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![Cyclone intensity grouped by categories according to the SS scale for decades between 1851-2019 where: a) green line represents the sum of the cyclones that do not reach the hurricane category; yellow line sum of hurricanes categories 1, 2, and 3; red line sum of hurricanes categories 4 and 5; and dotted black line, total cyclones. b) equivalent to -a) -but standing for the percentage concerning the cyclone's total per decade. Where: TD = Tropical Depression; TS = Tropical Storm; and H = Hurricane categories 1 to 5. Source: Adapted from Webster [37], updated with data from IBTrACS [10]](https://www.researchgate.net/profile/Julio-Morales-Hernandez/publication/351453199/figure/fig2/AS:1021898725003270@1620651050646/Cyclone-intensity-grouped-by-categories-according-to-the-SS-scale-for-decades-between_Q320.jpg)
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Tropical Cyclones in the North Atlantic Basin and Yucatan Peninsula, Mexico:
Identification of Extreme Events
Gabriel Sánchez-Rivera1,5*, Oscar Frausto-Martínez1,5, Leticia Gómez-Mendoza2,5, Ángel Refugio Terán-Cuevas3,
Julio Cesar Morales Hernández4,5
1 Laboratorio de Observación e Investigación Espacial, Universidad de Quintana Roo; Avenida Andrés Quintana Roo, S/N, Frente
a colonia San Gervasio, Cozumel 77600, Quintana Roo, Mexico
2 Colegio de Geografía, Facultad de Filosofía y Letras, Universidad Nacional Autónoma de México, Ciudad Universitaria,
Coyoacán 04510, Ciudad de Mexico, Mexico
3 Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo, Instituto Politécnico Nacional
(CIIEMAD-IPN), Departamento de Territorio y Ambiente, 30 de Junio de 1520 s/n, La Laguna Ticomán, Gustavo A. Madero,
07340, Ciudad de México, Mexico
4 Centro de Estudios Meteorológicos de la Costa, Departamento de Ciencias Exactas, Centro Universitario de la Costa,
Universidad de Guadalajara, Av. Juárez No. 976, Colonia Centro, Guadalajara 44100, Jalisco, Mexico
5 Red de Desastres Asociados a Fenómenos Hidrometeorológicos y Climáticos (REDESClim), CONACYT, Av. Insurgentes Sur
1582, Col. Crédito Constructor, Alcaldía Benito Juárez, Ciudad de México 03940, México
Corresponding Author Email: grivera@uqroo.edu.mx
https://doi.org/10.18280/ijdne.160204
ABSTRACT
Received: 19 October 2020
Accepted: 22 March 2021
Tropical cyclones impact continental and island lands annually, so attention to the
adverse effects of associated winds and rains is focused on damage to human settlements,
growing areas, infrastructure, loss of human life, and ecosystems. Defining the concept
of extreme events is complex since it depends on the focus with which it is approached.
The study aimed to provide a long-term and large-scale Serie (168 years) concerning
cyclonic activity and characterization of extreme cyclones in the North Atlantic Basin,
considering as a reference the World Meteorological definition Organization. The trend
of extreme events in the 1851-2019 period was calculated from the records of the
International Best Track Archive for Climate Stewardship and the statistical method
"Exceeding a relative threshold," from the variables: "maximum speed of sustained
winds" and "minimum atmospheric pressure." The thresholds considered were a) 10 and
90; b) 1 and 99; and c) extraordinary maximum threshold of 0.1 and 99.9 (percentiles).
The cyclone trajectories were classified based on a) did not landfall; b) They impacted
continental and insular lands; c) They impacted Mexico; and d) They impacted the
Yucatan Peninsula. The results showed that of the total of 2,220 events registered, 59%
(n=1,302) touched land. Considering the laxest thresholds (10 and 90 percentiles), the
number of extreme events identified for the North Atlantic was 5.95% (n=132), while for
the Yucatan Peninsula, it was 0.72% (n=16). The findings do not show a significant trend
of increasing cyclone numbers. As for intensity (magnitude), a slight increase in
hurricanes in categories H4 and H5 on the Saffir-Simpson scale has been observed in the
last two decades.
Keywords:
extreme events, hurricanes, percentiles, North
Atlantic, Yucatan Peninsula
1. INTRODUCTION
Tropical cyclones (TCs) impact continental and island lands
annually, the National Hurricane Center (NHC) defines them
as: "a closed atmospheric circulation that rotates
counterclockwise in the northern hemisphere and clockwise in
the southern hemisphere" [1], represented by an organized
system of clouds and electrical storms originating in tropical
or subtropical waters with a closed low-level circulation [2],
which can be characterized on the measurement and
estimation of their physical and temporal properties, such as
intensity (magnitude), measured as a function of the maximum
speed of sustained winds (Max. Win.) and minimum
atmospheric pressure (Min. Pres.), trajectory (location),
precipitation, duration, travel speed, among others [3]. The
measurement of the intensity and destruction capacity of
tropical cyclones is estimated based on a scale developed by
the wind engineer Herb Saffir and the meteorologist Bob
Simpson, which is called the "Saffir-Simpson scale (SS)" [4].
To compensate for the catastrophic perception of the effect
of cyclones, Vink and Ahsan [5] carried out a study where they
identified through the literature records those benefits that
these events provide; their results reported the finding of 14
types of direct benefits and two indirect, classified based on
the categories of provisioning, regulation, support, and
cultural aspects, proposed for ecosystem services of the United
Nations Environment Program (UNEP) [6].
According to Appendini et al. [7], TCs are events that can
cause the economy, the population's physical integrity, and
severe environmental damages. The effects of climate change
will be reflected in the increase in the number and intensity of
such events, which increase exposure to higher intensity winds
International Journal of Design & Nature and Ecodynamics
Vol. 16, No. 2, April, 2021, pp. 145-160
Journal homepage: http://iieta.org/journals/ijdne
145
and rainfall will have repercussions on the vulnerability of
social and environmental systems [8].
The design and implementation of mitigation and
adaptation measures require identifying and characterizing
changes in extreme events' dynamics.
The main objectives of this study were to provide a long-
term scenario of cyclonic activity in the North Atlantic Basin
(NAB) and the characterization of extreme cyclones,
according to the World Meteorological Organization (WMO)
[9], who states that the events considered "rare" are those that
exceed the 90th and 95th percentiles. In contrast, those
considered "very rare" would exceed the rank of 99 or higher.
Therefore, an added reference criterion (extreme threshold) of
the order of 0.1 and 99.9 percentiles were included; with this,
we sought to identify events that can be considered
"extraordinarily rare."
To answer the questions and objectives raised, we used the
records of cyclones that occurred in the last 168 years from the
database of the International Best Track Archive for Climate
Stewardship (IBTrACS) [10], considering the "maximum
speed sustained winds" and "minimum atmospheric pressure."
