SYLVATROP Editorial Staff

Antonio M. Daño Editor-in-Chief Adreana Santos-Remo Veronica O. Sinohin Liberato A. Bacod Managing Editor Layout Artists

Liberty E. Asis Gliceria B. De Guzman Adreana Santos-Remo Eduardo M. Tolentino Editors Circulation 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.

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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 in- depth 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 Rudolfo Espada, Jr. Supervising Science Research Specialist Planning Officer I Ecosystems Research and Development Service Email address: [email protected] DENR-National Capital Region North Avenue, Quezon City, Philippines Eduardo C. Calzeta Email address: [email protected] Forester II Email address: [email protected] Manuel S. Sabater Community Enviroment and Natural Riza C. Arjona Resources Officer Science Aide Email address: [email protected] Email address: [email protected]

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 2 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 GIS- based 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 4 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 eco- environmental 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 Vulnerability assessment of the La Mesa Watershed Reservation 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, 121o1 0 ” 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 Critical factors Agro-climatic type, soil type, slope, vegetative Soil erosion cover/landuse, conservation practices

Slope, soil genesis/morphology, proximity to fault- Landslide line, 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 6 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 upwith 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. Vulnerability assessment of the La Mesa Watershed Reservation 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. 8 E. Andres et al.

Table 2 Qualitative classification of areas vulnerable to identified hazards, with corresponding classification value Vulnerability classification value Degree of vulnerability 1 Very low 2 Low 3 Moderate 4 High 5 Very high

Table 3 Critical factor’s degree of influence

Hazard Critical factor Degree of influence (%) Slope 30.00 Soil genesis/morphology 10.00 Proximity to faultline 15.00 Climate 10.00 Landslide 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 Fire Proximity to fire sources 16.67 Accessibility 16.67 Infrastructure 16.67 Slope 20.00 Road and river 20.00 Biodiversity loss Natural disturbances 20.00 Market 20.00 Encroachment 20.00 Vulnerability assessment of the La Mesa Watershed Reservation 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 Description

Elevation 40-260 meters With gentle slopes and relatively flat areas around above sea level the watershed indicate low sediment loss or (masl) surface runoff. Slope 0-50% = 834.64

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. 10 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). Vulnerability assessment of the La Mesa Watershed Reservation 11

Table 5 Conservation status of some plant species at the LMWR

Common name Scientific name Conservation status Tanglin Adenanthera intermedia Vulnerable Antipolo Artocarpus blancoi Vulnerable Pili-liitan Canarium luzonicum Vulnerable Dao Dracontomelon dao Vulnerable Hamindang Macaranga bicolor Vulnerable Narra Pterocarpus indicus Vulnerable Almon Shorea almon Vulnerable White lauan Shorea contorta Vulnerable Tanguile Shorea polysperma Vulnerable Molave Vitex parviflora Vulnerable Palosapis Anisoptera thurifera Endangered Hingiw Ichnocarpus volubilis Endangered Dapong kahoy Loranthus philippinensis Endangered Nito vine Lygodium flexuosum Endangered Anchoan-dilau Senna spectabilis Endangered Payong-payong Tacca palmate Endangered Ayo Tetrastigma harmandii Endangered Kamagong Diospyros discolor Critically endangered Dalingdingan Hopea foxworthyi Critically endangered Baguilumbang Reutealis trisperma Critically endangered Philippine teak Tectona philippinensis Critically endangered

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. 12 E. Andres et al.

Table 6 Avifaunal species at the LMWR which are listed under CITES Appendix II Local name Scientific name Guiabero Bolbopsittacus lunutatus Brahminy kite 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: Vulnerability assessment of the La Mesa Watershed Reservation 13

USLE A = RKLSCP where: A = annual soil erosion in t/ha/yr R = rainfall erositivity factor K = soil erodibility factor L = slope length factor S = slope gradient factor C = cover and management factor P = 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.

Spatial analysis using the ArcView/ArcGIS Model Builder was used to identify 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 14 E. Andres et al.

Table 7 Reclassification of critical factors and corresponding values Assigned Critical factor Reclassified critical factor value Agro-climatic Type 1 390 Loamy sand 0.11 Soil type Sandy clay loam 0.20 Sandy loam 0.10 Agro-climatic Type 1 390 Loamy sand 0.11 Soil type Sandy clay loam 0.20 Sandy loam 0.10 0-3% 0.009 3-8 0.024 8-18 0.069 Slope 18-30 0.083 30-50 0.182 > 50 0.520 Closed forest broad-leaf 0.002 Open forest broad-leaf 0.003 Other build-up 0.300 Landuse Other land, Natural barren land 0.500 Other land, natural grassland 0.500 Other wooded land, shrub 0.100 Other wooded land, wooded grassland 0.100 Conservation management 1.000 practice

Table 8 Soil erosion estimate (t/ha/yr) with corresponding class rating

Soil loss (t/ha/yr) Vulnerability class value

0.0001-0.5 1 0.5-1.0 2 1.0-2.0 3 2.0-4.0 4 >4.0 5 Vulnerability assessment of the La Mesa Watershed Reservation 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. 16 E. Andres et al.

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.

Spatial analysis using the ArcView/ArcGIS Model Builder was used to identify 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 1 0.1-0.5 2 0.5-1.0 3 1.0-1.5 4 >1.5 5

Table 10 Soil loss per hectare per year in the LMWR Soil erosion Soil erosion Qualitative e s ti m a t e s Area (ha) classification classification (t/ha/yr) Erosion class 1 0.0001-0.5 None to slightly eroded 2098.15 2098.15 Class 2 0.5-1.0 Moderately eroded 172.81 172.81 Class 3 1.0-2.0 Severely eroded 19.26 19.26 Class 4 2.0-4.0 Very severe eroded 1.86 1.95 Class 5 >4.0 Very severe eroded 0.09 Reservoir NA - 367.18 367.18 Total Vulnerability assessment of the La Mesa Watershed Reservation 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.

