Vulnerability Assessment of the Quinali “A” Watershed in

Baseline Assessment: Vulnerability Assessment of Quinali “A” Watershed

Philippines Biodiversity and Watersheds Improved for Stronger Economy and Ecosystem Resilience (B+WISER)

22 June 2015

This publication was produced for review by the United States Agency for International Development. It was prepared by Chemonics International Inc. The Biodiversity and Watersheds Improved for Stronger Economy and Ecosystem Resilience Program is funded by the USAID, Contract No. AID-492-C-13-00002 and implemented by Chemonics International in association with:

Fauna and Flora International (FFI)

Haribon Foundation

World Agroforestry Center (ICRAF)

The author’s views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.

Vulnerability Assessment of Quinali “A” Watershed

Philippines Biodiversity and Watersheds Improved for Stronger Economy and Ecosystem Resilience

(B+WISER) Program

Implemented with:

Department of Environment and Natural Resources Other National Government Agencies Local Government Units and Agencies

Supported by:

United States Agency for International Development Contract No.: AID-492-C-13-00002

Managed by:

Chemonics International Inc. in partnership with

Fauna & Flora International (FFI) Haribon Foundation

World Agroforestry Center (ICRAF)

22 June 2015

VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES | i

CONTENTS

List of Figures ............................................................................................................... ii

List of Tables ................................................................................................................ iii

List of Plates ................................................................................................................. iii

Acronyms ...................................................................................................................... v

Rationale ...................................................................................................................... vii

The Quinali “A” Watershed .......................................................................................... 1

The Climate Profile ........................................................................................................ 3

Baseline Climate .................................................................................................... 3

Climate scenario for Albay province in 2020 and 2050 ........................................... 5

Assessment Framework and Methodology ................................................................. 7

Data Collection....................................................................................................... 7

Vulnerability Assessment ....................................................................................... 7

Hazards Assessment ............................................................................................. 8

Flood Hazard Assessment .................................................................................... 8 Drought Hazard Assessment .............................................................................. 10 Landslide Hazard Assessment ............................................................................ 12

Land Capability Classification .............................................................................. 13

Generation of Soil Erosion Potential ................................................................... 14 Rainfall Factor (R) ............................................................................................... 14 Soil Erodibility Factor (K) ..................................................................................... 15 Slope Length and Slope Gradient Factor (LS) .................................................... 16 Creation of Soil Loss Tolerance .......................................................................... 16 Determination of Soil Erosion Index .................................................................... 17 Generation of Land Capability Classification ....................................................... 17

Validation ............................................................................................................. 18

Findings ....................................................................................................................... 20

Hazards Assessment ........................................................................................... 20

Flood Hazard Assessment .................................................................................. 20 Drought Hazard Assessment .............................................................................. 35 Landslide Hazard Assessment ............................................................................ 52

Land Capability Classification .............................................................................. 67

Conclusions................................................................................................................. 69

Recommendation ........................................................................................................ 70

References ................................................................................................................... 71

ii | VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES

LIST OF FIGURES Figure 1. Location of the Quinali “A” Watershed in Albay .......................................................... 2

Figure 2. Tracks of tropical cyclones which crossed the province of Albay (1948 – 2009) ....... 4

Figure 3. Monthly rainfall based on CNCM3 model for 2020s and 2050s periods in Albay Province ...................................................................................................................... 6

Figure 4. Framework of the vulnerability assessment ............................................................... 8

Figure 5. Framework for erosion-based land capability classification system ......................... 14

Figure 6. The Quinali “A” Watershed validation site ................................................................ 19

Figure 7. Flood vulnerable areas based on CNCM3 model observed scenario in the Quinali “A” Watershed .............................................................................................. 24

Figure 8. Flood vulnerable areas based on CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed .................................................................................... 25

Figure 9. Flood vulnerable areas based on CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed .................................................................................... 26

Figure 10. Flood vulnerable areas based on CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed .................................................................................... 27

Figure 11. Flood vulnerable areas based on CNCM3 model A2 scenario for 2050s period of the Quinali “A” Watershed .................................................................................... 28

Figure 12. Standardized precipitation index based on A1B scenario in Albay Province ........... 36

Figure 13. Standardized precipitation index based on A1B scenario in Albay Province ........... 37

Figure 14. Drought vulnerable areas based on CNCM3 model observed scenario in the Quinali “A” Watershed .............................................................................................. 39

Figure 15. Drought vulnerable areas based on CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed ......................................................................... 40

Figure 16. Drought vulnerable based on CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed ........................................................................................ 41

Figure 17. Drought vulnerable areas based on CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed ......................................................................... 42

Figure 18. Drought vulnerable areas based on CNCM3 model A2 scenario for 2050s period of the Quinali “A” Watershed ......................................................................... 43

Figure 19. Landslide vulnerable areas based on CNCM3 model Observed scenario in the Quinali “A” Watershed .............................................................................................. 62

Figure 20. Rain-induced landslide vulnerable areas based on the CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed .......................................... 63

Figure 21. Rain-induced landslide vulnerable areas based on the CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed .......................................... 64

Figure 22. Rain-induced landslide vulnerable areas based on the CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed .......................................... 65

Figure 23. Rain-induced landslide vulnerable areas based on the CNCM3 model A2 scenario for 2050s period of the Quinali “A” ............................................................. 66

Figure 24. Prescribed land capability classification in the Quinali “A” Watershed ..................... 68

VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES | iii

LIST OF TABLES Table 1. Distribution of barangays and area in the watershed ................................................. 1

Table 2. List of damaging typhoons and tropical storms for the last 10 years in Albay Province ...................................................................................................................... 4

Table 3. Projected monthly rainfall based on CNCM3 model with A1b and A2 scenarios for 2020s and 2050s periods in Albay Province ......................................................... 5

Table 4. Change anomalies of rainfall in Albay Province based on CNCM3 model ................ 6

Table 5. Available datasets for the assessment ....................................................................... 7

Table 6. Summary of the classified ranges for the different layers/factors considered in the flood susceptibility for Quinali “A” Watershed ....................................................... 9

Table 7. Summary of the classified ranges for the different layers/factors considered in the drought susceptibility for Quinali “A” Watershed ................................................ 11

Table 8. Summary of the classified ranges for the different layers/factors considered in the landslide susceptibility modeling for Quinali “A” Watershed ............................... 12

Table 9. K-values for the Quinali “A” Watershed .................................................................... 15

Table 10. Prescribed soil loss tolerance in the watershed ....................................................... 17

Table 11. Land capability classification criteria ........................................................................ 18

Table 12. Distribution of vulnerability ratings to flooding in the Quinali “A” Watershed ............ 20

Table 13. Summary of barangays that are highly vulnerable to flooding within the Quinali “A” Watershed ........................................................................................................... 21

Table 14. Vulnerability to flooding by barangay of the Quinali “A" Watershed ......................... 29

Table 15. Drought vulnerability and its area coverage in the Quinali “A” Watershed ............... 37

Table 16. Distribution of highly vulnerable areas to drought within the Quinali “A” Watershed................................................................................................................. 37

Table 17. Drought vulnerability ratings by barangays in Quinali “A” Watershed ...................... 44

Table 17. Rain-induced landslide vulnerability of the Quinali “A” Watershed........................... 53

Table 18. Distributions of highly vulnerable areas to landslide within the Quinali “A” Watershed................................................................................................................. 53

Table 19. Rain-induced landslide vulnerability by barangay in the Quinali “A” Watershed ...... 54

Table 20. Recommended land capability classification of the Quinali “A” Watershed ............. 67

LIST OF PLATES Plate 1. Participants during the vulnerability assessment validation workshop on

January 14, 2015 ...................................................................................................... 19

Plate 2. Portion of flooded areas in San Rafael, Guinobatan, Albay during typhoon Amang in 2015 .......................................................................................................... 23

Plate 3. Rice fields identified as flood prone areas in Oas, Albay ......................................... 23

Plate 4. Vulnerable areas to drought within the Quinali “A” watershed ................................. 51

Plate 5. Landslide in a portion of Mt. Masaraga watershed forest reserve............................ 54

iv | VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES

VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES | v

ACRONYMS

B

B+WISER Biodiversity and Watersheds Improved for Stronger Economy

and Ecosystem Resilience

BSWM Bureau of Soils and Water Management

C

CNCM3/ CNRM-CM3 Centre National de Recherches Météorologiques

D

DEM Digital Elevation Model

DENR Department of Environment and Natural Resources

E

EDC Energy Development Corporation

F

FAO Food and Agriculture Organization

FMB Forest Management Bureau

FS Flood Susceptibility

G

GIS Geographic Information System

L

LCC Land Capability Class

LCCS Land Capability Classification System

LGU Local Government Unit

M MDG Millennium Development Goals

N

NAMRIA National Mapping and Resource Information Authority

NDVI Normalized Difference Vegetative index

NOAH Nationwide Operational Assessment of Hazards

NSO National Statics Office

P

PAGASA Philippine Atmospheric, Geophysical and Astronomical Services

Administration

PHIVOLCS Philippine Institute of Volcanology and Seismology

Q

QAW Quinali “A” Watershed

vi | VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES

S

SAFDZ Strategic Agriculture and Fisheries Development Zone

SEI Soil Erosion Index

SEP Soil Erosion Potential

SPI Standardized Precipitation Index

U

USAID United States Agency for International Development

USLE Universal Soil Loss Equation

V

VA Vulnerability Assessment

VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES | vii

RATIONALE

Ecosystem vulnerability assessment is an approach used in determining the degree to which a

system is susceptible to the adverse effects of climate related hazards such as soil erosion,

flooding, drought, landslides, etc. It is regarded as a planning tool as it serves as basis in making

decisions that will help minimize the vulnerability of the watersheds to environmental and

climate related disasters. Natural events such as typhoons and heavy rains can be hazardous and

can pose a major threat both to the ecosystems and human beings.

Watersheds play significant role in pursuing sustainable development (Lasco et al. 2006). More

than 70% of the country’s total land area lies within watersheds. Around 20 to 24 million people

– about one fourth of the country’s total population – inhabit the watersheds and are dependent on

them for survival (Cruz et al., 2005). Thus, in order to minimize further destruction and

degradation of watersheds due to climate related extreme events, assessing their vulnerabilities to

soil erosion, landslides, drought and flooding is of utmost importance. The results of the

assessment will provide basis in crafting mitigation and adaptation measures that have to be

integrated in the management plan of Quinali “A” watershed and development plans of the LGUs

covered by the Quinali “A” watershed.

viii | VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES

VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES | 1

THE QUINALI “A” WATERSHED

Quinali “A” Watershed is one of the major tributaries of the Bicol River basin. It is located within

the province of Albay. It lies between geographical coordinates 13˚ 7’ 30” to 13˚ 24’ 0” north

latitude and 123˚ 14’ 0” to 123˚ 41’ 30” east longitude (Figure 1). The headwaters of the Quinali

“A” Watershed originate from the upper slopes of the Mayon and Talisay sub-watersheds within

the forestlands. This watershed falls within the jurisdiction of eight (15) political or

administrative units comprising of three cities, namely: Ligao City, Iriga City, and Tobacco City,

and 12 municipalities, namely: Camalig, Daraga, Guinobatan, Libon, Malilipot, Malingo, Oas,

Polangui, Tiwi, Buhi and Nabua. It has an area of around 73,000 ha with almost 60% of the

watershed found in Ligao City, Polaqui and Oas (Table 1). The LGU with the biggest jurisdiction

is Libon, which occupies 14,837 ha and includes 41 barangays inside the watershed. This is

followed by the municipality of Plague which occupies 14,501 ha and includes 42 barangays.

The smallest area belongs to Malilipot, which has 1 barangay occupying an area of almost a

hectare.

Identification of the barangays covered by the watershed was done initially using the topographic

map from the National Mapping and Resource Information Authority (NAMRIA). The point

location of all barangays that fall within the boundary of the watershed and located partially or

entirely was considered part of and covered by the watershed. The results were then presented

and validated through a workshop with the key stakeholders.

Table 1. Distribution of barangays and area in the watershed

Municipality No. of barangays covered by the

watershed

Area of the municipality (ha)*

Area covered by the watershed (ha)*

Percent covered

Albay Province

Camalig 23 13,654 4,469 6.1

Daraga 1 13,567 4.45 0.0

Guinobatan 35 17,408 9,202 12.5

Libon 41 22,851 14,837 20.1

Ligao city 45 25,851 14,494 19.6

Malilipot 1 4,542 0.70 0.0

Malinao 2 10,678 427 0.6

Oas 42 23,958 13,086 17.7

Polangui 42 14,890 14,501 19.6

Tobaco city 1 11,224 47.3 0.1

Tiwi 1 12,440 259 0.4

Camarines Sur Province

Buhi 8 18,541 1,526 2.1

Iriga city 1 13,005 559 0.8

Nabua 1 9,661 3.0 0.0

Total 244 73,800 100.0 *GIS generated area

2 | VULNERABILITY ASSESSMENT OF THE QUINALI “A” WATERSHED IN ALBAY, PHILIPPINES

Figure 1. Location of the Quinali “A” Watershed in Albay


THE CLIMATE PROFILE

BASELINE CLIMATE

The Quinali “A” watershed climate falls under Type II and IV of the Modified Corona’s Climate

Classification System (Agpaoa et al., 1975). A large portion of the watersheds which is classified

under Type IV is located in the western portion. This category is characterized by an almost

evenly distributed rainfall during the whole year. The eastern side falls under Type IV climate,

which is characterized by a no dry season with a very pronounced rainfall from November to

January.

