Proposed updated Wind Zones and Contour Maps of the Philippines
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UPDATING THE WIND ZONE AND DEVELOPING CONTOUR MAPS FOR THE PHILIPPINES: ADAPTATION STRATEGIES FOR EXTREME WIND SPEEDS T. A. DE LEOZ 1 , E. R. KAW 1 , A. QUIDILLA 1 , J.G. VALBUENA 1 AND L. E. GARCIANO 2 1 Undegraduate student, De La Salle University - Manila 2 Associate Professor, De La Salle University - Manila ABSTRACT: It is evident that climate change will impact urban and sub-urban populations now and in the future. Some recent extreme events that battered the country are the storm surge in Tacloban City during super typhoon Haiyan, extreme flooding in Metro Manila during typhoon Ondoy and the massive flooding and landslides at the height of typhoon Pablo. As an initial step in climate-proofing our communities, the researchers updated the wind zone and developed a contour map. For this purpose, latest extreme wind speed data from fifty-five PAGASA stations all over the county were fitted to the Generalized Extreme Value (GEV) distribution. The return level wind speeds from the different distributions were used as basis in developing a wind zone and surface contour map using ArcGIS software. The developed contour and wind zone maps with 50, 75 and 100- year return periods are then compared with the recent wind zone map of the National Structural Code of the Philippines. Keywords: typhoons, extreme wind speed, GEV distribution, ArcGIS. 1. INTRODUCTION The World Risk Report 2013 ranked the Philippines as the third most disaster-prone country due to its high exposure to natural hazards such as typhoons, earthquakes, floods, storm surge, and volcanic eruption. Recent extreme events have highlighted the vulnerability of the country’s urban and sub-urban areas as enumerated below: a. Super typhoon Yolanda (2013) caused widespread damage specifically in Tacloban City due to an unexpected storm surge from sustained extreme wind speeds. Damage to low to mid- rise structures were also seen in other areas along the path of this typhoon. The estimated damage caused by typhoon Yolanda was pegged at 40 billion pesos leaving more than a million homes destroyed, infrastructure and agriculture devastated and more than 6000 casualties (NDRRMC 2013). b. Magnitude 7.2 earthquake in Bohol (October 2013) – a reverse thrust movement of a new fault caused widespread damage to houses, bridges, schools, roads, highways, buildings, hospitals and national cultural treasures such churches (Garciano and Taclibon 2013). c. Typhoon Ondoy (September 2009) - Metro Manila was submerged due to extreme flooding during this event. Damage was estimated at US$ 237 million. d. Typhoon Pablo (December 2012) - sub-urban flooding and landslide occurred during this event. Damaged was estimated to be around US$ 898 million with over 1000 casualties. The Philippines is a typhoon-prone country as it is located on the west side of the pacific ocean where strong typhoons are frequently formed (ADRC 2008). The Philippine Atmospheric, Geophysical and Astronomical Service Administration (PAGASA) estimates an average of twenty tropical cyclones enter the Philippine Area of Responsibility (PAR) with an annual intensity distribution of 4 tropical depressions, 5 to 6 tropical storms and 10 typhoons. In Table 1
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UPDATING THE WIND ZONE AND DEVELOPING CONTOUR MAPS FOR THE PHILIPPINES: ADAPTATION STRATEGIES FOR EXTREME WIND SPEEDS
T. A. DE LEOZ1, E. R. KAW1, A. QUIDILLA1, J.G. VALBUENA1 AND L. E. GARCIANO2 1Undegraduate student, De La Salle University - Manila
2Associate Professor, De La Salle University - Manila
ABSTRACT: It is evident that climate change will impact urban and sub-urban populations now and in the future. Some recent extreme events that battered the country are the storm surge in Tacloban City during super typhoon Haiyan, extreme flooding in Metro Manila during typhoon Ondoy and the massive flooding and landslides at the height of typhoon Pablo. As an initial step in climate-proofing our communities, the researchers updated the wind zone and developed a contour map. For this purpose, latest extreme wind speed data from fifty-five PAGASA stations all over the county were fitted to the Generalized Extreme Value (GEV) distribution. The return level wind speeds from the different distributions were used as basis in developing a wind zone and surface contour map using ArcGIS software. The developed contour and wind zone maps with 50, 75 and 100- year return periods are then compared with the recent wind zone map of the National Structural Code of the Philippines. Keywords: typhoons, extreme wind speed, GEV distribution, ArcGIS. 1. INTRODUCTION The World Risk Report 2013 ranked the Philippines as the third most disaster-prone country due to its high exposure to natural hazards such as typhoons, earthquakes, floods, storm surge, and volcanic eruption. Recent extreme events have highlighted the vulnerability of the country’s urban and sub-urban areas as enumerated below: a. Super typhoon Yolanda (2013) caused widespread damage specifically in Tacloban City due
to an unexpected storm surge from sustained extreme wind speeds. Damage to low to mid-rise structures were also seen in other areas along the path of this typhoon. The estimated damage caused by typhoon Yolanda was pegged at 40 billion pesos leaving more than a million homes destroyed, infrastructure and agriculture devastated and more than 6000 casualties (NDRRMC 2013).