Spatial analysis using the ArcView/ArcGIS Model Builder was used to identify 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 Reclassification value Area (ha.) classification 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 E. Andres et al.

Figure 4 Erosion hazard map of the La Mesa Watershed Reservation Vulnerability assessment of the La Mesa Watershed Reservation 19

Figure 5 Landslide hazard map of the La Mesa Watershed Reservation

Figure 8. Landslide hazard map of the LMWR. 20 E. Andres et al.

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 21

Figure 7 Biodiversity loss hazard map of the La Mesa Watershed Reservation

Figure 8 Ecological hazards map of the La Mesa Watershed Reservation. Vulnerability assessment 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 Area covered vulnerability class (ha) Slightly vulnerable 1.0 and below 199.13 Fairly vulnerable 1.1 – 2.0 1555.81 Moderately vulnerable 2.1 – 3.0 440.32 Highly vulnerable 3.1 – 4.0 95.41 Very highly vulnerable > 4.0 (vulnerable to 1.14 biodiversity loss) Reservoir 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); ABS- CBN 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.

Literature cited

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Chatterjee S, Krisna A, Sharma A. 2014. Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India. Environmental Earth Sciences. January 2014 (71):1, p357- 374. 18p.

[DENR] Department of Environment and Natural Resources . 2007. Profiling and characterization of the La Mesa Watershed Reservation. Terminal Report. ERDS NCR, Quezon City. Vulnerability assessment of the La Mesa Watershed Reservation 25

Daño A. 2006. Guidelines on vulnerability assessment of watersheds. Ecosystems Research and Development Bureau. Department of Environment and Natural Resources, College, Laguna.

Erener A, Duzgun H. 2012. Landslide susceptibility assessment: What are the effects of mapping unit and mapping method? Environmental Earth Sciences. 66(3):859- 877.

Huang P, Tsai J, & Lin W. 2010. Using multiple-criteria decision-making techniques for eco-environmental vulnerability assessment: A case study on the Chi-Jia-Wan Stream watershed, Taiwan. Environmental Monitoring & Assessment. 168(1- 4):141-158.

Lanuza RL. 2014. Geospatial modeling of soil erosion in Buhisan Watershed Forest Reserve, Cebu City, Philippines: Model Application and Validation. Sylvatrop. Tech. J. of Phil. Ecosystems and Nat. Resources. 24(1&2):47-78.

Lopez AV, et al. 2008. Vulnerability assessment of the Pudong Watershed within the Upper Amburayan River Basin in Kapangan, Benguet. Ecosystems Research Digest. 13(2).

Lopez A, Tubal R, Andrada M, Baldo H, Maddumba H. 2014. Vulnerability assessment of the Naguilian River Watershed to landslide and forest/grass fire. Sylvatrop. Tech. J. of Phil. Ecosystems and Nat. Resources. 24(1&2):19-46.

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. ERDS NCR, Quezon City.

Tagliavini F, Mantovani M, Marcato G, Pasuto A, Silvano S and Staffler H. 2007. Validation of landslide hazard assessment by means of GPS monitoring technique – a case study in the Dlomites, Eastern Alps, Italy. Natural Hazards and Earth System Sciences. 7(1):185-193.

Tiburan CL Jr, et al. 2012. Geospatial-based vulnerability assessment of an urban watershed. Procedia Environmental Science. Paper presented at: 3rd International Conference on Sustainable Future for Human Security, SUSTAIN 2012, 3-5 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. Supervising Science Research Specialist 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 fast- paced 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 man- made 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.

Review of literature

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 watershed- based 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 long- term 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 A.M. Daño and K.R.M. Fortus

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: Vulnerability to soil erosion and water pollution assessment 31 a. Biophysical assessment ŸŸ Soil (soil physical and chemical properties) ŸŸ Climate (annual/monthly rainfall, evaporation, typhoon occurrence and frequency) ŸŸ Hydrology (monthly streamflow pattern) ŸŸ 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 A.M. Daño and K.R.M. Fortus 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 Level of chemical and Class to pollutants biological contaminants

Negligible sediments None. Class A/AA water 1 Slight discoloration after Low. Class B water 2 heavy rainfall Moderate discoloration Low level contaminants 3 after heavy rainfall which are still within DENR limits Severe discoloration Levels of contaminants 4 exceeded DENR standards Vulnerability to soil erosion and water pollution assessment 33

Table 1 Rating class for vulnerability to water pollution (Continued) Water discoloration due Level of chemical and Class to pollutants biological contaminants Very severe discoloration Levels of contaminants are 5 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 5 High agricultural activity 4 Moderate agricultural activity 3 Minimal agricultural activity 2 No agricultural activity 1

Industrial and household Very large discharge or 5 impact very heavy impact on the surrounding Large discharge or heavy 4 impact on the surrounding Moderate discharge or 3 moderate impact on the surrounding Minimal discharge or 2 minimal impact on the surrounding No industry or households 1

Transportation avenue National and provincial 5 road system Provincial/municipal 4 road system Paved roads in most of 3 the watershed Unimproved road or dirt road 2 throughout the watershed area No road system 1 34 A.M. Daño and K.R.M. Fortus

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.

