Return period and Pareto analyses of 45 years of tropical cyclone data (1970–2014) in the Philippines
Introduction
Tropical cyclones (TCs) are one of the most destructive natural disasters affecting many societies around the world. In a Western North Pacific (WNP) country like the Philippines, its climate and weather conditions are greatly influenced by TCs. Together with the Asian southwest monsoon, both events contributed to high rainfall in the northern Philippines (Bagtasa, 2017) and caused flash floods over the low lying areas and landslides along the mountain slopes (Cayanan, Chen, Argete, Yen, & Nilo, 2011).
While the rainfall associated to TCs is beneficial to the country's agricultural farming and water supply systems, it also causes devastation when it is coupled with massive flooding and landslides. When cyclones make a landfall, strong winds can destroy agricultural crops and residential houses while storm surges can cause death and significant damages to the country's coastal towns and cities. Of all the natural disasters, tropical cyclones with its epiphenomena such as landslides, storm surges, and floods cause the largest loss of life and property in the Philippines (Huigen & Jens, 2006).
As the global climate has warmed, the TC intensity has increased over the past 40 years (Emanuel, Sundararajan, & William, 2008). Several studies have also shown the increasing trends in TC frequencies in the WNP (Walsh et al., 2016). Results from the other studies argue, however, that the longer-term records do not show changes in the frequency or severity and the recent upward trend is within the natural climatic variability (Landsea, Harper, Hoarau, & Knaff, 2006). Aimed to provide a framework to combine atmospheric science and economics, it was found that the tropical cyclone damage from climate change tends to be concentrated in North America, East Asia and the Caribbean-Central American region (Mendelsohn, Emanuel, Chonabayashi, & Bakkensen, 2012). In Southeast Asia, a recent study was conducted to analyse the trend in the annual total number and intensity of TCs within the Philippines Area of Responsibility (PAR) (Cinco et al., 2016).
Previous studies mainly focused on the largescale weather events that correspond to macrometeorological fluctuations in wind and used wind speed data to estimate 50- or 100-year return levels (Steinkohl, Davis, & Klüppelberg, 2013). The primary interest of this study lies in the spatiotemporal mapping of return periods of most intense and extremely destructive cyclones and then use the result to apply the Pareto principle.
Following the NHC-NOAA's (2018) definition, tropical cyclone is described as a rotating, organised system of clouds and thunderstorms that originates over tropical or subtropical waters and has a closed low-level circulation. In the Philippines, they are classified based upon their degree of intensity as follows (PAGASA, 2018):
- •
Tropical Depression (TD) - a tropical cyclone with maximum sustained winds of up to 61 kilometres per hour (kph) or less than 33 nautical miles per hour (knots).
- •
Tropical Storm (TS) - a tropical cyclone with maximum wind speed of 62–88 kph or 34–47 knots.
- •
Severe Tropical Storm (STS) - a tropical cyclone with maximum wind speed of 89–117 kph or 48–63 knots.
- •
Typhoon (TY) - a tropical cyclone with maximum wind speed of 118–220 kph or 64–120 knots.
- •
Super Typhoon (STY) - a tropical cyclone with maximum wind speed exceeding 220 kph or more than 120 knots.
Unless specified, this study used the term “tropical cyclones” (or TCs for short) referring to all these types of cyclones.
The return period (RP) of TCs' maximum wind speeds can be defined as the average period in which an event is expected to recur once (Chu & Wang, 1998a, b). The use of the term “return period”, however, has been criticised as it is confusing to decision-makers; hence, the National Oceanic and Atmospheric Administration (NOAA) used the term “average recurrence interval” (ARI) to describe the frequency of an event (Parzybok, Clarke, & Hultstrand, 2011). Often referred to as the Annual Exceedance Probability (AEP) (USGS, 2016), the ARI can also be expressed as the probability or percent chance of occurrence (CoC) of an event for any given year (Parzybok et al., 2011).
