flood susceptibility assessment of mt. makiling...

Key words: computer modelling, flooding, hazard mapping, rainfall

Flood Susceptibility Assessment of Mt. Makiling, Philippines Using Two-Dimensional Meteorological

and Hydrological Modelling

National Institute of Geological Sciences, University of the Philippines Diliman, Quezon City 1101 Philippines

*Corresponding author: rich.ybañ[email protected]

Richard L. Ybañez, Bernard Alan B. Racoma, Audrei Anne B. Ybañez, and Maria Ines Rosana D. Balangue-Tarriela

In a data-poor, hazard-prone country like the Philippines, interpolating distant data points and computer modelling have become the go-to methods for determining the hazards that may affect an area. The absence of monitoring stations and gauges necessitates the application of modelling techniques to build on the little data available and generate reliable hazard maps. In this study – the devastating Sep 2009 Tropical Cyclone Ketsana (local name: Ondoy) event, its atmospheric characteristics, and its effects near Mt. Makiling, Laguna – is analyzed utilizing two modelling software: the Weather Research and Forecasting (WRF) model to assess the amount of rainfall, and FLO-2D to map the flood hazard areas around the volcano using the output of the WRF. A lone meteorological observation station on Mt. Makiling provided rainfall data for comparison with the results of the meteorological and hydrological models. The WRF model yielded a mean rainfall amount in the study area of 129.92 mm over 24 h for the storm against the observed rainfall amount for the same duration at 182.3 mm from the meteorological station. The flood model using the WRF data yielded minimal inundated areas, while the flood model of the observed rainfall data showed several low-lying urban areas inundated by up to 1.5 m of floodwaters. Comparison with flood data collected by responding agencies and groups after the event shows good correlation of affected areas and flood heights, with discrepancies being attributed to the swelling of Laguna de Bay because of excess runoff from other surrounding provinces – a factor that the models could not consider. Despite this, the WRF model generated from global atmospheric data and the flood model using the WRF product appears as a feasible substitute in the absence of on-site observation points and monitoring stations.

Philippine Journal of Science147 (3): 463-471, September 2018ISSN 0031 - 7683Date Received: 22 May 2017

INTRODUCTIONLocated in the tropics, the Philippines is subject yearly to tropical cyclones (TCs) and monsoon rains. In the Philippine Area of Responsibility (PAR) – an area stretching from Taiwan to Palau in which the Philippine Atmospheric, Geophysical, and Astronomic Services Administration (PAGASA) monitors TC activity – as

much as 20 TCs enter annually with seven to eight of these making landfall (Yumul et al. 2011). While the total number of TCs that pass through the Philippines has slightly decreased in recent years according to David and colleagues (2014), the current 10-year moving average number of TCs is listed at 28.4 annually. Based on this number, TC formation in the Western Pacific, and east to west movement of most of the TCs, the Philippines is at significant risk from TCs and extreme rainfall related

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Figure 1. Location map of the study area.

disasters. As such, the occurrence of these storms defines the types and frequencies of natural hazards – such as flash floods and landslides – that the Philippines experiences. With enormous amounts of rainfall, buildup of runoff into floodwaters is inevitable; for a country whose major city centers are dominantly located along rivers and on floodplains, this causes regular occurrences of inundation and flash floods.

In the month of Feb 2018 alone, three tropical storms (Kai-tak, Tembin, and Sanba) that made landfall in the Philippines caused 220 fatalities plus 96 injuries and affected nearly three million people. Numerous flash floods and landslides caused by these storms contributed to the damage caused by these TCs (NDRRMC 2018).

This study focuses on Mt. Makiling, whose high-relief and large spatial extent of approximately 60 km2 pose flood hazards to communities located in its ridges and valleys (Figure 1). Four different municipalities can be found in and around Mt. Makiling, with the municipality of Los Baños occupying the volcano’s northwest-northeast-southeast flanks and foothills – the largest area of all. The entire southwest quadrant of the volcano is found at the municipality of Santo Tomas in the province of

Batangas. The city of Calamba and municipality of Bay also contain some of Makiling’s foothills to the northwest and southeast, respectively. Calamba, Los Baños, and Bay are all part of the province of Laguna and share a coast with Laguna de Bay, making these areas the drainage outlets of Mt. Makiling. While the southwest quadrant of Makiling drains into Santo Tomas, there are no outlets into any large body of water here. The stream thus flows north into the city of Calamba before draining into Laguna de Bay.