Results and discussion

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 inthe upstream to about 20 m near the bridge along the Calamba-Cabuyao highway. In most Vulnerability to soil erosion and water pollution assessment 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 9,842.5 69.5 8 - 18 Undulating to rolling 2,525.2 17.8 18 - 30 Rolling to moderately steep 1,243.8 8.8 30 - 50 Steep 485.4 3.4 >50 Very steep 66.1 0.47 Total 14,162.0 100.00 36 A.M. Daño and K.R.M. Fortus 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 582.8 4.11 Tagaytay sandy loam 1,102.4 7.78 Tagaytay loam 389.3 2.74 Carmona sandy clay loam 3,875.4 27.70 Mountain soil (undifferentiated) 1,091.2 7.70 Taal fine sandy loam 130.8 0.92 Lipa loam 6,990.0 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). Vulnerability to soil erosion and water pollution assessment 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 1996 Imagery 2007 Imagery category Area Percent Area Percentage Percent change (ha) total (%) (ha) total (%) from 1997 imagery Agricultural 5,865 41.4 4,926 34.7 -6.7 areas Grassland/ 5,621 39.7 2,926 20.7 -19.0 shrubland areas Built-up areas 2,488 17.6 5,084 35.9 +18.3 Open canopy 188 1.3 1,226 8.7 +6.4 Total 14,162 100.0 14,162 100.0

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 A.M. Daño and K.R.M. Fortus

Figure 2 Drainage map of San Cristobal Watershed

Figure 3 Soil map of San Cristobal Watershed Vulnerability to soil erosion and water pollution assessment 39

Figure 4 Land cover map of San Cristobal Watershed

Figure 5 Isohytal map of San Cristobal Watershed 40 A.M. Daño and K.R.M. Fortus

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 Vulnerability to soil erosion and water pollution assessment 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) 29.50 30.5 32.4 pH 8.29 8.17 7.13

BOD5 (mg/l) 2.20 12.280 19.20 DO (mg/l) 6.70 1.3 0 12.8 Total P (mg/l) 0.55 0.24 0.54 Total N (mg/l) 2.51 1.58 0.61 Sulfate (mg/l) 5.30 0.07 16.0 Conductivity 0.22 0.292 0.592 (mS/cm) TDS (mg/l) 114.0 145.0 294.0 Total Coliforms >1.6 x 106 4.9 x 105 >1.6 x 107 MPN/100ml Fecal Coliforms >1.6 x 106 7.8 x 104 >1.6 x 107 MPN/100ml 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 A.M. Daño and K.R.M. Fortus

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 non- agricultural 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 Municipality Sta. Rosa Cabuyao Calamba Entrepreneurial activities n.d. n.d. 36,225 Farming and Fishing 7,969 304 n.d. Oversees Remittances n.d. 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; Vulnerability to soil erosion and water pollution assessment 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 A.M. Daño and K.R.M. Fortus

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). Vulnerability to soil erosion and water pollution assessment 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 A.M. Daño and K.R.M. Fortus

Table 7 Areas in the watershed that are highly vulnerable to erosion Municipality/City Highly vulnerable areas (ha) Cabuyao 18 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. Vulnerability to soil erosion and water pollution assessment 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 A.M. Daño and K.R.M. Fortus

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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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 Email address: [email protected]

Kisloyan subwatershed is one of the crucial sources of water to the Mag- asawang 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.

Review of literature

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 Infanta- Nakar 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 Vendiola E and Limpiada M

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). Vulnerability assessment of the Kisloyan subwatershed 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 Vendiola E and Limpiada M

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.

Vulnerability assessment of the Kisloyan subwatershed 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: G = geology factor with consideration L = landslide vulnerability to formation (f), age (a) and relative f = formation distance to the fault line (f) Sl = slope factor S = soil factor as soil type (t) and soil Cl = climate factor morphological classification (c) r = rainfall amount Lu = land use factor t = typhoon frequency

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: R = rainfall Lp = landslide vulnerability due to G = geology factor physical factors S = soil factor Sl = slope factor Lu = land use factor 58 Vendiola E and Limpiada M

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 Class Description A. Slope (30%) 1 Slope, in general, is not steep (<8%) 2 Slope, in general, is slightly steep (8.1-18%) 3 Slope, in general, is fairly steep (18.1–30%) 4 Slope, in general, is moderately steep (30.1-50%) 5 Slope, in general, is very steep (>50%) B. Soils(15%) Morphology (5%) Soil type 1 Tropaquepts with entropepts, tropepts and oxisols 2 Tropopsamments with troporthents 3 Tropudalfs with tropepts 4 Entropepts with dystropepts 5 Tropudults with tropudalfs, mountain soils Erosion (10%) 1 Severe sheet and rill erosion 2 Moderate sheet, rill and gully erosion 3 Moderate sheet and rill; slight gully 4 Slight sheet and rill; no gullying 5 Almost no active erosion Vulnerability assessment of the Kisloyan subwatershed 59