The rainfall pattern in the watershed is highly influenced by the southwest and the northeast

monsoon systems that are responsible for the tropical storms that batter the Bicol Region during

the rainy season. The southwest monsoon sets in during late May and peaks during the months of

November and December. The northeast monsoon then comes in during late October and

intensifies in January and February.

The watershed is characterized by high rainfall intensity from June to January and relatively

lesser rainfall from February to May. However, the mountainous areas normally get higher

rainfall until February. Rainfall in the mountains ranges from 3,000 to 4,000 mm per year. The

onset of the rainy season is normally observed in the late May in the mountainous areas and about

early to mid-June in the lowlands. Rain slows down normally in February for the uplands and in

late January in the lower elevations. Rainfall peaks in the months of October to December in the

entire watershed.

The climate in Albay is generally mild with no specific extreme seasons. The frequency of

tropical cyclones is high in the province, which accumulated to 40 each year with an average of

two major destructive typhoons per year (Figure 2). In November 2006, it was hardest-hit by

typhoon Reming which was one of the most deadly and destructive tropical cyclones in the record

of history of the country (Table 2). The typhoon brought 466 mm of rainfall, the highest in 40

years. That rainfall caused debris and volcanic materials from the slopes of Mayon Volcano to

rush down as mudflows that buried the communities lying at the foot slopes of the volcano.

Aside from Reming, five other major typhoons hit the province since 2006.


Figure 2. Tracks of tropical cyclones which crossed the province of Albay (1948 – 2009)

Table 2. List of damaging typhoons and tropical storms for the last 10 years in Albay

Province

Typhoon Date Rainfall amount (mm)*

Reming November 28 – December 3, 2006 466

Juaning July 26, 2011 220

Yolanda November 12, 2012 325

Durian November 30, 2013 457

Glenda July 21, 2014 325

Amang January 17, 2015 186

*based on PAGASA’s records and the Project NOAH


CLIMATE SCENARIO FOR ALBAY PROVINCE IN 2020 AND 2050

In order to assess future vulnerabilities to flooding, drought and landslide, projections of future

changes in rainfall in 2020 and 2050 were prepared by the PAGASA using the CNRM-CM3

model (also known as CNCM3 model) with two scenarios. CNRM-CM3 coupled generation

circulation model is the sum of the updated version of the different model components already

present in CNRM-CM2 (Salas-Melia et al., 2005).

In this assessment, the model outputs under the two scenarios were within a planning horizon of

up to 2050. Outputs of the model under the A1B and A2 scenarios will only diverge after 2050

due to the long lifetimes of the greenhouse gases. The outputs of the model runs for the observed

monthly, and changes in the monthly rainfall both in 2020 and 2050 were used in the

vulnerability assessments.

The simulated monthly rainfall ranges from 29 mm to 652 mm. The mean monthly rainfall of

two (2) scenarios (A1B and A2) was insignificantly different from each period. The driest

month, April, still sees on average, over 62 mm of precipitation per year. The wettest months are

August and December with a monthly mean of more than 300 mm (Table 3 and Figure 3).

In particular, the monthly precipitation fluctuated each month for two periods. However, the

most distinct changes were predicted to be in the 2020s period under A2 scenario where the

months of May, August and December had indicated a potential decrease. Other periods closely

followed the trends and patterns. Overall, an increase of annual rainfall was predicted in each

scenario for two periods (Table 4).

Table 3. Projected monthly rainfall based on CNCM3 model with A1b and A2 scenarios

for 2020s and 2050s periods in Albay Province

Month Observed A1B A2

2020 2050 2020 2050

Jan 220.9 213.0 103.4 255.3 376.3

Feb 141.3 340.0 80.4 100.8 262.7

Mar 144.7 142.8 67.9 123.3 346.6

Apr 110.9 62.4 86.8 83.4 154.3

May 158.1 251.7 302.9 153.7 262.7

Jun 234.3 208.6 299.4 289.8 37.6

Jul 267.8 170.4 505.3 144.0 29.6

Aug 288.3 367.4 651.5 399.4 146.3

Sep 220.5 273.7 395.3 321.9 189.5

Oct 296.6 506.6 288.6 379.6 408.9

Nov 285.4 358.9 222.5 201.1 444.2

Dec 367.2 540.9 205.0 293.0 473.8

Total 2736.1 3436.5 3209.0 2745.3 3132.3

Min 110.9 62.4 67.9 83.4 29.6

Max 367.2 540.9 651.5 399.4 473.8

SD 77.34 143.02 181.77 109.27 152.10

Ave 228.0 286.4 267.4 228.8 261.0


Figure 3. Monthly rainfall based on CNCM3 model for 2020s and 2050s periods in Albay Province

Table 4. Change anomalies of rainfall in Albay Province based on CNCM3 model

Month A1B A2

2020 2050 2020 2050

Jan -4 -53 16 70

Feb 141 -43 -29 86

Mar -1 -53 -15 139

Apr -44 -22 -25 39

May 59 92 -3 66

Jun -11 28 24 -84

Jul -36 89 -46 -89

Aug 27 126 39 -49

Sep 24 79 46 -14

Oct 71 -3 28 38

Nov 26 -22 -30 56

Dec 47 -44 -20 29

Total 26 17 0 14


ASSESSMENT FRAMEWORK AND METHODOLOGY

DATA COLLECTION

An exhaustive collection, examination and analysis of existing documents were conducted. The

GIS geodatabase used in the overlay analysis was derived from the information contained in the

sources and satellite data (Table 5).

Table 5. Available datasets for the assessment

Layer Description Source

Quinali “A” Watershed boundary

Extent of assessment Interpolated from DEM; FMB-DENR

Fault lines Fault lines of the Philippine Islands PHIVOLCS

Geology Soil morphology FAO datasets; BSWM

Soil series/ Soil texture Soil series map BSWM (1965; 2005)

Barangay Barangay map NAMRIA; www.philgis.org

Town and city Administrative boundaries based on town and city

NAMRIA; www.philgis.org

DEM Digital Elevation Model of the Philippines

ASTER-GDEM

River River networks within the area Interpolated from DEM

Standardized Precipitation Index

A drought at a given time scale of interest

Computed based on monthly average rainfall

Land cover 2010 Land Cover Map NAMRIA-DENR

Vegetative Index Derived from land satellite 8 imageries www.earthexplorer.usgs.gov

Rainfall Daily rainfall data Legaspi Weather Station

Population Density Based on 2010 population distribution by barangay

National Statics Office

Watershed shape Based on sub-watersheds shape Interpolated from DEM

VULNERABILITY ASSESSMENT

The assessment was undertaken by determining inherently sensitive areas due to topography and

their exposure to climate hazards. Vulnerability or hazard maps were prepared to show which

areas in the watershed require immediate attention to minimize the adverse impacts of changing

climate. The assessment made use of simulated hazard maps derived from overlay analyses

associated with different variables based on the observed and projected climate scenarios.

http://www.philgis.org/

http://www.philgis.org/

http://www.earthexplorer.usgs.gov/


Climate change related hazards that are of common concern in the country include flood, drought,

and landslide. Vulnerability maps to climate hazards for two (2) climate scenarios were

developed following the process illustrated in Figure 4. Climate future scenarios are based on

2020 (base year 2006-2035) and 2050 (base year 2036-2065) periods.

Figure 4. Framework of the vulnerability assessment

The assessment used the land capability evaluation tool in conducting adaptive capacity

assessment. Land capability evaluation is a strategic planning tool in integrating climate change.

The tool was used as part of vulnerability assessment under the MDG-F 1656 project for the

purpose of developing an integrated watershed management plan. The tool was patterned after

the vulnerability assessment (VA) framework by observing the process of problem identification,

implementation and assessment following steps of Land Capability Classification Process.

HAZARDS ASSESSMENT

Flood Hazard Assessment

Flood hazard maps for 2020 and 2050 were generated by adjusting the existing flood hazard maps

based on the projected mean annual frequency of days with rainfall of at least 100 mm. The

susceptibility flood hazard map was generated based on different factors and their relative

weights. The flood modeling is based on the overlay of six (6) contributing factors namely slope,

Vulnerability

Maps

(Flood, Drought

and Landslide)

Bio-physical Characteristics• Soil • Geology• Land cover/

Vegetative cover• Drainage• Road networks• Fault lines• Elevation and slope

Socio-demographic Characteristics

• Population by barangay

• Population density• Farming systems

Climate Characteristics• Rainfall• Standard

Precipitation Index

Land Capability Classification

• Soil Erosion Potentials

• Production areas• Protection areas

Climate Scenarios

(Observed, 2020s

and 2050s)

Ground Validation and

Consultation

ZONING


land cover/land use, soils, elevation, sub-watershed shape, and stream buffer (Table 6). Each

factor is classified into five (5) categories ranging from very low to very high classes. The

different factors are described below:

a. Slope: The slope of the different sites was generated using a Digital Elevation Model

(DEM) with a resolution of 30 m. A slope of >30% is considered to have very low

susceptibility and a slope range of 0-3% is classified to be highly susceptible to flood.

b. Land Cover: The 2010 land cover data was used for this factor. Water bodies and open

areas are classified as highly susceptible to flood because they can generate high surface

runoff while forested areas are considered to have low susceptibility to flooding.

c. Soils: The different soil textural classes, more commonly known as the soil series, mainly

describe the soil factors. These textural classes ranged from clay to sandy types. Water-

holding capacity of soils at field capacity and wilting point of different soil textures are

considered in the classification. Hence, clay types are deemed to be highly susceptible

and sandy types are classified to have normal susceptibility to flood.

d. Elevation: The elevation was generated from the DEM. Higher elevations are considered

to be resistant to flooding and these are classified to have low susceptibility while slower

elevations are regarded as areas with very high susceptibility to flooding.

e. Watershed Shape: Shape of different sub-watersheds with the watershed was interpolated

from the DEM. Almost elongated watershed is classified to be less susceptible while a

watershed with nearly circular in shape is highly susceptible to flood.

f. Stream Buffer: Streams were generated from the 30 m DEM and then buffers were

interpolated. Distance within 30 m from the stream is classified to be highly susceptible

to flood while buffers with >1,000 m distance from the stream is regarded to have very

low susceptibility.

Table 6. Summary of the classified ranges for the different layers/factors considered in

the flood susceptibility for Quinali “A” Watershed

Layer/ Factor Classes/ Ranges

Relative weights

Elevation (ranges in m asl)

>150 1

80 – 150 2

40 – 80 3

20 – 40 4

<20 5

Slope (% ranges)

>30 1

18 - 30 2

8 - 18 3

3 - 8 4

0 - 3 5

Stream buffer (buffer ranges in m)

<100 1

100 – 200 2

200 – 300 3

300 – 500 4

>500 5

Soil texture (categories)

Fine sand 1

Sandy loam; Fine sandy loam 2

Loam; Sandy clay loam; Sandy clay; Silty clay; Silt loam 3



Relative weights

Silty clay loam; Clay loam; 4

Clay 5

Land cover (categories)

Closed forest 1

Open forest; Plantation 2

Shrubs; Natural grassland 3

Agricultural/Cultivated; Pasture land; Built-up 4

Bare; Water bodies; Inland water 5

Watershed shape (ratio; descriptive)

<0.25 (almost elongated) 5

0.25 – 0.40 4

0.40 – 0.60 3

0.60 – 0.80 2

>0.80 (almost circular) 1

The flood susceptibility (FS) map was generated using a map overlay analysis of the six criteria

or factors namely, slope, soils, stream buffer, elevation and land cover. The highest relative

weight was given to elevation (38%), it was followed by slope factor (24%), stream buffer (17%),

shape of the watershed (12%) and soil series (6%). The low relative weight was calculated for

the land cover factor (3%). Relative weights were applied to determine the flood susceptibility by

using the following equation:

𝐹𝑆 = (𝐸𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛 × 0.38) + (𝑆𝑙𝑜𝑝𝑒 × 0.24) + (𝑆𝑡𝑟𝑒𝑎𝑚 𝑏𝑢𝑓𝑓𝑒𝑟 × 0.17)+ (𝑆ℎ𝑎𝑝𝑒 × 0.12) + (𝑆𝑜𝑖𝑙 𝑠𝑒𝑟𝑖𝑒𝑠 × 0.06) + (𝐿𝑎𝑛𝑑 𝑐𝑜𝑣𝑒𝑟 × 0.03)

Based on the overlay analyses of these factors, the different flood susceptibility models were

generated.

