Debris Flow Hazard Map Simulation using FLO-2D for Selected Areas in the Philippines

PK Ferrer (a,b), F Llanes (a,b), M dela Resma (a,b), V II Realino (a,b), J Obrique (a), RC Gacusan (a,b), IJ Ortiz (a,b), C Quina (a), D Aquino (a), RN Eco (a,b), AMF Lagmay (a,b)

(a) Nationwide Operational Assessment of Hazards, University of the Philippines, Diliman, Quezon City, Philippines
(b) National Institute of Geological Sciences, University of the Philippines, Diliman, Quezon City


On December 4, 2012, Super Typhoon Bopha wreaked havoc in the southern region of Mindanao, leaving 1,067 people dead and causing USD 800 million worth of damage. Classified as a Category 5 typhoon by the Joint Typhoon Warning Center (JTWC), Bopha brought intense rainfall and strong winds that triggered landslide and debris flows, particularly in Barangay (village) Andap, New Bataan municipality, in the southern Philippine province of Compostela Valley. The debris flow destroyed school buildings and covered courts and an evacuation center. Compostela Valley also suffered the most casualties of any province; 612 out of a total 1,067. In light of the disaster in Compostela, measures were immediately devised to improve available geohazard maps to raise public awareness about landslides and debris flows. A debris flow is a very rapid to extremely rapid flow of saturated non- plastic debris in a steep channel. They are generated when heavy rainfall saturates sediments, causing them to flow down river channels within an alluvial fan situated at the base of the slope of a mountain drainage network. Many rural communities in the Philippines, such as Barangay Andap, are situated at the apex of alluvial fans and in the path of potential debris flows. In this study, we conducted simulations of debris flows to assess the risks in inhabited areas throughout the Philippines and validated the results in the field, focusing on the provinces of Pangasinan and Aurora as primary examples. Watersheds that drain in alluvial fan using a 10-m resolution Synthetic Aperture Radar (SAR)-derived Digital Elevation Model (DEM) was first delineated, and then a 1 in 100-year rain return rainfall scenario for the watershed was used to simulate debris flows using FLO-2D, a flood routing software. The resulting simulations were used to generate debris flow hazard maps which are consistent with danger zones in alluvial fans delineated previously from satellite imagery and available DEMs. The simulation was further verified with field assessment. Results show that a total of 135 barangays with 252,405 people in Pangasinan and 25 barangays with 34,495 people in Aurora are at risk of flood and debris flows.

1. Introduction

On December 4, 2012, Super Typhoon Bopha wreaked havoc in the southern region of Mindanao in the Philippines. The Joint Typhoon Warning Center (JTWC) classified the typhoon as Category 5. In the Saffir-Simpson Hurricane Scale, a Category 5 typhoon reaches wind speed of more than 156 miles per hour. Typhoon Bopha brought intense rainfall and strong winds all over the Philippines. The eye of the typhoon crossed Mindanao through the provinces of Agusan del Sur, Bukidnon, Davao Oriental, Misamis Oriental and Compostela Valley as seen in Figure 1. Of all the provinces affected by the typhoon, the province of Compostela Valley is the worst hit. Intense rainfall made the soil oversaturted with water creating voluminous landslides and debris flows. Out of the recorded 1,067 casualties brought by the typhoon, 612 came from Compostela Valley [1]. Many of which came from Barangay (village) Andap in the municipality of New Bataan. The debris flow destroyed school buildings, covered courts, and an evacuation center. It was very catastrophic. The typhoon caused USD 800 million worth of damage. Mass wasting events in a tropical country like the Philip-pines are quite expected especially in times of heavy rainfall. Of all the mass wasting events, debris flows are considered to be one of the most dangerous. Debris flow is a very rapid to extremely rapid flow of saturated non-plastic debris in a steep channel. The conceptual sketch of a debris flow is seen in Figure 2. Debris flows are churning, water-saturated masses of fine sediment, rocks, and assorted detritus that originate on mountain slopes and course down stream channels when they reach valley floors [2].


Mountainous areas with high slope instability, high seismic activities, and extreme rainfall condition are the main triggering factors [4]. In one of the studies presented in Tulane University, velocities of debris flows may reach between a meter per year to hundreds of meters per hour. They can occur suddenly and inundate an entire town in a matter of minutes [5]. In the event of a debris flow, many elements are at risk whether affected directly or indirectly. Several infrastructures and buildings are destroyed and most especially, the people’s lives. In light of the disaster in Compostela, measures were immediately devised to improve available geohazard maps to raise public awareness about landslides and debris flows. Many rural communities in the Philippines, such as Barangay Andap, are situated at the apex of alluvial fans and in the path of potential debris flows. For physical planning, reconstruction, prevention, debris flow mitigation, risk assessment and risk management reasons, there is the need for a detailed debris flow modeling [5]. Simulating debris flow is in fact needed and must be understood by government administrators, decision makers, planners and practitioners who have to protect the life, property, and economic activities of people who live in debris flow prone areas [6]. In this study, we conducted simulations of debris flows to assess the risks in inhabited areas throughout the Philippines and validated the results in the field, focusing on the provinces of Pangasinan and Aurora as primary examples.