The description of the information's characteristics and the
source data's uncertainty degrees are addressed in the "data"
section.
In the characterization of extreme events, the statistical
model "exceeding a relative threshold" was applied, taking as
reference three sets of thresholds based on the percentiles
proposed by the Intergovernmental Panel on Climate Change
(IPCC) [11, 12] and Camuffo, della Valle, & Becherini [13].
Results are presented from general to particular cases,
beginning with the events' characterization at the NAB scale
and ending with identifying the extreme events that have
impacted the Yucatan Peninsula (YP). For this, four phases
were defined and executed, described in the "methods" section.
The study allowed us to identify the trend of the formation
(quantity) and intensity (magnitude) of the tropical cyclones in
the extreme category, 260 for the NAB, of which 30 crossed
the YP. The identification of four hurricanes characterized as
"extremely rare" stands out, of which Gilbert (1988) and
Wilma (2005) landfall the YP.
The research will allow having a frame of reference
regarding the classification of tropical cyclones extreme
events under the definition proposed by the WMO, which will
serve as a reference to correlate the effects caused by TCs
based on the probability of the increase in the according to the
CC scenarios.
2. BACKGROUND
The characterization of extreme events involves a
conceptualization problem; Stephenson [14] mentions that
there is no consensus as to what is meant by "extreme," so the
lack of a single definition has given rise to various statements,
which depend on the context and the attributes considered,
such as frequency, intensity, duration, spatial scale, effects on
environmental components and human infrastructure, among
others. Also, the words "severe," "rare," "high impact," and
"extreme" are often used interchangeably and synonymously,
making it even more confusing and complicated to reach an
agreement to define what is an "extreme event." The author
proposes a classification that allows ranking extreme
meteorological and climatic phenomena, differentiating and
integrating the "rare," "severe," "extreme" and "high-impact"
concepts, where cyclones being in the extreme category, those
whose occurrence is "rare," with "severe" and "acute" effects,
where "rare" is defined as that event with a low probability of
happening.
In the scientific literature consulted, no single method was
found to characterize extreme events; on the contrary,
definitions such as those proposed from meteorology and
climatology vs. ecological and social approaches tend to be
opposite. In the first case, the WMO [7] proposes as a general
principle that the definition of extreme events must consider
the physical and spatio-temporal characteristics of the
phenomenon studied (the threat or danger), independent of the
impact that could cause, while in the second case states that
they must be defined upon the impact and the effects caused
(vulnerability) on the natural and social environment [13-16].
Due to the complexity in defining extreme events, the
WMO [17] presented a compilation of the findings in the
scientific literature on the perspectives used for its
characterization, distinguishing eight classes: a) basic
descriptive variables; b) derived variables; c) threshold
categorization; d) human perspective of extremes; e)
hydrological/agriculture; f) derived indices; g) basic
hydrological/climatological indices, and h) expert
interpretation/classification of drought. The "h" type category,
in turn, is divided into three subcategories: 1) extremes of
atmospheric weather and climate variables, 2) large-scale
phenomena accounting for extremes, and 3) collateral effects
of extremes on the physical environment. The most widely
used classifications in the climate science field are single,
variable, and compound (multivariable) ends.
The definition of the IPCC [11, 12] falls within class "h,"
category "1", which defines them as those that present values
of a climatic or weather variable above or below a relative
threshold value to the upper or lower extremes of the range of
observed values of the considered variable, being in the 10th
or 90th percentile of the probability density function. Camuffo
et al. [13] point out that the percentiles should be on the order
of 1 and 99, given that the proposed by the IPCC is unrealistic.
Stephenson [16] and Camuffo et al. [13] identified among
the main statistical models to characterize extreme climatic
events the following: a) Peak over a threshold [11]; b) Extreme
value theory [18, 19]; c) Exceeding a relative threshold [12];
d) exceeds a threshold and a return period [17]; and e)
Effectiveness: events that trigger a disaster or emergency [12]
or capable of causing injury and loss of life, etc. [17].
Statistically, the occurrence of extreme events is rare in
terms of frequency, magnitude, or duration in a particular
ecosystem, so recognizing them will be a function of the length
and quality of the observation records and cannot be described
from a single quantity, so it is advisable to include variables
that allow identifying the effects and damages caused by
phenomena that landfall [13, 14].
The characterization of hurricanes as extreme events is of
interest when considering the climate change scenarios, which
foresee an increase in their quantity and intensity [20]; an
increase that will not occur suddenly but gradually, so that the
detection of changes will be possible through the analysis of
long time series.
From another perspective, several authors have sought to
identify patterns that allow understanding and comprehend the
dynamics related to the occurrence of tropical cyclones
through the correlation of various variables such as ocean
surface temperature, sunspot activity, Southern Oscillation
(ENSO), and the multidecadal oscillation of the Atlantic
146
Ocean (AMO). Examples are the works published by Holland
& Bruyère [21]; Doval & Rodríguez [22]; Vecchi & Knutson
[23] and Klotzbach [24].
In Mexico's case, numerous studies have addressed the
analysis of cyclones in the YP, among which those of Palacio-
Aponte [25] and Ihl & Frausto [26] stand out. They estimate
the areas of greatest danger and probability of impact,
highlighting the north and northeast coastal areas of the
Peninsula as "extreme danger" areas, followed by the south of
Quintana Roo. Furthermore, others like Frausto et al. [27]
based on the design and implementation of indicators to
monitor and control the resilience capacities of areas
susceptible to cyclones' impact.
However, those studies do not address the concept of
extreme events considering the definitions of the WMO and
the IPCC. The same situation occurs with the National
Oceanic and Atmospheric Administration (NOAA), IBTrACS,
and the National Water Commission (CONAGUA, due to its
Spanish acronym) databases, which get limited to presenting
information about socio-economic impacts and loss of human
life according to the SS Scale.
Given the above, the following questions were asked: a) has
there been an increase in the number and intensity of cyclones
formed in the North Atlantic Basin between the 1851–2019
period? b) which cyclones formed in the North Atlantic Basin
does it meet the definitions of extreme events proposed by the
WMO and the IPCC? and c) which cyclones categorized as
extreme has landfall the Yucatan Peninsula?
3. MATERIALS AND METHODS
3.1 Study area
The study area corresponds to the North Atlantic Basin. It
includes the regions of Canada and the USA's eastern coasts
to the northwest and the sub-basins of the Gulf of Mexico and
the Caribbean Sea to the east and southeast (Figure 1).
According to data from the Flanders Marine Institute [28], the
basin covers an approximate surface of 59.5 million km2,
where 3 million correspond to the Caribbean Sea and 1.9 to the
Gulf of Mexico. Along its perimeter, many countries and
coastal populations are susceptible to suffering the direct
impacts of cyclones in the extreme category. The Caribbean
Sea region alone includes 28 islands recognized as countries,
with a population of the order of 43 million inhabitants
estimated by 2020 [29].
