial data needed for each component and the methods
for obtaining them. Accordingly, there is a very fast development in the application
of digital tools such as GIS, digital image processing, digital photogrammetry and
Global Positioning Systems (GPS). Landslide inventory databases are now available
and accessible even through the internet. A comprehensive landslide inventory is a
must to quantify both landslide hazard and risk.
Landslide is one of the various natural processes that shape the surface of the
earth. Hazard can only be realized when landslides threaten mankind. Landslide is
one of the mass movements which include all those processes that involve movement
of slope-forming material under the influence of gravity either outward or downward
(Crozier 1999a).
54
In the DENR report on Landslide Mapping and Vulnerability Assessment of
the Department of Environment and Natural Resources-Comprehensive Development
and Management Plan (DENR-CDMP 2005), the factors that were identified to cause
landslide include weak rock or soil; foliated/fractured rocks due to earthquakes and
natural weathering process; steep mountainous terrain; high drainage density; and
climatic factors particularly high rainfall and frequency of typhoons. Gunther (2006)
gave an overview of methods in landslide assessment which include geomorphologic
mapping; heuristic analysis (index-based); analysis of inventories; statistical modeling;
and process-based (conceptual).
Factors that promote slope instability are important considerations in
landslide vulnerability. Among them are the triggering factors that initiate movement,
namely, shifting of slope from a marginally stable to an actively unstable state. The
most common triggering factors are intense rainstorms, prolonged periods of wet
weather, seismic shaking, and slope undercutting. Hence, if a slope is marginally
stable, it is possible to recognize a threshold value for the triggering factor that is
responsible for initiating movement. The common triggering factors are usually
external forces imposed on the slope and the initiating thresholds are referred to as
extrinsic thresholds (Schumm 1979).
In certain occasions, there is mass movement even in the absence of particular
external triggering force, thus, it is assumed that some intrinsic threshold has been
surpassed within the slope. The Mount Cook rock avalanche from New Zealand’s
highest mountain in1991 is an example of this case (Mc Saveney 2002).
However, in most cases, an extrinsic triggering threshold for landslide
occurrence is identifiable and presents two useful opportunities for hazard estimation.
The first recognizes that the triggering threshold varies with the inherent stability of
the terrain and that spatial differences in the value of triggering thresholds can provide
a relative measure of the geographic distribution of terrain susceptibility to landslide
occurrence (Glade1998). Having identified the triggering threshold for a given
terrain, the triggering value may be used in determining the frequency of occurrence
of landslide generating conditions with reference to the seismic or climatic record for
the region (Brooks et al. 2004). Climate records are usually much longer and more
reliable than historical landslide records. In addition, these thresholds can be used for
warning systems and forecasting of landslide activity (Crozier 1999b).
55
Methodology
This study was conducted in 2010 within Mag-asawang Tubig Watershed
which is administratively shared by Occidental Mindoro and Oriental Mindoro.
Kisloyan Subwatershed is a component of the Mag-asawang Tubig Watershed with
a total land area of 1,435.90 ha covering the municipalities of Victoria, Oriental
Mindoro and Sablayan, Occidental Mindoro (Roberto 2006). As of May 1, 2010,
the Mag-asawang Tubig has a total population of 605 according to the Philippine
Statistics Authority (2010).
To facilitate the smooth implementation of the project, a multidisciplinary
team from various sectors of DENR-MIMAROPA Region was created. A workshop
was conducted to level off on the concepts and methodologies as well as to delineate
responsibilities of each member in the conduct of the project.
Watershed characterization and vulnerability assessment generally followed
the sequence of activities proposed by Daño (2005) in his Watershed Vulnerability
Activity Flow Diagram.
Assessment of watershed characterization data
This activity involves the review of the watershed characterization report to
determine data gaps that should be augmented through field visits or other means.
DENR MC 2008-05 can be used to assess the sufficiency of the characterization
report. Further environmental scanning was done to determine the availability of
watershed characterization data in other government agencies. This way, time and
substantial resources were saved as there was no need to get primary information
for data that are already available. Aerial photographs as well as other documents on
the history of flooding in the area were gathered from the provincial government of
Oriental Mindoro.
56
To have a good understanding of the entire Kisloyan subwatershed at
the macro level, the research team conducted a reconnaissance survey to gather
information on significant features within the study area. On-the-ground survey of the
Kisloyan subwatershed area was done traversing the main channel from the mouth to
the headwaters.
Determination and establishment of observation sites
With the use of topographic map, soils map and vegetation map, the
preliminary points were identified. The preliminary/tentative observation sites were
determined on the ground for evaluation. The identified areas were marked on the
ground.
Hazard identification, analysis, and mapping
The following methodologies and procedures were generated from the
Vulnerability Assessment Manual of ERDB (2011).
Hazards occurring in the Kisloyan subwatershed, both upstream and
downstream portions were identified from watershed characterization data and site
visitations together with the information gathered from the Mines and Geosciences
Bureau (MGB), Philippine Institute of Volcanology and Seismology (PHILVOCs),
Philippine Atmospheric, Geophysical and Astronomical Services Administration
(PAGASA) and other agencies.
This component is equivalent to a scoping activity in environmental
assessment. Its purpose is to focus on priority and critical hazards in the watershed.
Hazards and its contributory factors were initially determined through:
• Analysis of watershed characterization data
• Field observation of hazards occurring in the watershed (i.e., landslide)
• Conduct of focus group discussion (FGD) with the occupants of the
watershed and other key informants
Specific locations where the hazards were observed were recorded in the
field map during the field surveys and inputted to maps generated using geographic
information system (GIS) software. The observed hazard locations are useful in
validating GIS generated model. A crucial element in reducing vulnerability to natural
hazards is the analysis of human settlements and infrastructures as gathered during
field validation and FGDs.
57
For assessing landslide vulnerability due to physical factors, the different
thematic maps (slope, soil, geology/seismic, land use, and climate) are assigned with
corresponding rates/weights and overlaid based on the following relationships:
L = f [Sl, Cl (r + t), G (f + a + f), S (t + c), Lu]
Where:
L = landslide vulnerability
f = formation
Sl = slope factor
Cl = climate factor
r = rainfall amount
t = typhoon frequency
G = geology factor with consideration
to formation (f), age (a) and relative
distance to the fault line (f)
S = soil factor as soil type (t) and soil
morphological classification (c)
Lu = land use factor
Each factor was assigned a relative weight according to their influence in
landslide occurrence. Each factor class was also assigned a class rating as presented
in Table 1.
Table 1 Landslide vulnerability class rating
Landslide vulnerability class
Rating
Slightly vulnerable (SV)
<2.1
Failrly vulnerable (FV)
2.1 – 2.79
Moderately vulnerable (MV)
2.8 – 3.49
Highly vulnerable (HV)
3.5 – 4.19
Very highly vulnerable (VHV)
>4.2
Vulnerability was classified (slight, fair, moderate, high, and very high) based
on the hazard value; thus, a map was zoned into vulnerability classes. Landslide
vulnerability map due to physical factors were calculated for each hazard unit as the
sum of weighted product of individual factors, as shown in Equation 2:
Lp = 0.35Sl + 0.2R + 0.2G + 0.1S + 0.15Lu
Where:
Lp = landslide vulnerability due to
physical factors
Sl = slope factor
R = rainfall
G = geology factor
S = soil factor
Lu = land use factor
58
Geographic Information System and spatial analysis
To assess the vulnerability of the study area, historical accounts from the
respondents as to the occurrence of floods and landslides in the area were gathered.
Using tools such as remote sensing and geographic information systems (GIS), the
potential vulnerability was analyzed.
The overlay and index method which involved combining various watershed
attributes (e.g., geology, soils, slope, climate, landuse, anthropogenic factors) was
used. In this approach, all attributes are assigned with class (Class 1-5) and weights
(1-100%) (Table 2). This method was considered to be the simplest approach that
can easily be adopted in conducting vulnerability assessment. This tends to be more
quantitative by assigning different numerical scores and weights to the attributes in
developing a range of vulnerability classes which are then displayed in the map.
This approach involved assigning values to the identified factors affecting the
vulnerability of watershed resources to landslide. Factors which are considered to
have high influence in the vulnerability of the watershed to landslide were rated as 5
while those with very minimal effect were given a rating of 1 (Daño 2005).
Table 2 Guide in scaling of factors for landslide vulnerability of Kisloyan
subwatershed (ERDB 2011)
Physical factors
A. Slope (30%)
Class
1
2
3
4
5
Description
Slope, in general, is not steep (<8%)
Slope, in general, is slightly steep (8.1-18%)
Slope, in general, is fairly steep (18.1–30%)
Slope, in general, is moderately steep (30.1-50%)
Slope, in general, is very steep (>50%)
B. Soils(15%)
Morphology (5%)
Erosion (10%)
Soil type
1
2
3
4
5
Tropaquepts with entropepts, tropepts and oxisols
Tropopsamments with troporthents
Tropudalfs with tropepts
Entropepts with dystropepts
Tropudults with tropudalfs, mountain soils
1
2
3
4
5
Severe sheet and rill erosion
Moderate sheet, rill and gully erosion
Moderate sheet and rill; slight gully
Slight sheet and rill; no gullying
Almost no active erosion
59
subwatershed (ERDB 2011) (Continued)
Physical factors
Class
C. Climate
Monthly Rainfall
(7%)
Description
Maximum monthly rainfall
1
2
3
4
5
Very low (<100 mm)
Low (100.1-200mm)
Moderate (200.1-300mm)
High (300.1-500mm)
Very high (>500mm)
Typhoon frequency
Typhoon
Frequency (3%)
(See Philippine
Typhoon
frequency map)
1
2
3
4
5
Very low frequency
Low frequency
Moderate frequency
High frequency
Very high frequency
D. Geology (3%)
1
Pliocene-Quaternary (QV); Paleocene (sedimentary
and metamorphic rocks); Pre-jurassic
Undifferentiated (UV; KPg1; KPg2)
Oligocene (SPg2); Paleocene-Eocene (SPg1)
Pliocene-Pleistocene (N3+Q1); Upper Miocenepliocene (N2)
Recent (R); Quaternary (QAV); Pliocene-quaternary
(QPV)
2
3
4
5
E. Geohazards
(40%)
Proximity to fault
lines (20%)
1
2
3
4
5
Fault lines are not nearer than 5 km from the watershed
Fault lines are within 4-4.9 km
Fault lines within 1.9 km from the watershed
Earthquake
triggered landslides
susceptibility
(10%)
1
2
3
4
5
< 20% of the area is susceptible
20-30% of the area is susceptible
60
subwatershed (ERDB 2011) (Continued)
Physical factors
Rain-induced
landslides
susceptibility
(10%)
Class
1
2
3
4
5
Description
< 20% of the area is susceptible
Vegetative cover
F. Vegetative
cover/ Land-use
(2%)
1
2
3
4
5
>71% of the area is open/grassland/bare/cultivated
50-70% open/grassland/bare/cultivated
<20% open/grassland/bare/cultivated
Formulation of mitigating measures
Having identified the hazards, series of FGDs with the community as well as
workshops by the technical team and key persons were conducted to come up with
appropriate mitigating measures to prevent the occurrence of disaster. The mitigating
measures focused on interventions that may reduce the effects of the identified hazard
or improve the adaptation of the watershed to the landslide.
Review, analysis, and policy recommendation
Existing policies, including national policies, gathered during the conduct of
watershed characterization were reviewed and analyzed as to their relevance. Series
of in-house workshops were initiated by the team to come up with needed policy
recommendations that will serve as legal support to address the identified problems
and minimize damage that can be caused by landslide.
The Kisloyan subwatershed is located at the central part of the Mindoro Island.
Geographically, it lies between 13o02‘42’’ to 13o6’54’’ N, and 121o06’54’’ to 121o11‘
06’’ E (Fig. 1). It has an approximate area of 1,435.90 ha of which 1,133.70 ha or 78.95%
is within the Occidental side while 302.20 ha or 21.05% is within the Oriental side.
The subwatershed is covered by two municipalities: Victoria in Oriental Mindoro, and
Sablayan in Occidental Mindoro (Fig. 2).