b. Magnitude 7.2 earthquake in Bohol (October 2013) – a reverse thrust movement of a new fault caused widespread damage to houses, bridges, schools, roads, highways, buildings, hospitals and national cultural treasures such churches (Garciano and Taclibon 2013).
c. Typhoon Ondoy (September 2009) - Metro Manila was submerged due to extreme flooding during this event. Damage was estimated at US$ 237 million.
d. Typhoon Pablo (December 2012) - sub-urban flooding and landslide occurred during this event. Damaged was estimated to be around US$ 898 million with over 1000 casualties.
The Philippines is a typhoon-prone country as it is located on the west side of the pacific ocean where strong typhoons are frequently formed (ADRC 2008). The Philippine Atmospheric, Geophysical and Astronomical Service Administration (PAGASA) estimates an average of twenty tropical cyclones enter the Philippine Area of Responsibility (PAR) with an annual intensity distribution of 4 tropical depressions, 5 to 6 tropical storms and 10 typhoons. In Table 1
below, we can see that storms account for almost 50 percent of natural disasters (2000 – 2012) in the country (CRED 2013).
Table 1. Number of Natural Disasters in the Philippines Year Drought Earthquake
Climate change is real and taking place and this is backed up by scientific evidence and real extreme events (Kit et. al. 2010, Korndorfer et. al 2010). Figure 1 below shows
(a) the current NSCP wind zone map; and the typhoon tracks of (b) Super typhoon Yolanda with maximum sustained winds of 235 kph near the center and
gustiness of up to 275 kph at first landfall at Guian, Eastern Samar (NDRRMC 2013) (c) Typhoon Pablo with maximum sustained winds of 175 kph near the center and gustiness of
up to 210 kph at first landfall at Baganga, Davao Oriental (NDRRMC 2012).
Figure 1. Recent typhoon tracks and the current wind zone map
(b)
(a)
(c)
These strong typhoons, a year and a month apart, have exceeded the basic wind speeds of zones I and II of the NSCP. Furthermore southern Philippines, which is rarely hit by strong typhoons, are now experiencing the brunt of the changes in weather patterns that caused damage to life, livelihood and infrastructure (Alonzo 2011, BBC News Asia 2009). Therefore in light of the above discussions, the researchers make the following conjectures as shown in Figure 2 below: (a) extreme wind speeds are increasing in magnitude but structural resistance (without
retrofitting) remains the same such that the probability of failure increases (b) the probability density function of extreme wind speeds maybe shifting from north to south
such that the vulnerability of structures in southern areas increased due to this shift.
Figure 2. Probability density function shift of extreme wind speeds Analysis and design of almost all structures in the country are regulated by the National Structural Code of the Philippines (NSCP 2010). Chapter 2 provides “the minimum design load requirements for the design of buildings, towers and other vertical structures”. Section 207 of the NSCP specifies the basic wind speed v (see Figure 2a), corresponding to a 3-second gust speed at 10 m above ground in exposure category C. This v is also associated with 50-year return period or an annual probability of 0.02 of being equaled or exceeded. Garciano et. al 2004 developed a 6-zone regional wind zone map of the Philippines using PAGASA data from 1961 to 2000. However in this paper, the main objective is to update this map using extreme wind speed data from 1961 up to 2013 and also propose a 4-zone map. Furthermore, contour maps with 50, 75 and 100-year return periods were also developed. In the succeeding sections, the authors discuss the methodology, the analysis and develop the wind zone and contour maps using geographic information technology (GIT).
