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Home>FFTC Document Database>Extension Bulletins>Development and Application of High- Resolution Gis-Based Atlas to Enhance Watershed Management in the Philippines
Development and Application of High- Resolution Gis-Based Atlas to Enhance Watershed Management in the Philippines
Nathaniel C. Bantayan
Institute of Renewable Natural Resources
College of Forestry and Natural Resources
University of the Philippines at Los Banos,
College, Laguna, Philippines, 2002-10-01


This Bulletin uses GIS and modeling to enhance the management of four important watersheds in the Philippines. The analyzed data was combined into an Atlas showing important features of the watersheds, including their susceptibility to fire, floods and erosion. It is proposed to develop similar Atlases for the remaining important watersheds of the Philippines. Knowledge of their characteristics, and changes in these over time because of changes in land use, will enable the government to take a preventative rather than proactive response to environmental damage, and thus safeguard the country's water resources.


Our watershed resources provide the "engine" for the economic development of the Philippines. Why is this so? Just take the basic and vital watershed resource called Water. Everyone should know how important water is. However, for many, the importance of water may not go beyond the provision of their basic domestic needs for drinking, cooking, bathing and washing. They may not realize the strategic role of water in developing the country.

For example, water from the watersheds supports irrigation systems all over the Philippines. These irrigation systems sustain the country's food production program. In addition, many people may not know the role of water in generating electricity. President Gloria Macapagal-Arroyo recently approved the Power Reform Law. This Law included the privatization of the National Power Corporation. This corporation manages the country's watersheds which support strategic hydroelectric dams. The Power Reform Law will significantly influence the stability of the watersheds which supply water to these key hydroelectric structures. Whether or not there will be a sustainable supply of usable water will depend on the way in which the watersheds are managed.

The importance of effective management of watersheds to ensure their sustainability as a source of usable water for hydroelectric generation and irrigation cannot be overemphasized. Effective watershed management requires updated, reliable and easy-to-access information about the watersheds. It has been said that "Information is power". Information about our watersheds, including the bio-physical aspects, is indispensable.

Key watershed information can be obtained from various sources. A watershed Atlas should provide much of the available information for overall effective watershed management and development. Hence, we have developed an Atlas of selected watersheds for the Philippines.

The Gis-Based Atlas: Historical Background

The idea of a watershed Atlas for the Philippines began as early as 1989. Initial key watersheds were selected where a prototype watershed Atlas would be prepared. Consequently, a project proposal to develop this Atlas was prepared and submitted. The project was approved in principle in 1991, but unfortunately, the project was shelved after a key official left the government service.

Effects to pursue the project continued, because of several concerns. Among these were the need to have readily accessible, standardized maps of key watersheds with a uniform scale, and the need to have fully characterized watersheds, including both bio-physical and climatic data. All these data should be included in a watershed Atlas.

It was not until 1998 that the project to prepare an Atlas of selected watersheds in the Philippines finally began, funded by the Government. It had two components, namely: research and institutional development. The research component included hazard estimation and the forecasting of soil erosion, fire and flood in the study watersheds.

As important, if not more so, was the institutional development. Through the project, the University of the Philippines at Los Banos (UPLB) established the Environmental Remote Sensing and Geo-Information (ERSG) Laboratory under the Institute of Renewable Natural Resources (IRNR). The ERSG Laboratory was equipped with the latest hardware and software, to enable it to become a world-class GIS laboratory. This laboratory is presently used, not only for research, but also for teaching and extension. As well as training students, both from the Philippines and abroad, the ERSG Laboratory provides technical services (particularly land use planning) to the entire University of the Philippines system. Local government units also make use of the facilities and expertise of the ERSG Laboratory in their planning and development. However, the key output of this project has been a printed Atlas, plus a CD-ROM, of four key watersheds: Makiling, Angat, Ambuklao-Binga and Pantabangan-Carranglan.

Development of a Gis Infrastructure

The development of a GIS infrastructure highlights the importance of information technology in national development. GIS is an important tool in the management of the environment and natural resources, and more specifically watershed management. It is estimated that more than 80% of all issues and problems related to the environment are geographic in nature.