Figure 1 Location map of Kisloyan subwatershed
61
62
Although a larger portion of the said watershed is within the Occidental side,
the area is more accessible via the Oriental side route. Consequently, the inhabitants
living inside the watershed are having a more active interaction with the residents
of Oriental Mindoro than the other part of the island. However, the administrative
jurisdiction over the Kisloyan subwatershed is shared between Occidental Mindoro
and Oriental Mindoro.
Victoria
Sablayan
River Network
Kisloyan Subwatershed
Barangay Boundary
Municipal Boundary
Figure 2 Administrative map of Kisloyan subwatershed
63
Watershed behavior of the Kisloyan Sub-watershed is quite stable, relative to the
conditions of adjoining watersheds. Except for occasional “kaingins” done by the
Mangyan’s, which are also being allowed to fallow (as a soil conservation measure)
after 2 to 3 years of cropping, the vegetative condition of Kisloyan is quite good.
Geomorphological features
A. Slope
Figure 3 shows the slope characteristic of the subwatershed. Table 3 presents
the area covered per slope classification. It can be noted that a significant portion
falls under the gentle to moderately slope category representing 45.18% of the entire
area or 648.43 ha. Obviously, the rate of soil erosion and probability of landslide
occurrence are directly correlated with steepness of slope.
Figure 3 Slope map of Kisloyan subwatershed
64
Table 3 Slope characteristics of Kisloyan subwatershed
Slope (%)
Area (ha)
Percent share
Not steep (0-8)
261.04
18.18
Slightly steep (8-18)
369.43
25.74
Gentle to moderately steep (18-50)
648.43
45.18
Very steep (50 and above)
156.40
10.90
B. Soils
The entire Kisloyan subwatershed is classified by the Bureau of Soils and
Water Management as rough mountain soils. Composite soil analysis was done from
three different elevation ranges as shown in Table 4. Clay is the major component
of the soil within the subwatershed. These results show that the subwatershed is less
vulnerable to erosion which can also mean it is less vulnerable to landslide in terms
of its soil type.
Table 4 Soil characteristics of Kisloyan subwatershed
Soil
characteristics
Lower elevation
Topsoil
Subsoil
Middle elevation
Topsoil
Subsoil
Higher elevation
Topsoil
Subsoil
Texture
Clay
loam
Clay
Clay
loam
Loamy
clay
Clay
loam
Loamy
clay
Soil depth (m)
0.15
0.35
0.10
0.25
0.10
0.25
Bulk density
(g/cc)
1.15
1.25
1.08
1.12
1.02
1.08
C. Geology
In terms of mineral deposits, the Kisloyan subwatershed is the most active
object of mining exploration in the whole island of Mindoro. It is estimated that the
Kisloyan-Ibolo-Aglubang complex will yield about 500,000 tons of purified nickel
metal alone in addition to the significant volume of cobalt.
Geologic formation of the subwatershed shows that almost the entire area
(95.45%) evolved from the Pre-Jurassic to Jurassic era while the rest were of recent
(up to 1 million years old) origin. It was in the Pre-Jurassic and Jurassic eras that the
nickel and cobalt ore materials likely originated.
65
The general composition of the rock deposits in the area are classified into
four, namely: silt-sand-gravel component generally deposited along the channel,
green schist with mica schist generally associated with the Halcon metamorphics
and, dunite and peridotite generally associated with the ultramafic complex (Fig. 4).
Figure 4 Composition of rock deposits in
Kisloyan subwatershed
D. Climate
The subwatershed falls under Climatic Type III based on Corona’s Revised
Classification where rainfall is not pronounced and dry season lasts from one to three
months only. Rain mostly occurs in October, November, and December while the
driest period is during March and April.
Figure 5 shows the monthly mean rainfall during the two time periods: baseline
period (1951-1999) and climate change period (2000-2010). The selected time period
is in line with the PAGASA study on climate change which considers the period 1999
and earlier as the baseline period. The latter was found to have higher monthly mean
rainfall as compared to the baseline period (Daño et al. 2013). It shows that climate
change has a significant effect in the area and in the province as well. Rainfall almost
occurs from May to December. This underscores the fact that the Kisloyan subwatershed
is one of the major sources of water of the Mag-asawang Tubig River.
49.70
53.00
135.20
149.20
1370.5
65.40
4.55%
MW 6
MW 7
MW 8
Total
Percent (%)
95.45%
159.70
MW 5
456.80
MW 4
51.90
MW 2
89.90
276.90
13.5
MW 1
PreJurassic
and
Jurassic
(ha)
MW 3
Quaternary (ha)
Microwatershed
(MW)
Geologic formation
4.55%
65.40
51.90
13.50
Alluvial
deposits
(ha)
15.64%
224.60
17.40
156.20
51.00
Halcon
metamorphics
(ha)
Lithology
79.81%
1145.90
131.90
135.20
53.00
49.70
159.70
276.90
300.60
38.80
Ultramafic
complex
(ha)
4.55%
65.40
51.90
13.50
15.64%
224.60
17.40
156.20
51.00
544.80
123.10
37.40
15.60
16.20
114.60
199.00
38.80
Perido tite
(ha)
41.86% 37.95%
601.10
8.70
97.90
37.40
49.70
143.50
162.30
101.60
0.10
Dunite
(ha)
Composition
Green
Silt/Sand/ Schist
Gravel
& Mica
(ha)
Schist
(ha)
Table 5 Extent of the various geological characteristics of Kisloyan subcatchment
67
Figure 5 Mean monthly rainfall in the subwatershed based on PAGASA Calapan
Station (Daño et al. 2013)
E. Geological hazards
The Kisloyan subwatershed is crisscrossed with major fault lines such as the
Aglubang River Fault and the Central Mindoro Fault. As recorded by the US Geological
Survey, the Kisloyan area was the epicenter of three moderately strong earthquakes
in the past. Further, there are three general geological hazards identified in the area.
These are landslides, soil erosion, and floods. Figure 6 shows the geohazard map of
the Kisloyan subwatershed.
Of the three hazards, landslide is considered as most likely to occur with
tremendous impact in the area. Therefore, to be thoroughly prepared for this most
likely occurrence, landslide is further dissected on the basis of the most likely cause,
which is either earthquake-triggered or rainfall-triggered landslides (Tables 6a and
6b). It is very essential for disaster managers to lay out specific preparations based on
the nature of the landslides.
As shown in Table 6a, 56% of the entire area is vulnerable to earthquaketriggered landslides. It is noticeable that of the entire 1,435.90 ha of subwatershed
area, about 800.20 ha are vulnerable to landslides triggered by earthquakes (Fig. 7).
Presumably, these are remnants of the Magnitude 7.2 earthquake that hit Mindoro in
68
Figure 6 Geohazard Map of Kisloyan Sub-watershed
1994. On the other hand, about 42% of the area is vulnerable to landslides caused by
intense rainfall (Fig. 8). Although some areas are interchangeably vulnerable to these
two forms of landslides, it is obvious that for the most part, Kisloyan subwatershed is
particularly vulnerable to landslides in general.
F. Land classification
Legal Status. The entire subwatershed is classified into three major land classes. These
include: timberland, alienable and disposable, and school reservation. Table 7 shows
that approximately 50% of the area is considered under the Timberland status. This
constitutes about 711 ha. The rest of the areas are under the School Reservation status
(MinSCAT) and Alienable and Disposable (A&D).
Land Capability. In terms of land capability, the entire subwatershed is categorized
into four classes, namely: (1) agricultural production with intercropping of permanent
crops as a soil conservation measure, (2) areas where clean cultivation may apply, (3)
69
Table 6a Extent of earthquake-triggered landslide hazards in Kisloyan subwatershed
Not vulnerable
635.8
800.2
44%
56%
Victoria
Figure 7 Earthquake-trigerred landslide map of Kisloyan subwatershed
70
Table 6b Extent of rain-induced landslide hazards in Kisloyan subwatershed
Not vulnerable
Low vulnerability
Moderate vulnerability
834.6
78.3
316.2
58%
20%
22%
Figure 8 Rainfall-induced landslide map of Kisloyan Sub-Watershed.
71
areas with severe limitation for crop production, and (4) definitely unfit for agricultural
production (Table 8). As shown in Table 7b, 81% of the area or 1,169.10 ha is not
suited for agricultural activities where constant soil working is done. This includes
55% of the area under severe limitation and the 26% classified under not suited for
agricultural production. These areas had to stay as woodland and covered with trees.
The present classification of the area, where 21% had been declared as Alienable and
Disposable, fits relatively well with the inherent geophysical attribute and capability
of the land.
G. Land use and vegetative cover
Four categories of present landuses and vegetative cover are observed in the
area. These are: closed canopy, cropland mixed with coconut plantation, cultivated
area mixed with brushland/grassland, and mossy forest (Fig. 9). Table 9 shows details
of the area covered under each category. This shows that a major portion of the area
is cultivated with mixed brushland/grassland but still with the presence of mossy and
closed canopy of mature trees covering >50% in the higher elevation.
The area is believed to contain rich amount of nickel and cobalt. Therefore,
it is anticipated that the extent of the variability in the proposed land management
modalities will largely depend on the perception of the various interest groups as to
how the area should be managed and utilized.
Table 7 Land classification of Kisloyan subwatershed
Land classification (ha)
School reservation
Timberland
A&D
413.80
711.00
311.00
29%
50%
21%
Table 8 Land capability of Kisloyan subwatershed
Land capability (ha)
Agricultural
production
Clean cultivation
Severe limitation
Not for
agricultural
production
176.10
90.80
795.00
374.10
13%
6%
55%
26%
72
Figure 9 Landuse and land cover maps of Kisloyan subwatershed
Table 9 Landuse of Kisloyan subwatershed
Land use
Area (ha)
Percentage (%)
Closed canopy, mature
trees covering >50%
302.09
21
Crop land mixed with
coconut plantation
10.27
0.7
Cultivated area mixed
with brushland/grassland
938.06
65.4
Mossy forest
184.89
12.9
Vulnerability assessment
A. Landslide vulnerability analysis
Based on the analysis of factors and parameters, as presented in the ensuing
tables, the entire Kisloyan subwatershed is a source of possible landslide hazard.
Table 10 shows the summary ratings of each parameter based on the guide of
scaling of factors for landslide vulnerability of Kisloyan subwatershed (ERDB 2011).
73
Table 10 Summary of landslide vulnerability rating of Kisloyan subwatershed
Parameters
1. Slope (30%)
Vulnerability rating
4
2. Climate (10%)
- Monthly rainfall (7%)
5
- Typhoon frequency (3%)
4
3. Soils (15%)
- Morphology (5%)
5
- Erosion (10%)
2
3. Geology (3%)
1
4. Geo-Hazards (40%)
-Fault Lines (20%)
5
-Earthquake triggered landslide susceptibility (10%)
4
-Rain-induced landslide susceptibility (10%)
2
5. Vegetative cover (2%)
1
After assigning values to factors and analyzing them, results showed that six out of ten
factors contributed to the landslide vulnerability of the Kisloyan subwatershed. These
are slope, climate (rainfall and typhoon frequency), soil morphology, geohazards
(nearness to fault lines and earthquake-triggered susceptibility to landslide).
The weights of the assigning values to each factors based on their influence
in the occurrence of landslide in the subwatershed were analyzed to determine the
vulnerability class of the subwatershed as presented in Table 11. This result is further
verified through the landslide vulnerability map in Fig. 10 .
Conclusion
Despite the presence of upland dwellers or Mangyans in the area, the
Kisloyan subwatershed can still be considered as sufficiently forested. For centuries,
the means of living for these Mangyans is primarily through slash-and-burn farming
(kaingin making) following the traditional set of rules in the observance of fallow
period. This farming system is particularly attuned to the physical environment of
Kisloyan; otherwise, these forests had long been gone. This system of cropping,
however, can only be as effective as the availability of large tracts of forest lands
relative to the number of dweller-families. In a situation where lands become scarce
as a result of population influx, fallow period becomes shortened and subsequent
74
Table 11 Vulnerability class of the Kisloyan subwatershed
Parameters
Vulnerability analysis
1. Slope
1.20
2. Climate
0.45
3. Soils
0.52
4. Geology
0.03
5. Geohazards
1.35
6. Vegetative cover
0.02
Total rating
3.57
Vulnerability class
Highly vulnerable
Landslide Vulnerability Map
Naujan
Victoria
Sablayan
River Network
Municipal Boundary
Low
Moderate
High
Figure 10 Landslide vulnerability map
75
soil fertility loss sets in. Once fallow period is shortened, sustainability becomes a
critical issue. Notwithstanding the relative richness of its forest resources, the Kisloyan
subwatershed is threatened by its vulnerability to landslide.