Table 2 Guide in scaling of factors for landslide vulnerability of Kisloyan subwatershed (ERDB 2011) (Continued) Physical factors Class Description C. Climate Maximum monthly rainfall Monthly Rainfall 1 Very low (<100 mm) (7%) 2 Low (100.1-200mm) 3 Moderate (200.1-300mm) 4 High (300.1-500mm) 5 Very high (>500mm) Typhoon frequency Typhoon 1 Very low frequency Frequency (3%) 2 Low frequency (See Philippine 3 Moderate frequency Typhoon 4 High frequency frequency map) 5 Very high frequency D. Geology (3%) 1 Pliocene-Quaternary (QV); Paleocene (sedimentary and metamorphic rocks); Pre-jurassic 2 Undifferentiated (UV; KPg1; KPg2) 3 Oligocene (SPg2); Paleocene-Eocene (SPg1) 4 Pliocene-Pleistocene (N3+Q1); Upper Miocene- pliocene (N2) 5 Recent (R); Quaternary (QAV); Pliocene-quaternary (QPV) E. Geohazards (40%) Proximity to fault 1 Fault lines are not nearer than 5 km from the watershed lines (20%) 2 Fault lines are within 4-4.9 km 3 Fault lines are within 3-3.9 km 4 Fault lines are within 2-2.9 km 5 Fault lines within 1.9 km from the watershed Earthquake 1 < 20% of the area is susceptible triggered landslides 2 20-30% of the area is susceptible susceptibility 3 31-50% of the area is susceptible (10%) 4 51-70% of the area is susceptible 5 71-100% of the area is susceptible 60 Vendiola E and Limpiada M

Table 2 Guide in scaling of factors for landslide vulnerability of Kisloyan subwatershed (ERDB 2011) (Continued) Physical factors Class Description Rain-induced 1 < 20% of the area is susceptible landslides 2 20-30% of the area is susceptible susceptibility 3 31-50% of the area is susceptible (10%) 4 51-70% of the area is susceptible 5 71-100% of the area is susceptible Vegetative cover F. Vegetative 1 >71% of the area is open/grassland/bare/cultivated cover/ Land-use 2 50-70% open/grassland/bare/cultivated (2%) 3 30-49% open/grassland/bare/cultivated 4 21-30% open/grassland/bare/cultivated 5 <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.

Results and discussion

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). Vulnerability assessment of the Kisloyan subwatershed 61

Figure 1 Location map of Kisloyan subwatershed 62 Vendiola E and Limpiada M

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 Vulnerability assessment of the 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 Vendiola E and Limpiada M

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 Lower elevation Middle elevation Higher elevation characteristics Topsoil Subsoil Topsoil Subsoil Topsoil Subsoil Texture Clay Clay Clay Loamy Clay Loamy loam loam clay loam clay Soil depth (m) 0.15 0.35 0.10 0.25 0.10 0.25 Bulk density 1.15 1.25 1.08 1.12 1.02 1.08 (g/cc)

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. Vulnerability assessment of the Kisloyan subwatershed 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. Table 5 Extent of the various geological characteristics of Kisloyan subcatchment

Geologic formation Lithology Composition Pre- Green Micro- Halcon Ultra- Jurassic Alluvial Silt/Sand/ Schist watershed Quater- meta- mafic Dunite Perido tite and deposits Gravel & Mica (MW) nary (ha) morphics complex (ha) (ha) Jurassic (ha) (ha) Schist (ha) (ha) (ha) (ha) MW 1 13.5 89.90 13.50 51.00 38.80 13.50 51.00 0.10 38.80 MW 2 51.90 456.80 51.90 156.20 300.60 51.90 156.20 101.60 199.00 MW 3 276.90 276.90 162.30 114.60 MW 4 159.70 159.70 143.50 16.20 MW 5 49.70 49.70 49.70 MW 6 53.00 53.00 37.40 15.60 MW 7 135.20 135.20 97.90 37.40 MW 8 149.20 17.40 131.90 17.40 8.70 123.10 Total 65.40 1370.5 65.40 224.60 1145.90 65.40 224.60 601.10 544.80 Percent (%) 4.55% 95.45% 4.55% 15.64% 79.81% 4.55% 15.64% 41.86% 37.95% Vulnerability assessment of the Kisloyan subwatershed 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 earthquake- triggered 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 Vendiola E and Limpiada M

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)

Figure 9. Rainfall-induced landslide Map of Kisloyan Sub-watershed Vulnerability assessment of the Kisloyan subwatershed 69

Table 6a Extent of earthquake-triggered landslide hazards in Kisloyan subwatershed

Not vulnerable Moderately vulnerable 635.8 800.2 44% 56%

Victoria

Figure 7 Earthquake-trigerred landslide map of Kisloyan subwatershed

Figure 9. Rainfall-induced landslide Map of Kisloyan Sub-watershed 70 Vendiola E and Limpiada M

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. Vulnerability assessment of the Kisloyan subwatershed 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) Not for Agricultural Clean cultivation Severe limitation agricultural production production 176.10 90.80 795.00 374.10 13% 6% 55% 26% 72 Vendiola E and Limpiada M

Figure 9 Landuse and land cover maps of Kisloyan subwatershed

Table 9 Landuse of Kisloyan subwatershed

Land use Area (ha) Percentage (%) Closed canopy, mature 302.09 21 trees covering >50% Crop land mixed with 10.27 0.7 coconut plantation Cultivated area mixed 938.06 65.4 with brushland/grassland 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). Vulnerability assessment of the Kisloyan subwatershed 73

Table 10 Summary of landslide vulnerability rating of Kisloyan subwatershed Parameters Vulnerability rating 1. Slope (30%) 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 Vendiola E and Limpiada M

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 Kisloyan Subwatershed Naujan

Victoria

Sablayan

River Network Kisloyan Subwatershed Municipal Boundary

Low Moderate High

Figure 10 Landslide vulnerability map Vulnerability assessment of the Kisloyan subwatershed 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 low- statured 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 Vendiola E and Limpiada M

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 low- lying 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. Vulnerability assessment of the Kisloyan subwatershed 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 Vendiola E and Limpiada M

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 Daisy Luisa S. Camello Supervising Science Research Specialist Statistician II Ecosystems Research and Development Bureau ERDB-BCWERC Antonio M. Daño, PhD Banilad, Mandaue City Supervising Science Research Specialist Email address: [email protected] 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.