In applying the extreme value theory to wind speed data, one key objective is often the determination of the RPs (Steinkohl et al., 2013). The generated information can be used then for building designs and disaster preparedness (Chu & Wang, 1998a, b). The motivations of various studies have been founded on this principle. Using the 1987 HURISK Program, for example, NOAA generates spatial information on the RPs of major cyclones passing through various locations on the U.S. Coast (NOAA, 2017a, b). Malmstadt, Elsner, and Jagger (2010) and Trepanier and Scheitlin (2014) employed the spatial and temporal approaches to calculate the RPs of TCs. The combination of these approaches such as the works of Andrews (2004), Keim, Muller, and Stone (2007), Della-Marta et al. (2009), and Hoque, Phinn, Roelfsema, and Childs (2017) provides a unique picture when and where certain areas were mostly threatened by TCs (Keim et al., 2007). In a recent study conducted by Hong, Li, and Duan (2016), they estimated the RPs of the annual maximum typhoon wind speed for a set of grid points and then interpolated using ordinary kriging to develop the typhoon wind hazard contour maps.
Using the CoC in the present study, the generated spatial information shows the zones of cyclone tracks with corresponding probability values for a particular year during the cyclone season. While several cyclones can originate in different locations and travel much different paths from the average, mapping of the TCs' maximum wind speed gives a better picture of the average cyclone season (NOAA, 2017a, b). In the present study, this gives a better picture of the average recurrence interval or probability of occurrence of cyclones within PAR.
While the RP-based approach can be used to obtain an annual rate of return on the extreme winds with significant application and was proven useful for setting building codes (Rupp & Lander, 1996) including insurance and risk management (Elsner, Jagger, & Tsonis, 2006), the idea of this present study is to translate the annual rate of RP or percent CoC of extreme winds into spatially-explicit construct. This method does not only go beyond the empirical methods of storm counting by intensity category (Elsner et al., 2006), but also provides the annual or decadal spatial distribution of RPs or percent CoC of cyclone tracks within PAR over a forty-five-year time-period. This approach has never been substantially explored within PAR despite the fact that the WNP is the most active region on Earth for tropical cyclone occurrences (Rupp & Lander, 1996). Furthermore, the TC intensity is highly influenced by its genesis location and it is significant to investigate its spatial changes (Park, Ho, & Kim, 2014). Hence, this present study gives a better picture of cyclone occurrences within PAR with the aid of maps.
The temporal decrease in the year-to-year variability of the annual number of TCs in WNP was observed from 1959 to 1991 (Rupp & Lander, 1996) and the same trend was observed within PAR crossing the Philippines from 1951 to 2013 (Cinco et al., 2016). In a recent study conducted by Lee, Tippett, Sobel, and Camargo (2018), the Philippines has observed to experience 2–3 years return period of TCs. However, mapping the RP or percent CoC of TCs to depict the country's most active cyclone zones has never been explored. Through this approach, it is possible to identify and classify areas within PAR where tropical depressions (TD), tropical storms (TS), severe tropical storms (STS), typhoons (TY), or super typhoons (STY) are mostly and actively formed through the maximum sustained wind analysis.
In most developing countries such as the Philippines, lack of financial resources, few opportunities, and the politicisation of disaster risk management hindered the country from immediate rehabilitation, recovery and reconstruction (Alcayna, Bollettino, Dy, & Vinck, 2016) (Kure, Jibiki, Iuchi, & Udo, 2016). Some of the contributing issues include the poor implementation of vulnerability assessment to support survival funds (Blanco, 2015), lack of support to youth council participation for disaster risk reduction (Fernandez & Shaw, 2013), and the alleged funnelling of funds by corrupt politicians and political elites (Hodes, 2013; Webb, 2013). While the country is a leading regional actor in disaster risk management in South East Asia, the full picture of who is doing what, how, where and when the resilience and disaster preparedness should take place does not exist (Alcayna et al., 2016). Regardless of these issues, both the national government and local government must play an active and coordinated role in disaster governance on a limited resource (Blanco, 2015). The use of the Pareto principle can help direct this inadequate resource in order to make the maximum impact.