Like most other provinces in the Philippines, Laguna is vulnerable to flooding caused by consistent and strong rainfall (GMANews.TV 2009; Benaning 2013). During the wet season of Jun to Oct, a monthly average of 248.4 mm of rainfall is precipitated in the province (IRRI 2015). Laguna de Bay, an expansive lake north of the province, serves as the drainage basin for the adjacent provinces of Metro Manila, Rizal, and Laguna itself. As such, flooding in Laguna is nearly inevitable in extreme cases of rainfall. As an example, in Sep 2009, Tropical Storm (TS) Ondoy – a TC with 62-118 kph winds – passed through central to southern Luzon and precipitated unprecedented amounts of rainfall unto affected provinces (Ramos et al. 2009); in Metro Manila, the recorded rainfall reached 448.5 mm

Philippine Journal of ScienceVol. 147 No. 3, September 2018

Ybañez et al.: Flood Susceptibility Assessment of Mt. Makiling

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in only 12 h (Abon et al. 2011). The extreme rainfall in these provinces resulted in an increased amount of runoff draining into Laguna de Bay. Swelling of the lake occurred resulting in water moving inland into the coastal areas of Laguna where reclaimed land and floodplains were inundated by floodwaters up to several meters (Inquirer Southern Luzon 2012). Since drainage into Laguna de Bay ceased because of the swelling, floodwaters accumulated at the coastal and lowland areas remaining in place for months to come. It was through this event that Laguna province’s flood hazard along its coastal areas was observed and understood, as the volume of floodwaters coupled with lake water level rise resulted in the coastal areas’ inundation (Carcamo 2013).

Rapid development of the province of Laguna has led to unintended consequences such as pollution and flooding (Cinco et al. 2016). With calls for rapid rehabilitation being made, tools and models that may shed light between the interactions of the lake and nearby settlements are needed to help manage hydrological risk in light of urban planning (Morelli et al. 2012). Geographic Information Systems (GIS)-based analyses can produce information that can help rapidly help public administrations with their assessments for hazard prone areas (Morelli et al. 2014). Finally, models accompanied with local community knowledge can help bolster a community’s response in cases of impending hazards (Tran et al. 2008).

The lack of a dense rainfall observation network in the study area for use in creating hydrologic models necessitates the utilization of a meteorological model to approximate atmospheric conditions to get an estimate of rainfall distribution over the area. The rainfall product, in turn, is fed into the hydrological model to approximate the flood hazards that may affect the area. With the lack of monitoring stations for torrential rain and increasing stream levels, this modelling tandem may prove useful as an alternative to determining the possible hydrological hazards in a study area.

METHODOLOGYThis study uses GIS-based and modelling-derived of the two referenced natural hazards to present a consolidated and advanced analysis of areas susceptible to rainfall and flooding near Mt. Makiling. With the help of advanced computer modelling techniques – coupled with standard GIS processes – accurate and reliable data for potential rainfall and flooding was produced for the study area. Computer-modelled hazard maps could play a significant role in disaster preparedness, mitigation, and response for the local and national government as well as the public. Analysis of the products of each model product

provides new perspective into an accurate understanding of Mt. Makiling’s susceptibility to torrential rainfall and subsequent flooding. These maps will allow engineers and urban planners to design better strategies for disaster management against flooding events.

Case Study: Precipitation Modelling of Tropical Storm OndoyBy 2009, a total of only 57 synoptic weather stations were installed by PAGASA and made available via the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (https://www.ncdc.noaa.gov/cdo-web/). Unfortunately, there is a lack of density of rainfall observation points with only one – the University of the Philippines Los Baños Agromet station – located in Mt. Makiling and the next one located in Sangley Point Cavite, 45 km away from the study site. As such, because of the lack detailed rainfall data for the area during Tropical Storm Ondoy, the WRF Model 3.5 was used to simulate the spatial extent of Tropical Storm Ondoy’s accumulated precipitation along Laguna. The WRF Model is used both by researchers and forecasters to simulate atmospheric conditions such as wind speed and direction, amount of precipitation, humidity, and temperature (Skamarock et al. 2005). Deriving from simulated and observed data, WRF can capture the extent of mesoscale events of specified resolutions. A two-dimensional, 1 x 1 km accumulated precipitation map of TS Ondoy was produced in this study and was used to evaluate climate induced hazards particularly flood hazards in Mt Makiling. While the rainfall distribution of the WRF model along the area captured the distribution of the rainfall pretty well, the validation of the aforementioned precipitation dataset was done in a separate study by Racoma and co-workers (2016).