Results of the vulnerability assessment in the area indicated that a large part
of the subwatershed is highly vulnerable to landslide. The vulnerability of these areas
is particularly precipitated by the convergence of the effects of slope, rainfall, soils,
and fault lines. These parameters are the principal operating factors that rendered
a major portion of Sablayan, Occidental Mindoro particularly vulnerable to these
geological risks. Although the Victoria, Oriental Mindoro side shares similar rainfall
and fault line characteristics with the rest of the areas, the aggravating effect of slope
stood out as a very critical factor in the analysis.
Recommendations
Landuse prescriptions. Inasmuch as the Kisloyan subwatershed is one of
the principal watersheds of the Mag-asawang Tubig River, the management regimes
in the area must be geared towards watershed conservation and protection. This
does not preclude the government from allowing the use of other resources in the
area, however, the mode of resource-use must be designed in such a way that the
watershed character of Kisloyan will not be jeopardized. Appropriate technologies
must be earnestly applied to achieve such goal.
Natural hazards. As indicated in the results, there are specific areas in the
Kisloyan subwatershed that are particularly prone to geological perturbations, such as
ground movements, due to the presence of fault lines. The presence of these active
faults rendered the area vulnerable to landslides. Therefore, to forestall loss of life and
property, the long-term use of this subwatershed must be restricted to forest purposes.
The dwellers in these areas must be advised to move to safer grounds.
Several landslides are still visible in some portions of the area. These are
presumably results of the 1994 earthquake. Although many years had already passed,
these landslides have not been substantially repopulated with vegetation. It would be
beneficial to apply Assisted Natural Regeneration (ANR) using grasses and other lowstatured flora as pioneer or succession species.
It is critically important that no area within the Kisloyan subwatershed will
remain open without any vegetation for more than a year. This can be done by
limiting the annual usable area that is being provided to mining companies based on
their capability to immediately rehabilitate mined-out/opened areas after the mining
activity.
76
The vulnerability map shows that majority of the areas in the subwatershed
are highly vulnerable to landslide. Therefore, the following mitigating measures,
among others, should be prioritized:
1. Immediate rehabilitation of the 28.40 ha of severely eroded/landslide areas in
the southern portion of Sablayan, Occidental Mindoro. The surface condition
of these landslide areas are poor such that the soils are very porous and loose,
vegetative rehabilitation must be done thru planting of grasses, vines and
other low-statured pioneer species. The introduction of leguminous vines and
shrubs will improve the fertility of the soils. The leguminous crops may include
the following: Centrosema pubescens (Centro), Centrosema macrocarpum
(Centro), Pueraria lathyroides (Kudzu), Macroptilium atropurpureum (Siratro),
Calopogonium mucusoides (Calopogonium), Stylosanthes guianensis (Stylo),
Calliandra calothyrsus (Calliandra), Desmanthus virgatus (Desmanthus).
2. Application of the following soil conservation measures in the cropping
regimes in the portions of Victoria, Oriental Mindoro: contour cropping, buffer
strip or hedge-row planting, and mixed cropping of short-duration crops with
perennials. Some 266.70 ha of land in this subwatershed must be managed
using the above prescription.
3. Installation of rain gauge within the watershed. Since Kisloyan is one of the
principal sources of water to Mag-asawang Tubig River, the river could also
be one of the major contributors to the inundation of the floodplains. Several
studies pointed out that the triggering effect of rainfall to landslide start once
an area receives >100 mm rainfall in a 24-hour rainfall event. The rain gauge
will give a signal to any impending landslide situation in the area. With the
early warning of an unusually heavy precipitation, the residents in the lowlying areas of Mag-asawang Tubig River can be warned against a possible
flooding as a result of damming due to landslides, and the abrupt release of
the impounded waters in the Kisloyan River. This rain gauge can be installed
in the built-up areas where several dwellers live.
4. Determination of optimum Kisloyan subwatershed's carrying capacity for
kaingin farming. Prohibiting the practice of kaingin farming could serve as one
of the best prescriptions in the area. However, this is difficult to implement
because it is basically the Mangyans' only source of living. Hence, the
sustainable area for kaingin farms must be set to ensure that proper fallow
intervals are maintained.
77
5. Local Government Units (LGUs) should come up with Information, Education
and Communication (IEC) materials on the vulnerabilities within the watershed.
Literature cited
Brooks SM, Crozier MJ, Glade T, Anderson MG. 2004. Towards establishing climatic thresholds
for slope instability: Use of a physically-based combined solid hydrology-slope stability
model. Pure and Applied Geophysics. 161.
Crozier MJ. 1999a. Landslides. Alexander DE and Fairbridge RW, Editors. Encyclopedia of
Environmental Science. Dordrecht, Kluwer.
Crozier MJ. 1999b. Prediction of rainfall-triggered landslides: A test of the antecedent water
status model. Earth surface processes and landforms. 24:825-833.
Daño AM. 2005. Vulnerability assessment of watersheds in the Philippines. Unpublished
Research Proposal. ERDB, College, Laguna.
Daño AM, Ebora JB, Ociones FT, Olvida AF. 2007. Guidelines on vulnerability assessment of
watersheds. Unpublished paper. College, Laguna.
Daño AM, Vendiola EE, Reaviles RS, Mauricio RA, Chicano DS. 2013. Impacts of climate
change on the extent and magnitude of flooding in Mag-asawang Tubig-Bucayao River
Basin in Oriental Mindoro. Unpublished Terminal Report. Ecosystems Research and
Development Bureau, College, Laguna.
(DENR) Deparment of Environment and Natural Resources. 2005. Comprehensive Development
and Management Plan (CDMP). Diliman, Quezon City.
(ERDB) Ecosystems Research and Development Bureau. 2011. Manual on vulnerability
assessment of watersheds. Ecosystems Research and Development Bureau, Department of
Environment and Natural Resources, College, Laguna.
Glade T. 1998. Establishing the frequency and magnitude of landslide-triggering rainstorm
events in New Zealand. Environmental Geology. 35:160-74.
Gunther A. 2006. Landslide susceptibility assessments.
McSaveney MJ. 2002. Recent rockfalls and rock avalanches in Mount Cook National Park,
New Zealand. Evans SGand DeGraff JV (Eds.) Catastrophic landslides: Effects, occurrence,
and mechanisms. 15:35-70.
Roberto IS. 2006. Philippine Disaster Management System: Case of the Oriental Mindoro
December 2005 Floods.
78
Schumm SA. 1979. Geomorphic thresholds: the concept and its applications. Transactions
Institute of British Geographers (New Series). 4:485-515.
Van Westen CJ, et al. 2008. Spatial data for landslide susceptibility, hazard and vulnerability
assessment: An overview. Engineering Geology, doi:10.1016/j.enggeo.2008.03.010.
Sylvatrop, The Technical Journal of Philippine Ecosystems and Natural Resources 23 (1 & 2): 79 - 120
Application of analytic hierarchy process
and GIS in landslide vulnerability assessment
of Matutinao Watershed, Cebu, Philippines:
A case study anchored on the climate change
framework
Reynaldo L. Lanuza
Ecosystems Research and Development Bureau
ERDB-BCWERC
Banilad, Mandaue City
Daisy Luisa S. Camello
Statistician II
Antonio M. Daño, PhD
ERDB, College, Laguna
Bruno O. Carreon
Science Research Specialist I
The study was conducted in the ecologically and economically
significant Matutinao Watershed in Cebu. Ongoing developmental activities
in the area necessitates a landslide vulnerability assessment to avoid possible
losses of lives and properties. A GIS-assisted approach was developed to
a) evaluate the utility of GIS with regard to landslide vulnerability assessment
anchored on the climate change framework; b) identify and map out the
areas vulnerable to landslide; recommend appropriate measures to avoid
loss of lives and properties; and c) formulate policy recommendations.
Using the Analytical Hierarchy Process (AHP) in determining the
relative importance of factors identified and GIS, the landslide vulnerability
anchored in a climate change perspective was determined. Exposure
to landslide was based on 2020 climate projections. The sensitivity
was computed based on the model derived from AHP, expressed as L
= 0.8297[0.3160Sl + 0.0973R + 0.0973T + 0.0912Ga + 0.0912Gf +
0.1729Gfl+ 0.0698So + 0.0633Lu] + 0.1703[0.2532FS + 0.3175H +
0.4349GD].
Keywords: GIS-assisted approach, landslide, vulnerability assessment, Matutinao
Watershed, Geographic Information System, Analytic Hierarchy Process
80
R. Lanuza et al.
Adaptive capacity was derived based on the response of the
community. It was predicted that about 3,278.47 ha or approximately
65.12% have high vulnerability to landslide. This is followed by moderate
landslide vulnerability with 1,666.11 ha and very high vulnerability covering
about 89.60 ha.
The GIS-assisted model predicted the location of areas that are
vulnerable. Generally, areas vulnerable to landslides are located in steeper
slopes and in unstable geology. This can be further triggered by high rainfall
that causes the soil saturation and mass movement downslope. Moreover,
the results also showed the capability of GIS-assisted approach with AHP in
assessing areas vulnerable to landslide.
THE PHILIPPINES IS DUBBED AS THE "PEARL OF THE ORIENT" BECAUSE IT IS
endowed with rich natural resources, fascinating landscapes and splendid white
beaches. The Philippines’ rainforests and its extensive coastlines make a suitable
habitat to a diverse range of floral and faunal species. However, the country is
geographically located in the western side of the Pacific Ocean “Ring of Fire” which
is continually threatened by natural hazards that adversely affect the lives of Filipino
people. Therefore, landslides should be addressed with urgency, readiness, and good
plans and actions to avoid the loss of human lives and damage to infrastructures and
properties.
Landslides pose serious threats to life and property as demonstrated by a
disastrous rockslide-debris avalanche in Guinsaugon, Leyte, Philippines in February
2006 where over 1,100 people died. This is considered as one of the largest
landslides to have occurred in recent years (Evans et al. 2007). The occurrences of
catastrophic landslides in the country have highlighted the significance of assessing
the vulnerable areas to serve as inputs in the formulation of development plans. The
new Department of Environment and Natural Resources (DENR) management headed
by Secretary Ramon J.P. Paje reiterated the need to address these environmental
hazards. This paved way to the Banner Program on Vulnerability Assessment of
Characterized Watersheds in the Philippines with landslide assessment as one of the
project components.
In Central Visayas, Matutinao Watershed was the target site for CY 2014. It is
one of the important watersheds in Cebu because it supplies majority of the domestic
and agricultural waters in the municipalities of Alegria and Badian. It is also an
ecotourism site known for the fantastic beauty of Kawasan Falls. Thousands of local
and foreign tourists visit the place to commune with nature and to enjoy the cool
and refreshing waters and the beautiful landscape. It also generates power for nearby
Application of AHP and GIS in landslide vulnerability assessment
81
municipalities of Alegria and Badian with hydroelectric power plant of CEBECO in
Matutinao, Badian, Cebu. However, despite these uses, Matutinao Watershed is
threatened by degradation caused by natural and human-related activities. One of the
common hazards in the watershed is landslide. Hence, the need to identify and map
out the areas that are vulnerable in order to prevent future calamities that endanger
human lives and properties.
In conducting vulnerability assessment, computer-based tools are found to be
useful in the hazard mapping of landslides. One of the significant tools for landslide
hazard mapping is GIS coupled with Analytic Hierarchy Process (AHP). Hazard
occurrence models are further enhanced by evaluating their results and adjusting the
relative importance of input variables. Thus, the application of GIS-assisted approach
coupled with AHP is a useful approach to analyze the complicated process affecting
landslide.
This study focused on the following: integration of AHP and GIS in landslide
vulnerability assessment anchored on a climate change perspective; evaluation of
the utility of GIS with regard to landslide assessment and mapping; identification and
mapping of areas that are vulnerable to landslides; recommendation of appropriate
measures to avoid loss of lives and properties due to landslides; and formulation of
policy recommendations.