Drought Hazard Assessment

Vulnerability to drought is the relationship of susceptibility to physical factors, exposure to

climatic factors; and adaptability to anthropogenic factors. Basically, a relative weight according

to their influence was assigned to each factor. Each factor with the specific hazard values was

prepared and analyzed for simulation. All factors followed the same scaling factor procedure to

assess and map out vulnerable areas (Table 7). Overall, drought hazard maps for observed, 2020s

and 2050s periods were produced founded on different factors and their relative weights.

Different factors are described below:

a. Standardized Precipitation Index: The Standardized Precipitation Index (SPI) is a tool

developed primarily for defining and monitoring drought. It determines the rarity of a

drought at a given time scale of interest for the given station. It can also be used to

determine periods of anomalously wet events. It must be noted that the SPI is not a

drought prediction tool. Mathematically, the SPI is based on the cumulative probability

of a given rainfall event occurring in the station.

The SPI was generated from the variation in a gamma distribution function. The function

was a standard deviation and a mean, which depends on the rainfall characteristics of the

area. The SPI can effectively represent the amount of rainfall over a given time scale,

with the advantage that it provides information on the amount of rainfall , but is also an

indication of the relation of this amount to the normal range, thus leading to the definition

of whether a station is experiencing drought or not. In essence, the SPI value of greater


than 0 is considered to be wet to extremely wet. Higher exposure values are greater than

2, which were classified as extremely dry.

b. Elevation: The elevation was generated using the digital elevation model. Higher

elevations are classified as resistant to drought and have low susceptibility while low

elevations are regarded as areas with severe susceptibility to drought.

c. Soils: The different soil textural classes, more commonly known as the soil series,

describe the soil factors. These textural classes ranged from fine sand to clay types.

Water retention of several soil textures is already reported in literature (Plaster, 2003).

Hence, fine sand types are deemed to be highly susceptible while silt loam types are

classified to have low susceptibility to drought.

d. Irrigation Canal and River: Streams and canals assessment is based on the available

datasets and then buffers were interpolated. Distance within 500 m from the stream and

canal is classified as not susceptible to drought while buffers with >2,000 m distance

from the stream is regarded to have low susceptibility.

e. Population Density: Population density was estimated based on the 2010 population and

area per barangay. The barangays with more than 200 person/ha are classified to be

severely susceptible to drought while barangays with less than 10 person/ha are assigned

to have low susceptibility.

f. Vegetative Index: The latest land satellite imageries were used for this factor. The

influence of water bodies is considered low with values ranging from -1 to 0. Open and

built up areas are classified as severely susceptible to drought because they can generate

high soil and surface evaporation losses.


the drought susceptibility for Quinali “A” Watershed


Relative weights

Standardized Precipitation Index

>0 wet to extremely wet 1

0 – -1 (near normal) 2

-1 to -1.5 (moderately dry) 3

-1.5 to -2.0 (severely dry) 4

>-2 (extremely dry) 5

Elevation (m asl)

>1,000 1

500 – 1,000 2

200 – 5,000 3

100 - 200 4

0 – 100 5

Distance of existing irrigation canal and river (buffer ranges in m)

0-250 1

250-500 2

500-1,000 3

2,000-3,000 4

>3,000 5

Soil texture (categories)

Silt loam 1

Clay loam; Loam 2



Relative weights

Fine sandy loam 3

Sandy loam; Clay; Sandy loam 4

Fine sand 5

Vegetative index (NDVI range index)

-1.0 - 0 1

0.5 – 1.0 2

0.30 – 0.50 3

0.15 – 0.30 4

0 - 0.15 5

Population Density (person/ha) by barangays

<10 1

10 -50 2

50 - 100 3

100 - 500 4

>500 5

Landslide Hazard Assessment

It is essential for landslide susceptibility assessment to involve the detailed knowledge of slope

process that leads to landslides. Such information includes geology, geomorphology and

hydrogeology. Sufficient geotechnical information about the slopes improve slope failure

modeling. Important data that includes soil thickness and rainfall-landslide thresholds are not yet

available at the moment with the Albay Province. Hence, additional constraints were

incorporated in the landslide susceptibility modeling to improve its reliability. In order to define

the landslide susceptibility, the matrix method in a GIS environment was applied (e.g., Irigaray et

al., 2007; Jimenez- Peralvarez, 2009).

The vulnerability to landslide is a function of different physical factors, thematic maps (slope,

soil, geology (geo-hazard), land cover; and climate. Essentially, each factor assigned a relative

weight according to their influence in landslide occurrence. Each factor with the specific hazard

values was prepared and analyzed for simulation (Table 8). All physical factors followed the

same scaling factor procedures. Degrees within each factor were given relative weights (from

low to high) depending on the degree by which they could influence landslide susceptibility. The

geomorphologic and heuristics analyses were utilized to assess and map out areas vulnerable to

landslide.


the landslide susceptibility modeling for Quinali “A” Watershed


Relative weights

Elevation

(ranges in m asl)

<200 1

200 – 400 2

400 – 600 3

600 – 800 4

>800 5

Slope

(% ranges)

<8 1

8 – 18 2

18 - 30 3

30 - 50 4

>50 5



Relative weights

Rainfall

(buffer ranges in mm)

<100 1

100 – 200 2

200 – 300 3

300 – 500 4

>500 5

Soil Morphology

(categories)

Tropaquepts w/ Entropepts; Udorthents & Tropepts 1

Tropopsamments w/ Troporthents; Eutrandepts w/ Eutropepts 2

Tropudalfs w/ Tropepts 3

Entropepts w/ Dystropepts 4

Tropudults w/ Tropudalfs; Mountain soils w/ Entisols, Inecptisols, Ultisols and Alfisols

5

Land cover

(categories)

Closed forest 1

Open forest; Plantation 2

Shrubs; Natural grassland 3

Agricultural/Cultivated; Pasture land; Built-up 4

Bare 5

Fault lines

(buffer ranges in meters)

<500 5

500 – 2,000 4

2,000 – 5,000 3

5,000 – 8,000 2

>8,000 1

Road Network

(buffer ranges in meters)

<150 5

150 - 300 4

300 - 500 3

500 – 1,000 2

>1,000 1

LAND CAPABILITY CLASSIFICATION

Land capability is the capability of the land to sustain the forest ecosystem. Rainfall, soil and

topography are the factors considered for determining the survival of a forest ecosystem. These

factors are assessed for land capability assessment for sustaining forests and (other) ecosystems.

Before, land capability assessment is being conducted without the consideration of climate

change. Today, climate change has been incorporated with land capability given its perceived

impact on forest ecosystems over time.

Land capability evaluation process looks at the characteristics of each factor and determines how

it affects the capability of the land to sustain the forest ecosystem. This process was applied as a

product of land capability classification, which was undertaken using the potential soil erosion of

an area as basis.

Figure 5 shows the framework derived from the erosion-based Land Capability Classification

System (LCCS) developed by Warren et al. (1989) in the United States and applied by Cruz

(1990) in Ibulao Watershed, by De Asis (1998) in UP Land Grant, Quezon-Laguna, and by Cruz

et al. (2010) in Pantabangan and Ambuklao-Binga Watersheds, Philippines, by EDC (2012) in

five (5) geothermal project sites, and by DENR-R4 (2013) in San Juan River Watershed.

Soil erosion is a suitable indicator of land capability because of common key determinants (i.e.,

rainfall, soil and topography). Soil erosion is also a good measure of the sustainability of land

productivity which is the primary success indicator of land capability. The premise of an erosion-


based LCCS is that any use that is compatible with a specific Land Capability Class (LCC) or

zone will not cause significant soil erosion that will lead to the deterioration of land productivity

and soil and water resources. Further, the planned use should not bring about adverse offsite

impacts. Climate change related hazards, such as floods, rain-induced landslides and other

natural hazards, impose limitations on the potential uses of LCC.

Following the procedure described by Warren et al. (1989) and with the aid of GIS analytical

techniques, erosion index was developed and used for land capability classification.

Figure 5. Framework for erosion-based land capability classification system

Generation of Soil Erosion Potential

Soil erosion potential (SEP) was estimated using the principle of the Universal Soil Loss

Equation (USLE) developed by Wischmeier and Smith (1978). Originally, the equation includes

the rainfall erosivity factor (R), soil erodibility factor (K), topographic factors (slope, S and

length, L), plant cover and farming techniques (C), and erosion control practices (P). However,

SEP was computed the same except C factor. The C was excluded because it can easily be

altered by the activities of man. In particular, the equation is as follows:

𝑺𝑬𝑷 = 𝑹 × 𝑲 × 𝑳𝑺 × 𝑷

Rainfall Factor (R)

In 1987, David and Collado adopted an equation to estimate the value of R given the limited

rainfall data in Northern and Central Luzon, Philippines. The equation is shown as follows:

m

ij PAR

where:

Rj = number of erosion index units on a given year j;

Pi = daily precipitation total for a given day i in any year j;

m = an exponent

Rainfall

Topography

Soil Land Capability Classification

Soil Erosion Potential


A = an empirical constant designed to relate the precipitation amount P

with raindrop erosive energy

In the application of the above equation, only rainfall totals, Pi exceeding the threshold value of

25 mm is used, while values of m and A are 2.0 and 0.002, respectively.

In this assessment, the computation of R was based on the above equation. According to David

(1988), the use of an A value of 0.002 renders the R estimates compatible with those of the USLE.

The R factor is commonly computed from the daily rainfall records exceeding or equal to 25 mm

which is considered commonly as erosion producing rainfall events. These records were obtained

from Legaspi weather station in Albay Province.

Soil Erodibility Factor (K)

Owing to the lack of a detailed soil map, the K-values were estimated using the Wischmeier and

Mannering (1969) equation as modified by David (1987) and used by Cruz (1990), Pudasaini

(1992), Singh (1993), Bantayan (1996), De Asis (1998), and Combalicer (2000). This equation

was estimated on the basis of particle size distribution, organic matter content, and pH. It was

also simplified and adjusted for Philippine conditions. The equation is as follows:

SCSaOM

pHK

0062.00082.0

621.0043.0

where:

K = erodibility factor

OM = organic matter content in percent

Sa = percent sand

C = silt % sand %

%clay ratioclay

S = 100

silt %

The K values for the different soil series identified within the Quinali “A” Watershed are shown

in Table 9.

Table 9. K-values for the Quinali “A” Watershed

Soil series K-value

Annam clay loam 0.25

Faraon clay 0.15

Guinobatan sandy loam 0.20

Lava flow 0.30

Legaspi fine sandy loam 0.20

Legaspi fine sandy loam, stony phase 0.20

Legaspi sandy clay loam 0.25

Libon silty clay 0.23

Ligao loam 0.28

Macolod - PIli complex 0.26

Macolod sandy loam 0.27


Soil series K-value

Malinao fine sandy loam 0.20

Mauraro gravelly sandy loam 0.23

Mayon gravelly sandy loam 0.23

Mountain soil (undifferentiated) 0.20

Pili loam 0.28

Sevilla clay 0.15

Tigaon clay 0.15

Umingan clay 0.15

Umingan silt loam 0.33

Slope Length and Slope Gradient Factor (LS)

The topographic factor is the combined effects of slope length (L) and slope steepness (S) on soil

erosion. Slope length is the horizontal distance downslope from the point where overland flow

begins up to where runoff enters a waterway or where deposition starts. Slope gradient is the field

or segment slope, usually expressed as a percentage.

Slope length and slope gradient have significant roles in the erosion process. Since they are

related, the effects of both factors were evaluated as a single topographic factor. Using

combination equations of Smith and Wischmeier (1957) and Williams and Berndt (1972) as

adopted by Cruz (1990), Sing (1993), Pudasaini (1993), Oszaer (1994), and Combalicer (2000).

LS can be computed as follows:

200076000530007601322

7054 S. S . . .

L . LS

m

where:

LS = topographic factor (unitless)

L = slope length factor

S = average slope in %

m = an exponent

m = 0.5 if S>5

m = 0.4 if 5>S>3

m = 0.3 if 3>S>1

m = 0.2 if S<1

Creation of Soil Loss Tolerance

Soil loss tolerance limit of a watershed is a common expression of the SEP estimates. The T

value is an expression of the maximum soil loss that an area can sustain without regressing in

productivity permanently or temporarily. It is a function of the rate of soil accumulation in an

area that is dependent on the slope of an area. Hence, the slope was reclassified according to its

soil loss tolerable limits (Table 10).


Table 10. Prescribed soil loss tolerance in the watershed

Slope Soil Loss Tolerance (ton/ha)

0 - 3 20

3 - 8 15

8 - 18 12

18 - 30 10

30 – 50 7

>50 5

Determination of Soil Erosion Index

The computation of soil erosion index (SEI) is essential to standardize the SEP estimates. As it

is, the SEP per se when directly used as indicator of sensitivity or susceptibility of an area to soil

erosion does not capture the full weight of slope as a determinant of soil erosion in an area.