2. Geology and Geography of the Study Area

The provinces of Pangasinan and Aurora are found on the central portion of Luzon Island in the Philippines. Pangasinan (Figure 3) which is enclosed in red box is situated on the western side of the Philippines. On the other hand, Aurora (Figure 3) which is enclosed in blue, is situated on the eastern side. The study area in Pangasinan is mostly underlain by Late Pliocene sedimentary rocks. It is part of the Ilocos-Central Luzon Sedimentary Basin. Various sedimentary rocks can be found such as tuffaceous sandstone, interbedded siltstone, shale and conglomerate, including minor lenses of limestone. Igneous rocks such as andesite, diorite, and basalts are the dominant lithology in Aurora Province. Moreover, pyroclastic rocks can also be associated to the lithology of both study area.


When combined with active faulting and bedding, it may produce many potential failure surfaces in the rock slopes [5]. Figure 4 shows the distribution of active faults and trenches in the Philippines. The Philippine fault in Central Luzon consists of four left-stepping fault segments with a total of 150 km: the San Manuel Fault, San Jose, Digdig and Gabaldon [8]. Figure 5 shows the Philippine Fault in Central Luzon island. The San Manuel and San Jose Fault are two fault systems affecting the lithology and structural geology of Pangasinan Province. Dingalan Fault and Casiguran Fault, on the other hand, are fort he province of Aurora. As said earlier in the previous section, high seismic activities which are caused mostly by active faults coupled with high instability on mountain slopes and intense rainfall will trigger a potential debris flow.

Based on the monthly rainfall received, Pangasinan Province is classified to have a Type I climate. A type I climate is characterized to have two (2) distinct seasons: wet from June to November, and dry for the rest of the year. Aurora Province is different from Pangasinan for it is classified to have a Type II climate. A type II climate is identified to have no dry season at all throughout the year, but a pronounced wet season from November to February. Figure 6 shows the climate classification map of the Philippines.

3. Methodology

There are three (3) major steps in generating debris flow hazard maps. First, watershed is created in ArcGIS. Second, debris flow is simulated in FLO-2D and third, the resulting hazard map is assessed upon conducting a field validation of the study area. In generating watersheds, a Digital Elevation Model (DEM) of the study area is required. A DEM file consists of a sampled array of elevations for a number of ground positions at regularly spaced intervals [11]. This DEM file will serve as the base layer that is needed in generating the watersheds. The type of elevation data used in this study is a 10-meter resolution Synthetic Aperture Radar (SAR) DEM. Figure 7 and 8 show the digital elevation model of the study area. It is also important to have the shapefile of the alluvial fans of the area. The shapefile is overlain on the DEM file because this is essential in identifying where exactly in the DEM watersheds should be generated. The process in alluvial fan identification will not be discussed thoroughly in this article. The software used in generating the watersheds is ArcMap, which is a main component of ArcGIS. ArcMap is an application used for analyzing editing, and mapping geospatial data [12]. It is integral to note that the projection used for the SAR image and the alluvial fan shapefile should have the same data projection. This can be verified through the Data Management Tools of the ArcToolbox. In generating the streams and catchments of the study area, a special kind of tool in ArcGIS is utilized. This tool creates various sizes of streams and catchments ranging from small, medium, and large. The catchments contributing to the area of a potential debris flow are carefully selected. The selected watersheds are in raster form and this should be converted into a shape file before incorporating in FLO-2D.



Debris flow simulation is done by utilizing the FLO-2D software. FLO-2D is a software that is used basically for flood hazard mapping. Moreover, this flood-routing software is also used to simulate mud and debris flows. This software uses dynamic-wave momentum equation and a finite-difference routing scheme [5]. FLO2D was chosen for this research study because it incorporates rainfall and digital elevation parameters to be able to model or simulate debris flows. The following procedures were followed in this FLO-2D simulation. Initially, GDS or Grid Developer System was used to facilitate the preparation and graphical editing of the FLO-2D grid system and its attributes [13]. A 10-meter SAR DEM file was imported into the software as a PTS file. The PTS file was obtained from converting an XYZ grid file in Global Mapper software. The grid size used in the simulation is defined to be 15. Interpolation is done in the model to be able to assign a representative elevation in each grid element. Manning coefficients and in-filtration time were also incorporated in the model. Lastly, a 100-year return period was used as the rainfall data in the FLO-2D debris flow simulation. The result of the simulation is post-processed in the Mapper software. Mapper is the primary post-processing program for viewing the FLO-2D simulation results. [13].