Landslides: Its causes and occurrence
Landslide is one of the forms of erosion called mass wasting when the
force of gravity pulls rock, debris or soil down a slope. It may occur when the stress
produced by the force of the gravity exceeds the resistance of the material due to
the determining and triggering factors (Varnes 1978). A landslide may be defined as
a “downhill and outward movement of slope-forming materials under the influence
of gravity” (Cruden 1991). According to Petley (2010), majority of landslides are
triggered by external processes that cause the slope for failure. These processes are
termed as “causes” which includes geomorphology, physical processes or features,
and human actions. In most cases, the final failure of slope occurs as a result of a
clear trigger. Rainfall triggered landslides happen when the rainwater sinks through
the earth on top of a slope, percolates through cracks and pore spaces in underlying
sandstone, and encounters a layer of slippery material, such as shale or clay, inclined
toward the valley (Petley 2010).
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R. Lanuza et al.
Petley (2010) stated that landslide hazard assessment identifies areas
potentially affected by slope failures, quantifies the probability of occurrence and
estimates the magnitude of the event. Determining the extent of landslide hazard
requires identifying those areas which could be affected by a damaging landslide
and assessing the probability of the landslide occurring within a period of time.
The methods used to assess probability of land sliding and other hazards have been
discussed by Leroi (1996). Unfortunately, specifying a time frame for the occurrence
of a landslide is difficult to determine even under ideal conditions. As a result, Brabb
(1984) stressed that landslide hazard is often represented by landslide susceptibility.
It only identifies areas potentially affected and does not imply a time frame when
a landslide might occur. Esmali and Ahmadi (2003) stated that the ultimate aim of
investigating and studying landslides is to look for ways to reduce and/or to avoid
their damages. This can be possibly achieved by determining the hazardous areas
or through landslides hazard zonation and by providing mitigation measures and
regulations for appropriate uses or avoidance of these areas.
GIS-assisted landslide assessment
The advancement of computer-based tools with capabilities on geographically
referenced data is useful in landslide assessment and hazard mapping. Geographic
Information Systems (GIS) is found to be effective as a key tool for natural hazard
management of spatial and temporal data in the context of integrated development
planning (Parsons and Frost 2000; Lan et al. 2004; Kohler et al. 2006; Lan et al. 2009).
GIS is a systematic means of geographically referencing a number of “layers”
of information to facilitate overlaying, quantification, and synthesis and analysis of
data to aid in decision-making (Burrough and McDonnell 1998). GIS also provides
effective tools for the handling, integrating, and visualizing diverse spatial data sets
(DeMers 2000; Brimicombe 2003; Lan et al. 2009). Arunkumar et al. (2013) pointed
out that the advanced GIS computational tools offer numerous advantages in multigeodata handling as evident from various geo-environmental studies. Furthermore, the
integration of GIS with information on natural hazards, natural resources, population,
and infrastructure can help planners in identifying less hazard-prone areas that
are suitable for development activities, areas where further hazard evaluations are
required, and areas where mitigation strategies should be prioritized.
Soeters and van Westen (1996) discussed the application of various GIS
methods with respect to the characteristics of the area, the extent and type of
landslides, data types, and mapping scale. However, GIS applications in natural
hazard management and development planning are limited only by the amount of
information available and by the imagination of the analyst. Mukhlisin et al. (2010)
83
used GIS in analyzing and mapping landslide hazardous areas with four main factors
such as slope gradient aspect, geology, surface cover/landuse, and precipitation
distribution. They produced a hazard map with five different indexes (i.e., very low,
low, medium, high, and very high hazard). The results of the analysis were verified
using the landslide location data which showed that the model was very suitable in
predicting landslide hazard and generating landslide hazard maps.
In Iran, Hassanzadeh (2000) had successfully applied multiple regression
method and GIS techniques for landslides hazard zonation considering four factors
such as lithology, slope angle, precipitation, and land use. Ajalloeian et al. (2000)
also investigated the role of land use change and its relation to landslide using GIS
with Arc Info Software. They found out that landslide zonation can be successfully
done and that the most important landslide points are in the areas with changes in
landuse (e.g., replacement of forest to grassland and civil activities).
In Mangalore, Karnataka, Sivakumar Babu and Mukesh (2007) emphasized
that GIS is a promising tool for an effective analysis associated with the study of
geologic hazards such as landslide modeling because of its flexibility in handling
large set of information as well as in providing a good avenue for analysis and display
of results. Their study has demonstrated the ability of the GIS in incorporating the
spatial variation of ground elevation, soil properties, and other factors in slope
stability analysis.
The GIS-based Multi-Criteria Evaluation (MCE) methods have also been
applied in several studies. They provide good computational ability in determining
the relative importance of factors. Typically, MCE has been approached in two
ways. First, all criteria are allowed as Boolean type statement. However, problems
were noted in the methods for site selection and resource evaluation that rely on
classical Boolean logic (Carver 1991). Loss of information might occur in situations
where the threshold value is not precise. Furthermore, the method does not offer any
analytical possibility for examining which of the areas fulfilling the criteria are the
most appropriate method for the purpose of the study. With these constraints, the
MCE methods have been applied instead of the Boolean logic (Pereira and Duckstein
1993) for suitability analysis and vulnerability analysis were identified. An index
model produces for each unit area an index value rather than a simple yes or no.
The weighted linear combination method is probably the most common
method for computing the index value for each unit area and produces a ranked map
based on the index values (Saaty 1980). According to Saaty (1980), AHP is one of the
widely used methods in computing the criteria weights in MCE via an expert pairwise comparison matrix using the respective weights. It has been suggested by Rao
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R. Lanuza et al.
et al. (1991) that for the development of criteria weights, the procedure of pairwise
comparison in AHP is a logical process. Weighted linear combination operator
commonly used with such factors lies on a continuum with these operators.
However, in spite of some uncertainties, several studies have successfully
applied the AHP in various fields (Banai-Kashani 1989; Malczewski 2000; Gil and
Kellerman 1993; Eastman et al. 1995; Jiang and Eastman 1996, Esmali and Ahmadi
2003). These studies acknowledged successful application of the AHP approach
combined with weighted linear combination in GIS for strong theoretical framework
and standardization of factors.
Landslide vulnerability and climate change
Kelly and Adger (2000) and Dolan and Walker (2004) have summarized the
various definitions of vulnerability that have previously appeared in the literatures.
Dolan and Walker (2004) identified three perspectives of vulnerability from climate
change and hazards research to address the dynamic and integrated nature of social
and environmental vulnerability. The first perspective delves on the exposure to
hazards and how this affects people and structures. The second perspective views
vulnerability as a human relationship (social vulnerability) while the third is the
integration of both the physical event and the underlying causal characteristics
leading to risk exposure and limited capacity of communities to respond.
Similarly, the Intergovernmental Panel on Climate Change (IPCC) described
vulnerability as the function of the degree of exposure of the system to climatic
hazards, sensitivity of a system to changes in climate (the degree to which a
system will respond to a given change in climate, including beneficial and harmful
effects), adaptive capacity (the degree to which adjustments in practices, processes,
or structures can moderate or offset the potential for damage or take advantage of
opportunities created by a given change in climate). The integration of exposure and
sensitivity will result to the potential impact to the human populations.
Previous vulnerability assessment studies in the Philippines by the DENR
Research Sector failed to include the future climate scenarios as indicated in the
climate change framework. The procedures have been described in the ERDB
vulnerability manual and implemented nationwide (ERDB 2000). In the course of
implementation, the GIS-assisted methodology was developed and found to be
a useful alternative tool in aid of watershed planning (Lanuza 2007). One of the
most important applications of GIS is spatial analysis of geospatial data to support
the process of environmental decision-making. Malczewski (1999) noted that a
decision can be between alternatives, where the alternatives may be different actions,
locations, and the like. Since 80% of data used by decision makers is location based,
85
GIS can provide better information in decision making. Heywood et al. (1995) opined
that GIS allows the decision maker to identify a list of pre-defined set of criteria with
the overlay process.
Methodology
Project Site
Matutinao Watershed is located in the southwestern portion of Cebu Province
and within the political jurisdiction of the Second Congressional district covering
the municipalities of Alegria and Badian. The watershed is geographically located
at coordinates at 123o22’ to 123o27’N latitude and 9o42’ to 9o44’34.5” E longitude
(Fig. 1). It is approximately 93 km from Cebu City via Cebu-Barili-Bato route. It
has a total land area of 5,735.7 ha. Of this, 3,915.6 and 1,820.1 ha are classified
as timberland, and alienable and disposable lands, respectively. It covers wholly or
partly four barangays in Lepanto, Compostela, Valencia, and Guadalupe in Alegria
and Barangays Matutinao, Balhaan, and Sulsogan in Badian (Table 1 and Fig. 2).
The watershed is characterized with a relatively rolling terrain that varies
from plain along valleys to slightly rolling along hills and moderately steep to steep
along mountains. The flat portions of the watershed are located in the coastal area
near the outlet of Matutinao River. There are some portions that can be classified as
mountainous with 30-50% slope or over almost majority of the total land area belongs
to this topographic classification. Slightly rolling to moderately undulating relief is
mostly characterized in the middle portion. The highest elevation is 841 m above sea
level (masl) in Libo, Lepanto, Alegria while the lowest is 0 masl in Matutinao, Badian.
Table 1 Component barangays and the respective areas within Matutinao
Watershed in Alegia and Badian, Cebu
Municipality
Alegria
Badian
Barangay
Area (ha)
Compostela
Guadalupe
Lepanto
Valencia
Balhaan
Matutinao
Solsogan
720.265
1,300.368
928.141
1,717.892
96.345
66.840
212.184
10800 00
1085000
54000 0
54000 0
10710 00
10800 00
545000
545000
Universal Transverse Mercator 51N
Luzon Datum (excluding Mindanao)
54900 0
54900 0
Figure 1 Location map of the Matutinao Watershed
540000
540000
550000
550000
Location of Matutinao Watershed
1080000
December 2014
10710 00
1080000
1085000
1075000
Department of Environment and
Natural Resources
S
E
20000 Meters
Political Boundary
Scale : 1:1535938
0
: 5,735.677 hectares
: Matutinao Watershed
: Alegria, Badian, Dalaguete and
Malabuyoc
: Cebu
: Cebu
Barangay
Matutinao Watershed
Legend
20000
Province
Island
Area
Watershed
Municipality
A MAP showing
location of Matutinao Watershed
W
N
86
R. Lanuza et al.
1075000
1085000
1080000
December 2014
540000
545000
Palaypay
Lepanto
Nug-as
545000
Valencia
Guadalupe
Compostela
Sulsogan
Balhaan
Matutinao
540000
Dugyan
Source: NAMRIA, FMS
550000
550000
Barangay Boundary
S
E
475000
570000
570000
2000 Meters
This Site
665000
Watershed Boundary
Scale : 1:100000
0
Malabuyoc
: Cebu
: Cebu
Barangay
Balhaan
Compostela
Dugyan
Guadalupe
Lepanto
Matutinao
Nug-as
Palaypay
Sulsogan
Valencia
Legend
2000
Province
Island
Area
Watershed
Municipality
A MAP showing
barangay boundary within Matutinao
Watershed
W
475000
665000
1045000
Figure 2 Map showing the covered barangays of Matutinao Watershed
1075000
1080000
1235000
1085000
1140000
1140000
Ta
n
on
Str
ait
N
1235000
1045000
Natural Resources
87
1075000
88
R. Lanuza et al.
Data sources
Secondary information were gathered from various sources. These included
the topographic map from National Mapping and Resource Information Authority
(NAMRIA) with a scale of 1:50,000; landuse and land classification map (DENR
2007); geology map (Bureau of Mines 1984); soil map (Bureau of Soil and Water
Management as cited by DENR 2007); rainfall data (DENR 2007); political boundary
(LGU Badian and Alegria as digitized by DENR 2007); road network from the
topographic map; and fault line (PHIVOLCS).