Hence, the equation is as follows:

𝑺𝑬𝑰 = 𝑺𝑬𝑷

𝑺𝑳𝑻

Generation of Land Capability Classification

Land capability classification was derived based on soil erosion index and other criteria shown in

Table 11. Two (2) major zones, namely protection and production were identified. Each major

zone was further classified into subzones. The output zonation and the indicative land uses in the

watersheds are intended to provide a scientific basis for allocating the lands in the watershed to

various uses. Zoning is not meant to be prescriptive in any absolute sense. The land capability

zoning map is an ideal physical framework for allocating the lands inside the watersheds. The

primary goal is to sustain the long-term productivity of the land and promote the sustainability of

biodiversity, soil and water resources and the delivery of key services of ecosystems in and out of

the watersheds.

Land use zones were delineated based on land capability as indicated by Soil Erosion Index and

other criteria. Two major zones, namely protection and production, were identified. Each major

zone was further classified into subzones. The output zonation and the indicative land uses in the

area are intended to provide a scientific basis for allocating the lands in the municipalities to

various uses. Zoning is not meant to be prescriptive in any absolute sense. The land capability

zoning map is an ideal physical framework for allocating the lands within the watershed. The

primary goal is to sustain the long-term productivity of the land and promote the sustainability of

biodiversity, soil and water resources and the delivery of key services of ecosystems in and out of

the area. The decision on how the lands are ultimately used still rests with the managers, farmers,

and other stakeholders.


Table 11. Land capability classification criteria

Class Land Classification SEI Indicative Land Uses

I PROTECTION AREAS

IA

Strict Protection Zone All remaining natural forests, all areas with high erosion potential and slope >50%, all key biodiversity areas, all areas categorized as SAFDZ, all other areas with SEI > 5

>5

Strict protection, limited collection of ornamental plants, herbs, vines, fruits and other non-timber products may be allowed

IB

Protection Buffer Zone All areas within 40 m of stream banks, all areas within 50 m of major watershed divides;

0

Permanent crops (fruit trees, bamboo), harvesting of fruits and bamboo shoots and culms will be allowed but no harvesting of trees will be allowed

IC Key Biodiversity Area Biodiversity conservation

II PRODUCTION AREAS 0 - 5

IIA Unlimited Production Zone Grasslands and brush lands; built up and cultivated areas

0 - 1

Timber and fruit tree plantations, agriculture and agroforestry can be allowed with suitable soil and water conservation measures, settlement can be allowed

IIB Multiple Use Zone Grasslands and brush lands; built up and cultivated areas

1 - 3

Multi-story timber and fruit tree plantations, agroforestry can be allowed with suitable soil and water conservation measures

IIC Limited Production Zone 3 - 5 Multi-story timber and fruit tree plantations

VALIDATION

Results of simulation based on physical, demographic, vegetative and climatic data were

validated on site. Different stakeholders from municipalities within the watershed were

considered as key informants in the area. Key informants are primarily the Municipal Planning

and Development Officer and the Disaster Risk Reduction and Management Officer (Plate

No. 1). Each informant was asked of his/her observation on the degree of hazard susceptibility of

every barangay. High susceptible barangays are considered to have previous experience of

landslide, drought and flood.

Site visit followed after interviews and documents gathering in the entire watershed. The location

of sites visited is shown in Figure 6.


Plate 1. Participants during the vulnerability assessment validation workshop on January 14, 2015

Figure 6. The Quinali “A” Watershed validation site


FINDINGS

HAZARDS ASSESSMENT

Flood Hazard Assessment

Flood is commonly defined as an overflow of water on normally dry land. It is also described as

the inundation of a normally dry area caused by rising water in an existing river networks and

waterways. In this assessment, flood scenarios were generated given physical factors, vegetative

conditions and rainfall amounts based on CNCM3 model with two scenarios during the 2020s and

2050s periods.

Table 12 shows the summary of vulnerability ratings and area coverage in the Quinali “A”

Watershed. Almost 16,500 ha of the Quinali “A” Watershed was found highly vulnerable as

described under the observed and projected scenarios for 2020s and 2050s periods. Moderate to

high vulnerable areas is likely increasing under A1B scenario that would affect as much as 29,500

hectares. The A2 scenario depicts also an increase of affected areas but less in terms of its area

coverage (4,000 to 10,000 ha).

The climate change phenomenon certainly affects the Quinali “A” Watershed. Five

municipalities and one city are largely covered and surrounded by tributaries of the Bicol River

system which discharge towards Lake Bato. Based on simulated flood vulnerability maps, about

92 inland barangays are highly susceptible to flooding (Table 13). Other barangays are

considered moderately susceptible in near future.

Table 12. Distribution of vulnerability ratings to flooding in the Quinali “A” Watershed

Vulnerability Observed A1B Scenario A2 Scenario

2020 2050 2020 2050

Low 46,679 25,734 25,734 46,679 37,553

Moderate 20,582 29,554 29,554 20,582 23,493

High 4,402 16,375 16,375 4,402 10,617

Total 71,662 71,662 71,662 71,662 71,662


Table 13. Summary of barangays that are highly vulnerable to flooding within the Quinali “A” Watershed

Municipality Area of the municipality (ha)* No. of barangays with high

vulnerability rating

Albay Province

Camalig 13,654 4

Daraga 13,567 0

Guinobatan 17,408 18

Libon 22,851 21

Ligao city 25,851 11

Malilipot 4,542 0

Malinao 10,678 0

Oas 23,958 20

Polangui 14,890 18

Tobaco city 11,224 0

Tiwi 12,440 0


Buhi 18,541 0

Iriga city 13,005 0

Nabua 9,661 0

Total 92

Flooding is one of the problems in urban areas in the Philippines. This catastrophe has been

experienced in the Quinali “A” Watershed brought about by typhoons and tropical storms that

lead to occurrence of disastrous floods and mudflows. Significant flooding was recorded for the

last 10 years with rainfall ranging from 180 to 466 mm (See Table 3). These records were

triggered by typhoons with heavy rains and antecedent rains. Flooding in rivers in Quinali “A”

were intensified by factors associated with the drainage network and stream channels. Most of

the channels operate to speed up the movement of water within the watershed. In addition, the

watershed is occupying almost 73,000 hectares, which is clearly important factor in the sense that

the larger the watershed, the larger is the flood produced from the watershed-wide rainfall event.

Meanwhile, five municipalities and one city, namely: Camalig, Guinobatan, Libon, Oas, and

Polangui, and Ligao City, have experienced the most devastating flood events. These affected

residents in terms of lost and damage to lives, properties, infrastructure, and crops (Plates No. 1

and 2). These conditions were due to intensive commercial and residential developments that

have overtaken the investment in infrastructure, particularly in terms of drainage facilities and

flood control infrastructure. Since the existing urban core is downstream of the Mayon Unit

which is at higher elevation, the risk of flooding is a perennial occurrence. This is affecting

residences and businesses, prompting to the low lying municipalities and city. Frequent flooding

is also associated with the change of urban land use. The change dramatically increased the

paved areas and correspondingly increased the discharge of surface run-off. Urban drainage

networks that were previously designed for lower runoff capacity suddenly became under-

capacitated which resulted to flooding in some of the City’s streets. In addition, silt deposits over

the years have also lessened the area of flow inside drainage lines, making these easily filled up


which also resulted in street flooding. Several barangays of each municipality in the watershed

were identified as flood prone areas.

Figures 7 to 11 present the observed and simulated flood vulnerability maps for 2020s and 2050s

periods.

Based on key informant interviews, the occurrence of flood in Camalig, Guinobatan, Ligao City,

Libon, Oas, and Polangui have unusual flash floods and river floods caused by the Inter-Tropical

Convergence Zone. With more than 100 tributaries abounding the Province of Albay, almost all

the provinces are affected by the flooding. The back flow effects from the outflows of Quinali

“A” River System, Talisay River, Polangui River and other tributaries from the tertiary hills of

Talisay, Mayon Volcano and Mt. Masaraga converges to Lake Bato resulting to severe flooding

in various barangays.

Table 14 presents the vulnerability assessment of each barangay to flooding based on the

observed and simulated future scenarios.

It was predicted that there would be shifting in weather patterns that brought degrees of variation

of monthly rainfall in near future. Damaging typhoons and tropical storms would usually happen

in the months of July, November, and December and might be extended up to January. This

change would likely contribute to significant increase of surface runoff in the watershed. This

pattern and trend may increase the incidence and magnitude of flooding as the amount and

variation of rainfalls change in the area.

Stakeholders believed that there is lack of clear and effective program for watershed

management. With the denudation of the watershed areas at the headwaters of the rivers, such as

in the San Francisco and Cabagsay Rivers in Guinobatan, and Matanglad River, which drains into

the alluvial flood plains of the municipalities of Camalig, Guinobatan, Oas, Libon and Polangui,

and Ligao City.


Plate 2. Portion of flooded areas in San Rafael, Guinobatan, Albay during

typhoon Amang (Taken January 15, 2015)

Plate 3. Rice fields identified as flood prone areas in Oas, Albay, January 14, 2015


Philippine Biodiversity and Watersheds Improved

for Stronger Economy and Ecosystem Resilience (B+WISER) Program

Figure 7. Flood vulnerable areas based on CNCM3 model observed scenario in the Quinali “A” Watershed




Figure 8. Flood vulnerable areas based on CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed




Figure 9. Flood vulnerable areas based on CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed




Figure 10. Flood vulnerable areas based on CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed




Figure 11. Flood vulnerable areas based on CNCM3 model A2 scenario for 2050s period of the Quinali “A” Watershed