Review of literature

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). 82 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 multi- geodata 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) Application of AHP and GIS in landslide vulnerability assessment 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 pair- wise comparison matrix using the respective weights. It has been suggested by Rao 84 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, Application of AHP and GIS in landslide vulnerability assessment 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 Barangay Area (ha) Alegria Compostela 720.265 Guadalupe 1,300.368 Lepanto 928.141 Valencia 1,717.892 Badian Balhaan 96.345 Matutinao 66.840 Solsogan 212.184 86 R. Lanuza et al.

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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. Application of AHP and GIS in landslide vulnerability assessment 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. E x p o s u r e VL L M H VH VL VL VL L L M 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 L VL L L M H among which the best decision can be made. Essentially, with contrasting criteria, the M L L M H H 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). H L M H H VH S e n s I t v y Furthermore, AHP allows some small inconsistency in judgment because human is VH M H H VH VH not always consistent. The ratio scales are derived from the principal Eigenvectors and the consistency index is derived from the principal Eigen value. Potential Impact = (Exposure + Sensitivity) 2 An expert pair-wise comparison matrix was formulated and the weights to the Note: The formula was adapted from Allison et al. 2009 factors/criteria were assigned. Using the pair-wise comparison matrix, all identified Vulnerability Values relevant factors/criteria were compared against each other with reproducible preference Very Low < 2.0 factors to calculate the needed weights of factors. Table 2 shows the numerical values Low 2.0 – 2.75 expressing a judgment of the relative importance of one factor against another. The Moderate 2.75 – 3.5 values range from 1 to 9 which describe the intensity of importance (Saaty and Vargas High 3.5 – 4.2 1991). A value of 1 expresses “equal importance” and a value of 9 is given for factors Very High > 4.2 with “extreme importance” over another factor. Figure 4 Potential impact of the ecosystem as a function of exposure and sensitivity 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. Adaptive Capacity Similarly, F1 is moderately important to F3, F4 and F5, equally to moderately VL L M H VH 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 VL M L L VL VL 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. L H M L L L

In the next step, the assigned preference values are synthesized to determine M H H M L L 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 H VH H H M L details about patterns in the data matrix are computed. The square matrix of order Potential Impact 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 VH VH VH H H M 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 Vulnerability = Potential Impact – Adaptive Capacity is normalized and the Eigen values of the normalized matrix, which represent the Note: The formula was adapted from Gletibouo and Ringler 2009; Allison et al. 2009 parameter weights, are computed as shown in Table 3. The final equation of sensitivity Vulnerability Values function is expressed in Equation 1: Very Low < -1.0 Low -1.0 to 0 L = 0.8297[0.3160Sl + 0.0973R + 0.0973T + 0.0912Ga + 0.0912Gf + 0.1729Gfl+ Moderate 0 to 0.5 0.0698So + 0.0633Lu] + 0.1703[0.2532FS + 0.3175H + 0.4349GD] High 0.5 to 1.5 Very High > 1.5

(1) E x p o s u r e VL L M H VH VL VL VL L L M 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 L VL L L M H among which the best decision can be made. Essentially, with contrasting criteria, the M L L M H H 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). H L M H H VH S e n s I t v y Furthermore, AHP allows some small inconsistency in judgment because human is VH M H H VH VH not always consistent. The ratio scales are derived from the principal Eigenvectors and the consistency index is derived from the principal Eigen value. Potential Impact = (Exposure + Sensitivity) 2 An expert pair-wise comparison matrix was formulated and the weights to the Note: The formula was adapted from Allison et al. 2009 factors/criteria were assigned. Using the pair-wise comparison matrix, all identified Vulnerability Values relevant factors/criteria were compared against each other with reproducible preference Very Low < 2.0 factors to calculate the needed weights of factors. Table 2 shows the numerical values Low 2.0 – 2.75 expressing a judgment of the relative importance of one factor against another. The Moderate 2.75 – 3.5 values range from 1 to 9 which describe the intensity of importance (Saaty and Vargas High 3.5 – 4.2 1991). A value of 1 expresses “equal importance” and a value of 9 is given for factors Very High > 4.2 with “extreme importance” over another factor. Figure 4 Potential impact of the ecosystem as a function of exposure and sensitivity 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. Adaptive Capacity Similarly, F1 is moderately important to F3, F4 and F5, equally to moderately VL L M H VH 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 VL M L L VL VL 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. L H M L L L

In the next step, the assigned preference values are synthesized to determine M H H M L L 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 H VH H H M L details about patterns in the data matrix are computed. The square matrix of order Potential Impact 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 VH VH VH H H M 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 Vulnerability = Potential Impact – Adaptive Capacity is normalized and the Eigen values of the normalized matrix, which represent the Note: The formula was adapted from Gletibouo and Ringler 2009; Allison et al. 2009 parameter weights, are computed as shown in Table 3. The final equation of sensitivity Vulnerability Values function is expressed in Equation 1: Very Low < -1.0 Low -1.0 to 0 L = 0.8297[0.3160Sl + 0.0973R + 0.0973T + 0.0912Ga + 0.0912Gf + 0.1729Gfl+ Moderate 0 to 0.5 0.0698So + 0.0633Lu] + 0.1703[0.2532FS + 0.3175H + 0.4349GD] High 0.5 to 1.5 Very High > 1.5 Figure 5 Vulnerability anchored to climate change as a function of potential impact

and adaptive capacity

(1) 92 R. Lanuza et al.