Pareto principle has been generalised to mean that approximately 80% of given effects (“trivial many”) can be attributed to the 20% of the possible causes (“vital few”) (Scott, 2017). Various studies were conducted to apply this principle in information security management (Scott, 2017), fire insurance claims (Embrechts, Resnick, & Samorodnitsky, 1999), and the insurability of climate risks (e.g. hurricane losses) (Charpentier, 2008). Never been explored in the Philippine setting, the tool was used in this present study to account the relationship between RPs and the cost of socio-economic damages and number of deaths caused by TCs.
Having identified the research gaps as discussed above, this study aimed to:
- 1.
calculate and map the return periods or probability of occurrence of TCs within PAR using the geospatial technique rather than a single extreme event calculation or the empirical method of storm counting by intensity category;
- 2.
establish the linkage between SST anomaly associated with the Interdecadal Pacific Oscillation (IPO) and the percent CoC of tropical cyclones; and
- 3.
investigate the relationship of return periods with economic damages and deaths caused by the extreme tropical cyclones.
Section snippets
Description of the study area
The Philippines is geographically located at 12.8797° N, 121.7740° E. Its climate is generally described as tropical and maritime with relatively high temperature, high humidity and abundant rainfall (PAGASA, 2016). Located along the typhoon belt in the Pacific, the Philippines is threatened by an average of 20 typhoons every year, five of which are destructive (Asian Disaster Reduction Center (ADRC), 2008) (see Fig. 1, Fig. 2). As of 2016, the country has a population of over 103 million (The
Results and discussion
Nine hundred ninety five (995) TCs were identified from the 45-year data as shown in Fig. 3 and summarised in Table 2. On the average, 22 cyclones entered the PAR annually. Found to have physical connection in the determination of the Weibull distribution function (Wang & Li, 2016), the calculated mean and standard deviation of the maximum sustained wind by cyclone event were used in this study to implement Equations (1), (2). The results of the analyses are discussed in the subsequent sections.
Conclusion
Known to be the “exporter of typhoons”, the Philippines experienced a range of tropical cyclones from 1970 to 2014 which include 9 tropical depressions (1%), 263 tropical storms (26%), 248 severe tropical storms (25%), 454 typhoons (46%), and 21 super typhoons (2%). On the average, 6 tropical storms, 6 severe tropical storms, and 10 typhoons entered the PAR per year. With 3-yr RP (30% CoC), the mean estimate of TCs' maximum sustained wind speed (38 m/s) falls in the 60th percentile and within
Acknowledgments
The author sincerely thanks the anonymous reviewers for their constructive comments and significant contributions to improve this study. Also thanks to Ms. Adreana Santos-Remo of the Ecosystems Research and Development Bureau (ERDB) for the proofreading assistance.
References (101)
On interpolating between probability distributions
Applied Mathematics and Computation
(1996)- et al.
Socio-economic impact of super typhoon Harurot in San Mariano, Isabela, the Philippines
World Development
(2006) - et al.
Analysis of early warning systems: The case of super-typhoon Haiyan
International Journal of Disaster Risk Reduction
(2016) Devastating storm surges of typhoon Haiyan
International Journal of Disaster Risk Reduction
(2015)- et al.(2015)
- et al.
Prediction of wind speed time series using modified Taylor Kriging method
Energy
(2010) - et al.
Analysis of the coastal Mississippi strom surge hazard
Ocean Engineering
(2010) Two and three-parameter Weibull distribution in available wind power analysis
Renewable Energy
(2017)A review of Weibull functions in wind sector
Renewable and Sustainable Energy Reviews
(2017)- et al.