The following sections provide a brief outline of the important steps in the development of a GIS infrastructure. These can be summarized into three phases: encoding and processing, analysis and modeling, and composition and display.

Phase 1: Gis Encoding and Processing

GIS encoding involves compiling all relevant data, and converting them into GIS format. In other words, a digital database should be established by scanning maps and transforming them into digital images. GIS encoding involves georeferencing (the process of locating features within a model of the surface of the earth), geocoding (the process of assigning a geographic reference to non-geographic data), and the creation of topology (the branch of mathematics that defines the relationships between features). As a result of these, it become possible to associate systematically the different geographical features with their corresponding attributes.

The project gathered data from all possible sources. All these secondary data were encoded into GIS format. Maps were digitized, tables were entered into spreadsheets, and the written reports were made into standard computer files.

Phase II: Gis Analysis and Modeling

Once the data have been processed, thematic overlays can be generated according to the requirements of the analysis. Analytical procedures may include distance and proximity analysis, buffering, connectivity analysis, optimal paths, neighborhood characteristics, variability, shape and pattern assessment, overlay analysis, prescribing mitigation measures based on vulnerabilities, suitability and capability assessments, etc.

As a modeling tool, GIS facilitates the development of integrative models that are able to address in a more or less hierarchical manner resource allocation and location problems. Such models must be able to deal with the complexity of the decision to be made, considering the three (spatial) dimensions, multiple objectives, multiple alternatives and multiple social interests and preferences.

Phase III: Gis Display and Output

This phase includes presentations to policy makers and other interested parties. As much as possible, members of the public should have access to Phase III, to generate the necessary response and debate over the results of the analysis.


The project was applied in three ways; modeling of erosion, modeling of fire hazards, and modeling of liability to floods.

Modeling of Susceptibility to Erosion

There are basically three types of erosion models: empirical, conceptual and those based on physical data.

Physically-based models are intended to represent a synthesis of the individual components which affect erosion, including the complex interactions between various factors and their spatial and temporal variabilities.

Empirical models are based on inductive logic, and are generally applied to conditions for which the parameters have been calibrated, while conceptual models lie somewhere between the two.

Conceptual models are based on equations dealing with spatially lumped forms of water and sediment continuity. Some of these models include Zingg's model, the Musgrave equation, and the USLE (universal soil loss equation).

The USLE is an empirically-derived equation that estimates soil loss on the basis of four groups of physical factors, namely: climatic erosivity; soil erodibility; topography; and land use, management and conservation practices. It evolved out of the modeling work of Zingg, Musgrave and others. In our project, we adjusted the equation to represent the circumstances occurring in the selected watersheds.

The popularity of the USLE probably lies in its simplicity and ease of use. Most process-based erosion models require the collection of substantial amounts of complex data, in addition to their complex mechanics. The USLE gives an approximation of the extent of soil erosion. However, users should not try to extend the use of the equation in order to estimate soil loss from drainage basins, because it is not intended to estimate gully and streambank erosion.

What is perhaps more important is that the basic principles of the equation can be applied in countries which do not have a vast accumulation of research data. The important thing is to use whatever information is already available, while leaving room for the system to be improved or modified as new information comes in from research.

In equation form, soil loss is: A = R l K l LS l C l P (4.1)

where A - Average soil loss per unit area

R - Rainfall erosivity factor

K - Soil erodibility factor

LS - Topographic factor

C - Cropping management factor

P - Erosion control practices factor

For all the watersheds, the modified Universal Soil Loss Equation (USLE) was used to assess susceptibility to soil erosion. The factors used were soil, slope, landuse and rainfall. Five soil erosion susceptibility classes were used, namely: very low, low, moderate, high and very high.

The susceptibility to soil erosion of Makiling Watershed was generally higher in 1997 compared to 1992, because of changes in land use ( Fig. 1(1218) and Fig. 2(1087)). In 1997, there was an increase in the areas associated with low, moderate and high soil erosion susceptibility. In addition, fewer areas (58%) had very low susceptibility to soil erosion in 1997, compared to 61.50% in 1992. However, the areas with very high susceptibility to soil erosion remained the same.