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 Experience and judgment slightly 3 Moderate importance favor one activity over another Essential or strong Experience and judgment strongly 5 importance favor one activity over another Very strong An activity is strongly favored and its 7 importance dominance demonstrated in practice

The evidence favoring one activity 9 Extreme importance over another is of the highest possible order of affirmation Intermediate values 2, 4, 6, 8 between the two When compromise is needed adjacent judgments If activity i has one of the above numbers assigned to it when Reciprocals compared with activity j, then j has the reciprocal value when compared with i If consistency were to be forced by Ratios arising from the Rationals obtaining n numerical values to span scale the matrix Table 3 Pair-wise comparison matrix of the factors/criteria affecting landslide sensitivity

PHYSICAL FACTORS ANTHROPOGENIC FACTORS Priority Relative Occupancy CRITERIA Rainfall Typhoon Geologic Soil Farming Ground Vector Slope Geologic Distance Landuse and Amount Frequency Formation Type System Disturbance (PV) (F1) Age (F4) to Faultline (F8) Habitation (F2) (F3) (F5) (F7) (F9) (F11) (F6) (F10) Slope (F1) 1 3 3 3 3 2 4 4 8 7 4 0.2624 Rainfall 1/3 1 1 1 1 1/2 2 2 2 1 1 0.0808 amount (F2) Typhoon 1/3 1 1 1 1 1/2 2 2 2 1 1 0.0808 frequency (F3) Geologic age 1/3 1 1 1 1 1/2 1 1 2 2 1 0.0757 (F4) Geologic 1/3 1 1 1 1 1/2 1 1 2 2 1 0.0757 formation (F5) Relative distance to 1/2 2 2 2 2 1 2 3 3 3 2 0.1436 faultline (F6) Soil type (F7) 1/4 1/2 1/2 1 1 1/2 1 1 1 1 1 0.0580 Landuse (F8) 1/4 1/2 1/2 1 1 1/3 1 1 1 1 1/2 0.0526 Farming 1/8 1/2 1/2 1/2 1/2 1/3 1 1 1 1 1/2 0.0429 system (F9) Occupancy and habitation 1/7 1 1 1/2 1/2 1/3 1 1 1 1 1 0.0538 (F10) Ground disturbance 1/4 1 1 1 1 1/2 1 2 2 1 1 0.0737 (F11) SUM 3.8512 12.50 12.50 13.00 13.00 7.00 17.00 19.00 25.00 21.00 14.00 1.0000 SUM*PV 1.0121 1.0091 1.0091 0.98536 0.985360 1.0179 0.9756 0.9867 1.0747 1.1039 1.0338 Lambda Max 11.1937 CI 0.0194 CR 0.0127 94 R. Lanuza et al.

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 GIS- based 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 Moderately vulnerable 2.8 – 3.5 Highly vulnerable 3.5 – 4.2 Very highly vulnerable > 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 Application of AHP and GIS in landslide vulnerability assessment 95

Landuse Ground disturbance Soil Type Habitation and Occupancy Relative distance to fault line Farming System Geological formation Geological age Typhoon Frequency Rainfall amount Slope

FactorsFactors forfor Sensitivitysensitivity Analytic Hierarchy Process Parameter Class Weighing (0-100) Landslide Hazard Model on Sensitivity

Parameter s Weighing Weighted Linear GIS Sum Σ=1

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

Landslide Hazard Model on Exposure

GIS Factors for Exposure Average Weight

Self-help System Availability of Structures Availability and Use of Facilities Special Skills and Training External Assistance Model on the IEC Activities Leadership Potential Formal Safety Association impact Indigenous Practices Impact Availability of Technology Adoption of Technology

Landslide Hazard Model Landslide Vulnerability on Adaptive Capacity Model

Factors for Adaptive Average Weight GIS Capacity

Figure 5 Schematic diagram of the GIS-assisted approach on landslide assessment based on the result of Analytic Hierarchy Process (AHP). 96 R. Lanuza et al. communities away from vulnerability to landslide-prone areas to prevent and avoid damage or loss of human lives and properties.

Results and discussion

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. Application of AHP and GIS in landslide vulnerability assessment 97

Table 5 Geographic location of landslide incidents and landslide prone areas within Matutinao Watershed determined using a GPS handset (Lanuza et al. 2014) Elevation Location Easting Northing Remarks (masl) Alegria Compostela 540163 1081046 236 Minor landslide area 540145 1081062 231 Minor landslide area 540133 1081079 232 Minor landslide area 540115 1081105 232 Minor landslide area 540022 1081134 238 Minor landslide area 540022 1081119 248 Landslide prone (4 people affected) 540189 1080989 222 Landslide prone 540206 1080811 220 Landslide prone Guadalupe 539706 1076139 416 Landslide prone (5 houses affected) near on somewhat a lake 539606 1076207 424 Landslide area (rock falls along road at the back of Guadalupe Elementary School) 539573 1076180 415 Landslide prone (road at the back of Guadalupe Elementary School) 539696 1076253 391 Landslide prone (abandoned Guadalupe Elementary School) 539678 1076253 392 Landslide prone (abandoned Guadalupe Elementary School) 539655 1076266 423 Landslide prone (abandoned Guadalupe Elementary School) 539675 1076300 427 Landslide prone (very steep area at the back of Guadalupe Elementary School) Lepanto 542548 1073654 633 Landslide area (occurring at road side) 98 R. Lanuza et al.