The results presented by Knapp et al. [31] place the North
Atlantic Basin in third place in terms of the number of
cyclones reported between the years 1940 and 2010,
occupying first and second place the Western and Eastern
Pacific Basins, respectively.
Figure 1. North Atlantic Basin. Source: elaborated with data from Amante & Eakins [30], Flanders Marine Institute [28]
147
The main development zone for cyclonic events is located
between the coordinates: 10º N - 20º N and 20º W - 60º W,
between Cape Verde's islands and the Caribbean's Lesser
Antilles [32].
3.2 Data
Information on the physical characteristics and trajectory of
tropical cyclones in the NAB was obtained from the IBTrACS
[10] repertoire; best track data are free to access, updated daily
in a time interval of 3-hour and contain provisional tracks of
recent storms; the data is available in 3 formats (netCDF, CSV,
shapefiles). We use the release "v04r00" on 09-09-2019. The
files are presented in alphanumeric and vector format, which
contains 119,626 individual records that make up 2,220
meteors reported between June 23, 1851, and September 05,
2019.
The data results from the post-cyclone season analysis when
all the information available for each event is processed. So,
the historical record is not homogeneous in its construction
[33]; additionally, the origin of the records from various
historical sources made from different techniques and ranges
of precision present different spatial-temporal variations of
origin depending on the agencies and sources consulted [31].
In the case of wind speed, the degree of uncertainty
measured in knots decreases temporarily, reaching the
following maximum levels: years before 1965 of ± 30, 1965 -
1978 of ± 20; 1978 - 1984 of ± 15; 1984-2000 of ± 10; and
from 2000 to date, it drops to ± 7 knots. The atmospheric
pressure oscillates in the order of 1 mb, and the position is
reported at a resolution of approximately 0.1°, which results in
an uncertainty of approximately ± 10 km [3].
Regarding atmospheric pressure, the primary source of
records begins from the end of the Second World War with
manned aircraft equipped with sensors for their measurement
and later with the incorporation of satellites in the 1960s.
Therefore, the absence of data for more than 50% of the NAB's
cyclones stands out. Additionally, spatial variations can also
occur because of the intensity of cyclonic phenomena [34].
To identify the cyclones that made landfall, we used the
global vector files of the coasts of the: "A Global Self-
consistent, Hierarchical, High-resolution Geography
Database" [35, 36] and the polygons that delimit the NAB and
the sub-basins of the Gulf of Mexico and the Caribbean Sea of
the Flanders Marine Institute [28].
3.3 Methods
The processes to reach the aims of the study were grouped
into four phases (Figure 2):
Figure 2. Methodological diagram
148
Phase 1: Data acquisition and pre-processing. The vector
layers were re-projected to UTM-GCS_WGS_1984, the units
of the alphanumeric files were converted to the decimal metric
system. Subsequently, two base files were created: the
trajectory vector layer and alphanumeric database with each
cyclone's records.
Given that the precision of the information is a function of
each cyclone's particular characteristics and that it is not
reported individually, for the analysis of the distribution and
trend of cyclonic events, the data reported by the IBTrACS
were considered as average values and preadjusted from the
origin. Nevertheless, during the analysis of results, uncertainty
was always considered, particularly for those events before the
'80s of the 20th century.
Phase 2: Data processing. A correlation database was
designed in the MS-Access program, and various queries were
generated through the SQL language to classify and analyze
meteors, such as the number of events per year, per month, per
category, among others. The impact zones were identified by
geoprocessing the vector layers in the ArcGis 10.3 program,
classifying them into four classes: a) they did not landfall; b)
impacted continental and insular lands; c) impacted Mexican
lands, and d) impacted the Yucatan Peninsula. The results
were integrated into the database to generate queries by type
of impact zone.
Phase 3: Models' choice, variables, and reference
parameters. To identify the trend patterns of the analyzed
events were applied, statistical models of change rates, linear
regression (least squares), and calculation of Pearson and
Spearman's linear correlation coefficients. In the
characterization of extreme cyclonic activity, the models
"probability density function" and "Exceeding a relative
threshold" was selected, considering the variables "wind
speed" and "atmospheric pressure," which were analyzed
taking the percentiles as reference parameters 10-90; 1-99 and
0.1-99.9.
Phase 4: Statistical analysis. a) identifying trends of change
in cyclones' number and intensity through queries in the
correlated database for spatio-temporal classification, by
decades, categories (intensity), and impact zones. The data
was exported to the MiniTab 17 statistical program to calculate
the basic descriptive statistics by analysis variable, the
probability density function, the rates of change based on the
trend line equation, and the regression and linear correlation
models, which that allowed estimating the annual distribution
of the number of cyclones vs. intensity and speed of the wind
vs. atmospheric pressure; b) identification and
characterization of extreme hurricanes from the "Exceeding a
relative threshold" model at two levels: i) independent
variables (wind speed and atmospheric pressure), and ii)
compound variables (wind speed vs. atmospheric pressure),
contrasting the sets of "reference thresholds" vs. "analysis
variables"; and c) queries in the correlated database to identify
extreme hurricanes over YP.
The study presents the results as follows: a) characterization
of cyclonic phenomena at the NAB level by impact zones; b)
characterization of extreme cyclones based on the selected
analysis criteria and variables; and c) extreme hurricanes on
the YP.
4. RESULTS
4.1 Characterization of the seasons 1851 to 2019
4.1.1 Analysis of trends in the number and intensity of tropical
cyclones
IBTrACS records report a total of 2,220 storms for NAB, of
which 2,193 (99%) reached the SS scale. To identify trends in
cyclones' number and intensity, they were grouped into three
categories: i) depressions and tropical storms; ii) hurricanes
H1 to H3; and iii) hurricanes H4 and H5 (Figure 3).
Figure 3. Cyclone intensity grouped by categories according to the SS scale for decades between 1851-2019
where: a) green line represents the sum of the cyclones that do not reach the hurricane category; yellow line sum of hurricanes categories 1, 2, and 3; red line sum
of hurricanes categories 4 and 5; and dotted black line, total cyclones. b) equivalent to -a) - but standing for the percentage concerning the cyclone's total per decade.