Flood Modelling of WRF ProductThe flood modelling component of this study was generated using the software FLO-2D version 2009.06, a flood routing software used to map flood extent and hazard. With given input conditions, FLO-2D uses hydrological equations to produce a two-dimensional map of flood extent, depth, and hazard over an affected area (O’Brien et al. 1993). Geographic Information Systems (GIS) data such as Digital Elevation Models (DEM), watershed boundary and stream shapefiles, and land use shapefiles are used to create a virtual environment for the simulation. Rainfall data is then introduced to simulate a complete rainfall event and the subsequent drainage and runoff process. Simulated flooding is monitored and recorded, producing a generalized flood hazard map derived from horizontal flood extent and flow depth and velocity.

For the simulation used in this study, a resampled digital



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terrain model derived from Interferometric Synthetic Aperture Radar (IfSAR) with a resolution of 30 x 30 m was used as the topographic base of the model. The final resolution of the product flood hazard map is defined by the resolution as set by the digital elevation model. All watersheds draining Mt. Makiling were included in the simulation. Surface roughness – defined by the n-Manning’s coefficient – was set at a value of 0.03 for streams, 0.01 for urban surfaces, 0.20 for agricultural lands, 0.30 for grasslands, and 0.40 for forested areas; these values were derived and generalized from the more specific surface cover features and their corresponding coefficients found in the FLO-2D Pocket Guide. Rainfall data used was from the precipitation output of the WRF model over the area which, for the duration of 0800 h

26 Sep 2009 to 0800 h 27 Sep 2009, is at 129.92 mm for the 24-hr period. This value was then applied as 10-min point rainfall using the United States Natural Resources Conservation Service rainfall distribution for a 24-hour model storm as seen in Figure 2 (Merkel et al. 2015). For comparison, rainfall data from the University of the Philippines Los Baños (UPLB) Agromet station during the same period, 182.3 mm, was also used. The accumulated rainfall for the two rainfall scenarios are shown in Figure 3.

Outflow nodes were set along the boundary of the watershed at the coast of Laguna de Bay. This boundary assumes unimpeded flow into the lake and does not consider possible backflow by tide, lake water level, or surge.

Figure 2. Accumulated rainfall over Mt. Makiling (26-27 Sep 2009)

Figure 3. Point rainfall over Mt. Makiling (26-27 Sep 2009).



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RESULTS

WRF Modelling for Precipitation EstimationBased on the WRF model, during TS Ondoy, as much as 326.06 mm of accumulated rain over 24 h fell over the northern boundaries of Calamba, Laguna and Laguna de Bay (Figure 4) while the average accumulated rainfall across the WRF simulation grid points is 129.92 mm. The nearest Agromet station in UPLB recorded 182.3 mm in 24 h, a value not too far from the WRF simulation average. While the storm’s center did not directly pass over the Laguna area with a distance of roughly 90 km away from the northern tip of Laguna (red track in Figure 4), the precipitation it brought was enough to cause an overflow in the lake that directly affected coastal areas (Ubalde 2009).

FLO-2D ModellingThe flood hazard map (Figure 5) generated for southwest Laguna where the study area is located shows drainage of numerous tributaries into Laguna de Bay to the north. Most sources of runoff are located around Mt. Makiling, as well as some high-relief areas to the west going into Cavite province. Tributaries located along Makiling’s flank only run for a few kilometers before draining into the lake. An

exception is the nearly 40 km-long San Juan River, which serves as drainage for areas farther south into Batangas province as well as the southern to western flanks of Mt. Makiling. With rainfall from the WRF product, minimal flooding is observed in the study area. Small pockets of inundation are observed along flood plains and urban areas near the tip of the tributaries leading into Laguna de Bay.

Figure 6 shows the flood hazard map using data from the Agromet Station. There is significantly more flooding along tributaries to the east and along coastal areas at the foot of Mt. Makiling in this map compared to the WRF flood hazard map. Flood heights of up to 1.5 m high are observed along coastal urban areas beside tributaries between the foothills of Mt. Makiling and the coastline of Laguna de Bay – an area approximately 1.5 km wide and 8.0 km long.