Comparative study of five methods to estimate Weibull parameters for wind speed on Phangan island, Thailand
Energy Procedia
(2015)
Poverty and climate Change: Reducing the vulnerability of the poor through adaptation, s.l
Resilience and disaster trends in the Philippines: Opportunities for national and local capacity building
PLOS Currents Disasters
Spatial and temporal variability of tropical strom and hurrican strikes in the Bahamas, and the greater and lesser antilles
Information on disaster risk reduction of the member countries: Philippines
Severe tropical cyclone chloe
Contribution of tropical cyclones to rainfall in the Philippines
Journal of Climate
A history of poverty: The politics of natural disasters in the Philippines, 1985-1995
The Pacific Review
New Philippine floods kill dozens
Disaster governance in the Philippines: Issues, lessons learned, and future directions in the Post-Yolanda super typhoon aftermath
International Journal of Public Administration
Western North pacific tropical cyclone intensity and ENSO
Journal of Climate
The effect of tropical cyclones on southwest monsoon rainfall in the the Philippines
Journal of the Meteorological Society of Japan
Modeling return periods of tropical cyclone intensities in the vicinity of Hawaii
American Meteorological Society
Modeling return periods of tropical cyclone intensities in the vicinity of Hawaii
Journal of Applied Meteorology
Observed trends and impacts of tropical cyclones in the Philippines
International Journal of Climatology
Return periods of extreme events
Kriging nonstationary data
Journal of American Statistical Association
CEDIM forensic disaster analysis - super typhoon Haiyan/Yolanda - report No. 2b - supplement, Eggenstein-Leopoldshafen
The retrun period of wind storms over Europe
International Journal of Climatology
The Pareto principle
The Plymouth Student Scientist
An analysis of super typhoon tip (October 1979)
Monthly Weather Review
Natural disasters threaten Philippine growth
Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon
PLoS One
Estimated return periods for hurricane Katrina
Geophysical Research Letters
Statistical models for tropical cyclone activity
On estimating hurricane return periods
Journal of Applied Meteorology and Climatology
Tropical cyclones and global warming: Results from downscaling IPCC AR4 simulations
Bulletin of American Meteorological Society
Extreme value as a risk management tool
North American Actuarial Journal
Understanding simple kriging
Geohazards, tropical cyclones and disatser risk management in the Philippines: Adaptation in a changing climate regime
Journal of Environmental Science and Management
Youth council participation in disaster risk reduction in Infanta and Makati, Philippines: A policy review
International Journal of Disaster Risk Science
Isotropic and anistropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions
Geomatics, Natural Hazards and Risk
Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity
Climate Dynamics
Haiyan and the other Philippines typhoon: The untold political scandal underpinning this tragedy
Typhoon wind hazard estimation and mapping for coastal region in mainland China
Natural Hazards Review
Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques
International Journal of Digital Earth
Estimation of Weibull parameters for wind speed calculation at Kanyakumari in India
International Journal of Innovative Research in Science, Engineering and Technology
RSMC Tokyo typhoon center 1951-2017
Spatiotemporal analysis of old World diseases in North America, A.D. 1519-1807
American Antiquity
Spatiotemporal patterns and return periods of tropical storm and hurricane strikes from Texas to Maine
Journal of Climate
Cited by (6)
Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series
2022, International Journal of Applied Earth Observation and GeoinformationIntegrating information and communications technology (ICT) assets in assessing tropical cyclone risk in the Philippines
2019, International Journal of Disaster Risk ReductionCitation Excerpt :Cyclones are one of the most dangerous natural hazards that occur in the ocean-atmosphere system [1]. With strong winds and extreme rainfall, they can create destructive impacts [2], catastrophic economic damages and considerable loss of life due to storm surges, widespread flooding and landslides [3–5]. Human agency and societal processes play a critical role in properly understanding and preventing disasters [6] which include the timely information and early warning of potential hazards [7].
Regional tropical cyclone impact functions for globally consistent risk assessments
2021, Natural Hazards and Earth System SciencesProcess audit in a beverage industry - Line efficiency and downtime analysis, a case study
2021, International Journal of Productivity and Quality Management