Each element is assigned a color depending on the value of the hazard level. In this case study, red indicates a high level of hazard. Flow depth in this level is greater than one (1) meter. Orange areas indicate intermediate hazard with flow depth ranging from 0.2 to 1 meter. The raw and colored debris flow simulation shapefile is overlain on the SAR DEM image together with the delineated alluvial fan shapefile in ArcMap in order to generate the final debris flow hazard map. The resulting debris flow hazard map is compared with actual data upon conducting field validation and assessment of the study area. The assessment of the study area involves soil sampling and mapping of the extent of the alluvial fan. The deposits on the area are noted and observed for this may be a factor whether the area is prone to mudslides or debris flows. In the case where debris flow deposits are observed, the data on the exact location, slope, and elevation are gathered. Upon post-examination of the data gathered during the field, an update on the debris flow simulation is eventually conducted for further analysis. Figure 9 provides a summarized flow of the process in debris flow simulation.

4. Results and Discussion

4.1. Pangasinan



d11 d12


Fieldwork validation was done after the initial debris flow hazard map was generated. Out of the seven alluvial fans presented in the section above, only four were successfully validated. The fans that were assessed are Rosario, Pozzorubio, San Manuel, and San Nicolas. A river deposit found along the southern portion of Pangasinan was also observed. The Rosario alluvial fan’s apex (Figure 11) is located near Camp 1 bridge in Benguet, Philippines. Bued River is the tributary system that contributes to the deposition of sediments in the foot of the mountain. Outcrops along the Bued River are boulder-sized. It also shows a slight reverse grading. According to interviews with the locals, the scouring that was seen in the wall rock is higher than the average height of a person. Southeast of the Rosario alluvial fan is the Pozzorubio alluvial fan (Figure 12). Even though the investigated area is relatively far from the Pozzorubio’s apex, deposits of coarse sand to boulder- sized rocks are observed in the mountain drainage. Along the sides of the deposits, normal grading is observed. There are also gravelly point bars identified in the middle of the river. Further southeast of Pozzorubio, after the Binalonan alluvial fan is the San Manuel alluvial fan (Figure 12). Brown sit-sized deposits to boulder-sized rocks are observed in this area. The apex of the San Manuel alluvial fan is located in Brgy. Lapalo, municipality of San Manuel. The last fan to be assessed is the San Nicolas (Figure 13) alluvial fan. It is located northeast of San Manuel. The area is directly downstream of a major dam system in the country namely the San Roque Dam. The apex of the of the fan is within the confines of the San Roque Power Corporation. All of the fans mentioned are part of the northern Pangasinan bajada. The alluvial fans as they grow and develop laterally with each other tends to coalesce and form as one continuous landform. In southern Pangasinan, a couple of minor alluvial fans were identified but were not investigated thoroughly because its size is too small to be significant. These are the Aguilar (Figure 14) and Mangatarem (Figure 15) alluvial fan. However, a stopover in a river located downstream of one of the mini fans show deposits that are volcanic in origin. It shows poor sorting, boulder-sized imbrications, and composed of cobbles and boulders. Also, a pointbar with similar deposits is also observed. Figure 16 shows some of the photos taken during the field validation in Pangasinan.