(a)
(b) (source: Salazar, 2014)
2. METHODOLOGY The development of the wind zone map and the wind contour map consists of three phases. Data gathering and processing
In stage, data needed for the research which includes the following are obtained: Extreme wind speed data from PAGASA, Weather Philippines, National Power
Corporation Longitude and latitude coordinates and elevation of each PAGASA synoptic station Digital elevation map (DEM) of the Philippines in shape file (for GIT processing) Philippine map in shape file (for GIT processing)
Wind speed modelling and simulation Verify if correction factors to account for topography and terrain exposure were applied
for each station. Applying correction factor is necessary since it affects the magnitude of the wind speed.
Fit extreme wind speed data from each station to a generalized extreme value (GEV) distribution. The parameters of the GEV distribution are estimated using Maximum Likelihood Estimate (MLE). R-software (freeware) was used to estimate the parameters of the GEV distribution.
Extrapolate 50, 75, and 100-year return level extreme wind speeds using the estimated GEV parameters
Geographic information technology (GIT)
ArcGIS software is used to create the maps using Kriging interpolation method and surface contouring package. The final outputs of the study are wind zone maps and wind contour map of the Philippines.
Generalized extreme value (GEV) model The GEV model is defined by the following equation:
1 (1)
where is the location parameter, (>0) is the scale parameter and is the shape parameter. The shape parameter is important in extreme value modelling since it determines the rate of tail
decay. If we let = 0 the Type I (Gumbel) distribution is obtained. For > 0, the Type II
(Frechet) is obtained while a reversed Weibull, is obtained when < 0. The parameters of the GEV distribution is determined using maximum likelihood estimate (MLE). To obtain estimates of return level wind speeds, the following equations are used:
1 1 /1
(2)
where G(xp= 1 – p and xp is the return level associated with the return period 1/p.
3. DATA GATHERING / PROCESSING Annual extreme wind speeds from 55 PAGASA stations (see Table 2) all over the country were obtained. However, only wind speed equal or greater than 8 m/s were used in the GEV inference.
Table 2. PAGASA Station Name and ID No. Station ID No. Station ID1 Alabat, Quezon 435 29 Itbayat, Batanes 132 2 Ambulong, Batangas 432 30 Laoag City, Ilocos Norte 223 3 Aparri, Cagayan 232 31 Legaspi City, Albay 444 4 Baguio City, Benguet 328 32 Lumbia Airport, Misamis Oriental 747 5 Baler, Quezon 333 33 Maasin, Southern Leyte 648 6 Basco, Batanes 135 34 Mactan Airport 646 7 Butuan City, Agusan Del Norte 752 35 Malaybalay, Bukidnon 751 8 Cabanatuan, Nueva Ecija 330 36 Masbate, Masbate 543 9 Cagayan de Oro, Misamis Oriental 748 37 NAIA (MIA), Pasay City 429 10 Calapan, Oriental Mindoro 431 38 Port Area (MCO), Manila 425 11 Calayan, Cagayan 133 39 Puerto Princesa, Palawan 618 12 Casiguran, Quezon 336 40 Romblon, Romblon 536 13 Catarman, Northern Samar 546 41 Roxas City, Aklan 538 14 Catbalogan, Western Samar 548 42 San Francisco, Quezon 437 15 Coron, Palawan 526 43 San Jose, Occ. Mindoro 531 16 Cotabato City, Maguindanao 746 44 Sangley Point, Cavite 428 17 Cuyo, Palawan 630 45 Science Garden, Quezon City 430 18 Daet, Camarines Norte 440 46 Surigao, Surigao del Norte 653 19 Dagupan Cit, Pangasinan 325 47 Tacloban City, Leyte 550 20 Davao City, Davao del Sur 753 48 Tagbilaran City, Bohol 644 21 Dipolog, Zamboanga del Norte 741 49 Tanay, Rizal 433 22 Dumaguete City, Negros Oriental 642 50 Tayabas, Quezon 427 23 General Santos, South Cotabato 851 51 Tuguegarao, Cagayan 233 24 Guiuan, Easter Samar 558 52 Vigan, Ilocos Sur 222 25 Hinatuan, Surigao del Sur 755 53 Virac Radar, Catanduanes 447 26 Iba, Zambales 324 54 Virac Synop, Catanduanes 446 27 Iloilo City, Iloilo 637 55 Zamboanga City, Zambo del Sur 836 28 Infanta. Quezon 434
Note that the stations did not operate simultaneously from 1961 and extreme wind speed data were missing at random from different stations due to various sources. Figure 3 shows a time series scatter plot of Baguio City, Benguet station where the minimum wind speed was 13 m/s in 1961 and the maximum wind speed recorded in 1974 was 46 m/s.