Where: TD = Tropical Depression; TS = Tropical Storm; and H = Hurricane categories 1 to 5. Source: Adapted from Webster [37], updated with data from IBTrACS
[10]
149
(a)
(b)
Figure 4. Annual distribution and linear trend analysis in the North Atlantic: a) total (number) and SS category (intensity) of
cyclones; b) minimum atmospheric pressure and maximum speed of sustained winds
where: Spearman's rho linear correlation coefficient has a value of R2 = -0.95, which corroborates the proportional inverse relationship between both variables
Figure 3a shows the sum of meteors by category per decade,
where the '20s of the 20th century stand out with the lowest
number (61 cyclones) and the first in which a category H5
cyclone is recorded, in addition to being the only one with
more hurricanes H1 to H3 recorded than depressions and
tropical storms, a fact that may be due to the loss of records at
sea during and after World War I [38]. In contrast, the 1970s
reported the most with 200 cyclones.
The percentages to identify the behavior of the intensity of
cyclones were calculated by groups of categories concerning
the total by decade; in Figure 3b, it can be seen that between
1930 and 1970, there was a rebound of hurricanes H4 and H5,
but it is in the past three decades when there is the most
significant tendency to increase in proportion to the total
number of meteors formed per decade. 2005 was characterized
by presenting the most considerable amount with four
hurricanes in H5 category (Emily, Katrina, Rita, and Wilma),
and maintaining itself as the year with the most intense
cyclonic formation recorded in the history of the North
Atlantic, represented by Hurricane Wilma.
The calculation of the trend line for the variables number of
cyclones and category reveals that the values given their
variability do not present a significant adjustment, returning
R2 values of the order of 0.029 and 0.199, respectively (Figure
4a). The rates of change based on the trend lines' equations
show in the number of cyclones an increase of less than 2%
and the intensity (SS category) of only 1%.
Considering that the Saffir-Simpson scale represents ranges
and not absolute speeds, to identify the behavior of cyclone
intensity, the linear trend of the variables "atmospheric
pressure" and "wind speed" was calculated, obtaining values
of R2 of the order of 0.17 and 0.2, respectively, indicating a
low adjustment with the trend line (Figure 4b). Calculating the
change rates based on the trend line equation showed an
average decrease of -19% for atmospheric pressure and an
increase of close to 38% for wind speed.
Linear regression showed an inversely proportional
significant relationship with an R2 value of 89.5, consistent
150
with the results of the calculation of Pearson and Spearman
rho linear correlation coefficients (values of -0.95); which
corroborates that at lower pressure, higher wind intensity, a
behavior known and widely described in the meteorological
literature [39-42].
4.1.2 Spatial distribution of tropical cyclones
Of the registered tropical systems, 59% touched continental
or insular lands; of this, 21% impacted Mexico, and 13% the
Yucatan Peninsula; the spatial distribution is presented in
Figure 5.
Figure 5. Tropical cyclones track classified by impact zone. a) Did not landfall; b) Continental and insular landfall; c) Mexico
landfall; and d) Yucatan Peninsula landfall
where: Continental and insular landfall include Mexico, and Mexico landfall includes the Yucatan Peninsula
4.2 Characterization of tropical cyclones as extreme events
in the period 1851 to 2019
Wind speed data are available for 84% of cases and
atmospheric pressure around 50% (Table 1). Regarding
extreme values, the maximum reported wind speed is 305
km/h for hurricane Allen (1980, H5)), and the minimum
pressure is 882 mb for hurricane Wilma (2005, H5).
4.2.1 Characterization of extreme cyclones
To identify the values that exceeded the selected relative
thresholds, we applied the "Exceeding a relative threshold"
model for each of the reference variables independently. The
box diagrams (Figure 6) show that of the total, five cyclones
reported values higher than 296 km/h and ten underneath than
908 mb, being considered as outliers given that they exceed
the respective interquartile range by more than 1.5 times.
The values that exceed the established thresholds and are
considered "rare" were obtained using the probability density
function. Figure 7 shows the ranges for maximum winds and
minimum pressure for each set of thresholds (percentiles)
selected.
The number of extreme events to each variable analyzed
independently and based on the thresholds proposed by the
IPCC [11, 12] is 257 (11.58%) for wind speed and 132 (5.95%)
for atmospheric pressure. The total extreme hurricanes
identified for each of the three selected thresholds by their
trajectories are shown in Figures 8 and 9.
151
Table 1. Basic descriptive statistics for each variable
where: N = records with data and N * = records without data
Variable
N
N*
Percent
Mean
StDev
Min.
Max Wind [km/h]
1,859.00
361.00
83.74
125.96
54.32
46.30
Min Pres [mb]
1,090.00
1,130.00
49.10
978.51
24.46
882.00
Variable
Max.
Q1
Q3
Mode
Skewness
Kurtosis
Max Wind [km/h]
305.58
83.34
166.68
92.60
0.61
-0.20
Min Pres [mb]
1,016.00
963.00
999.00
1,000.00
-0.91
0.27
Figure 6. Statistical summary (histogram and box plot): a) Maximum speed of sustained winds [km/h]; b) Minimum atmospheric
pressure [mb]
Figure 7. Probability function: a) maximum speed of sustained winds [km/h]; b) minimum atmospheric pressure [mb]
Figure 8. Extreme cyclones in the North Atlantic Basin by thresholds by analysis variable
152
Figure 9. Cyclones that exceed the thresholds per variable: a)> = 90.0 and <99.0; b)> = 99.0 and <99.9; c)> = 99.9; d) <= 10.0
and > 1.0; e) <= 1.0 and > 0.1; and f) <= 0.1
4.2.2 Multivariate analysis between "maximum speed of
sustained winds" and "minimum atmospheric pressure."
When comparing the thresholds' results for the selected
variables, the resulting number of combinations was 10, with
a total of 260 cyclones that exceed the reference indices. The
summary by combination and categories (SS) minimum and
maximum achieved is shown in Figure 10.
Figure 10. Extreme cyclones by combination of reference thresholds
where: P = Percentile; () = Reference units; y [] = Min. and Max. Saffir-Simpson category
153
Figure 11. Comparison of linear regression and box plots for variables "maximum sustained wind speed" vs. "low atmospheric
pressure."
where: Linear regression equation is: "Min. Pres = 1036 - 0.4125 Max. Winds ", with an R2 of the order of -0895, corroborate the relationship between the two
variables inversely proportional
The values considered outliers decrease in quantity as the
thresholds tend towards the extremes (85, 11, and 4 cyclones,
respectively). Observing Figure 11, the presence of 4
hurricanes: Not named (1935; H5), Allen (1980, H5); Gilbert
(1988, H5); and Wilma (2005, H5)) categorized as "extremely
rare" when exceeding the extraordinary reference indices of
0.1 and 99.9, with temporal equidistance between them of 45,
8 and 17 years respectively, which does not show a defined
pattern regarding the occurrence of events of such
characteristics.
4.2.3 Extreme cyclones in the North Atlantic Basin
Discarding the reference thresholds suggested by the IPCC
[11, 12], 47 cyclones that meet the category of extreme events,
where hurricane Jose (2017, H4) was the only one that did not
reach continental or insular lands (Table 2).