Table 14. Vulnerability to flooding by barangay of the Quinali “A" Watershed

Municipality/Barangay Observed Validated* A1B Scenario A1B Scenario

2020 2050 2020 2050

BUHI

Amlongan low low low low Low

Cagmaslog low low low low Low

Delos Angeles low moderate moderate low Low

Divino Rostro low low low low Low

Macaangay low low low low Low

Monte Calvario low low low low Low

Namurabod low low low low low

Santa Isabel moderate moderate moderate moderate moderate

CAMALIG

Anoling low low low low low low

Baligang low low low low low low

Bantonan low low low low low low

Barangay 1 low moderate moderate moderate low low

Barangay 2 low moderate moderate moderate low moderate





Barangay 7 low moderate moderate moderate low low

Gapo low moderate moderate moderate low low

Gotob low moderate moderate moderate low low

Ilawod moderate high moderate moderate moderate moderate

Libod moderate high moderate moderate moderate moderate

Ligban low moderate low low low low

Mina low low low low low low

Palanog low low moderate moderate low low

Quirangay low moderate low low low low

Salugan low moderate low low low low

Sua low moderate low low low low

Tagaytay low high moderate moderate low low

Tinago moderate moderate high high moderate moderate

Tumpa low moderate moderate moderate low low

DARAGA

Mi-Isi low low low low low

GUINOBATAN

Agpay low low moderate moderate low low

Banao low high moderate moderate low low



2020 2050 2020 2050

Binogsacan Lower low high moderate moderate low moderate

Binogsacan Upper low low moderate moderate low moderate

Bololo low low low low low low

Bubulusan low high moderate moderate low low

Calzada moderate high high high moderate moderate

Catomag low low low low low low

Dona Tomasa low high low low low low

Inamnan Grande low low moderate moderate low moderate

Inamnan Pequeno low low moderate moderate low moderate

Inascan low low moderate moderate low low

Iraya moderate high moderate moderate moderate moderate

Lomacao low low moderate moderate low moderate

Maguiron low high moderate moderate low low

Maipon low high moderate moderate low low

Malabnig low low low low low low

Maninila low high moderate moderate low low

Mapaco low low low low low low

Masarawag low high low low low low

Mauraro low low moderate moderate low low

Minto low high moderate moderate low low

Morera low high moderate moderate low low

Muladbucad Grande low high moderate moderate low low

Muladbucad Pequeno low high low low low low

Poblacion moderate high moderate moderate moderate moderate

Quibongbongan low low moderate moderate low moderate

Quitago low moderate moderate moderate low low

San Francisco low high moderate moderate low moderate

San Jose low moderate moderate moderate low moderate

San Rafael low high moderate moderate low moderate

Tandarora low high moderate moderate low moderate

Travesia moderate high moderate moderate moderate moderate

IRIGA CITY

Santo Nino moderate moderate moderate moderate moderate

LIBON

Bacolod high moderate high high high high

Bariw low low moderate moderate low low

Bonbon moderate high high high moderate high

Buga high high high high high high

Bulusan high high high high high high



2020 2050 2020 2050

Burabod low moderate low low low low

Caguscos low low low low low low

East Carisac high high high high high high

Harigue low low low low low low

Libtong low low moderate moderate low moderate

Linao low low low low low low

Mabayawas low low moderate moderate low low

Macabugos low low low low low low

Magallang moderate low high high moderate moderate

Malabiga low low low low low low

Marayag moderate moderate high high moderate high

Matara low low low low low low

Molosbolos low low low low low low

Natasan moderate low moderate moderate moderate moderate

Nino Jesus low low moderate moderate low moderate

Nogpo moderate moderate high high moderate high

Pantao low moderate low low low low

Sagrada Familia moderate moderate moderate moderate moderate moderate

Salvacion low low moderate moderate low moderate

Sampongan low high low low low low

San Agustin high high high high high high

San Antonio low low moderate moderate low moderate

San Isidro high moderate high high high high

San Jose low low moderate moderate moderate moderate

San Pascual moderate low moderate moderate moderate moderate

San Ramon low low low low low low

San Vicente moderate low moderate moderate moderate moderate

Santa Cruz high high high high high high

Villa Petrona moderate low high high moderate moderate

West Carisac high high high high high high

Zone I high moderate high high high high

Zone II high moderate high high high high

Zone III high moderate high high high high

Zone IV high high high high high high

Zone V high moderate high high high high

Zone VI high moderate high high high high

Zone VII high high high high high high

LIGAO

Allang low moderate moderate moderate low low



2020 2050 2020 2050

Amtic low moderate low low low low

Bagumbayan high high high high high high

Balanac

Barayong moderate low moderate moderate moderate moderate

Basag moderate low moderate moderate moderate low

Batang moderate low moderate moderate moderate moderate

Bay high high high high high moderate

Binanowan moderate low moderate moderate moderate moderate

Binatagan high high high high high high

Bobonsuran high low high high high high

Bonga moderate low moderate moderate moderate low

Busac high low high high high high

Busay low low low low low low

Calsada

Cavasi high high high high high moderate

Culliat moderate low moderate moderate moderate moderate

Dunao high moderate high high high moderate

Francia moderate low moderate moderate moderate moderate

Guilid high high high high high moderate

Herrera low low low low low low

Layon moderate low moderate moderate moderate moderate

Macalidong low low low low low low

Mahaba high low high high high moderate

Malama moderate low moderate moderate moderate moderate

Nabonton moderate low moderate moderate moderate low

Nasisi moderate low moderate moderate moderate moderate

Palapas low low low low low low

Pandan high high high high high high

Paulba moderate low moderate moderate moderate low

Paulog moderate low moderate moderate moderate moderate

Pinamaniquian moderate low moderate moderate moderate moderate

Pinit high high high high high moderate

Ranao-Ranao high high high high high moderate

Tagpo high high high high high moderate

Tambo low low low low low low

Tastas moderate low moderate moderate moderate moderate

Tinampo high high high high high moderate

Tinago moderate moderate moderate moderate moderate moderate

Tiongson moderate low moderate moderate moderate low



2020 2050 2020 2050

Tomolin high low high high high moderate

Tuburan moderate low moderate moderate moderate moderate

Tula-Tula Grande moderate low moderate moderate moderate moderate

Tula-Tula Pequeno low low low low low low

Tupas low low low low low low

MALILIPOT

Canaway low low low low low

MALINAO

Bagatangki low low low low low

Quinarabasahan low low low low low

NABUA

Lourdes Young moderate low low moderate moderate

OAS

Badbad moderate low moderate moderate moderate moderate

Bagsa high high high high high moderate

Balogo low moderate low low low low

Bangyawon low low low low low low

Bongoran high moderate high high high high

Busac high

Cadawag low low low low low low

Calaguimit low low low low low low

Calpi high low high high high low

Camagong moderate moderate moderate moderate moderate low

Casinagan moderate low moderate moderate moderate moderate

Centro Poblacion high moderate high high high high

Coliat moderate low moderate moderate moderate low

Del Rosario moderate low moderate moderate moderate low

Gumabao high high high high high moderate

Ilaor Norte high moderate high high high high

Ilaor Sur high moderate high high high high

Iraya Norte high moderate high high high high

Iraya Sur high moderate high high high high

Manga high high high high high high

Maporong high high high high high high

Matambo high low high high high high

Mayag moderate low moderate moderate moderate low

Mayao high moderate high high high high

Moroponros low low low low low low

Obaliw-Rinas high moderate high high high high



2020 2050 2020 2050

Pistola high high high high high moderate

Ramay moderate low moderate moderate moderate moderate

Rizal high moderate high high high high

Saban high low high high high high

San Agustin moderate

San Isidro moderate

San Juan high high high high high moderate

San Miguel high low high high high moderate

San Pascual low

San Vicente low

Tablon low low low low low low

Talisay moderate low moderate moderate moderate moderate

Talongog high moderate high high high high

Tobgon moderate low moderate moderate moderate moderate

Tobog high moderate high high high high

POLANGUI

Agos high high high high high high

Alnay high high high high high low

Alomon high high high high high high

Amoguis moderate low moderate moderate moderate moderate

Anopol low low low low low low

Apad high high high high high moderate

Balaba low low low low low low

Balangibang high high high high high high

Balinad moderate moderate moderate moderate moderate moderate

Basud high high high high high high

Binagbangan (Pintur) low low low low low low

Buyo moderate low moderate moderate moderate moderate

Centro Occidental high high high high high moderate

Centro Oriental high high high high high moderate

Cepres moderate moderate moderate moderate moderate low

Cotmon low low low low low low

Cotnogan moderate moderate moderate moderate moderate moderate

Danao moderate low moderate moderate moderate moderate

Gabon high high high high high moderate

Gamot high low high high high low

Itaran moderate low moderate moderate moderate moderate

Kinale high high high high high high

Kinuartilan low low low low low low



2020 2050 2020 2050

La Medalla moderate low moderate moderate moderate moderate

La Purisima moderate low moderate moderate moderate moderate

Lanigay moderate moderate moderate moderate moderate moderate

Lidong moderate low moderate moderate moderate low

Lourdes moderate moderate moderate moderate moderate low

Magpanambo moderate moderate moderate moderate moderate moderate

Magurang high high high high high high

Matacon high moderate high high high high

Maynaga moderate low moderate moderate moderate low

Maysua low low low low low low

Mendez high high high high high high

Napo high moderate high high high moderate

Pinagdapugan moderate low moderate moderate moderate moderate

Ponso high moderate high high high low

Salvacion low

San Roque moderate low moderate moderate moderate low

Santa Teresita moderate low moderate moderate moderate low

Sta Cruz low

Santicon moderate moderate high high moderate high

Sugcad moderate moderate moderate moderate moderate moderate

Ubaliw high high high high high high

TOBACO CITY

Buang low low low low low

TIWI

San Bernardo low Low low low low

*Validation was conducted in selected municipalities

Drought Hazard Assessment

Drought is described as the unavailability of water due to extreme weather conditions such as

long period of abnormally low rainfall. It is also a condition of moisture shortage to have an

effect on vegetation, animals, and man over a sizeable area. Basically, a drought-related hazard is

an event in which a significant reduction of water brings about severe economic, social and

environmental hardships to the population.

The vulnerability to drought was assessed primarily based on precipitation exposure, influence

and distribution. Usually, drought would be experienced from January to May under the

observed scenario. For A1B scenario, months of January to July in 2020s and November to April

in 2050s periods were registered as nearly normal (0 to -1) to moderately dry (-1.0 to -1.5) based

on CNCM3 model simulation. The A2 scenario, on the other hand, the months of February to

July (2020s) and April to September (2050s) were simulated as nearly normal to moderately dry

season. Change in monthly patterns of dry months would be extended up to September. The


remaining months were considered to be wet season (Figures 12 to 13). In essence, the computed

SPI in Albay Province can effectively represent the amount of rainfall over a given time scale,

with the advantage that it provides not only information on the amount of rainfall. It also gives

an indication of what this amount is in relation to the normal.

Table 15 presents the distribution of vulnerability rating from the watershed. Results show that

there are about 26,000 (36%) to 32,000 (43%) hectares that are highly susceptible to drought

given the projected scenarios and periods. These areas, which are found from 7 municipalities

and 1 city, with a total of 100 barangays are considered as highly vulnerable to drought

(Table 16).

The drought vulnerability spatial distribution is shown in Figures 14 to 18. Highly vulnerable

areas would likely be concentrated in the southern portion of the municipalities of Oas, Libon,

Guinobatan, and Camalig, and Ligao City. Most of the barangays were rated as high in two

different periods (Table 17). The low to moderate rating of vulnerability was also established in

the remaining barangays.

Based on key informant interviews conducted during the validation workshop, there was an

evidence of drought, which affected cropping season in the municipality (Plate No. 4). The

drought prone areas are mostly identified in the low-lying areas. Changes from moderate to low

affected areas are increasing which mainly concentrated on the upper and mountainous

barangays. This can be attributed to the influence of its topography and water availability during

summer season.

Figure 12. Standardized precipitation index based on A1B scenario in Albay Province


Figure 13. Standardized precipitation index based on A1B scenario in Albay Province

Table 15. Drought vulnerability and its area coverage in the Quinali “A” Watershed


2020 2050 2020 2050

Low 3,042 3,042 3,182 3,182 3,042

Moderate 38,789 38,789 42,245 42,245 38,789

High 31,977 31,977 26,282 26,282 31,977

Total 73,808 73,808 73,808 73,808 73,808

Table 16. Distribution of highly vulnerable areas to drought within the Quinali “A”

Watershed



Albay Province

Camalig 13,654 11

Daraga 13,567 0

Guinobatan 17,408 24

Libon 22,851 23

Ligao City 25,851 16

Malilipot 4,542 0

Malinao 10,678 0

Oas 23,958 13

Polangui 14,890 10

Tobaco City 11,224 0

Tiwi 12,440 0





Buhi 18,541 2

Iriga City 13,005 1

Nabua 9,661 0

Total 100




Figure 14. Drought vulnerable areas based on CNCM3 model observed scenario in the Quinali “A” Watershed




Figure 15. Drought vulnerable areas based on CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed




Figure 16. Drought vulnerable based on CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed




Figure 17. Drought vulnerable areas based on CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed




Figure 18. Drought vulnerable areas based on CNCM3 model A2 scenario for 2050s period of the Quinali “A” Watershed