Figure 3. Scatter Plot of Annual Maxima at Baguio City, Benguet Station
4. GEV MODELLING OF ANNUAL EXTREME WIND SPEEDS To obtain the MLE of the GEV parameters for each PAGASA station extreme wind speed data, the Extremes Toolkit of R software was used. The MLEs of the GEV parameters from Baguio City, Benguet station are = 25.91, = 6.58 and = -0.25. Since the shape parameter is negative the distribution model is classified as Type III or the reversed Weibull distribution. The standard errors of the parameters are = 0.98, = 0.67 and = 0.07 and the approximate 95% confidence intervals (CI) for is [-0.09, -0.37]. The return wind speeds and CI are estimated by substituting the MLEs into Eq. 2 and setting p = 0.02 (50 years), 0.013 (75 years) and 0.01 (100 years). The results are shown in Table 3 while the return wind speed plot is shown in Figure 6.
Table 3. Return wind speeds for Baguio City Station 50-year return wind speed 75-year return wind speed 100-year return wind speed m/s 95% CI 95% CI 95% CI
Figure 4. Return wind speeds for Baguio City Station The same procedure was applied to the remaining extreme wind speed data from PAGASA stations and the results summarized in Table 4.
Table 4. GEV distribution parameters for each station Station Name GEV Parameters Station Name GEV Parameters
Cabanatuan, Nueva Ecija 16.71 5.30 0.32 Maasin, Southern Leyte 16.42 4.39 0.22 Cagayan de Oro, Misamis O. 10.99 2.16 0.12 Mactan International Airport 18.33 3.90 0.30 Calapan, Oriental Mindoro 19.55 7.56 -0.06 Malaybalay, Bukidnon 12.73 3.46 0.04 Calayan, Cagayan 20.20 8.59 0.08 Masbate, Masbate 18.10 6.62 0.18 Casiguran, Quezon 23.40 10.55 0.00 NAIA (MIA), Pasay City 20.83 5.06 0.05 Catarman, Northern Samar 19.57 4.96 0.19 Port Area (MCO), Manila 21.85 6.74 0.05 Catbalogan, Western Samar 16.94 5.07 0.32 Puerto Princesa, Palawan 13.81 3.89 0.15 Coron, Palawan 14.75 5.27 0.18 Romblon, Romblon 22.27 8.40 0.00 Calayan, Cagayan 20.20 8.59 0.08 Roxas City, Aklan 20.52 4.80 0.21 Casiguran, Quezon 23.40 10.55 0.00 San Francisco, Quezon 15.36 3.77 0.08 Catarman, Northern Samar 19.57 4.96 0.19 San Jose, Occidental Mindoro 23.05 5.72 -0.07 Catbalogan, Western Samar 16.94 5.07 0.32 Sangley Point, Cavite 22.05 5.60 0.19 Coron, Palawan 14.75 5.27 0.18 Science Garden, Quezon City 19.27 6.45 -0.05 Cotabato City, Maguindanao 18.77 5.18 -0.44 Surigao, Surigao del Norte 18.91 6.05 0.11 Cuyo, Palawan 17.01 4.58 0.03 Tacloban City, Leyte 20.47 5.15 0.27 Daet, Camarines Norte 20.35 7.02 0.19 Tagbilaran City, Bohol 14.03 3.95 0.01 Dagupan City, Pangasinan 19.86 6.87 0.01 Tanay, Rizal Radar 16.87 4.39 0.27 Davao City, Davao 13.65 2.55 0.07 Tayabas, Quezon 16.18 5.16 0.10 Dipolog, Zamboanga 17.66 5.43 -0.24 Tuguegarao, Cagayan 22.46 9.35 -0.13 Dumaguete City, 15.75 4.04 -0.10 Vigan, Ilocos Sur 20.82 7.95 0.04 General Santos City 12.58 3.55 -0.11 Virac Radar, Catarman 30.91 14.12 0.05 Guiuan, Eastern Samar 27.09 11.08 0.08 Virac Synop, Catarman 25.13 11.63 0.21 Hinatuan, Surigao del Sur 15.31 3.94 0.03 Zamboanga City, Zamboanga 15.97 3.61 0.17
5. KRIGING INTERPOLATION AND SURFACE CONTOUR IN ArcGIS The Philippine map with regional boundaries (Figure 5a) in shape file format was stored in ArcGIS as well as a digital elevation map (Figure 5b). The PAGASA stations were likewise stored in the map with the following attributes: latitude, longitude and elevation as shown in black dots in Figure 5c.