Table 5 Geographic location of landslide incidents and landslide prone areas within Matutinao Watershed determined using a GPS handset (Lanuza et al. 2014) (Continued) Elevation Location Easting Northing Remarks (masl) Valencia 541786 1077941 420 Landslide prone (occurring at sitio Banahaw) 541825 1077964 404 Landslide prone 541863 1077985 414 Minor landslide area (near 3 houses) 541923 1078006 396 Landslide prone (2 houses) 541933 1078012 395 Landslide prone (cliff) 542097 1078054 394 Landslide prone (1 house) 542191 1077857 416 Landslide prone (1 house) 542170 1077866 409 Landslide area (rock falls) 542136 1077884 406 Landslide prone (near 2 houses) 542225 1077851 410 Landslide prone (1 house) 542246 1077851 409 Landslide prone (1 house) 542379 1077883 405 Landslide prone (2 houses) 542403 1077859 401 Landslide prone 543417 1077989 490 Landslide area (basketball court with rock falls at the upper portion of Inghoy Elementary School) 543423 1077964 493 Landslide area (rock falls affecting 3 houses) 543968 1077709 525 Landslide area (minor rock falls going to sitio Mayana) 542405 1077830 423 Landslide prone (after Cambais falls) 540878 1079546 377 Landslide prone Badian Matutinao 540489 1084126 13 Landslide area (area due to earthquake) 540533 1084133 12 Landslide area (rock falls) 540651 1084093 15 Landslide area (located at the other side of the river (portion of the adjacent mountain) which occurred few years ago Solsogan 542627 1082901 313 Landslide prone Application of AHP and GIS in landslide vulnerability assessment 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: self- help 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 Application of AHP and GIS in landslide vulnerability assessment 101

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Figure 6 Location of landslide and landslide-prone areas within Matutinao Watershed 102 R. Lanuza et al. 2007 1,764.80 2006 1,559.71 2005 1,398.80 2004 1,401.53 2003 1,954.90 2002 Year 1,134.50 2001 (1997-2007) 2,125.10 2000 1,980.01 1999 2,057.80 1998 1,117.50 1997 Annual Rainfall Amount from PAG-ASA, Mactan Station Mactan PAG-ASA, from Amount Rainfall Annual 1,364.30 - 500.00

2,500.00 2,000.00 1,500.00 1,000.00 Rainfall Amount (mm) Amount Rainfall Figure 7 Annual rainfall from 1997 to 2007 at PAGASA Station, Mactan, Cebu Application of AHP and GIS in landslide vulnerability assessment 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. 104 R. Lanuza et al. Category Moderate High Very High High Moderate High Moderate nd nd nd Average 3.38 3.88 4.25 3.75 3.00 3.88 3.13 nd nd nd E8 5 5 5 5 5 5 5 nd nd nd E7 4 4 4 4 4 4 4 nd nd nd E6 3 3 5 3 3 3 3 nd nd nd E5 Exposure 4 2 2 4 3 2 3 nd nd nd E4 3 3 3 3 3 2 3 nd nd nd E3 2 5 5 2 2 5 2 nd nd nd E2 4 4 5 4 2 5 3 nd nd nd E1 2 5 5 5 2 5 2 nd nd nd Location A. Alegria 1. Compostela 2. Guadalupe 3. Lepanto 4. Valencia B. Badian 1. Balhaan 2. Matutinao 3. Solsogan C. Dalaguete 1. Dugyan * D. Alcoy 1. Nug-as * E. Malabuyoc 1. Palaypay * Table 7 Exposure to landslide of barangays within Matutinao Watershed based on 2020 climate projections 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 2020 (0-5%=1; 5-10%=2; 10-15%=3; 15-20%=4; and >20%=5) E8 - Percentage of Exposure Community ((0-2%=1; 2-4%=2; 4-7%=3; 7-10%=4; and >10%=5) nd - No data Application of AHP and GIS in landslide vulnerability assessment 105 nd nd nd low Low Low Low Low Low Very Moderate Category Category Moderate High Very High High Moderate High Moderate nd nd nd 2.36 2.55 2.91 2.73 1.36 2.45 2.27 nd nd nd Average Average 3.38 3.88 4.25 3.75 3.00 3.88 3.13 nd nd nd 1 1 3 3 1 3 2 nd nd nd AC11 E8 2 2 2 2 2 2 2 5 5 5 5 5 5 5 nd nd nd nd nd nd AC10 - Availability of Technology AC11 - Adoption of Technology AC10 E7 4 4 3 3 2 2 2 nd nd nd AC9 4 4 4 4 4 4 4 nd nd nd 4 5 3 4 1 1 1 nd nd nd E6 AC8 3 3 5 3 3 3 3 nd nd nd 2 2 4 2 1 4 4 nd nd nd AC7 E5 Exposure 4 2 2 4 3 2 3 nd nd nd 3 3 3 3 3 3 3 nd nd nd Adaptive Capacity AC6 E4 4 4 3 4 1 3 1 3 3 3 3 3 2 3 nd nd nd nd nd nd AC5 AC5 - External Assistance AC6 - IEC AC7 - Leadership AC8 - Formal Safety Association AC9 - Common Safety Indigenous Practices 2 2 1 2 1 2 2 E3 nd nd nd AC4 2 5 5 2 2 5 2 nd nd nd 2 2 3 2 1 2 3 nd nd nd AC3 E2 4 4 5 4 2 5 3 nd nd nd 1 1 3 3 1 3 3 nd nd nd AC2 E1 2 5 5 5 2 5 2 nd nd nd 1 2 4 2 1 2 2 nd nd nd AC1 Location Location A. Alegria 1. Compostela 2. Guadalupe 3. Lepanto 4. Valencia B. Badian 1. Balhaan 2. Matutinao 3. Solsogan C. Dalaguete 1. Dugyan * D. Alcoy 1. Nug-as * E. Malabuyoc 1. Palaypay * A. Alegria 1. Compostela 2. Guadalupe 3. Lepanto 4. Valencia B. Badian 1. Balhaan 2. Matutinao 3. Solsogan C. Dalaguete 1. Dugyan * D. Alcoy 1. Nug-as * E. Malabuyoc 1. Palaypay * Table 7 Exposure to landslide of barangays within Matutinao Watershed based on 2020 climate projections 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 2020 (0-5%=1; 5-10%=2; 10-15%=3; 15-20%=4; and >20%=5) E8 - Percentage of Exposure Community ((0-2%=1; 2-4%=2; 4-7%=3; 7-10%=4; and >10%=5) nd - No data Table 8 The present adaptive capacity of communities to landslide within Matutinao Watershed Note: AC1 - Self-help System AC2 - Availability of Structures AC3 - Availability and use of Facilities AC4 - Special Skills and Training Table 9 Hazard rating of various themes Landslide parameters /Sensitivity Subclass of parameters Hazard rating 1. Slope Very Steep (>50%) 5 Steep (30-50%) 4 Moderate (18-30%) 3 Gentle (8-18%) 2 Very Gentle (0-8%) 1 2. Rainfall (Annual) > 2000 mm 5 1500 – 2000 mm 4 1000 mm – 1500 mm 3 500 mm – 1000 mm 2 < 500 mm 1 3. Typhoon Frequency 3 times a year 3 4. Geologic Age Upper Miocene-Pliocene, Pliocene-Pleistocene 4 Oligocene-Miocene 3 5. Geologic Formation Carcar Formation, Maingit Formation 4 Barili Formation 3 6. Relative Distance to Fault 0 – 0.5 km line 5 0.5 – 2 km 4 2 – 5 km 3 5 – 8 km 2 > 8 km 1 7. Soil Type Clay loam, silt loam 4 Clay, loam 3 8. Landuse Cultivated area (annual crops) 5 Shrublands 4 Woodland with grassland, natural grassland, open forest with broadleaves species 3 9. Farming System Upland farms (Cropland) 5 Brushland 4 Open forest interspersed with broadleaves species 3 10. Occupancy and Habitation 0 – 100 m 5 100 – 200 m 4 200 – 300 m 3 300 – 400 m 2 > 500 m 1 11. Ground Disturbance 0 – 200 m 5 200 – 400 m 4 400 – 600 m 3 600 – 800 m 2 > 800 m 1 Note: Values in bold are the hazard ratings of the landslide parameters (sensitivity) of Matutinao Watershed. Application of AHP and GIS in landslide vulnerability assessment 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 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 RI 0.00 0.52 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 Note: Hazard rating 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 Estimated area by landslide vulnerability class (ha) Location 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 B. Badian Balhaan 0 0 0 6.48 89.60 Matutinao 0 0 0 65.64 0 Solsogan 0 0 1.58 209.56 0 C. Dalaguete 1. Dugyan* * * * * * D. Alcoy