Table 17. Drought vulnerability ratings by barangays in Quinali “A” Watershed

Municipality/ Barangay

Observed Validated* A1B Scenario A1B Scenario

2020 2050 2020 2050

BUHI

Amlongan moderate moderate moderate moderate moderate

Cagmaslog moderate moderate moderate moderate moderate

Delos Angeles high high high high high

Divino Rostro moderate moderate moderate moderate moderate

Macaangay moderate moderate moderate moderate moderate

Monte Calvario moderate moderate moderate moderate moderate

Namurabod moderate moderate moderate moderate moderate

Santa Isabel high high high high high

CAMALIG

Anoling moderate high moderate moderate moderate moderate

Baligang moderate moderate moderate moderate moderate moderate

Bantonan moderate high moderate moderate moderate moderate

Barangay 1 moderate low moderate moderate moderate moderate







Gapo moderate moderate high moderate moderate high

Gotob moderate moderate high moderate moderate high

Ilawod moderate moderate moderate moderate moderate moderate

Libod high moderate high high high high

Ligban moderate moderate moderate moderate moderate moderate

Mina moderate high moderate moderate moderate moderate

Palanog moderate moderate high moderate moderate high

Quirangay moderate high moderate moderate moderate moderate

Salugan moderate high moderate moderate moderate moderate

Sua low high low low low low

Sumpang moderate

Tagaytay moderate moderate moderate moderate moderate moderate

Tinago low high low low low low

Tumpa high high high high high high

DARAGA

Mi-Isi moderate moderate moderate moderate moderate

GUINOBATAN

Agpay moderate high high moderate moderate high




2020 2050 2020 2050

Banao high moderate high high high high

Binogsacan Lower

moderate high moderate moderate high

Binogsacan Upper

moderate high moderate moderate high

Bololo moderate moderate moderate moderate moderate moderate

Bubulusan moderate moderate moderate moderate moderate moderate

Calzada moderate high moderate moderate moderate moderate

Catomag high high high high high high

Dona Tomasa low low moderate low low moderate

Inamnan Grande moderate moderate high moderate moderate high

Inamnan Pequeno

moderate moderate high moderate moderate high

Inascan moderate moderate moderate moderate moderate moderate

Iraya moderate moderate high moderate moderate high

Lomacao moderate moderate high moderate moderate high

Maguiron high low high high high high

Maipon high high high high high high

Malabnig high high high high high high

Maninila moderate high high moderate moderate high

Mapaco high high high high high high

Masarawag low low moderate low low moderate

Mauraro moderate moderate high moderate moderate high

Minto moderate moderate high moderate moderate high

Morera moderate moderate high moderate moderate high

Muladbucad Grande

low low moderate low low moderate

Muladbucad Pequena

low low moderate low low moderate

Poblacion moderate moderate high moderate moderate high

Quibongbongan moderate moderate high moderate moderate high

Quitago moderate moderate moderate moderate moderate moderate

San Francisco moderate moderate high moderate moderate high

San Jose high high high high high high

San Rafael moderate low high moderate moderate high

Tandarora high moderate high high high high

Travesia moderate moderate moderate moderate moderate

IRIGA CITY

Santo Nino high high high high high

LIBON

Bacolod high high high high high high

Bariw high high high high high high




2020 2050 2020 2050

Bonbon moderate moderate moderate moderate moderate moderate

Buga moderate moderate moderate moderate moderate moderate

Bulusan high high high high high high

Burabod moderate moderate moderate moderate moderate moderate

Caguscos moderate moderate moderate moderate moderate moderate

East Carisac moderate moderate high moderate moderate high

Harigue moderate moderate moderate moderate moderate moderate

Libtong high high high high high high

Linao moderate moderate moderate moderate moderate moderate

Mabayawas high high high high high high


Magallang high high moderate high high moderate

Malabiga high high high high high high

Marayag moderate moderate moderate moderate moderate moderate

Matara high high high high high high

Molosbolos high high high high high high

Natasan high high high high high high

Nino Jesus high high high high high high

Nogpo high high high high high high

Pantao high high high high high high

Sagrada Familia high high high high high high

Salvacion moderate moderate high moderate moderate high

Sampongan high high high high high high

San Agustin high high high high high high

San Antonio high high high high high high

San Isidro moderate moderate moderate moderate moderate moderate

San Pascual high high high high high high

San Ramon high high high high high high

San Vicente high high high high high high

Santa Cruz high high high high high high

Villa Petrona high high high high high high

West Carisac moderate moderate moderate moderate moderate moderate

Zone I moderate moderate moderate moderate moderate moderate

Zone II moderate moderate moderate moderate moderate moderate

Zone III moderate moderate moderate moderate moderate moderate

Zone IV moderate moderate moderate moderate moderate moderate

Zone V moderate moderate moderate moderate moderate moderate

Zone VI moderate moderate moderate moderate moderate moderate

Zone VII moderate moderate moderate moderate moderate moderate




2020 2050 2020 2050

LIGAO CITY

Abella moderate moderate moderate moderate moderate moderate

Allang high high high high high high

Amtic low low low low low low

Bagumbayan low low moderate low low moderate

Barayong moderate moderate moderate moderate moderate moderate

Basag moderate moderate moderate moderate moderate moderate

Batang moderate moderate moderate moderate moderate moderate

Bay moderate moderate moderate moderate moderate moderate

Binanowan moderate moderate moderate moderate moderate moderate

Binatagan moderate moderate moderate moderate moderate moderate

Bobonsuran moderate moderate moderate moderate moderate moderate

Bonga moderate moderate moderate moderate moderate moderate

Busac high high high high high high

Busay moderate moderate high moderate moderate high

Cavasi moderate moderate moderate moderate moderate moderate

Culliat high high high high high high

Dunao moderate moderate moderate moderate moderate moderate

Francia high high high high high high

Guilid moderate moderate moderate moderate moderate moderate

Herrera high moderate high high high high

Layon moderate moderate moderate moderate moderate moderate

Macalidong high high high high high high

Mahaba moderate moderate moderate moderate moderate moderate

Malama high high high high high high

Nabonton moderate moderate moderate moderate moderate moderate

Nasisi high low high high high high

Palapas moderate moderate moderate moderate moderate moderate

Pandan moderate moderate moderate moderate moderate moderate

Paulba high high high high high high

Paulog high high moderate high high moderate

Pinamaniquian high high high high high high

Pinit moderate moderate moderate moderate moderate moderate

Ranao-Ranao moderate moderate moderate moderate moderate moderate

Tagpo moderate moderate moderate moderate moderate moderate

Tambo moderate moderate moderate moderate moderate moderate

Tastas high moderate high high high high

Tinampo moderate moderate moderate moderate moderate moderate

Tiongson high high high high high high




2020 2050 2020 2050

Tomolin moderate moderate moderate moderate moderate moderate

Tuburan moderate moderate moderate moderate moderate moderate

Tula-Tula Grande high high high high high high

Tula-Tula Pequeno

moderate moderate moderate moderate moderate moderate

Tupas moderate moderate high moderate moderate high

MALILIPOT

Canaway moderate moderate moderate moderate moderate

MALINAO

Bagatangki low low low low low

Quinarabasahan moderate moderate moderate moderate moderate

NABUA

Lourdes Young moderate moderate moderate moderate moderate

OAS

Badbad high moderate high high high high

Bagsa moderate moderate moderate moderate moderate moderate

Balogo high high high high high high

Bangiawon moderate moderate moderate moderate moderate moderate

Bongoran moderate moderate moderate moderate moderate moderate

Cadawag moderate moderate moderate moderate moderate moderate

Calaguimit moderate moderate moderate moderate moderate moderate

Calpi high high high high high high

Camagong high high high high high high

Casinagan high high high high high high

Centro Poblacion moderate moderate moderate moderate moderate moderate

Coliat high high high high high high

Del Rosario high high high high high high

Gumabao moderate moderate moderate moderate moderate moderate

Ilaor Norte moderate moderate moderate moderate moderate moderate

Ilaor Sur moderate moderate moderate moderate moderate moderate

Iraya Norte moderate moderate moderate moderate moderate moderate

Iraya Sur moderate moderate moderate moderate moderate moderate

Manga moderate moderate moderate moderate moderate moderate

Maporong moderate moderate moderate moderate moderate moderate

Matambo moderate moderate moderate moderate moderate moderate

Mayag high moderate high high high high

Mayao moderate moderate moderate moderate moderate moderate

Moroponros moderate moderate moderate moderate moderate moderate

Obaliw-Rinas moderate moderate moderate moderate moderate moderate

Pistola high high high high high high




2020 2050 2020 2050

Ramay high low high high high high

Rizal moderate moderate moderate moderate moderate moderate

Saban moderate moderate moderate moderate moderate moderate

San Juan moderate moderate moderate moderate moderate moderate

San Miguel moderate moderate high moderate moderate high

Tablon moderate moderate moderate moderate moderate moderate

Talisay high high high high high high

Talongog moderate moderate moderate moderate moderate moderate

Tobgon high high high high high high

Tobog moderate moderate moderate moderate moderate moderate

POLANGUI

Agos moderate moderate moderate moderate moderate moderate

Alnay moderate moderate moderate moderate moderate moderate

Alomon moderate moderate moderate moderate moderate moderate

Amoguis moderate moderate moderate moderate moderate moderate

Anopol low low moderate low low moderate

Apad moderate moderate moderate moderate moderate moderate

Balaba low low moderate low low moderate

Balangibang moderate moderate moderate moderate moderate moderate

Balinad moderate moderate moderate moderate moderate moderate

Basud moderate moderate moderate moderate moderate moderate

Binagbangan moderate moderate moderate moderate moderate moderate

Buyo moderate moderate high moderate moderate high

Centro Occidental

moderate moderate high moderate moderate high

Centro Oriental moderate moderate moderate moderate moderate moderate

Cepres moderate moderate moderate moderate moderate moderate

Cotmon moderate high high moderate moderate high

Cotnogan moderate moderate moderate moderate moderate moderate

Danao moderate moderate moderate moderate moderate moderate

Gabon moderate moderate moderate moderate moderate moderate

Gamot moderate moderate high moderate moderate high

Itaran moderate moderate moderate moderate moderate moderate

Kinale moderate moderate moderate moderate moderate moderate

Kinuartilan moderate high high moderate moderate high

La Medalla moderate high high moderate moderate high

La Purisima moderate high moderate moderate moderate moderate

Lanigay moderate moderate moderate moderate moderate moderate

Lidong moderate moderate moderate moderate moderate moderate

Lourdes moderate moderate moderate moderate moderate moderate




2020 2050 2020 2050

Magpanambo moderate high moderate moderate moderate moderate

Magurang moderate moderate moderate moderate moderate moderate

Matacon moderate moderate moderate moderate moderate moderate

Maynaga moderate moderate moderate moderate moderate moderate

Maysua moderate low high moderate moderate high

Mendez moderate moderate moderate moderate moderate moderate

Napo moderate moderate moderate moderate moderate moderate

Pinagdapugan moderate moderate moderate moderate moderate moderate

Ponso moderate moderate moderate moderate moderate moderate

San Roque moderate moderate moderate moderate moderate moderate

Santa Teresita moderate high moderate moderate moderate moderate

Santicon moderate moderate moderate moderate moderate moderate

Sugcad moderate moderate moderate moderate moderate moderate

Ubaliw moderate moderate moderate moderate moderate moderate

TOBACO CITY

Buang moderate moderate moderate moderate moderate

TIWI

San Bernardo low low low low low



Plate 4. Vulnerable areas to drought within the Quinali “A” watershed (Oas, Albay, January 14, 2015)


Landslide Hazard Assessment

Landslide is essentially described as the downward movement of a relatively dry mass of earth

and rock. It is a process where soil particles detached, transported and deposited from one place

to another. It is usually triggered by excessive rainfall or the occurrence of an earthquake strong

enough to cause instability in the underlying rock layer. The rain-induced landslide hazard maps

for the Quinali “A” Watershed were assessed and generated based on physical conditions,

vegetated factors, and climate change influences given the CNCM3 model and scenarios.

The entire watershed has low susceptibility to landslide based on two scenarios in each period. It

was estimated that high vulnerable landslide areas range from 300 ha to 2,500 ha. Changes of

highly vulnerable areas appeared not really significant to the forest landscape during the 2020s

and 2050s periods. This can be associated primarily with the similar monthly rainfall

distributions except during the 2050s A1B scenario. The A1B scenario predicts significant

fluctuating pattern for the duration of May to September.

Table 18 shows the distribution of landslide-affected areas (in hectares) in the different periods.

About 0.4 to 3.4 percent of the area is considered highly susceptible to landslide in near future

scenarios. Portions of eleven (11) barangays are evaluated as highly vulnerable areas (Table 19).

Most of these areas are apparent in the fragmented mountainous portions of Mayon and Talisay

watersheds (Plate No. 5). Overall, the extent of high landslide risk areas are not really alarming

in which vulnerable areas are fragmented in the mountain ranges.

The landslide-prone areas are mostly found in 6 barangays of Ligao City, namely: Amtic, Busac,

Culiat, Macalidong, Palapas and Pandan (Table 20). Two barangays are highly susceptible within

the municipality of Oas such as Balogo and Bangiawon (Table 21). These barangays are mostly

located at the south western section towards the coastal barangays where steep slopes are

prevalent and the soil therein is generally characterized as sandy loam. Further, extreme rainfall

may trigger landslides and excessive erosion of old pyroclastic deposits in the upper slopes of

Mayon Volcano, generating lahar in all river drainages.

Figures 19 to 23 show the spatial distribution of vulnerable areas to landslide within the Quinali

“A” Watershed.

In the upland areas, denudation of forest cover is one of the most reported environmental issues,

contributes significantly to climate change, landslides, and loss of soil productivity. The over-

extraction of forest resources had resulted to denudation of the upland areas. This is evident as

one travels along the national road to Pio Duran where hills and mountains are plainly deforested.

Upland agriculture and kaingin farming contributed much to this situation. With the soil exposed,

surficial erosion ensues, thus bringing down fertile deposits until the land loss its productivity.

This condition contributes to landslides, and eventually siltation of rivers and flashflood.


Table 17. Rain-induced landslide vulnerability of the Quinali “A” Watershed


2020 2050 2020 2050

Low 55,864 54,177 54,177 55,864 55,864

Moderate 15,544 15,065 15,065 15,544 15,544

High 301 2,466 2,466 301 301

Total 71,708 71,708 71,708 71,708 71,708

Table 18. Distributions of highly vulnerable areas to landslide within the Quinali “A”

Watershed



Albay Province

Camalig 13,654 1

Daraga 13,567 0

Guinobatan 17,408 1

Libon 22,851 0

Ligao City 25,851 6

Malilipot 4,542 0

Malinao 10,678 0

Oas 23,958 2

Polangui 14,890 1

Tobaco City 11,224 0

Tiwi 12,440 0


Buhi 18,541 0

Iriga City 13,005 0

Nabua 9,661 0

Total 11


Plate 5. Landslide in a portion of Mt. Masaraga watershed forest reserve (Oas, Albay, January 14, 2015)

Table 19. Rain-induced landslide vulnerability by barangay in the Quinali “A” Watershed