The paths by combinations of thresholds (percentiles) are
shown in Figure 12.
Figure 12. Cyclones tracks in North Atlantic Basin between 1851-2019, categorized as extreme events for exceeding the
thresholds selected for both variables
154
Table 2. Cyclones by the combination of percentiles [%] by variable. where: MxLf = Mexico landfall; YPLf = Yucatan Peninsula
landfall; and NLf = No landfall
Season
Name
S.S. Scale
Max. Wind [km/h]
Pwind [%]
Min. Pres [mb]
Ppres [%]
Impact zone
1935
Not named
5
296.32
99.9
892
0.1
1980
Allen
5
305.58
99.9
899
0.1
MxLf
1988
Gilbert
5
296.32
99.9
888
0.1
YPLf
2005
Wilma
5
296.32
99.9
882
0.1
YPLf
2019
Dorian
5
296.32
99.9
910
1.0
2005
Rita
5
287.06
99.0
895
0.1
1924
Not named
5
268.54
99.0
910
1.0
1932
Not named
5
277.8
99.0
918
1.0
1955
Janet
5
277.8
99.0
914
1.0
YPLf
1969
Camille
5
277.8
99.0
900
1.0
1998
Mitch
5
287.06
99.0
905
1.0
YPLf
2003
Isabel
5
268.54
99.0
915
1.0
2004
Ivan
5
268.54
99.0
910
1.0
2005
Katrina
5
277.8
99.0
902
1.0
2007
Dean
5
277.8
99.0
905
1.0
YPLf
2017
Irma
5
287.06
99.0
914
1.0
2017
Maria
5
277.8
99.0
908
1.0
1928
Not named
5
259.28
99.0
929
> P
1929
Not named
4
250.02
99.0
924
> P
1930
Not named
4
250.02
99.0
933
> P
1933
Not named
5
259.28
99.0
940
> P
MxLf
1933
Not named
5
259.28
99.0
929
> P
YPLf
1938
Not named
5
259.28
99.0
940
> P
1953
Carol
5
259.28
99.0
929
> P
1961
Carla
5
277.8
99.0
931
> P
1961
Hattie
5
259.28
99.0
920
> P
1965
Betsy
4
250.02
99.0
941
> P
1967
Beulah
5
259.28
99.0
923
> P
YPLf
1971
Edith
5
259.28
99.0
943
> P
YPLf
1977
Anita
5
277.8
99.0
926
> P
MxLf
1979
David
5
277.8
99.0
924
> P
1992
Andrew
5
277.8
99.0
922
> P
1998
Georges
4
250.02
99.0
937
> P
1999
Floyd
4
250.02
99.0
921
> P
1999
Lenny
4
250.02
99.0
933
> P
2005
Emily
5
259.28
99.0
929
> P
YPLf
2007
Felix
5
277.8
99.0
929
> P
MxLf
2008
Gustav
4
250.02
99.0
941
> P
2010
Igor
4
250.02
99.0
924
> P
2015
Joaquin
4
250.02
99.0
931
> P
2016
Matthew
5
268.54
99.0
934
> P
2017
Jose
4
250.02
99.0
938
> P
NLf
2018
Michael
5
259.28
99.0
919
> P
1932
Not named
5
259.28
99.0
> P
1964
Cleo
4
250.02
99.0
950
> P
1989
Hugo
5
259.28
99.0
918
> P
1995
Opal
4
240.76
< P
916
1.0
YPLf
4.2.4 Cyclones categorized as extremes in the Yucatan
Peninsula
According to the IPCC [11, 12] criteria, 30 extreme
cyclones have impacted the Yucatan Peninsula (Table 3), 9 of
which reached category H5. hurricanes Gilbert (1988) and
Wilma (2005) are listed as "extremely rare," and hurricanes
Janet (1955), Mitch (1998), and Dean (2007) in the "very rare"
category by exceeding the 1.0 and 99.0 percentiles as well as
Emily (2005) and Dean (2007) who exceeded the 99.0
percentile for wind speed. By discarding the IPCC thresholds,
the number of cyclones categorized as extreme events is
reduced to 10.
80% (n = 24) of the hurricanes impacting as extreme event
category crossed the Peninsula transversely and uniformly in
a southeast-northwest direction. 10% (n = 3) arrived from the
north in a southwest direction: Not named (1888, H3); Isidore
(2002, H3); and Mitch (1988, H5).
Cyclones as: Not named (1906, H3), Opal (1995, H4),
Wilma (2005, H5), and Isidore (2002, H3) presented erratic
trajectories, particularly Isidore, which enter the Peninsula
along the north coast of the state of Yucatan with a southern
direction, where little before reaching the state of Campeche,
he made a 360º loop to move to the northwest until he left the
Gulf of Mexico.
Extreme events are mainly concentrated in the north and
south, while the Peninsula center reported the minor cyclonic
activity (Figure 13).
155
Table 3. Cyclones by the combination of percentiles [%] by variable that landfall the YP
Season
Name
S.S. Scale
Max. Wind [km/h]
Pwind [%]
Min. Pres [mb]
Ppres [%]
1988
Gilbert
5
296.32
99.9
888
0.1
2005
Wilma
5
296.32
99.9
882
0.1
1955
Janet
5
277.8
99.0
914
1.0
1998
Mitch
5
287.06
99.0
905
1.0
2007
Dean
5
277.8
99.0
905
1.0
1933
Not named
5
259.28
99.0
929
10.0
1967
Beulah
5
259.28
99.0
923
10.0
1971
Edith
5
259.28
99.0
943
10.0
2005
Emily
5
259.28
99.0
929
10.0
1995
Opal
4
240.76
90.0
916
1.0
1880
Not named
4
240.76
90.0
931
10.0
1974
Carmen
4
240.76
90.0
928
10.0
2000
Keith
4
222.24
90.0
939
10.0
2002
Isidore
3
203.72
90.0
934
10.0
2017
Harvey
4
212.98
90.0
937
10.0
1855
Not named
3
203.72
90.0
> P
1888
Not named
3
203.72
90.0
> P
1893
Not named
4
212.98
90.0
948
> P
1903
Not named
3
194.46
90.0
958
> P
1906
Not named
3
194.46
90.0
953
> P
1909
Not named
3
194.46
90.0
> P
1931
Not named
4
212.98
90.0
> P
1938
Not named
3
194.46
90.0
979
> P
1944
Not named
3
194.46
90.0
973
> P
1951
Charlie
4
212.98
90.0
968
> P
1955
Hilda
3
194.46
90.0
952
> P
1970
Ella
3
203.72
90.0
967
> P
1975
Eloise
3
203.72
90.0
955
> P
2010
Karl
3
203.72
90.0
956
> P
1988
Keith
0
120.38
< P
945
10.0
Figure 13. Cyclones touched the Yucatan Peninsula between 1851-2019, categorized as extreme events for exceeding the
thresholds selected for: a) both variables, and b) in a single variable
5. DISCUSSION
The analysis results do not show a significant increase in the
number of cyclones because there are highlights a significant
variability between seasons, which coincides with Walsh [43],
who mentions that there has been little consensus regarding
the increase in meteors. However, authors like Walsh et al. [44]
agree that due to global warming, climate change scenarios
156
foresee an increase in cyclonic activity, which will be evident
towards the 1950s of the present centuries.