Actual survey within Matutinao Watershed was conducted to have ocular
observation on the occurrence of landslides in the area. Personal interview of key
informants in the seven barangays within the watershed was also done. About 25
households per barangay were interviewed. The interview schedule included the
following: a) sociodemographic characteristics, b) socioeconomic characteristics,
c) access to social services, and d) exposure, biological sensitivity and adaptive
capacity of the community. The study included other factors: possible impacts of
climate change; coping mechanism which include awareness of the status of climate;
mitigating measures extended by the government/institutions to address the changes
encountered by communities; and communities' adapted traditional/indigenous
practices associated to climate change perception.
The location of landslides and landslide prone areas were determined using
a Global Positioning System (GPS). Other primary data included the location of
infrastructures, farming systems and occupancy, and ground habitation which served
as inputs to the development of GIS-assisted model on landslide assessment.
Hazard identification and generation of thematic maps using GIS
Hazard areas due to landslide were identified and categorized. Actual
locations were determined using the GPS and mapped using GIS. The thematic maps
such as contour, geology, vegetation, climate, soil, landuse, infrastructures, among
others were generated using GIS. This involved digitizing and processing the spatial
information of individual map as well as downloading and converting the actual GPS
readings to GIS map. Moreover, geo-relational databases were also created for each
thematic map.
Factor analysis, hazard ratings, and determination of weights
The factors for each of the vulnerability functions such as exposure, sensitivity
and adaptive capacity were treated individually. Then, thematic maps were generated
for each factor.
89
For the exposure, the relative distance to landslide areas (household,
economic activities, and leisure activities), frequency of occurrence, projected
increase of rainfall in 2020, and percentage exposure of community were considered.
For the sensitivity, both the physical and anthropogenic causal factors on landslides
were included such as the slope, climate (rainfall and typhoon frequency), geology
(age, formation and relative distance to fault line), soil, landuse, farming systems,
habitation, and ground disturbance. The exposure and sensitivity constituted the
potential impact on landslide (Fig.3).
The resiliency is estimated based on the system’s adaptive capacity to
sustain the climate effects with minimum disruption or cost. The factors for adaptive
capacity included the self-help system, availability of structures, availability and use
of facilities, special skills and training, external assistance, Information Education and
Communication (IEC) activities, leadership, formal safety association, common safety
indigenous practices, availability of technology, and adoption of technology. The
final vulnerability index as a function of exposure, sensitivity and adaptive capacity
is presented in Fig. 4.
Scaling of factors affecting landslide vulnerability
The approach in landslide vulnerability assessment involved assigning values
to quantitative and qualitative factors considered to affect the vulnerability of the
watershed. Factors or attributes in each of the vulnerability functions are assigned
with hazard rating ranging from 1 to 5. The scale as presented in the Manual on
Vulnerability Assessment of Watershed by ERDB (2011) was adopted as follows:
1 – The factor plays a role in Very Low Vulnerability
2 – The factor plays a role in Low Vulnerability
3 – The factor plays a role in Moderate Vulnerability
4 – The factor plays a role in High Vulnerability
5 – The factor plays a role in Very High Vulnerability
Computing the relative weights of factors
The average weight of factors was directly used for the exposure and adaptive
capacity. The weights for the factors identified were equally divided by the number
of factors for each vulnerability function. On the other hand, the MCE approach
using the AHP was employed in determining the relative weights for the sensitivity.
The MCE approach can objectively solve a complex decision problem with multiple
criteria using the AHP method introduced by Saaty (1980). The AHP is a method to
derive ratio scales from paired comparisons. The input can be obtained from actual
90
R. Lanuza et al.
measurement from subjective opinion such as satisfaction, feelings, and preference.
The AHP considers a set of evaluation criteria as well as a set of alternative options
among which the best decision can be made. Essentially, with contrasting criteria, the
best option is not always the one which optimizes each single criterion, rather the
one which achieves the most suitable trade-off among various criteria (Saaty 1980).
Furthermore, AHP allows some small inconsistency in judgment because human is
not always consistent. The ratio scales are derived from the principal Eigenvectors
and the consistency index is derived from the principal Eigen value.
An expert pair-wise comparison matrix was formulated and the weights to the
factors/criteria were assigned. Using the pair-wise comparison matrix, all identified
relevant factors/criteria were compared against each other with reproducible preference
factors to calculate the needed weights of factors. Table 2 shows the numerical values
expressing a judgment of the relative importance of one factor against another. The
values range from 1 to 9 which describe the intensity of importance (Saaty and Vargas
1991). A value of 1 expresses “equal importance” and a value of 9 is given for factors
with “extreme importance” over another factor.
Table 3 shows a reciprocal matrix from pair-wise comparisons of order
11 where 11 criteria (F1, F2, …, and F11) are compared against each other. In the
comparison of criteria F1 and F2, criterion F1 is regarded as moderately important.
Similarly, F1 is moderately important to F3, F4 and F5, equally to moderately
important to F6, moderately to strongly important to F7, F8 and F11, very strongly
important to F9 and very to extremely strong importance to F10. Then, the relative
importance had been assigned to the remaining criterion. The transposed position for
F1 to F2 automatically gets a value of the reciprocal which is 1/3 or 0.33.
In the next step, the assigned preference values are synthesized to determine
a numerical value which is equivalent to the weights of the factors. Therefore, the
Eigen values and Eigen vectors of the square preference matrix showing important
details about patterns in the data matrix are computed. The square matrix of order
11 gives 11 Eigen values with which 11 Eigen vectors can be computed. Saaty and
Vargas (1991) remarked that it is sufficient to calculate only the Eigen vector resulting
from the largest Eigen value since this Eigen vector contains enough information to
provide the relative priorities of the factors being considered. The pair-wise matrix
is normalized and the Eigen values of the normalized matrix, which represent the
parameter weights, are computed as shown in Table 3. The final equation of sensitivity
function is expressed in Equation 1:
L = 0.8297[0.3160Sl + 0.0973R + 0.0973T + 0.0912Ga + 0.0912Gf + 0.1729Gfl+
0.0698So + 0.0633Lu] + 0.1703[0.2532FS + 0.3175H + 0.4349GD]
SensItIvIty
VL
L
Exposure
M
H
VH
VL
VL
VL
L
L
M
L
VL
L
L
M
H
M
L
L
M
H
H
H
L
M
H
H
VH
VH
M
H
H
VH
VH
Potential Impact = (Exposure + Sensitivity)
2
Note: The formula was adapted from Allison et al. 2009
Vulnerability
Very Low
Low
Moderate
High
Very High
Values
< 2.0
2.0 – 2.75
2.75 – 3.5
3.5 – 4.2
> 4.2
Potential Impact
Figure 4 Potential impact of the ecosystem as a function of exposure and sensitivity
Adaptive Capacity
M
H
VL
L
VH
VL
M
L
L
VL
VL
L
H
M
L
L
L
M
H
H
M
L
L
H
VH
H
H
M
L
VH
VH
VH
H
H
M
Vulnerability = Potential Impact – Adaptive Capacity
Note: The formula was adapted from Gletibouo and Ringler 2009; Allison et al. 2009
Vulnerability
Very Low
Low
Moderate
High
Very High
Values
< -1.0
-1.0 to 0
0 to 0.5
0.5 to 1.5
> 1.5
Figure 5 Vulnerability anchored to climate change as a function of potential impact
and adaptive capacity
R. Lanuza et al.
92
The relative contribution by the physical and anthropogenic factors was also
analyzed. It was done by summing up all the physical and normalizing the values.
The same was done for the anthropogenic factors. The relative weights were 82.97%
and 17.03% for the physical and anthropogenic factors.
Table 2 The fundamental scale (adapted from Saaty 1990)
Intensity of
importance on
Definition
Explanation
an absolute
scale
Two activities contribute equally to
1
Equal importance
the objective
3
Moderate importance
Experience and judgment slightly
favor one activity over another
5
Essential or strong
importance
Experience and judgment strongly
favor one activity over another
7
Very strong
importance
An activity is strongly favored and its
dominance demonstrated in practice
9
Extreme importance
The evidence favoring one activity
over another is of the highest possible
order of affirmation
2, 4, 6, 8
Reciprocals
Rationals
Intermediate values
between the two
When compromise is needed
adjacent judgments
If activity i has one of the above numbers assigned to it when
compared with activity j, then j has the reciprocal value when
compared with i
Ratios arising from the
scale
If consistency were to be forced by
obtaining n numerical values to span
the matrix
12.50
3.8512
1.0121
11.1937
0.0194
0.0127
SUM*PV
Lambda Max
CI
CR
1.0091
1
1/2
1/2
1/4
1/8
Farming
system (F9)
1
1/4
Landuse (F8)
1/2
1/7
1/4
Soil type (F7)
2
Occupancy
and habitation
(F10)
Ground
disturbance
(F11)
SUM
1/2
1
1/3
Relative
distance to
faultline (F6)
1
1/3
1
1
1/3
1/3
3
Rainfall
Amount
(F2)
1
Slope
(F1)
Geologic
formation (F5)
Slope (F1)
Rainfall
amount (F2)
Typhoon
frequency (F3)
Geologic age
(F4)
CRITERIA
1.0091
12.50
1
1
1/2
1/2
1/2
2
1
1
1
1
3
Typhoon
Frequency
(F3)
0.98536
13.00
1
1/2
1/2
1
1
2
1
1
1
1
3
Geologic
Age (F4)
0.985360
13.00
1
1/2
1/2
1
1
2
1
1
1
1
3
Geologic
Formation
(F5)
PHYSICAL FACTORS
1.0179
7.00
1/2
1/3
1/3
1/3
1/2
1
1/2
1/2
1/2
1/2
2
Relative
Distance
to Faultline
(F6)
0.9756
17.00
1
1
1
1
1
2
1
1
2
2
4
Soil
Type
(F7)
Table 3 Pair-wise comparison matrix of the factors/criteria affecting landslide sensitivity
0.9867
19.00
2
1
1
1
1
3
1
1
2
2
4
Landuse
(F8)
1.0747
25.00
2
1
1
1
1
3
2
2
2
2
8
Farming
System
(F9)
1.1039
21.00
1
1
1
1
1
3
2
2
1
1
7
Occupancy
and
Habitation
(F10)
1.0338
14.00
1
1
1/2
1/2
1
2
1
1
1
1
4
Ground
Disturbance
(F11)
ANTHROPOGENIC FACTORS
1.0000
0.0737
0.0538
0.0429
0.0526
0.0580
0.1436
0.0757
0.0757
0.0808
0.0808
0.2624
Priority
Vector
(PV)
R. Lanuza et al.
94
Development of GIS-assisted model and determination of vulnerability class
In assessing the landslide vulnerability anchored on climate change
framework, data on the exposure due to 2020 climate projection and other factors,
the physical and biological sensitivity, and the adaptive capacity of the community
were gathered and analyzed. These information were based from primary and
secondary data gathered. Under this framework, a highly vulnerable system would
be a system that is very sensitive to modest changes in climate, where the sensitivity
includes the potential for substantial harmful effects, and for which the ability to adapt
is severely constrained. Resilience is the flip side of vulnerability — a resilient system
or population is not sensitive to climate variability and change and has the capacity
to adapt (McCarthy et al. 2001).
The vector files of the thematic maps with the respective hazard ratings
were converted into grid formats in assessing the landslide vulnerability class due
to exposure, sensitivity and adaptive capacity (Lanuza 2007). For the exposure and
adaptive capacity, a straight-forward conversion of the vector files to grid format was
done by averaging the respective hazard ratings. In the case of sensitivity, the GISbased approach with relative weights derived from AHP was applied to calculate areas
that are vulnerable to landslide using Equation 1. The vulnerability class followed the
ranges as presented in Table 4.
Table 4 Vulnerability class for landslide as referred to sensitivity of the watershed
Landslide vulnerability class
Rating
Not vulnerable
< 2.1
Low vulnerable
2.1 – 2.8
2.8 – 3.5
Highly vulnerable
3.5 – 4.2
> 4.2
The potential impact of landslide with consideration of future climate
scenario within Matutinao Watershed was computed by getting the average hazard
ratings of exposure and sensitivity. Then, the final landslide vulnerability anchored
on the climate change framework was calculated by getting the difference of potential
impact and the adaptive capacity (Fig. 5).