DISCUSSIONOwing to its regional climate, the location of Laguna province and Mt. Makiling in the Philippine archipelago renders it vulnerable to both regular and extreme rainfall events for a large part of the year particularly during the wet months of Jul to Sep. Several tributaries formed from

Figure 4. WRF model extent of accumulated rainfall near Laguna for Tropical Storm Ondoy from 15:00, 24 Sep 2009 to 12:00, 26 Sep 2009; 6-hourly TS tracks in red taken from International Best Track Archive for Climate Stewardship (Knapp et al. 2010).



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Figure 5. Flood hazard map for Mount Makiling from the WRF model product (129.92 mm rainfall).

Figure 6. Flood hazard map for Mount Makiling from the Agromet station (182.30 mm rainfall).



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the flanks of Mt. Makiling serve as pathways for excess runoff that can, in turn, inundate low-lying urban areas at the foothills of Makiling. The lack of a dense network of hydrometeorological monitoring stations around Mt. Makiling makes the WRF model a workable substitute for determining rainfall distribution in the area for use in monitoring and hazards mapping.

Flood hazards from the model using WRF data are minimal, indicating that the average 24-hour value of rainfall at 129.92 mm is not enough to cause severe flooding in the area. Drainage to Laguna de Bay is adequate to prevent significant inundation of urban areas. However, in the flood hazard map using the Agromet Station data of 182.3 mm in a 24-hour period, significant – though not widespread flooding – can be observed along low-lying urban areas indicating the inadequacy of tributaries to drain the increase in runoff due to rainfall.

When compared to a situation map produced by the National Mapping and Resource Information Authority (NAMRIA) from data collected on the ground by the National Disaster Coordinating Council, the Agromet

Figure 7. Affected barangays by TS Ondoy and Typhoon Pepeng with corresponding water levels (NAMRIA 2009). This paper’s study area enclosed in red.

flood hazard map appears accurate in its depiction of affected areas. Coastal barangays in the study area are all marked as having experienced knee-high floodwaters after TS Ondoy (Figure 7), while the same area in the Agromet data flood model has urban areas that were inundated by 0.5-1.5 m of floodwaters (Figure 6).

As FLO-2D only models riverine flooding, the products shown above do not reflect lake waters that may have inundated coastal urbanized areas because of the swelling of Laguna de Bay in the hours and days after TS Ondoy as reported by news articles. The discrepancy between affected areas based on the flood models and those that are described in news articles and disaster response maps can be attributed to this limitation, indicating that inundated areas not shown on the map were caused by lake swelling and not river swelling from the various tributaries in the area. Similarly, while the WRF model captures the spatial distribution of rainfall quite well, it tends to overestimate the amount of rainfall in the modeled areas (Racoma et al. 2016). Future studies should take into consideration the propagation of error, especially when models are used as inputs for other models.



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CONCLUSIONSIn this study, rainfall data from the 26 Sep 2009 TS Ondoy event was taken from a hydrometeorological station in Mt. Makiling as well as modelled through WRF. The lack of a dense network of rain gauges in the area necessitates the use of a meteorological model to approximate the mean rainfall from the TS Ondoy event. The WRF model resulted in a coarse rainfall distribution of the study area with an average amount of 129.92 mm, compared to the observed 182.3 mm in the Agromet Station. These two were then used as input for hydrologic modelling to ascertain the flood hazards that low-lying communities experienced during that event. The mean rainfall amount of the WRF model was insufficient in generating the actual observed flood hazards and may hence be underestimated. The model from the Agromet Station data reflected more inundated areas depicting more accurately the observed flooding along the tributaries. However, the lack of hydrologic input from coastal swelling proved to be a significant limitation in closely recreating the floodwaters of the TS Ondoy event in the Mt. Makiling area.

For countries like the Philippines where the density of meteorological observation stations is lacking, the WRF model can provide rainfall distribution of a study area for use in hazards monitoring and modelling. The FLO-2D model can then utilize this generated rainfall data, in the absence of rainfall gauges or records in the study area, to simulate flood hazards that can possibly affect floodplains and other low-lying area.

ACKNOWLEDGMENTSThe authors would like to thank the following people and groups: the Local Government Units of Barangays Pansol, Camaligan, and Saimsim for providing valuable information and support, the drivers of the National Institute of Geological Sciences; the Geosciences Foundation Incorporated for providing us with some funds for our fieldworks; Project NOAH for providing the technical skills and capabilities to produce the data presented here; and finally, the other members of our team: Julius Judan, Karizz Morante, and Raymond Leuterio for conducting the fieldwork with us.

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