In the case of intensity (magnitude), results show rates
increase of change of approximately 37% in terms of the
"maximum speed of sustained winds" and 19% for the
"minimum atmospheric pressure. Although the results cannot
be considered definitive or conclusive due to variability of the
data's uncertainty, particularly before 1965, our estimates are
consistent with the forecasts of the current scenarios [12, 44-
47]. An example is an increase in cyclone intensity in
hurricane categories H4 and H5 reported by Elsner et al. [48],
Emanuel [49], and Webster [37], who studied periods ranging
from 25, 30, and 35 years, respectively. Notwithstanding the
differences in the time series' size, our findings are consistent
with such studies' results.
In contrast, Klotzbach [24] and Kossin et al. [50] found no
evidence of a trend of increasing magnitude of meteors, which
can be attributed to the limited periods analyzed (20 years).
According to Décamps [14] the detection of changes or trends
will be a function of the temporality of the analyzed data and,
as suggested by the WMO [9] periods of at least 30 years are
required to study the behavior of the atmosphere.
Nevertheless, according to the general circulation models
(GCMs) conducted by Appendini et al. [7], there is a clear
trend tendency for higher intensity events to become more
frequent due to a warming climate.
According to the definition of the IPCC [11, 12], 260
cyclones can be characterized as extreme events. However,
when individually analyzing each one's characteristics, it was
found that 39.62% barely reached the category at some point
in their trajectory. Maximum H3, even more, within this range,
are included the subtropical cyclone Charlie (1972) that did
not reach the SS scale and tropical storm Keith (1988), which
agrees with Camuffo et al. [13] who suggest that extreme
events must be at least in the order of the 1st and 99th
percentiles or higher.
When analyzing the events that exceed the reference
thresholds 1.0 and 99.0 (percentiles), all reached top categories
of hurricane H4 and H5, causing the most significant damages
and economic losses in the region according to the studies
published by Alarcón [51]; Bonilla-Moheno [52];
CENAPRED [53]; and Frausto et al. [54], among others.
Most studies related to the intensity and occurrence of TCs
consider as a parameter only the SS scale, which, being
conceived as a categorical scale, does not allow direct
comparisons between the values of the variables wind speed
and atmospheric pressure, with the damages and affectations
in the social and environmental systems.
The main difference between the studies found in the
literature with the present one is that the analysis and
classification of extreme events are carried out in terms of
intensity on the SS scale and the effects caused by these
phenomena, such is the case of the works published by
Palacio-Aponte [55] where he evaluates the effects of the
impact of hurricanes taking as a geo-indicator the post-
hurricane coastal morphology and the study of Emanuel [49]
who determines a potential destructiveness index of hurricanes
to through correlation with sea surface temperature.
Other cases are the studies carried out by Rey et al. [56] and
Ruiz-Salcines et al. [57], where the effects of TCs were
modeled as a direct function of the variables representing each
phenomenon.
It is important to note that the evaluation and
characterization of the damages caused by cyclonic events
must consider both the increase in the population in the areas
susceptible to being impacted, as well as the quality and type
of infrastructure (vulnerability) so that the comparison
between the Affectations in different spatio-temporal stages
can be subjective.
6. CONCLUSIONS
The methodology used allowed identifying and categorizing
extreme cyclones based on small-scale analysis (North
Atlantic Basin) and long-time series. However, in the case of
studies at larger scales focused on identifying and estimating
the effects and damages from a socio-ecological context, we
consider that reclassification should be carried out based on
the maximum and minimum values of the variables "wind
speed," "pressure atmospheric" and "precipitation" during its
proximity and passage through the land.
It is recommended to study and analyze the reports
generated by various instances on the characteristics and
effects of each cyclone, such as, among others: a) the
IBTrACS [58] online search engine that provides detailed
information on each meteor; b) the "summaries of tropical
cyclones" issued by CONAGUA [59]; c) case studies on the
effects of cyclones related to the number of deaths, economic
damages, damage to infrastructure and ecosystems, such as the
works published by CENAPRED [60]; Rodríguez-Esteves et
al. [61] and Tun et al. [62]; among others.
Given the probabilities of the occurrence of multiple
extreme events simultaneously, as was Hurricane Cristobal in
southern PY in June 2020, during the pandemic caused by
COVID-19 [63]. The emergency preparedness and response
processes will become increasingly complicated, resulting in
increased risk to the population and the environment [7].
Identifying and characterizing extreme events will provide
valuable information for the preparation of studies that provide
elements to decision-makers within the framework of
strengthening public policies.
The characterization of extreme events is useful to
strengthen the knowledge of the resilience and vulnerability of
infrastructure, means of production, and the environment.
The findings obtained in this study can provide helpful
information for the delimitation of priority areas for the
assessment of the vulnerability of urban areas and protected
natural areas, since of the four hurricanes considered as
"extremely rare," two of them (50%) impacted the Yucatan
Peninsula: Gilbert (1988, H5) and Wilma (2005, H5).
Given the results, we consider that the methodology applied
in this study could be adapted to catalog different disturbing
events such as the case of forest fires, drought, floods, among
others. It could be useful to release comparisons and analysis
between events based on their probabilities of occurrence,
intensity, spatio-temporal dimensions and estimate socio-
ecological systems' resilience capacities in specific areas.
ACKNOWLEDGMENT
The author thanked the University of Quintana Roo, the
Laboratory of Space Observation, the Sustainable
Development Division, and the National Council for Science
and Technology (CONACYT, due to its Spanish acronym) for
the scholarship granted (597620) to carry out doctoral studies
in Sustainable Development in the Cozumel Academic Unit.
M. Geóg. Gabriel Sánchez Rivera.
157
REFERENCES
[1] NHC. (2019). Glossary of NHC terms. Centro Nacional
de Huracanes de los Estados Unidos de América.
https://www.nhc.noaa.gov/aboutgloss.shtml.
[2] NHC. (2019). Tropical cyclone climatology. Centro
Nacional de Huracanes de los Estados Unidos de
América. https://www.nhc.noaa.gov/climo/.
[3] IBTrACS. (2019b). International best track archive for
climate stewardship (IBTrACS). Technical
Documentation. En National Oceanic and Atmospheric
Administration, National Climatic Data Center.
https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACS_versi
on4_Technical_Details.pdf.