Formulation of mitigation measures
A review of existing programs was made and the results of the vulnerability
assessment on landslide was used to come up with the proposed mitigation measures
to minimize and control the negative impact of the identified environmental hazards
within Matutinao Watershed. The strategies focused on measures that can keep the
Landuse
Soil Type
Relative distance to fault line
Geological formation
Geological age
Typhoon Frequency
Rainfall amount
Slope
Analytic Hierarchy
Process
95
Ground disturbance
Habitation and Occupancy
Farming System
Factors
sensitivity
Factors for
for Sensitivity
Parameter Class
Weighing (0-100)
Parameter s Weighing
Σ=1
Weighted Linear
Sum
Landslide Hazard Model
on Sensitivity
GIS
Distance to Household
Distance to Economic Activities
Distance to Leisure Activities
Frequency (Household)
Frequency (Economic Activities)
Frequency (Leisure Activities)
Projected Increase in Rainfall
Percentage of Exposure
on Exposure
Factors for Exposure
Average Weight
GIS
Self-help System
Availability of Structures
Availability and Use of Facilities
Special Skills and Training
External Assistance
IEC Activities
Leadership
Formal Safety Association
Indigenous Practices
Availability of Technology
Adoption of Technology
Model on the
Potential
impact
Impact
on Adaptive Capacity
Factors for Adaptive
Capacity
Average Weight
Landslide Vulnerability
Model
GIS
Figure 5 Schematic diagram of the GIS-assisted approach on landslide
assessment based on the result of Analytic Hierarchy Process (AHP).
R. Lanuza et al.
96
communities away from vulnerability to landslide-prone areas to prevent and avoid
damage or loss of human lives and properties.
Hazard identification and landslide occurrence
Landslides were among the natural hazards that had occurred within Matutinao
Watershed. Landslide refers to slides, rock falls, and/or lows of unconsolidated
materials. It can be triggered by heavy precipitation or groundwater rise that saturate
and loosen the soil, earthquakes, and river undercutting. Landslides are highly
localized but can be particularly hazardous due to their frequency of occurrence.
In Matutinao Watershed, landslides are in the form of rockfalls which consist
of free-falling rocks from overlying cliffs. In general, rockfalls are apparent dangers to
life and property but they cause only a localized threat due to their limited area of
influence. There are also cases of minor slides, a displacement of overburden due to
shear failure along a structural feature. In contrast to rockfalls, slides often have great
area of coverage which result in loss of lives and properties.
Based on personal interviews with key informants, landslide incidents and
landslide prone-areas are noticed in Barangays Compostela, Guadalupe, Valencia,
Lepanto, Matutinao, and Solsogan. Landslides were triggered by the combined effects
of soil type, soil exposure, and heavy rainfall. Areas prone to landslide are those along
the creeks and in steep slopes (Table 5 and Fig. 6).
Projected increase in rainfall
Matutinao Watershed belongs to the Type III Climate under the Corona
climate classification. This is characterized by no pronounced wet or dry season,
relatively dry from December to April, but wet during the rest of the year. It is situated
on less frequent typhoon zone making it favorable to vegetative interventions. The
highest amount of monthly rainfall was recorded in November 2001 having 523.7
mm. Moreover, the average monthly rainfall for the 9-year period (2000-2008) is
131.9 mm taken from three nearby stations. For the rainfall recorded in Philippine
Atmospheric, Geophysical and Astronomical Services Administration (PAGASA),
Mactan Station, the highest rainfall amount for the 11-year period (1997-2007)
was 423.5 in December 2003. Data on annual rainfall from three gauging stations
indicated that the month of May is the driest month with only 1,107.5 mm average
rainfall for the past seven years. In contrast, the month of March had the highest
rainfall with 1,835.6 mm followed by month of April that measured 1,663.1 mm
average rainfall.
97
Table 5 Geographic location of landslide incidents and landslide prone areas within
Matutinao Watershed determined using a GPS handset (Lanuza et al. 2014)
Location
Easting
Northing
Elevation
(masl)
540163
540145
540133
540115
540022
540022
540189
540206
539706
1081046
1081062
1081079
1081105
1081134
1081119
1080989
1080811
1076139
236
231
232
232
238
248
222
220
416
539606
1076207
424
539573
1076180
415
539696
1076253
391
539678
1076253
392
539655
1076266
423
539675
1076300
427
542548
1073654
633
Alegria
Compostela
Guadalupe
Lepanto
Remarks
Minor landslide area
Landslide prone (4 people affected)
Landslide prone
Landslide prone
Landslide prone (5 houses affected) near
on somewhat a lake
Landslide area (rock falls along road
at the back of Guadalupe Elementary
School)
Landslide prone (road at the back of
Guadalupe Elementary School)
Landslide prone (abandoned Guadalupe
Elementary School)
Elementary School)
Elementary School)
Landslide prone (very steep area at the
back of Guadalupe Elementary School)
Landslide area (occurring at road side)
R. Lanuza et al.
98
Table 5 Geographic location of landslide incidents and landslide prone areas within
Matutinao Watershed determined using a GPS handset (Lanuza et al. 2014)
(Continued)
Location
Valencia
Badian
Matutinao
Solsogan
Easting
541786
Northing
1077941
Elevation
(masl)
420
541825
541863
541923
541933
542097
542191
542170
542136
542225
542246
542379
542403
543417
1077964
1077985
1078006
1078012
1078054
1077857
1077866
1077884
1077851
1077851
1077883
1077859
1077989
404
414
396
395
394
416
409
406
410
409
405
401
490
543423
1077964
493
543968
1077709
525
542405
540878
1077830
1079546
423
377
Landslide area (rock falls affecting 3
houses)
Landslide area (minor rock falls going to
sitio Mayana)
Landslide prone (after Cambais falls)
Landslide prone
540489
1084126
13
Landslide area (area due to earthquake)
540533
540651
1084133
1084093
12
15
Landslide area (rock falls)
Landslide area (located at the other
side of the river (portion of the adjacent
mountain) which occurred few years ago
542627
1082901
313
Landslide prone
Remarks
Landslide prone (occurring at sitio
Banahaw)
Landslide prone
Minor landslide area (near 3 houses)
Landslide prone (2 houses)
Landslide prone (cliff)
Landslide prone (1 house)
Landslide area (rock falls)
Landslide prone (near 2 houses)
Landslide prone (2 houses)
Landslide prone
Landslide area (basketball court with
rock falls at the upper portion of Inghoy
Elementary School)
99
Furthermore, the rainfall pattern has two peaks from June to July and
September to October. Similarly, the general rainfall pattern of Cebu Province has
also two peaks around these months. The average annual rainfall within the watershed
is about 1,583.4 mm. Based on the 11-year rainfall data from PAGASA station in
Mactan, Cebu, the average annual rainfall is about 1,623.5 mm (Fig. 7).
Specifically for Cebu Province, the projected seasonal rainfall changes in
2020 are shown in Table 6. Generally, there will be an increase in the rainfall amount
with greatest during the northeast monsoon (DJF) having 17.7% increase or from
324 mm to 381.3 mm in 2020.
Table 6 Projected change in seasonal rainfall (%) in Cebu Province (Millennium Development Goal Achievement Fund 2010)
Observed rainfall a
Projected change
Projected amount in
Season
(mm)
in 2020 b (%)
2020 (mm)
DJF
324.0
17.7
381.3
MAM
228.3
0.8
230.1
JJA
595.1
7.7
640.9
SON
607.4
7.7
654.2
Total
1,754.8
8.5c
1,904.9
Note:
Seasonal rainfall: DJF (December-January-February); MAM (March-April-May); JJA (June-July-August); SON
(September-October-November); a - Rainfall observed from 1971-2000; b - Projected change (2006-2036),
c - Computed average from the seasonal rainfall
Determination of landslide vulnerability anchored in a Climate Change
Framework
Exposure, sensitivity, and adaptive capacity
The values of exposure and adaptive capacity per barangay are shown in
Tables 7 and 8 which were later converted to geospatial data using the average
weights of hazard ratings (Figs. 8 and 9). Meanwhile, the sensitivity was based on
GIS-assisted model as expressed in Equation 1 which was generated through Analytic
Hierarchy Process (AHP) and the hazard ratings are presented in Table 9. However,
before the relative weights of the GIS-assisted model were applied, the consistency
ratio (CR) was calculated. The CR is a measure of how consistent the judgments were
made relative to large samples of purely random judgments. The AHP always allows
for some level of inconsistencies which should not exceed a certain threshold (Saaty
1980).
Table 10 shows the Random indices (RI) developed by Saaty and Vargas
(1991) and is used to determine the CR. If the CR value is smaller or equal to 0.1,
100
R. Lanuza et al.
the inconsistency is acceptable. However, if the CR is greater than 0.1, the pair-wise
comparison may be revised as it implies that the judgments are unreliable because
they are too close for comfort to randomness.
Based on AHP, weights are calculated in percent as 26.24, 8.08, 8.08, 7.57,
7.57, 14.36, 5.80, 5.26, 4.29, 5.38, and 7.37 for slope, rainfall amount, typhoon
frequency, geologic age, geologic formation, relative distance to fault line, soil
type, landuse, farming system, habitation and occupancy, and ground disturbance,
respectively. The computed CR is 0.0127 which indicated a reasonable level of
consistency in the pair-wise comparison of the factors. Thus, the weights can be
accepted. The physical and anthropogenic sensitivity were further analyzed and the
final equation is:
L(sensitivity) = 0.8297[0.3160Sl + 0.0973R + 0.0973T + 0.0912Ga + 0.0912Gf +
0.1729Gfl+ 0.0698So + 0.0633Lu] + 0.1703[0.2532FS + 0.3175H + 0.4349GD].
The raster layer in grid format of each parameter is multiplied by their given
weight and summing them together by arithmetic weighted sum overlay tool using
GIS to generate the sensitivity (Fig. 10).
On the exposure, Barangay Lepanto has very high category followed by
Barangays Guadalupe, Valencia, and Matutinao having high category and Barangays
Compostela, Balhaan, and Solsogan with moderate category (Table 7). The factors
considered were distance to household, economic and leisure activities, frequency
of occurrence to household, projected increase in rainfall amount in 2020, and
percentage of exposed community. In terms of adaptive capacity, Barangay Balhaan
has very low adaptive capacity, Barangays Compostela, Guadalupe, Valencia,
Matutinao, and Solsogan have low adaptive capacity; and Barangay Lepanto has
moderate adaptive capacity (Table 8). This was based on the following factors: selfhelp system, availability of structures, availability and use of facilities, special skills
and training, external assistance, IEC activities, leadership, formal safety association,
common safety indigenous practices, availability of technology, and adoption of
technology. Then, the potential impact was computed as a function exposure and
sensitivity (Fig. 11). Finally, landslide vulnerability map was generated Fig. 12.
It was predicted that about 3,278.47 ha or approximately 65.12% have
high landslide vulnerability. This is followed by moderate landslide vulnerability
with 1,666.11 ha (33.10%) and then a very high vulnerability covering about 89.60
ha(1.78%). The extent of areas on landslide vulnerability per barangay is presented
in Table 11. Among the barangays, Balhaan has been estimated having 89.60 ha
with very high vulnerability. Further, it was estimated that Barangays Guadalupe,
Valencia, Compostela, Lepanto, and Solsogan have high vulnerability with 1,243.42
1085000
1080000
December 2014
540000
#####
###
#
#
#
#
#
545000
Palaypay
#
Lepanto
Nug-as
545000
Valencia
Guadalupe
#### #######
Compostela
Sulsogan#
Balhaan
###
Matutinao
540000
Dugyan
Source: NAMRIA, FMS
MDGF, 2010
550000
550000
(based on GPS Readings)
S
E
475000
570000
570000
2000 Meters
This Site
665000
Watershed Boundary
Scale : 1:100000
0
Malabuyoc
: Cebu
: Cebu
Hazards areas
#
Barangay
Balhaan
Compostela
Dugyan
Guadalupe
Lepanto
Matutinao
Nug-as
Palaypay
Sulsogan
Valencia
Legend
2000
Province
Island
Area
Watershed
Municipality
A MAP showing
the location of landslides and landslide
prone areas based on GPS readings
within Matutinao Watershed
W
N
475000
665000
1045000
1075000
Figure 6 Location of landslide and landslide-prone areas within Matutinao Watershed
1075000
1080000
1235000
1085000
1140000
1140000
Ta
n
o
n
Str
ait
Landslide and Landslide Prone Areas
1235000
1045000
Natural Resources
101
-
500.00
1,000.00
1,500.00
2,000.00
2,500.00
1997
1,364.30
1998
1,117.50
1999
2,057.80
2000
1,980.01
2001
2,125.10
Year
2002
1,134.50
2003
1,954.90
2004
2005
1,401.53 1,398.80
Figure 7 Annual rainfall from 1997 to 2007 at PAGASA Station, Mactan, Cebu
Rainfall Amount (mm)
2006
1,559.71
2007
1,764.80
Annual Rainfall Amount from PAG-ASA, Mactan Station
(1997-2007)
102
R. Lanuza et al.
103
ha, 826.23 ha, 715.07 ha, 212.07 ha, and 209.56 ha, respectively. However, the
information generated is only indicative.