The discussion on the field assessment conducted in Aurora can be divided into three areas: Northern Aurora (Casiguran and Dinalungan), Mid-Aurora (Dipaculao, Maria Aurora, and San Luis), and Southern Aurora (Dingalan). Based from the delineation of alluvial fans using 90-meter resolution SRTM images, Aurora has a total of 11 alluvial fans. Both Northern and Southern Aurora have 2 alluvial fans each while 7 alluvial fans are spread out around the municipalities of Dipaculao, Maria Aurora, and San Luis. There are two observation points in the northernmost alluvial fan of Aurora. Located primarily at Barangay Calabgan, in the municipality of Casiguran (Figure 17), the deposits fan out in a southeast direction leading to the coast. The two observation points are relatively far apart, with one located near the foot of the fan while the other is located near the apex. The deposits found are mostly silt-sized but a few boulders were able to make its way to this area. There is no definite grading in the deposits buried in the soily matrix but the boulders can be found at the top. The matrix is unconsolidated and sandy. Meanwhile clasts are polymictic with the lithology ranging from andesite to diorite. The second alluvial fan is located in Barangay Dibaraybay, in the municipality of Dinalungan (Figure 17). There are plenty of cobble to boulder-sized deposits that are igneous in origin. An outcrop investigated in the vicinity of this observation point is comprised of three layers and is 1.5 meters in height. The topmost layer is clast-supported and poorly sorted with pebble to boulder-sized clasts. The layer in between is composed of sandy dark brown soil in which plant remains can be identified. The bottommost layer is composed primarily of boulders that are clast-supported. In Mid-Aurora, four of the seven fans are located in Dipaculao (Figure 18). There are more cobble-sized deposits. Facing downstream, more boulders can be found. The deposits found at this point is similar in lithology as the floats found in the two fans in Northern Aurora. The next alluvial fan is located in Barangay Dibutunan. The river at this point is very narrow and is surrounded by boulder-sized deposits. Brecciated boulders have also been observed in the area. An outcrop found near the area is composed of poorly sorted pebble to boulder-sized clasts that are subangular to rounded. The outcrop is matrix- supported when it comes to the smaller deposits. The fan in Barangay Ditale in Dipaculao has deposits of igneous origin such as diorite, andesite, and breccias– similar to the igneous deposits of the northern alluvial fans. The nearest outcrop has deposits that are very poorly sorted and ranges in size from pebbles to boulders. The outcrop is matrix-supported and shows normal grading. The sub-angular to rounded clasts can be seen to have imbrication pointing downstream. The alluvial fan assessed in Maria Aurora is located in Barangay Ponglo. Cobble-sized to boulder-sized igneous rocks are observed in the river deposits. The stream is braided suggesting it’s a high-energy fluvial environment. Outcrop along the river reaches up to 1.5 meters in height. In southern Aurora (Figure 19), the alluvial fan assessed is located in Barangay Aplaya, Dingalan. The observation point is relatively far from the apex around 2.1 kilometers northwest. However, the outcrop observed in this area shows poor sorting, angular to sub-rounded rocks, clast-supported igneous to sedimentary type of rocks. It is slightly imbricated and the height of the outcrop is almost one (1) meter. Figure 20 shows some of the photos taken during the field validation in Aurora.



5. Conclusion

Debris flow simulation is very important in geohazard mapping. Knowing and understanding the debris prone areas in one place can save a multitude of life. In this case study, the province of Pangasinan and Aurora are given as examples. Results show that debris flows colored in red indicates a high level of hazard. Flow depth in this level is greater than one (1) meter. Orange areas indicate intermediate hazard with flow depth ranging from 0.2 to 1 meter. Most of the red-colored hazard zone in the simulation are found on the base of the slope of a mountain drainage network. The data on the number of the people that may be affected by a potential debris flow event is based on the Philippine Statistics Authority. Results show that a total of 135 barangays with 252,405 people in Pangasinan and 25 barangays with 34,495 people in Aurora are at risk of flood and debris flows. As of the present, simulation on debris flow simulation for the rest of the country is on-going.


6. Acknowledgements

The authors would like to thank DOST Project NOAH (Nationwide Operational Assessment of Hazards) Landslide Hazard Mapping Team of the University of the Philippines (UP) National Institute of Geological Sciences (NIGS).


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[3] M. Jakob, A size classification for debris flows, Engineering Geology 79 (2005) 151–161.

[4] R. Huang, W. Li, Analysis of the geohazards triggered by the 12 May 2008 Wenchuan earthquake, china, Bull Eng Geol Environ 68 (2009) 363–371.

[5] M. Adegbe, D. Alkema, V. Jetten, A. Agbor, I. Abdullahi, O. Shehu, A. Unibu, Post seismic debris flow modelling using Flo-2d; case study of Yingxiu, Sichuan province, China, Geography and Geology 5 (2013) 101–115.

[6] A. Petrascheck, H. Kienholz, Hazard assessment and hazard mapping of mountain risk-examples of Switzerland in: Debris flow hazard mitigation: Mechanics, prediction, and assessment (2003) 25–383rd International DFHM Conference, Davos, Switzerland.

[7] MGB, Geology of the Philippines, Book (2010).

[8] H. Tsutsumi, J. Perez, Large-scale active fault map of the Philippine fault based on aerial photograph interpretation, Active Fault Research 39 (2013) 29–37.

[9] PHIVOLCS, Distribution of active faults and trenches in the Philippines, Online (2000).

[10] PAGASA, Climate change in the Philippines, Online (2011).

[11] USGS, Digital elevation models, Online, accessed at: (1993).

[12] ESRI, Understanding the ArcGIS desktop applications: What is ArcMap?, Online, accessed at: (2008).

[13] FLO2D, Flo2d data input manual, Manual (2007).


  1. Pls provide us data/discussion in Calinog, Iloilo


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