Barangay Observed Validated A1B Scenario A1B Scenario

2020 2050 2020 2050

BUHI

Amlongan low low low low low

Cagmaslog low low low low low

Delos Angeles low low low low low

Divino Rostro low low low low low

Macaangay low low low low low

Monte Calvario low low low low low

Namurabod low low low low low

Santa Isabel low low low low low

CAMALIG

Anoling low low low low low low

Baligang low low moderate moderate low low

Bantonan low moderate moderate moderate low low

Barangay 1 low low low low low low




2020 2050 2020 2050






Gapo low low low low low low

Gotob low high moderate moderate low low

Ilawod low low low low low low

Libod low low low low low low

Ligban low low low low low low

Mina low low low low low low

Palanog low low low low low low

Quirangay low low low low low low

Salugan low low low low low low

Sua low low low low low low

Tagaytay low low low low low low

Tinago low low low low low low

Tumpa low low low low low low

DARAGA

Mi-Isi low low low low low low

GUINOBATAN

Agpay low low low low low low

Banao low low moderate moderate low low

Binogsacan Lower low low low low low low

Binogsacan Upper low low low low low low

Bololo low low moderate moderate low low

Bubulusan low low low low low low

Calzada low low low low low low

Catomag low low low low low low

Dona Tomasa low low low low low low

Inamnan Grande low low low low low low

Inamnan Pequeno low low low low low low

Inascan low low low low low low

Iraya low low low low low low

Lomacao low low low low low low

Maguiron low low low low low low

Maipon low low low low low low

Malabnig low high low low low low

Maninila low low low low low low



2020 2050 2020 2050

Mapaco low low moderate moderate low low

Masarawag low low low low low low

Mauraro low low low low low low

Minto low low low low low low

Morera low low low low low low

Muladbucad Grande low low low low low low

Muladbucad Pequeno

low low low low low low

Poblacion low low low low low low

Quibongbongan low low low low low low

Quitago low low low low low low

San Francisco low low low low low low

San Jose low low moderate moderate low low

San Rafael low low low low low low

Tandarora low low moderate moderate low low

Travesia low low low low low low

IRIGA CITY

Santo Nino low low low low low low

LIBON

Bacolod low low low low low low

Bariw low low low low low low

Bonbon low low moderate moderate low low

Buga low low low low low low

Bulusan low low low low low low

Burabod moderate moderate moderate moderate moderate moderate

Caguscos moderate moderate moderate moderate moderate moderate

East Carisac low low low low low low

Harigue moderate moderate moderate moderate moderate moderate

Libtong low low low low low low

Linao moderate moderate moderate moderate moderate moderate

Mabayawas low low low low low low


Magallang low low low low low low

Malabiga moderate moderate moderate moderate moderate moderate

Marayag low low low low low low

Matara moderate moderate moderate moderate moderate moderate

Molosbolos low low low low low low

Natasan low low low low low low

Nino Jesus moderate moderate moderate moderate moderate moderate

Nogpo low low low low low low



2020 2050 2020 2050

Pantao moderate moderate moderate moderate moderate moderate

Sagrada Familia moderate moderate moderate moderate moderate moderate

Salvacion low low low low low low

Sampongan moderate moderate moderate moderate moderate moderate

San Agustin low low low low low low

San Antonio moderate moderate moderate moderate moderate moderate

San Isidro low low low low low low

San Pascual moderate moderate moderate moderate moderate moderate

San Ramon moderate moderate moderate moderate moderate moderate

San Vicente moderate moderate moderate moderate moderate moderate

Santa Cruz low low low low low low

Villa Petrona low low low low low low

West Carisac low low low low low low

Zone I low low low low low low

Zone II low low low low low low

Zone III low low low low low low

Zone IV low low low low low low

Zone V low low low low low low

Zone VI low low low low low low

Zone VII low low low low low low

LIGAO CITY

Abella low low low low low low

Allang moderate moderate moderate moderate moderate moderate

Amtic low high low low low low

Bagumbayan low low low low low low

Barayong low low low low low low

Basag low low low low low low

Batang low low low low low low

Bay low low low low low low

Binanowan low low low low low low

Binatagan low low low low low low

Bobonsuran low low low low low low

Bonga moderate moderate moderate moderate moderate moderate

Busac moderate high high high moderate moderate

Busay moderate moderate moderate moderate moderate moderate

Cavasi low low low low low low

Culliat low high low low low low

Dunao low low low low low low

Francia moderate moderate moderate moderate moderate moderate



2020 2050 2020 2050

Guilid low low low low low low

Herrera low low low low low low

Layon low low low low low low

Macalidong moderate high high high moderate moderate

Mahaba low low low low low low

Malama moderate moderate moderate moderate moderate moderate

Nabonton low low low low low low

Nasisi low low low low low low

Palapas high high high high high high

Pandan low high low low low low

Paulba moderate moderate moderate moderate moderate moderate

Paulog low low low low low low

Pinamaniquian moderate moderate moderate moderate moderate moderate

Pinit low low low low low low

Ranao-Ranao low low low low low low

Tagpo low low low low low low

Tambo moderate moderate moderate moderate moderate moderate

Tastas moderate moderate moderate moderate moderate moderate

Tinampo moderate moderate moderate moderate moderate moderate

Tiongson moderate moderate moderate moderate moderate moderate

Tomolin low low low low low low

Tuburan low low low low low low

Tula-Tula Grande moderate moderate moderate moderate moderate moderate

Tula-Tula Pequeno high moderate high high high high

Tupas low low low low low low

MALILIPOT

Canaway low low low low low low

MALINAO

Bagatangki low moderate moderate low low

Quinarabasahan low low low low low

NABUA

Lourdes Young low low low low low

OAS

Badbad moderate moderate moderate moderate moderate moderate

Bagsa low low low low low low

Balogo moderate high high high moderate moderate

Bangiawon moderate moderate high high moderate moderate

Bongoran low low low low low low

Cadawag moderate low moderate moderate moderate moderate



2020 2050 2020 2050

Calaguimit moderate low moderate moderate moderate moderate

Calpi moderate low moderate moderate moderate moderate

Camagong moderate moderate moderate moderate moderate moderate

Casinagan moderate moderate moderate moderate moderate moderate

Centro Poblacion low low low low low low

Coliat moderate Low moderate moderate moderate moderate

Del Rosario low low low low low low

Gumabao moderate moderate moderate moderate moderate moderate

Ilaor Norte low low low low low low

Ilaor Sur low low low low low low

Iraya Norte low low low low low low

Iraya Sur low low low low low low

Manga low low low low low low

Maporong low low low low low low

Matambo low low low low low low

Mayag moderate low moderate moderate moderate moderate

Mayao low low low low low low

Moroponros moderate low moderate moderate moderate moderate

Obaliw-Rinas low low low low low low

Pistola moderate moderate moderate moderate moderate moderate

Ramay moderate moderate moderate moderate moderate moderate

Rizal low low low low low low

Saban low low low low low low

San Juan moderate moderate moderate moderate moderate moderate

San Miguel low low low low low low

Tablon moderate moderate moderate moderate moderate moderate

Talisay moderate moderate moderate moderate moderate moderate

Talongog low low low low low low

Tobgon low low low low low low

Tobog low low low low low low

POLANGUI

Agos low low low low low low

Alnay moderate moderate moderate moderate moderate moderate

Alomon low low low low low low

Amoguis low low low low low low

Anopol low low low low low low

Apad low low low low low low

Balaba low low low low low low

Balangibang low low low low low low



2020 2050 2020 2050

Balinad low low low low low low

Basud low low low low low low

Binagbangan low low low low low low

Buyo low low low low low low

Centro Occidental low low low low low low

Centro Oriental low low low low low low

Cepres low low low low low low

Cotmon low low low low low low

Cotnogan low low low low low low

Danao low low low low low low

Gabon low low low low low low

Gamot low low low low low low

Itaran low low low low low low

Kinale low low low low low low

Kinuartilan low low low low low low

La Medalla low low low low low low

La Purisima low low low low low low

Lanigay low low low low low low

Lidong low low low low low low

Lourdes low low low low low low

Magpanambo low low low low low low

Magurang low low low low low low

Matacon low low low low low low

Maynaga low low low low low low

Maysua low low low low low low

Mendez low low low low low low

Napo low low low low low low

Pinagdapugan low low low low low low

Ponso moderate moderate moderate moderate moderate moderate

San Roque low low low low low low

Santa Teresita low low low low low low

Santicon low low low low low low

Sugcad low low low low low low

Ubaliw low low low low low low

TABACO CITY

Buang low low low low low

TIWI

San Bernardo moderate moderate moderate moderate moderate




Figure 19. Landslide vulnerable areas based on CNCM3 model Observed scenario in the Quinali “A” Watershed






Figure 20. Rain-induced landslide vulnerable areas based on the CNCM3 model A1B scenario for 2020s period of the Quinali “A” Watershed


Figure 21. Rain-induced landslide vulnerable areas based on the CNCM3 model A1B scenario for 2050s period of the Quinali “A” Watershed




Figure 22. Rain-induced landslide vulnerable areas based on the CNCM3 model A2 scenario for 2020s period of the Quinali “A” Watershed




Figure 23. Rain-induced landslide vulnerable areas based on the CNCM3 model A2 scenario for 2050s period of the Quinali “A”




LAND CAPABILITY CLASSIFICATION

Table 21 presents the recommended land capability classification of the Quinali “A” Watershed.

Estimated protection zone is about 60,068 ha (84%) of which 53,751 ha are classified as strict

protection and 6,316 ha for buffer zone. Strict protection zones are commonly found in upland

barangays of the municipalities of Camalig, Guinobatan, and Ligao and other barangays which

have high erosion potentials. The buffer zones are identified from the upstream to downstream

areas. The zone is highly suitable for planting bamboos, banana, giant fern, and other fruit trees for

stream bank stabilization.

The total production zone is 11,639 ha of which, 5,830 ha are classified for multiple use production

while the remaining areas are mostly for limited production (5,748 ha). In Quinali “A” Watershed,

the unlimited production zone is predominantly agricultural, built-up, residential and commercial

areas. The multiple use zone is mainly dominated by coconut plantation mixed with maize, abaca,

rice, sugarcane, mango, gmelina, mahogany, cassava, sweet potato, and other fruit trees.

The land classification distribution within the watershed is shown in Figure 24.

Table 20. Recommended land capability classification of the Quinali “A” Watershed

Class Land Classification Indicative Land Uses Area

(ha) Hazard Limitations

1 PROTECTION AREAS (60,068 ha) 84%

1A Strict Protection

Zone

Strict protection, limited collection and

harvesting of plants, herbs, vines, fruits

and other non-timber products; No

cultivation should be allowed

53,751 Highly vulnerable to flooding (7 ha)

Highly vulnerable to drought (25,000 ha)

Highly vulnerable to landslide (289 ha)

1B Stream Buffer Zone

Permanent crops (fruit trees, bamboo,

giant fern); harvesting of fruits and

bamboo shoots and culms is allowed

but no harvesting of trees are allowed;

construction of dike, retaining walls, and

other soil and water conservation

measures

6,316 Highly vulnerable to flooding (414 ha)

Highly vulnerable to drought (1,300 ha)

Highly vulnerable to landslide (20 ha)

2 PRODUCTION AREAS (11,639 ha) 16%

2A Unlimited

Production Zone

Built up and cultivated areas 61 Highly vulnerable to drought (47 ha)

Highly vulnerable to flooding (60 ha)

2B Multiple Use Zone

Built up; cultivated areas (rice field);

banana plantation; coconut plantation;

camote plantation; sugarcane; abaca;

corn plantation; gmelina and mahogany

plantations; multi-story timber and fruit

tree plantations

5,830 Highly vulnerable to drought (2,800 ha)

Highly vulnerable to flooding (2,312 ha)

2C

Limited Production

Zone

Grasslands and

brushlands; built up

and cultivated areas

Built up and cultivated areas with soil

and water conservation measures

5,748 Highly vulnerable to drought (2,500 ha)

Highly vulnerable to flooding (1,793 ha)




Figure 24. Prescribed land capability classification in the Quinali “A” Watershed


CONCLUSIONS

The hazard assessment predicted the shifting in weather patterns that brought degrees of

variation of monthly rainfall in near the future. Tropical storms and extreme rainfall events

will likely occur in the months of July, November, and December and might be extended

up to January. Hence, this pattern and trend may increase the incidence and magnitude of

flooding as the amount and variation of rainfalls change in the area.

The risk of flooding is a perennial occurrence in the low-lying areas of the watershed.

Flooding is the topmost hazard wherein about 92 inland barangays are highly susceptible.

Five municipalities are found to have experienced the most devastating flood events due to

intensive commercial and residential developments in the existing urban core of the Mayon

Unit. Heavy to torrential rainfall events associated by tropical storms and typhoons, forest

degradation, siltation are described to be mainly causing this incidence.

Drought would likely be experienced from January to May under the observed scenario,

January to July in 2020s, and November to April in 2050s period. A total of 100 barangays

within the Quinali “A” Watershed are found highly vulnerable to drought. Highly

vulnerable areas would likely be concentrated in the southern portion of the Municipalities

of Oas, Libon, Guinobatan, and Camalig, and Ligao City.

About 11 barangays are categorically ranked as highly vulnerable to rain-induced landslide

in the near future scenarios. The extreme rainfall events resulting to rain-induced landslide

would primarily affect the mountain ranges and steep slopes of Mt. Mayon. Spatial

distributions and extents are found not significant to the forest landscape during the 2020s

and 2050s periods.

Based on land capability classification, about 84% of the Quinali “A” Watershed are

estimated to be likely suitable as protection areas which include the classification of strict

protection, stream buffer, and key biodiversity areas.

As agreed upon by the different stakeholders and the B+WISER team, results of the assessment

including shape files will be shared to them to ensure that results will be used by the targeted

clientele.


RECOMMENDATION

Results of the vulnerability assessment will serve as inputs to the management plan of Quinali “A”

Watershed as well as in the comprehensive land use plans of the different LGUs inside the

watershed and PAs. Mainstreaming climate change considerations such as results of the

vulnerability assessment will similarly enable the managers and LGUs to design mitigation and

adaptation strategies that will make the ecosystems and its components become resilient to the

adverse impacts of climate change. Vulnerability assessment is a critical part of any planning

exercise since climate change cuts across a wide array of various sectors.