The GIS-approach used by this study confirmed the capability of GIS
technology in assessing landslide vulnerability. The validation was based on actual
location of landslide and landslide prone areas. Out of 21 locations, 67% falls on high
vulnerability, 14% on very high vulnerability, and 19% on medium vulnerability.
Furthermore, it also conformed to the findings of other researchers, although remote
sensing was integrated (Yuan and Mohd 1997; Nagarajan et al. 1998; Hassanzadeh
2000; Ajalloeian et al. 2000; Ramakrishnan et al. 2007; Sivakumar Babu and Mukesh
2007).
Implications and mitigation/Adaptation measures
Generally, the areas with higher vulnerability to landslides are located in
steeper slopes, unstable geology, and near fault lines and the effects may be further
aggravated by high rainfall that causes the saturation of soil and some ground
disturbance which lead to mass movement downslope. Considering the projected
17.7% increase in rainfall in 2020, it is estimated that the aggregate coverage of
high and very high vulnerability is 3,368.07 ha or about 66.90% of the total area
of the barangay assessed (Fig. 12). With this finding, the GIS-assisted model for
landslide assessment can be used as a valuable tool in determining areas vulnerable
to landslides as input to the sustainable management of watersheds.
Landslide hazard is a function of location, type of human activity, intervention
and use, and frequency of landslide events. The effects of landslides on human
population and structures can be lessened by total avoidance of landslide hazard
areas or by restricting, prohibiting, or imposing conditions on hazard-zone activity.
Local governments can reduce landslide effects through appropriate landuse policies
and regulations. Individuals can reduce their exposure to hazards by educating
themselves on the past hazard history of a site and by making inquiries to planning
and engineering departments of local governments as well as mitigating the impacts
of climate change. They can also obtain the professional services of an engineering
geologist, a geotechnical engineer, or a civil engineer who can properly evaluate the
hazard potential of a site. The proposed mitigation measures for areas vulnerable to
landslide are presented in Table 12.
nd
nd
nd
nd
nd
E2
nd
2
5
3
4
5
2
5
2
4
4
5
2
5
5
E1
nd
nd
nd
2
5
2
2
2
5
5
E3
nd
nd
nd
3
2
3
3
3
3
3
E4
nd
nd
nd
3
2
3
4
2
2
4
Exposure
E5
nd
nd
nd
3
3
3
3
3
5
3
E6
5
5
4
4
nd
nd
nd
nd
nd
nd
3.00
3.88
3.13
4.25
3.75
3.88
3.38
Average
nd
E8
nd
nd
5
5
5
5
4
4
4
4
5
4
E7
Note:
E1 - Distance to Household
E2 - Distance to Economic Activities
E3 - Distance to Leisure Activities
E4 - Frequency of Occurrence to Household
E5 - Frequency of Occurrence to Economic Activities
E6 - Frequency of Occurrence to Leisure
E7 - Projected Increase in Rainfall in 2020 (0-5%=1; 5-10%=2; 10-15%=3; 15-20%=4; and >20%=5)
E8 - Percentage of Exposure of Community ((0-2%=1; 2-4%=2; 4-7%=3; 7-10%=4; and >10%=5)
nd - No data
1. Dugyan *
D. Alcoy
1. Nug-as *
E. Malabuyoc
1. Palaypay *
2. Matutinao
3. Solsogan
C. Dalaguete
1. Balhaan
2. Guadalupe
3. Lepanto
4. Valencia
B. Badian
A. Alegria
1. Compostela
Location
Table 7 Exposure to landslide of barangays within Matutinao Watershed based on 2020 climate projections
nd
nd
nd
Moderate
Moderate
High
Very High
High
High
Moderate
Category
104
R. Lanuza et al.
2
4
2
2. Guadalupe
3. Lepanto
4. Valencia
2
3. Solsogan
nd
nd
nd
nd
nd
3
3
1
3
3
1
1
AC2
Note:
AC1 - Self-help System
AC2 - Availability of Structures
AC3 - Availability and use of Facilities
AC4 - Special Skills and Training
1. Palaypay *
E. Malabuyoc
1. Nug-as *
D. Alcoy
1. Dugyan *
nd
2
2. Matutinao
C. Dalaguete
1
1. Balhaan
B. Badian
1
AC1
1. Compostela
A. Alegria
Location
nd
nd
nd
3
2
1
2
3
2
2
AC3
nd
nd
nd
1
3
1
4
3
4
4
AC5
nd
nd
nd
3
3
3
3
3
3
3
AC6
nd
nd
nd
4
4
1
2
4
2
2
AC7
nd
nd
nd
1
1
1
4
3
5
4
AC8
AC5 - External Assistance
AC6 - IEC
AC7 - Leadership
AC8 - Formal Safety Association
AC9 - Common Safety Indigenous Practices
nd
nd
nd
2
2
1
2
1
2
2
AC4
Adaptive Capacity
nd
nd
nd
2
2
2
3
3
4
4
AC9
nd
nd
nd
2
3
1
3
3
1
1
AC11
nd
nd
nd
2.27
2.45
1.36
2.73
2.91
2.55
2.36
Average
AC10 - Availability of Technology
AC11 - Adoption of Technology
nd
nd
nd
2
2
2
2
2
2
2
AC10
Table 8 The present adaptive capacity of communities to landslide within Matutinao Watershed
nd
nd
nd
Low
Very
low
Low
Low
Moderate
Low
Low
Category
105
Very Steep (>50%)
Steep (30-50%)
Moderate (18-30%)
Gentle (8-18%)
Very Gentle (0-8%)
> 2000 mm
1500 – 2000 mm
1000 mm – 1500 mm
500 mm – 1000 mm
< 500 mm
3 times a year
Upper Miocene-Pliocene, Pliocene-Pleistocene
Oligocene-Miocene
Carcar Formation, Maingit Formation
Barili Formation
0 – 0.5 km
0.5 – 2 km
2 – 5 km
5 – 8 km
> 8 km
Clay loam, silt loam
Clay, loam
Cultivated area (annual crops)
Shrublands
Woodland with grassland, natural grassland, open forest with broadleaves
species
Upland farms (Cropland)
Brushland
Open forest interspersed with broadleaves species
0 – 100 m
100 – 200 m
200 – 300 m
300 – 400 m
> 500 m
0 – 200 m
200 – 400 m
400 – 600 m
600 – 800 m
> 800 m
Subclass of parameters
Note: Values in bold are the hazard ratings of the landslide parameters (sensitivity) of Matutinao Watershed.
11. Ground Disturbance
10. Occupancy and Habitation
9. Farming System
8. Landuse
7. Soil Type
6. Relative Distance to Fault
line
5. Geologic Formation
3. Typhoon Frequency
4. Geologic Age
2. Rainfall (Annual)
1. Slope
Landslide parameters /Sensitivity
Table 9 Hazard rating of various themes
5
4
3
2
1
5
4
3
2
1
3
4
3
4
3
5
4
3
2
1
4
3
5
4
3
5
4
3
5
4
3
2
1
5
4
3
2
1
Hazard rating
107
Table 10 Random Index for matrices of various sizes (Saaty and Vargas 1991)
n
2
3
4
5
6
7
8
9
10
11
RI
0.00 0.52 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51
Note:
CR= CI/RI, where CI = ( max - n)/(n-1), RI = random index, n = number of criteria, λmax is priority vector
multiplied by each column total.
Table 11 Landslide vulnerability as a function of exposure, sensitivity, and
adaptive capacity within Matutinao Watershed by barangay based on
2020 climate projections
Location
Estimated area by landslide vulnerability class (ha)
Very Low
Low
Moderate
High
Very high
A. Alegria
Compostela
0
0
2.37
715.07
0
Guadalupe
0
0
56.32
1,243.42
0
Lepanto
0
0
715.34
212.07
0
Valencia
0
0
890.50
826.23
0
Balhaan
0
0
0
6.48
89.60
Matutinao
0
0
0
65.64
0
Solsogan
0
0
1.58
209.56
0
B. Badian
C. Dalaguete
1. Dugyan*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
D. Alcoy
1. Nug-as*
E. Malabuyoc
1. Palaypay*
Total of barangays
covered
%
0
0
1,666.11
3,278.47
89.60
0
0
33.10
65.12
1.78
Total
0
26.47
2,051.05
3,557.08
89.60
%
0
0.46
35.83
62.14
1.57
Note: The barangay boundary is based on the map provided by CENRO Argao (2007). The extent of
landslide is only indicative per barangay which also covered three other barangays (in asterisk) not
included in the assessment but with assumed values.
R. Lanuza et al.
108
Table 12 Proposed mitigation/adaptation measures for areas vulnerable to landslides
Landslide
Vulnerability
Class
Moderate
Vulnerability
Area
(ha)
1,666.11
Mitigation/Adaptation Measures
•
•
•
•
•
•
High
Vulnerability
3,278.47
•
•
•
•
•
•
•
The Municipal and Barangay Hazard Prevention
and Mitigation Council must be created through
legislation.
Results shall be included in the Comprehensive
Landuse Plan (CLUP).
Infrastructures and settlements must be avoided
in areas with moderate landslide. These areas
are located in steeper slopes, unstable geology
and near fault lines.
Intensive IEC campaign must be done through
barangay “pulong-pulong”, display of GIS maps
of affected areas in the municipal and barangay
halls.
LGU must put markings or warning signs on
the ground to warn would be developers and
investors of the potential hazards.
Strengthen the self-help systems
legislation.
Results shall be included in the CLUP.
in areas with high landslide. These areas are
located in steeper slopes, unstable geology and
near fault lines.
halls.
LGU must put markings or warning signs on
the ground to warn would be developers and
investors of the potential hazards.
There is a need of continuous monitoring of
landslides.
Warning devices to warn occurrence of
landslides must be installed.
109
Table 12 Proposed mitigation/adaptation measures for areas vulnerable to landslides
(Continued)
Landslide
Vulnerability
Class
Area
(ha)
Mitigation/Adaptation Measures
•
•
•
•
Very High
Vulnerability
89.60
•
•
•
•
•
•
•
•
•
•
•
•
Upper slopes of roads constructed in steeper
slopes must be supported with ripraps.
Procurement of equipment to respond to
landslide occurrence.
Provision of goods and basic services in
evacuation centers
legislation.
Results shall be included in the CLUP.
in areas with very high landslide. These areas
are located in steeper slopes, unstable geology
and near fault lines.
halls.
LGU must put markings or warning signs on the
ground to warn developers and investors of the
potential hazards.
Continuous monitoring of landslides.
Installation of warning devices to warn people
with the occurrence of landslides.
Upper slopes of roads constructed in steeper
slopes must be supported with ripraps.
Provision of goods and basic services in
evacuation centers
Procurement of equipment to respond to
landslide occurrence.
Relocation of communities living within these
areas.
Ta
n
o
nS
tra
it
540000
540000
545000
545000
Note: The watershed boundary
is based from the map
presented in the Watershed
Characterization Report (2007).
However, this map is for
indicative purposes only and
subject to further validation
and/or actual boundary survey.