Figure 5. Philippine map
(a) (b) (c)
These black dots (stations) also store the 50, 75 and 100-year return wind speeds attributes. Using Geostatistical Analyst (of ArcGIS), a 2D wind zone and contour map is generated using Kriging interpolation method.
5.1 PROPOSED WIND ZONE MAPS The maps produced using kriging method are shown in Figure 6. Four zones were also derived from the initial results consisting of Zone 1 (v = 300 kph), Zone 2 (v = 250 kph), Zone 3 (v = 200 kph) and Zone 4 (v = 150 kph) for the 50-year return period. Fo the 75 and 100-year return periods, the basic wind speeds for each zone are shown in Figure 6.
Figure 6. Proposed wind zone maps for different return periods For the proposed wind zone map, Zone 1 has a basic wind speed of 300 kph and the provinces of Albay, Batanes, Camarines Sur, Catanduanes, Eastern Samar and Sorsogon fall in this zone. Zone 2 has a basic wind speed of 250 kph, and some provinces in Zones 1 and 2 of the NSCP were included in this zone as shown in table 4. In the NSCP 2010 wind zone map, the provinces of Mindanao have a basic wind speed of 150 and 200 kph. However, in the proposed wind zone map, there were three provinces from Mindanao that increased their basic wind speed from 200 kph to 250 kph. In Luzon, the basic wind speed of the provinces of Nueva Ecija and Nueva Vizcaya increased from 200 to 250 kph. While in the Visayas their basic wind speed in Leyte, Southern Leyte and Masbate also an increased.
(a) 50-year return period (b) 75-year return period (c) 100-year return period
Zone 3 has a basic wind speed of 200 kph, and the provinces are the same with Zone 2 of NSCP 2010 with Northern Palawan as an addition. For Zone 4 with a basic wind speed of 150 kph, the provinces are the same with Zone 3 of the 2010 except of the additional provinces of Camiguin, Misamis Oriental and Siquijor were the basic wind speed was reduced from 200 kph to 150 kph. The zonings in 50, 75 and 100 year return period were maintain as much as possible in ArcGIS so that the provinces fall under each wind zone classification were the same for all the wind zone map with three different return periods. Tables 6, 7 and 8 give the basic wind speeds of the four wind zones for 50, 75 and 100- year return periods.