REFERENCES

Combalicer, E.A. 2000. Application of Soil Erosion Potentials in the Lower Magat Watershed,

Philippines. MS Thesis. UPLB, College, Laguna.

Cruz, R. V. O., Lasco, R. D., Pulhin, J. M., Pulhin, F. B., and Garcia, K. B. 2005. Assessment of

Climate Change Impacts, Vulnerability and Adaptation: Water Resources of Pantabangan-

Carranglan Watershed. Environmental Forestry Programme. College of Forestry and Natural

Resources. University of the Philippines Los Baños. Laguna. Philippines. Assessments of

Impacts and Adaptations to Climate Change (AIACC), AIACC-AS21 Working Paper_N09, a

joint project of START, the Third World Academy of Sciences, and the UN Environment

Programme.

Cruz, R.V.O. 1990 Land-use suitability assessment and land capability classification in Ibulao

Watershed, Philippines. Ph.D. Dissertation. Univ. of Arizona, USA. 180p.

De Asis, A. 1998. A GIS-aided soil erosion potential based solution to UP Land Grant allocation

problem. MS Thesis. UPLB, Coll., Lag., Phil. 86p.

DENR-R4. 2013. Watershed Characterization and Vulnerability Assessment of the San Juan River

Watershed. Volume 1. Calamba City. 156p.

EDC. 2012. EDC Water Balance Assessment Report. Ortigas, Manila. 59p.

Irigaray, C., Fernandez, T., El Hamdouni, R., and Chacon, J., 2007. Evaluation and validation of

landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic

Cordillera (southern Spain), Nat. Hazards, 41, 61–79.

Jimenez-Pelvarez, J., Irigaray, C., El Hamdouni, R., and Chacon, J., 2009. Building models for

automatic landslide-susceptibility analysis, mapping and validation in ArcGIS, Nat. Hazards,

50, 571– 590.

Lasco, R. D., K. G. MacDicken, F. B. Pulhin, I. Q. Guillermo, R. F, Sales and R.V. O. Cruz. 2006.

Carbon stocks assessment of a selectively logged dipterocarp forest and wood processing mill.

Plaster, E.J. 2003. Soil Science and Management. 4th Ed. NY: Clifton Park.

Salas-Mélia D., Chauvin, F., Déqué, M., Douville, H., Guérémy, J. F., Marquet, P., Planton, S.,

Royer, J. F., and Tyteca, S. 2005. Description and validation of the CNRM-CM3 global

coupled model, CNRM Working Note 103, 36 p.


ANNEX A. B+WISER DIRECTORY

DENR PROGRAM STEERING COMMITTEE FOR.RICARDO L. CALDERON, CESO III Chair Director, Forest Management Bureau (FMB) Department of Environment and Natural Resources FMB Bldg., Visayas Avenue, Diliman 1100 Quezon City Phone: 928-9313/927-4788; Fax: 920-0374 Email address: [email protected] MS. LOURDES G. FERRER Co-Chair Director for Program Implementation Office of the Undersecretary for Field Operations (OUFO) Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 928-4969; Fax: 929-4969 Email address: [email protected] DR. THERESA MUNDITA S. LIM Member Director, Protected Areas and Wildlife Bureau (PAWB) Department of Environment and Natural Resources Ninoy Aquino Parks and Wildlife Center Diliman, 1100 Quezon City Phone: 924-6031 to 35 local 203 & 204; Fax: 920-4417 Email address: [email protected] ENGR. EDWIN G. DOMINGO Member Overall Director, Foreign-Assisted and Special Projects Office (FASPO) Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 925-2344; Fax: 926-8065 Email address: [email protected] DR. HENRY A. ADORNADO Member Director, Ecosystems Research and Development Bureau (ERDB) Department of Environment and Natural Resources University of the Philippines at Los Baños College, Laguna Phone: (049) 536-3628; Fax: (049) 536-2850 Email address: [email protected]

mailto:[email protected]






FOR. ERIBERTO C. ARGETE, CESO IV Member Director, Planning and Policy Studies Office (PPSO) Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 929-6626 local 2043, 925-1184 Email address: [email protected] ENGR. LEO L. JASARENO Member Director, Mines and Geo-Sciences Bureau (MGB) Department of Environment and Natural Resources MGB Compound North Avenue, Diliman Quezon City Phone: 920-9120; 920-9130; Fax 920-1635 Email address: [email protected] DR. RIJALDIA N. SANTOS Member Director, Resource Data Analysis Branch National Mapping and Resource Information Authority (NAMRIA) Lawton Avenue, Fort Andres Bonifacio 1638 Taguig City Phone: 884-2857 / 816-1033 Email address: [email protected]



TECHNICAL WORKING GROUP FOR.MAYUMI MA. QUINTOS-NATIVIDAD OIC, Assistant Director Forest Management Bureau (FMB) Department of Environment and Natural Resources FMB Bldg., Diliman, 1100 Quezon City Phone: 927-4784; 928-2778; TeleFax: 920-8650 Email address: [email protected] FOR.EDNA D. NUESTRO Chief, Planning and Project Management Services Division Forest Management Bureau (FMB) Department of Environment and Natural Resources FMB Bldg., Diliman, 1100 Quezon City Phone: 927-6217; Telefax: 920-0368 Email address: [email protected] FOR.REMEDIOS T. EVANGELISTA Chief, Reforestation Division Forest Management Bureau (FMB) Department of Environment and Natural Resources FMB Bldg., Diliman, 1100 Quezon City TeleFax: 928-2891 Email address: [email protected] FOR.NORMA M. MOLINYAWE Chief, Biodiversity Management Division Protected Areas and Wildlife Bureau (PAWB) Department of Environment and Natural Resources Ninoy Aquino Parks and Wildlife Center Diliman, 1100 Quezon City Phone: 924-6031 to 35 local 232; TeleFax: 925-8947 Email: [email protected], [email protected] FOR. ARMIDA P. ANDRES Officer-in-charge, Planning Staff Protected Areas and Wildlife Bureau (PAWB) Department of Environment and Natural Resources Ninoy Aquino Parks and Wildlife Center Diliman, 1100 Quezon City Phone: 924-6031 to 35 local 210; TeleFax: 920-4486 Email: [email protected]

FOR. MARLYNN M. MENDOZA Chief, Protected Area Community Management Division Protected Areas and Wildlife Bureau (PAWB) Department of Environment and Natural Resources Ninoy Aquino Parks and Wildlife Center Diliman, 1100 Quezon City Phone: 924-6031 to 35 local 226; TeleFax: 925-8950 Email: [email protected]







DR. CARMELITA VILLAMOR Ecosystems Research and Development Bureau (ERDB) Department of Environment and Natural Resources University of the Philippines at Los Baños College, Laguna Phone: (049) 536-2229, TeleFax: 536-7746 Email address: [email protected] FOR. MONINA M. CUNANAN Chief, Project Development and Evaluation Division Planning and Policy Studies Office (PPSO) Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 929-6626 local 2042, 928-9737 Email address: [email protected] MS. LLARINA MOJICA OIC, Policy Studies Division Planning and Policy Studies Office (PPSO) Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 929-6626 local 2046, TeleFax: 925-1183 Email address: [email protected] Ms. SOLITA CASTRO Senior Remote Sensing Technologist National Mapping and Resource Information Authority (NAMRIA) Lawton Avenue, Fort Andres Bonifacio 1638 Taguig City Phone: 810-4831 loc. 741 / 810-2891 / 884-2867 Email address: [email protected] MR. CONRAD BRAVANTE OIC-Chief, Project Monitoring Division Foreign-Assisted and Special Projects Service Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City Phone: 929-6626 local 2118, TeleFax: 927-6755 Email address: [email protected] MS. MOONYEEN MANRIQUE Project Officer, Project Monitoring Division Foreign-Assisted and Special Projects Service Department of Environment and Natural Resources Visayas Avenue, Diliman 1100 Quezon City TeleFax: 928-0028 Email address: [email protected]



UNITED STATES AGENCY FOR INTERNATIONAL DEVELOPMENT (USAID) Mr. JEREMY GUSTAFSON Director Office of Environment, Energy, and Climate Change (OEECC) U.S. Agency for International Development Annex 2 Building, U.S. Embassy 1201 Roxas Boulevard 1000 Ermita, Manila, Philippines (632) 301-2129; Fax: (632) 301-6213 Email: [email protected] Mr. JOSEPH FOLTZ Deputy Director Office of Environment, Energy, and Climate Change (OEECC) U.S. Agency for International Development Annex 2 Building, U.S. Embassy 1201 Roxas Boulevard 1000 Ermita, Manila, Philippines Phone: (632) 301-4823; Fax: (632) 301-6213 Email: [email protected] Mr. OLIVER O. AGONCILLO Natural Resources Policy Advisor Office of Environment, Energy, and Climate Change (OEECC) U.S. Agency for International Development Annex 2 Building, U.S. Embassy 1201 Roxas Boulevard 1000 Ermita, Manila, Philippines Phone: (632) 301-4828; (632) 301-6000 local 4828; Fax: (632) 301-6213 Email: [email protected] Mr. RANDY JOHN N. VINLUAN Sustainable Landscape Specialist Office of Environment, Energy, and Climate Change (OEECC) U.S. Agency for International Development Annex 2 Building, U.S. Embassy 1201 Roxas Boulevard 1000 Ermita, Manila, Philippines Phone: (632) 301-4826; (632) 301-6000 local 4826; Fax: (632) 301-6213 Email: [email protected]






B+WISER PROGRAM KEY STAFF

Staff Designation E-mail Address Roberto B. Rapera Acting Chief of Party [email protected] Ferdinand S. Esguerra* Communications Specialist [email protected] Rojessa T. Saceda Communication Specialist [email protected] Rodolfo B. Santos, Jr. M&E Specialist [email protected] Nena O. Espiritu Sustainable Finance Specialist [email protected] Maria Zita B. Toribio Policy & Governance Specialist [email protected] Guillermo A. Mendoza REDD+/MRV Specialist [email protected] Elena Chiong-Javier Gender & Inclusion Specialist [email protected] Felix Gaschick Forestry & Biodiversity Specialist [email protected] Wilman C. Pollisco Legal & ADR Specialist [email protected] Siegfried L. Batucan* Mapping & GIS Specialist [email protected] Raul M. Caceres* Social Marketing & BCC Consultant [email protected] Calixto E. Yao Coastal Forest Ecosystem Specialist [email protected] Robert R. Araño Field Manager – NSMNP rarañ[email protected] Roldan R. Dugay Field Manager – UMRBPL-KWFR [email protected] Geoffrey E. Sa-ong Field Manager – QAW [email protected] Anselmo P. Cabrera Field Manager – BRWNP [email protected] Rodolfo V. Aragon Field Manager – MKRNP [email protected] Gregory Benjamin M. Luz Field Manager – MANP [email protected] Sarah M. Simmons Operations Manager [email protected] Susan R. Elizondo Procurement/SAF Manager [email protected] Catherine C. Pollisco Finance Manager [email protected] Nicanor P. Gonzalo Senior Accountant [email protected] Eugene C. Bennagen Technical Activity Manager [email protected] Ina Karissa D. Tobias PCU Coordinator [email protected] Jay Lowell H. Payuyo IT/MIS Specialist [email protected] Romero Y. Inamac Publications Associate [email protected] Leo Rex C. Cayaban Senior Program Associate [email protected] Ramil S. Alcala Program Associate [email protected] John Kevin D.G. Benico Program Associate [email protected] Desiree A. Donceras Program Associate [email protected] Joyce Lyn S. Molina Program Associate [email protected] Kent C. Tangcalagan Program Associate for IPs & Social Media [email protected] Ana Georgina C. Ciriaco Program Development Associate [email protected]

SUBCONTRACTORS FFI Neil Aldrin D. Mallari Biodiversity and Ecology Specialist [email protected] Jose Don T. de Alban RS/GIS Specialist [email protected] Edmund Leo B. Rico Carbon Inventory & Assessment Specialist [email protected] Orlando Arciaga Community Development Specialist [email protected] Angelica Kristina Monzon RS/GIS Data Analysis Associate angelica.monzon@fauna-flora. Christian Supsup RS/GIS Data Analysis Associate [email protected] Rizza Karen A. Veridiano Forest Carbon & Inventory Assess. Assoc. [email protected] Nevong Puna Biodiv Assess. (BA) & Monitoring Assoc. [email protected] Jackie Lou Wenceslao BA & Monitoring Associate [email protected] Laila Pornel Community Development Associate [email protected]








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ICRAF

Rodel P. Lasco REDD+ and Agro-Forestry Specialist [email protected] Florencia B. Pulhin Climate Change & Forest Biodiversity Sp. [email protected] Bhen A. Aguihon Researcher [email protected]

HARIBON FOUNDATION*

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Documents

Vulnerability Assessment of the Quinali “A” Watershed in