550000
(based on 2020 Rainfall Projection)
Exposure to Landslide
550000
Source: NAMRIA, FMS
MDGF, 2010
S
E
Watershed Boundary
Scale : 1:100000
0
475000
570000
570000
This Site
665000
2000 Meters
Malabuyoc
: Cebu
: Cebu
Exposure
0 - 2.1 (Very Low)
2.1 - 2.8 (Low)
2.8 - 3.5 (Moderate)
3.5 - 4.2 (High)
> 4.2 (Very Hig)
Legend
2000
Province
Island
Area
Watershed
Municipality
A MAP showing
the exposure to landslide based
on 2020 rainfall projection
W
N
475000
665000
1045000
Figure 8 Exposure to landslide within Matutinao Watershed based on 2020 rainfall projection
December 2014
1080000
1075000
1085000
1075000
1235000
1080000
1140000
1140000
1085000
1235000
1045000
Natural Resources
110
R. Lanuza et al.
Figure 9 Adaptive capacity of the communities on landslide within Matutinao Watershed
111
Ta
n
on
Str
ait
540000
540000
545000
545000
550000
(Physical and Biological)
Sensitivity to Landslide
550000
Source: NAMRIA, FMS
MDGF, 2010
S
E
475000
0
Watershed Boundary
570000
570000
This Site
665000
0 - 2.1 (Very Low)
2.1 - 2.8 (Low)
3.5 - 4.2 (High)
> 4.2 (Very Hig)
2000 Meters
Malabuyoc
: Cebu
: Cebu
Scale : 1:100000
Sensitivity
Legend
2000
Province
Island
Area
Watershed
Municipality
A MAP showing
the sensitivity to landslide
W
N
475000
665000
1045000
Figure 10 Sensitivity to landslide within Matutinao Watershed based on 2020 rainfall projection
December 2014
1080000
1075000
1085000
1075000
1235000
1080000
1140000
1140000
1085000
1235000
1045000
Natural Resources
112
R. Lanuza et al.
Ta
n
o
nS
tra
it
540000
540000
545000
545000
550000
(due to Exposure and Sensitivity)
Potential Impact
550000
Source: NAMRIA, FMS
MDGF, 2010
S
E
Watershed Boundary
Scale : 1:100000
475000
0
570000
570000
This Site
665000
0 - 2.1 (Very Low)
2.1 - 2.8 (Low)
3.5 - 4.2 (High)
> 4.2 (Very Hig)
2000 Meters
Malabuyoc
: Cebu
: Cebu
Potential Impact
Legend
2000
Province
Island
Area
Watershed
Municipality
A MAP showing
the potential impact on landslide
W
N
475000
665000
1045000
Figure 11 Potential impact as a function of exposure and sensitivity on landslide within Matutinao
Watershed based on 2020 climate projection
December 2014
1080000
1075000
1085000
1075000
1235000
1080000
1140000
1140000
1085000
1235000
1045000
Natural Resources
113
T
a
n
on
Str
ait
540000
540000
545000
545000
550000
550000
Source: NAMRIA, FMS
MDGF, 2010
S
E
475000
570000
570000
This Site
<= -1.0 (Very Low)
-1.0 - 0 (Low)
0 - 0.5 (Moderate)
0.5 - 1.5 (High)
> 1.5 (Very Hig)
2000 Meters
665000
Watershed Boundary
Scale : 1:100000
0
Malabuyoc
: Cebu
: Cebu
Legend
2000
Province
Island
Area
Watershed
Municipality
475000
665000
1045000
Figure 12. Landslide vulnerability as a function of exposure, sensitivity, and adaptive capacity
within Matutinao Watershed based on 2020 climate projection
December 2014
W
N
A MAP showing
the landslide vulnerability based
on potential impact and adaptive capacity
(based on Potential Impact and Adaptive Capacity)
1080000
1075000
1085000
1075000
1235000
1080000
1140000
1140000
1085000
1235000
1045000
Natural Resources
114
R. Lanuza et al.
115
Conclusions
The occurrence of landslides is highly localized based on the ground truthing
conducted. However, landslides can be particularly hazardous due to their frequency
of occurrence.
It was predicted that about 3,278.47 ha or approximately 65.12% have high
landslide vulnerability. This is followed by moderate landslide vulnerability with
1,666.11 ha and very high vulnerability with 89.60 ha. Among the barangays, Balhaan
has been estimated having 89.60 ha with very high vulnerability. Further, it was
estimated that Barangays Guadalupe, Valencia, Compostela, Lepanto, and Solsogan
have high vulnerability with 1,243.42 ha, 826.23 ha, 715.07 ha, 212.07 ha, and
209.56 ha, respectively. Generally, the areas with higher vulnerability to landslides
are located in steeper slopes, with unstable geology and near fault lines. The effects
may be further aggravated by high rainfall that causes the saturation of soil and some
ground disturbance such as road construction which lead to mass movement of soil
downslope. With consideration on the projected 17.7% increase in rainfall amount
in 2020, it is estimated that the aggregate area of high and very high vulnerability is
3,368.07 ha or about 66.90% of the total area of the barangays assessed.
The GIS technology has demonstrated its capability in assessing landslide
vulnerability in a watershed. The model predicted the location of landslides in a
climate change perspective and these areas have been mapped out using GIS. Out
of 21 locations, 67% falls on high vulnerability, 14% on very high vulnerability,
and 19% on medium vulnerability. Therefore, this approach can be a valuable tool
in watershed planning to avoid possible losses of lives and properties caused by
landslides.
The GIS technology coupled with the application of AHP has demonstrated its
capability in assessing landslide vulnerability in a watershed. The application of AHP
has provided a strong basis in determining the relative importance of the landslide
sensitivity factors. Moreover, the approach has also refined the previously reported
GIS-assisted model by incorporatinh the vulnerability functions (exposure, sensitivity
and adaptive capacity) anchored on climate change framework. The model predicted
the location of landslides in a climate change perspective and these areas have been
mapped out using GIS. Out of 21 locations, 67% falls on high vulnerability, 14% on
very high vulnerability, and 19% on medium vulnerability. Therefore, this approach
can be a valuable tool in watershed planning to avoid possible losses of lives and
properties caused by landslides.
R. Lanuza et al.
116
However, with the limited funding and insufficient actual observations on the
magnitude of landslide, there was no detailed validation of the GIS-assisted model.
With this limitation, this model needs to be validated using sufficient actual landslide
data in the area.
Recommendations
To address landslide hazards that pose constant threats to the communities
living within Matutinao Watershed, LGU Alegria and Badian must enact a Municipal
Ordinance on the Creation of Municipal Environmental Hazard Prevention and
Mitigation Council as the legal and policy support in dealing with areas vulnerable
to landslides. The draft ordinance is contained in the terminal report of Vulnerability
Assessment of Matutinao Watershed, Cebu, Philippines (Lanuza et al 2014). The
main provisions will be the creation of the council, its composition, duties and
responsibilities, and funding and allotment as well as the prevention and mitigation
of natural hazards but not limited to landslides, as follows:
1. Riparian restoration with bamboos and fruit trees to minimize stream
bank soil erosion and landslides.
2. Infrastructures and settlements must be avoided in areas with moderate
to high landslide vulnerability. These areas are located in steeper slopes,
unstable geology and near fault lines.
3. Legislation and implementation of consistent building and grading code
must be legislated and implemented.
4. Continuous monitoring of landslides.
5. Upper slopes of roads constructed in steeper slopes must be supported
with ripraps.
6. Installation of markings or warning signs to warn developers and investors
of the potential hazards.
7. Inclusion of results of the study to the municipal Comprehensive Land
Use Plan (CLUP).
8. Intensive IEC campaign through barangay “pulong-pulong” and display
of GIS maps of affected areas in the municipal and barangay halls.
117
Acknowledgement
The authors wish to express their deepest gratitude to the following persons and
institutions that in one way or another extended their assistance in the conduct of this research
endeavor;
Dr. Alicia L. Lustica and Mrs. Emma E. Melana for their support and technical inputs
in the research work; Samuel Laurino, for providing the GIS maps. Also, to MGB for the geology
maps, Philvocs for the faults map downloaded from their website, CENRO Argao and PAGASA
Mactan for providing the rainfall data, and to all ERDS personnel who in one way or another
assisted the project leader and the VA team;
Special thanks to OIC CENRO Flordeliza Geyrozaga, and For. Mardionne delos
Reyes, for their assistance in the project implementation;
The Local Government Units of Alegria and Badian headed by their respective
mayors, Mayor Verna Magallon and Mayor Rubbort Librando, the staff from MAO/MENRO,
and the barangay captains for their assistance during data gathering and facilitation of Focus
Group Discussion;
ERDS 7, FMS 7 and ERDB for providing funding support;
To our loved ones for their love, care, prayers, and understanding;
And above all, to the LORD GOD ALMIGHTY who gives life, knowledge, wisdom,
blessings and graces which make this work a reality.
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122
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(AS OF DECEMBER 31, 2015)
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Executive Adviser
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Director, ERDB
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Board Secretariat
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Chief, Land Management Division
Office of the Secretary
For. Cynthia A. Lopez
Community Development Officer IV
For. Carina C. Manlapaz
Technical Assistant
Human Resource Management Service, DENR (HRMS)
For. Manny Sabater
Administrative Officer V
Dexter Tindoc
Administrative Officer IV
National Mapping and Resources Information Authority (NAMRIA)
Rijaldia N. Santos, Ph.D.
Director II
Beata D. Batadlan
Chief, Land Classification Division
Laguna Lake Development Authority (LLDA)
Ms. Bileynnie P. Encarnacion
Biologist II
Mr. Eduardo R. Canawin
Planning Officer III
Sylvatrop, The Technical Journal of Philippine Ecosystems and Natural Resources 25 (1 & 2)
REVIEWERS
DR. DIOMEDES A. RACELIS
University of the Philippines
Los Banos, Laguna
Dr. Diomedes A. Racelis is an Associate Dean and
Professor at the University of the Philippines Los
Banos - College of Forestry and Natural Resources.
He has more than 25 years of experience in
watershed management, climate change and land
use planning - making him as one of the renowned
VA expert in the Philippines
FOR. MANOLITO U. SY
Ecosystems Research and
Development Bureau (Former researcher)
For. Manolito U. Sy has more than 35 years of
government service in the Ecosystems Research and
Development Bureau (ERDB) as a former Supervising
Science Research Specialist from 1992 to 2013. For.
Sy has completed research projects on forest and
plantation development, reforestation, silviculture,
and carbon sequestration. A prolific author cum
researcher, For. Sy has published five technical articles and 41 semi-technical articles on different
topics about forestry.
Sylvatrop, The Technical Journal of Philippine
Ecosystems and Natural Resources
REMINDERS TO CONTRIBUTORS
Sylvatrop is a medium of information exchange on scientific, technological and
descriptive articles, research notes and reviews of technical literatures on ecosystems
and natural resources topics.
Manuscripts should not have been published earlier or are not being submitted for
publication in any other journal.
The articles to be submitted should accompany an endorsement letter from the head
of agency of the author, addressed to the ERDB Director.
Ideally, an article should have the following parts: title, author (with designation
and address), abstract, introduction, review of literature, materials and methods/
methodology, results and discussion, conclusion, and literature cited.
An informative abstract and at least five keywords should be provided.
A brief acknowledgement may be included.
For the text of the article, submit four hard copies and an e-copy in MS Word format.
Submit quality photos/graphics, either hard copies or a cd of the raw files with a
resolution of at least 300 dpi.
Keep the minimum number of tables, illustrations, maps and photographs. Provide
the caption of each.
Normally, Sylvatrop publishes articles of approximately 10 printed pages or 24
manuscript pages, including figures, tables, and references. If the manuscript exceeds
normal length, but otherwise appropriate, it should be submitted. The editors will
suggest ways of condensing it.
For mechanical style, consult the Scientific Style and Format: The CounciI of Science
Editors (CSE) Style Manual for Authors, Editors and Publishers. 2006. 7th edition.
Use metric system.
Sylvatrop gives authors 10 offprints of each published article and two complimentary
copies of the issue where their articles appear.
This journal is being abstracted by:
Abstract Bibliography of Tropical Forestry (Philippines)
Documentation Centre on Tropical Forestry (Philippines)
Forestry Abstract (Oxford, UK)
Chemical Abstracts (Ohio, USA)
Asia Science Research Reference (India)

full version - Sylvatrop - Department of Environment and

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