Table 5. Changes in the basic wind speeds between NSCP and proposed wind zone map
Provinces Island Group Basic Wind Speed, v (in kph)
NSCP 2010 Wind Zone Map
proposed wind zone map
Albay, Batanes, Camarines Sur and Catanduanes, Sorsogon Luzon 250 300
Nueva Ecija and Nueva Vizcaya Luzon 200 250
Northern Palawan Luzon 150 200
Eastern Samar Visayas 250 300
Leyte, Southern Leyte and Masbate Visayas 200 250
Agusan del Sur, Surigao del Norte and Surigao del Sur Mindanao 200 250
Camiguin, Misamis Oriental and Siquijor Mindanao 200 150
Table 6. Wind Zone classification of the different provinces (NSCP 2010)
Abra, Agusan del Norte, Agusan del Sur, Aklan, Antique, Apayao, Bataan, Batangas, Benguet, Biliran, Bohol, Bulacan, Camiguin, Capiz, Cavite, Cebu, Compostela Valley, Davao Oriental, Guimaras, Ifugao, Ilocos Norte, Ilocos Sur, Iloilo, Kalinga, La Union, Laguna, Leyte, Marinduque, Masbate, Misamis Oriental, Mountain Province, National Capital Region, Negros Occidental, Negros Oriental, Nueva Ecija, Nueva Viscaya, Occidental Mindoro, Oriental Mindoro, Pampanga, Pangasinan, Rizal, Romblon, Siquijor, Southern Leyte, Surigao del Norte, Surigao del Sur, Tarlac, Zambales
Zone 3
(V = 150 kph)
Basilan, Bukidnon, Davao del Norte, Davao del Sur, Lanao del Norte, Lanao del Sur, Maguindanao, Misamis Occidental, North Cotabato, Palawan, Sarangani, South Cotabato, Sultan Kudarat, Sulu, Tawi-tawi, Zamboanga del Norte, Zamboanga del Sur, Zamboanga Sibugay
Table 7. Proposed basic wind speeds for a 50-year return period
Agusan del Sur, Aurora, Cagayan, Camarines Norte, Isabela, Leyte, Masbate, Northern Samar, Nueva Ecija, Nueva Vizcaya, Quezon, Quirino, Southern Leyte, Surigao del Norte, Surigao del Sur, Western Samar
Basilan, Bukidnon, Camiguin, Davao del Norte, Davao del Sur, Lanao del Norte, Lanao del Sur, Maguindanao, Misamis Occidental, Misamis Oriental, North Cotabato, Palawan, Sarangani, Siquijor, South Cotabato, Sultan Kudarat, Sulu, Tawi-tawi, Zamboanga del Norte, Zamboanga del Sur, Zamboanga Sibugay
Table 8. Proposed basic wind speeds for a 75-year return period Zone Classification (Basic Wind Speed)
Agusan del Sur, Aurora, Cagayan, Camarines Norte, Isabela, Leyte, Masbate, Northern Samar, Nueva Ecija, Nueva Vizcaya, Quezon, Quirino, Southern Leyte, Surigao del Norte, Surigao del Sur, Western Samar
Basilan, Bukidnon, Camiguin, Davao del Norte, Davao del Sur, Lanao del Norte, Lanao del Sur, Maguindanao, Misamis Occidental, Misamis Oriental, North Cotabato, Palawan, Sarangani, Siquijor, South Cotabato, Sultan Kudarat, Sulu, Tawi-tawi, Zamboanga del Norte, Zamboanga del Sur, Zamboanga Sibugay
Table 9. Proposed basic wind speeds for a 100-year return period
Agusan del Sur, Aurora, Cagayan, Camarines Norte, Isabela, Leyte, Masbate, Northern Samar, Nueva Ecija, Nueva Vizcaya, Quezon, Quirino, Southern Leyte, Surigao del Norte, Surigao del Sur, Western Samar
Basilan, Bukidnon, Camiguin, Davao del Norte, Davao del Sur, Lanao del Norte, Lanao del Sur, Maguindanao, Misamis Occidental, Misamis Oriental, North Cotabato, Palawan, Sarangani, Siquijor, South Cotabato, Sultan Kudarat, Sulu, Tawi-tawi, Zamboanga del Norte, Zamboanga del Sur, Zamboanga Sibugay
5.2 PROPOSED WIND CONTOUR MAPS Wind contour maps were also produced using 50, 75 and 100 return level wind speeds. Using the surface contour method in Arcmap, different contour lines of interval 20 kph were drawn in the map. The minimum contour line was the same, 100 kph, while the maximum contour lines were
320, 340 and 360 for three wind contour maps with 50, 75 and 100-year return period, respectively. The return wind speed value also has the same pattern compared to the wind zone map. Very strong wind speeds are located in the extreme eastern part of the country specifically in the places of Samar and Catanduanes in Visayas. Moderate wind speed ranging from 160 to 220 kph are assgined to provinces that fall under zone 3 of the proposed wind zone map. Provinces with low wind speeds ranging from 100 to 140 kph are located mostly in Mindanao and the western and southern part of Visayas. Figure 7 show the proposed wind contour maps with 50, 75 and 100-year return periods.