In the Ambuklao-Binga, Watershed, about 54,022 ha (63%) of the total land area had very low susceptibility to soil erosion, while an additional 28,975 ha (34%) of the total land area ha had low susceptibility. Approximately 33 ha (less than 1%) of the total area is highly susceptible to soil erosion ( Fig. 3(1004)).

In the Angat Watershed ( Fig. 4(1033)) about 39,791 ha, or 71% of the total land area, had very low susceptibility to erosion, while about 16,122 ha or 29% of the total land area had low susceptibility. Less than 1% of the total land area of the watershed was moderately or very susceptible to soil erosion.

In the Pantabangan-Carranglan Water-shed, a total of 66% of the total land area had very little soil erosion. Just under 6% of the total land area was associated with moderate susceptibility to soil erosion. The class of very high soil susceptibility to erosion included less than 1% of the watershed.

Modeling of Susceptibility to Fire

Susceptibility to fire of the four watersheds was assessed in terms of the availability of fuel and proximity to human activities. Variables associated with fuel are land use, drainage, aspect and slope. The variables associated with proximity to human activities are landuse (including residential use), drainage, elevation, and the presence of roads.

Three fire susceptibility classes were used: low, moderate and high.

In terms of availability of fuel, using the 1997 landuse of Makiling watershed, about 53.59% of the total land area of the reserve had a low level of susceptibility to fire. About 37% of the total land area was moderately susceptible, while only about 9% of the total land area was highly susceptible to fire.

In terms of proximity to human activities, about 84% of the total land area of the reserve had low susceptibility in 1992. In 1997, this area with low susceptibility to fire fell to 68%. The area with moderate susceptibility to fire rose from 14% to 68% of the total watershed area over the same period ( Table 1(985)).

 also shows the susceptibilities of the other study watersheds.

Modeling of Susceptibility to Flood

Key information was used to assess potential flooding on low-lying areas and flood plains of the four watersheds. This information included GIS-based elevation and drainage maps of the watersheds. Rainfall data were also used.

For the Makiling Forest Reserve, prolonged heavy rainfall could cause various levels of flooding in low-lying areas and flood plains. The same was true of the other three watersheds. By identifying these areas, we hope to help the authorities to take preventative action to avoid future problems.


It is clear that an Atlas of this kind for the most important watersheds in any country is indispensable for the overall effective management and development of the watersheds. This first Atlas is a valuable basis for the development of similar Atlases for other watersheds in the Philippines.


  • Bantayan, N.C. 1996. Participatory Decision Support System: Case Study of mt. Makiling Forest Reserve, Philippines. Department of Geomatics, The University of Melbourne, Australia.
  • Saplaco, S.R., N.C. Bantayan and R.V.O. Cruz. 2001. GIS-Based Atlas of Selected Watersheds in the Philippines. DOST-PCARRD, Philippiens

Index of Images

Figure 1 Soil Erosion Susceptibility from Land Use at Makiling Watershed in 1992

Figure 1 Soil Erosion Susceptibility from Land Use at Makiling Watershed in 1992

Figure 2 Soil Erosion Susceptibility from Land Use at Makiling Watershed in 1997

Figure 2 Soil Erosion Susceptibility from Land Use at Makiling Watershed in 1997

Figure 3 Soil Erosion Susceptibility of the Ambuklao-Binga Watershed

Figure 3 Soil Erosion Susceptibility of the Ambuklao-Binga Watershed

Figure 4 Soil Erosion Susceptibility of the Angat Watershed

Figure 4 Soil Erosion Susceptibility of the Angat Watershed

Figure 5 Soil Erosion Susceptibility of the Pantabanan-Gan-Carranglan Watershed

Figure 5 Soil Erosion Susceptibility of the Pantabanan-Gan-Carranglan Watershed

Table 1 Susceptibility to Fire of the Study Watersheds

Table 1 Susceptibility to Fire of the Study Watersheds

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