Subclass of parameters Very Steep (>50%) Steep (30-50%) Moderate (18-30%) Gentle (8-18%) Very Gentle (0-8%) > 2000 mm 1500 – 2000 mm 1000 mm – 1500 500 mm – 1000 < 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 open forest 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 1. Nug-as* * * * * * E. Malabuyoc 1. Palaypay* * * * * * Total of barangays 0 0 1,666.11 3,278.47 89.60 covered % 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 Landslide parameters /Sensitivity 1. Slope 2. Rainfall (Annual) 3. Typhoon Frequency 4. Geologic Age 5. Geologic Formation 6. Relative Distance to Fault line 7. Soil Type 8. Landuse 9. Farming System 10. Occupancy and Habitation 11. Ground Disturbance

Table 9 Hazard rating of various themes Note: Values in bold are the hazard ratings of landslide parameters (sensitivity) Matutinao Watershed. included in the assessment but with assumed values. 108 R. Lanuza et al.

Table 12 Proposed mitigation/adaptation measures for areas vulnerable to landslides Landslide Area Vulnerability Mitigation/Adaptation Measures (ha) Class

Moderate 1,666.11 • The Municipal and Barangay Hazard Prevention Vulnerability 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 High 3,278.47 • The Municipal and Barangay Hazard Prevention Vulnerability and Mitigation Council must be created through legislation. • Results shall be included in the CLUP. • Infrastructures and settlements must be avoided in areas with high 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. • There is a need of continuous monitoring of landslides. • Warning devices to warn occurrence of landslides must be installed. Application of AHP and GIS in landslide vulnerability assessment 109

Table 12 Proposed mitigation/adaptation measures for areas vulnerable to landslides (Continued) Landslide Area Vulnerability Mitigation/Adaptation Measures (ha) Class • Upper slopes of roads constructed in steeper slopes must be supported with ripraps. • Procurement of equipment to respond to landslide occurrence. • Strengthen the self-help systems • Provision of goods and basic services in evacuation centers Very High 89.60 • The Municipal and Barangay Hazard Prevention Vulnerability and Mitigation Council must be created through legislation. • Results shall be included in the CLUP. • Infrastructures and settlements must be avoided in areas with very high 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 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. • Strengthen the self-help systems • Provision of goods and basic services in evacuation centers • Procurement of equipment to respond to landslide occurrence. • Relocation of communities living within these areas. 110 R. Lanuza et al.

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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. 116 R. Lanuza et al.

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. Application of AHP and GIS in landslide vulnerability assessment 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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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

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