[4] Schott, T., Landsea, C., Hafele, G., Lorens, J., Thurm, H.,
Ward, B., Willis, M., Zaleski, W. (2012). The Saffir
Simpson hurricane wind scale. National Hurricane
Center, February, 1-4.
http://www.nhc.noaa.gov/pdf/sshws.pdf.
[5] Vink, K., Ahsan, M.N. (2018). The benefits of cyclones:
A valuation approach considering ecosystem services.
Ecological Indicators, 95(1): 260-269.
https://doi.org/10.1016/j.ecolind.2018.07.035
[6] UNEP. (2009). Environment for development:
Ecosystem management programme: A new approach to
sustainability | capacity4dev.
https://europa.eu/capacity4dev/unep/document/ecosyste
m-management-programme-new-approach-
sustainability.
[7] Appendini, C.M., Meza-Padilla, R., Abud-Russell, S.,
Proust, S., Barrios, R.E., Secaira-Fajardo, F. (2019).
Effect of climate change over landfalling hurricanes at
the Yucatan Peninsula. Climatic Change, 157(3-4): 469-
482. https://doi.org/10.1007/s10584-019-02569-5
[8] Wisner, B., Blaikie, P., Cannon, T., Davis, I. (2003). At
Risk: Natural Hazards, People’s Vulnerability and
Disasters (2nd ed.). Taylor & Francis.
https://www.preventionweb.net/publications/view/670.
[9] WMO. (2018). Guidelines on the definition and
monitoring of extreme weather and climate events. Final
Version. World Meteorological Organization.
[10] IBTrACS. (2019a). IBTrACS v04r00 - Online browsing.
International Best Track Archive for Climate
Stewardship (IBTrACS). NOAA.
https://www.ncdc.noaa.gov/ibtracs/index.php.
[11] IPCC. (2012). Managing the risks of extreme events and
disasters to advance climate change adaptation. En C. B.
Field, V. Barros, T. F. Stocker, & Q. Dahe (Eds.), A
Special Report of Working Groups I and II of the
Intergovernmental Panel on Climate Change. Cambridge
University Press.
https://doi.org/10.1017/CBO9781139177245
[12] IPCC. (2014). Future climate changes, risks and impacts.
en climate change 2014: Synthesis report. Contribution
of Working Groups I, II and III to the Fifth Assessment
Report of the Intergovernmental Panel on Climate
Change. https://doi.org/10.1017/CBO9781107415324
[13] Camuffo, D., della Valle, A., Becherini, F. (2020). A
critical analysis of the definitions of climate and
hydrological extreme events. Quaternary International,
538: 5-13. https://doi.org/10.1016/j.quaint.2018.10.008
[14] Décamps, H. (2008). Ecosystems and extreme climatic
events. Comptes Rendus Geoscience, 340(9-10): 553-
563. https://doi.org/10.1016/j.crte.2008.08.004
[15] Martínez-Yrízar, A., Jaramillo, V.J., Maass, M., Búrquez,
A., Parker, G., Álvarez-Yépiz, J.C., Araiza, S., Verduzco,
A., Sarukhán, J. (2018). Resilience of tropical dry forest
productivity to two hurricanes of different intensity in
western Mexico. Forest Ecology and Management, 426:
53-60. https://doi.org/10.1016/j.foreco.2018.02.024
[16] Stephenson, D.B. (2008). Definition, diagnosis, and
origin of extreme weather and climate events. En H. F.
Diaz & R. J. Murnane (Eds.), Diaz, H.F., Murnane, R.J.
(Eds.), Climate Extremes and Society. Cambridge
University Press, Cambridge. (pp. 11-23). Cambridge
University Press.
https://doi.org/10.1017/CBO9780511535840.004
[17] WMO. (2015). Guidelines on the definition and
monitoring of extreme weather and climate events. Draft
version – First review by TT-DEWCE, no. December.
World Meteorological Organization.
[18] Coles, S. (2001). An introduction to statistical modeling
of extreme values. Springer, London.
https://doi.org/10.1007/978-1-4471-3675-0
[19] Tiago de Oliveira, J. (1986). Extreme values and
meteorology. Theoretical and Applied Climatology,
37(4): 184-193. https://doi.org/10.1007/BF00867576
[20] Lazos-Chavero, E., Mwampamba, T.H., García-Frapolli,
E. (2018). Uncovering links between livelihoods, land-
use practices, vulnerability and forests after hurricane
Jova in Jalisco, Mexico. Forest Ecology and
Management, 426: 27-38.
https://doi.org/10.1016/j.foreco.2017.10.009
[21] Holland, G., Bruyère, C.L. (2014). Recent intense
hurricane response to global climate change. Climate
Dynamics, 42(3-4): 617-627.
https://doi.org/10.1007/s00382-013-1713-0
[22] Doval, J.P., Pérez, M.B., Acosta, J.O., Rodríguez, E.
(2013). Caracterización de las trayectorias de los
Ciclones Tropicales en la Cuenca Atlántica en relación
con la actividad solar y otras variables. XI Congreso
Cubano de Informática y Geociencias (GEOINFO’2013),
18.
[23] Vecchi, G.A., Knutson, T.R. (2008). On estimates of
historical north atlantic tropical cyclone activity. Journal
of Climate, 21(14): 3580-3600.
https://doi.org/10.1175/2008JCLI2178.1
[24] Klotzbach, P.J. (2006). Trends in global tropical cyclone
activity over the past twenty years (1986-2005).
Geophysical Research Letters, 33(10).
https://doi.org/10.1029/2006GL025881
[25] Palacio-Aponte, A.G. (1998). Susceptibilidad regional
del terreno y riesgos por huracanes en la zona costera del
Golfo de México. JAINA. EPOMEX, 9(2): 2.
[26] Ihl, T., Frausto-Martínez, O. (2014). El Cambio
Climático y los huracanes en la Península de Yucatán. En
Frausto Martínez, Oscar (Coord). Monitoreo de riesgo y
desastre asociados a fenómenos hidrometeorológicos y
cambio climático. (pp. 42–49). Universidad de Quintana
Roo - REDESClim - CONACYT.
https://www.mendeley.com/catalogue/monitoreo-
riesgo-y-desastre-asociados-fenómenos-
hidrometeorológicos-y-cambio-climático/.
[27] Frausto Martínez, O., Vazquez, A., Arroyo, L., Castillo,
L., Hernández Aguilar, M.L. (2016). Hurricane resilience
indicators in mexican caribbean coastal cities.
International Journal of Safety and Security Engineering,
6(4): 755-763. https://doi.org/10.2495/SAFE-V6-N4-
158
755-763
[28] Flanders Marine Institute. (2018). IHO Sea Areas,
version 3. https://doi.org/10.14284/323
[29] United Nations. (2019). Population Division, World
Population Prospects 2019, Online Edition. Rev. 1.