Figure 7. Proposed Wind Contour Map of the Philippines 6. CONCLUSIONS Climate change patterns that increases in rainfall, typhoon strength etc will certainly affect our communities. As an initial step in climate proofing our communities, the researches updated the wind zone map using latest extreme wind speed data from PAGASA. Three wind zone maps each with four zones were developed. These maps were compared with the NSCP 2010 wind zone map and the following were observed: eight provinces in Luzon, four provinces in the Visayas and six provinces from Mindanao have changes in the basic wind speed. A wind contour map was also developed in this paper. This shows a more detailed design wind speeds for all regions. Both maps can be used as reference for basic design wind speed of 50, 75 and 100-year return periods.
(a) 50-year return period (b) 75-year return period (c) 100-year return period
REFERENCES ADRC (2008). Information on Disaster Risk Reduction of the Member Countries. Date Accessed: March 15, 2014. Available: http://www.adrc.asia/nationinformation.php?NationCode=608&Lang=en&NationNum=14. Alliance Development Works . World Risk Report 2013. Date Accessed: March 15, 2014. Available: www.worldriskreport.org Alonzo, A (2011). “’Sendong’ among deadliest cyclones to enter PHL in 12 years”. Date Accessed March 15, 2014. GMA news online, Available: http://www.gmanetwork.com/news/story/242058/news/nation/sendong-among-deadliest-cyclones-to-enter-phl-in-12-years BBC News Asia (2009). “Dozens dead in Philippine floods”. Date Accessed March 15, 2014. Available: http://news.bbc.co.uk/2/hi/asia-pacific/8276347.stm
CRED. (2013). Emergency Events Database. Centre for Research on the Epidemiology of Disasters. Retrieved August 16, 2013, from EM-DAT: http://www.emdat.be/disaster-list Dinglasan, R. R. (2013). GMA News Online. Retrieved from SciTech: Science: http://www.gmanetwork.com/news/story/300616/scitech/science/manila-2nd-most-at-risk-from-climate-change-among-cities-in-197-countries-study Garciano, L. E., Hoshiya, M., and Maruyama, O. (2005). Development of a regional map of extreme wind speeds in the Philippines, JSCE Journal of Structural and Earthquake Eng., Vol. 22, No. 1, 15s - 26s. Garciano, L.E. and Taclibon, P. (2013). The October 15, 2013 Bohol Island, Philippines earthquake and its damaging effects: An investigative report. JSCE Journal of Disaster Factsheets, 6 pages. http://committees.jsce.or.jp/disaster/FS2013-D-0004 Guardian. (2013). Filipino super-typhoon an ominous warning of climate change impact. Date Accessed: July 7, 2013. Available: http://www.guardian.co.uk/world/2013/feb/17/filipino-super-typhoon-climate-change Kit, O., Ludeke, M., and Reckien, D. (2010). Assessment of Climate Change-Induced Vulnerability to Floods in Hyderabad, India, Using Remote Sensing Data, Resilient Cities: Cities and Adaptation to Climate Change Proceedings of the Global Forum 2010. pp. 35 - 44. Korndöfer, C., Teichmann, P., and Frenzel, F. (2010). Integrated Climate Adaptation in Dresden: Insights from Flood Prevention, Resilient Cities: Cities and Adaptation to Climate Change Proceedings of the Global Forum 2010. pp. 291 - 298. NDRRMC (2013). Effects of Typhoon Yolanda. Quezon: National Disaster Risk Reduction and Management Council.
NDRRMC (2012). SiteRep No. 05 Preparedness Measures for Typhoon “PABLO” (Bopha). National Disaster Risk Reduction and Management Council, Dec. 04, 2012. Salazar, M. (2014). “A methodology for site-specific wind contouring and hazard mapping: A case study in Surigao City” Undergraduate thesis of the Department of Civil Engineering, DLSU-M. April 2014.
ABOUT THE AUTHORS
Lessandro Estelito Garciano is an associate professor of the Department of Civil Engineering, De La Salle University - Manila. He received his Doctor of Engineering degree (Civil Engineering) from Tokyo City University. His research interests include wind hazard mapping, Monte Carlo simulation, reliability theory and stochastic filtering techniques in civil engineering. He may be contacted by e-mail at [email protected].
Tamara Beatrice de Leoz, Eldrich Kaw, Aaron Quidilla and Janssen Valbuena are undergraduate students of the Department of Civil Engineering, De La Salle University - Manila.
ACKNOWLEDGEMENT The authors would like to thank the staff of PAGASA for providing the necessary extreme wind speed data used in this research.