Department of Economic and Social Affairs.
https://population.un.org/wpp/Download/Standard/Popu
lation/.
[30] Amante, C., Eakins, B.W. (2009). ETOPO1 1 Arc-
Minute Global Relief Model: Procedures, Data Sources
and Analysis. NOAA Technical Memorandum NESDIS
NGDC-24. National Geophysical Data Center, NOAA.
https://doi.org/10.7289/V5C8276M
[31] Knapp, K.R., Diamond, H.J., Kossin, J.P., Kruk, M.C.,
Schreck III, C.J. (2018). International Best Track
Archive for Climate Stewardship (IBTrACS) Project,
Version 4. Subset: NA - North Atlantic. NOAA National
Centers for Environmental Information. non-
gonvernment domain. https://data.nodc.noaa.gov/cgi-
bin/iso?id=gov.noaa.ncdc:C01552.
[32] Saunders, M., Lea, A. (2019). Extended Range Forecast
for Atlantic Hurricane Activity in 2020. Tropical Storm
Risk (TSR), December 19 2019, 3.
http://www.tropicalstormrisk.com/docs/TSRATLForeca
stDec2020.pdf.
[33] Knapp, K.R., Kruk, M.C. (2010). Quantifying
interagency differences in tropical cyclone best-track
wind speed estimates. Monthly Weather Review, 138(4):
1459-1473. https://doi.org/10.1175/2009MWR3123.1
[34] Kruk, M.C., Knapp, K.R., Levinson, D.H. (2010). A
technique for combining global tropical cyclone best
track data. Journal of Atmospheric and Oceanic
Technology, 27(4): 680-692.
https://doi.org/10.1175/2009JTECHA1267.1
[35] Wessel, P., Smith, W.H.F. (1996). A global, self-
consistent, hierarchical, high-resolution shoreline
database. Journal of Geophysical Research: Solid Earth,
101(B4): 8741-8743. https://doi.org/10.1029/96JB00104
[36] Wessel, P., Smith, W.H.F. (2017). GSHHG Version
2.3.7. A Global Self-consistent, Hierarchical, High-
resolution Geography Database.
https://www.soest.hawaii.edu/pwessel/gshhg/.
[37] Webster, P.J. (2005). Changes in tropical cyclone
number, duration, and intensity in a warming
environment. Science, 309(5742): 1844-1846.
https://doi.org/10.1126/science.1116448
[38] Landsea, C.W., Vecchi, G.A., Bengtsson, L., Knutson,
T.R. (2010). Impact of duration thresholds on Atlantic
tropical cyclone counts. Journal of Climate, 23(10):
2508-2519. https://doi.org/10.1175/2009JCLI3034.1
[39] Sanders, C. (2016). A regression analysis of past
hurricane patterns and a projection for the 2016 hurricane
season. Florida Atlantic University.
[40] Harper, B.A. (2013). Best practice in tropical cyclone
wind hazard modelling: In search of data and emptying
the skeleton cupboard. 16th Australasian Wind
Engineering Society Workshop, 10.
[41] Farfán, L.M. (2004). Regional observations during the
landfall of tropical cyclone Juliette (2001) in Baja
California, Mexico. Monthly Weather Review, 132(7):
1575-1589. https://doi.org/10.1175/1520-
0493(2004)132<1575:RODTLO>2.0.CO;2.
[42] Rey, W., Mendoza, E.T., Salles, P., Zhang, K., Teng,
Y.C., Trejo-Rangel, M.A., Franklin, G.L. (2019).
Hurricane flood risk assessment for the Yucatan and
Campeche State coastal area. Natural Hazards, 96(3):
1041-1065. https://doi.org/10.1007/s11069-019-03587-3
[43] Walsh, K.J.E. (2004). Tropical cyclones and climate
change: Unresolved issues. Climate Research, 27(1): 77-
83. https://doi.org/10.3354/cr027077
[44] Walsh, K.J.E., McBride, J.L., Klotzbach, P.J.,
Balachandran, S., Camargo, S.J., Holland, G., Knutson,
T.R., Kossin, J.P., Lee, T., Sobel, A., Sugi, M. (2016).
Tropical cyclones and climate change. Wiley
Interdisciplinary Reviews: Climate Change, 7(1): 65-89.
https://doi.org/10.1002/wcc.371
[45] Solow, A.R. (2017). On detecting ecological impacts of
extreme climate events and why it matters. Philosophical
Transactions of the Royal Society B: Biological Sciences,
372(1723): 8-11. https://doi.org/10.1098/rstb.2016.0136
[46] Goodess, C.M. (2013). How is the frequency, location
and severity of extreme events likely to change up to
2060? Environmental Science & Policy, 27: S4-S14.
https://doi.org/10.1016/j.envsci.2012.04.001
[47] Tapia-Palacios, M.A., García-Suárez, O., Sotomayor-
Bonilla, J., Silva-Magaña, M.A., Pérez-Ortíz, G.,
Espinosa-García, A.C., Ortega-Huerta, M.A., Díaz-
Ávalos, C., Suzán, G., Mazari-Hiriart, M. (2018). Abiotic
and biotic changes at the basin scale in a tropical dry
forest landscape after Hurricanes Jova and Patricia in
Jalisco, Mexico. Forest Ecology and Management, 426:
18-26. https://doi.org/10.1016/j.foreco.2017.10.015
[48] Elsner, J.B., Kossin, J.P., Jagger, T.H. (2008). The
increasing intensity of the strongest tropical cyclones.
Nature, 455(7209): 92-95.
https://doi.org/10.1038/nature07234
[49] Emanuel, K. (2005). Increasing destructiveness of
tropical cyclones over the past 30 years. Nature,
436(7051): 686-688.
https://doi.org/10.1038/nature03906
[50] Kossin, J.P., Knapp, K.R., Vimont, D.J., Murnane, R.J.,
Harper, B.A. (2007). A globally consistent reanalysis of
hurricane variability and trends. Geophysical Research
Letters, 34(4): L04815.
https://doi.org/10.1029/2006GL028836
[51] Rodríguez-Alarcón, M. N. (2019). El huracán Janet y el
desastre de 1955 en Quintana Roo: estudio de una
coyuntura crítica. Antrópica. Revista de Ciencias
Sociales y Humanidades, 161-190.
https://antropica.com.mx/ojs2/index.php/AntropicaRCS
H/article/view/196.
[52] Bonilla-Moheno, M. (2010). Damage and recovery of
forest structure and composition after two subsequent
hurricanes in the Yucatan Peninsula. Caribbean Journal
of Science, 46(2-3): 240-248.
https://doi.org/10.18475/cjos.v46i2.a12
[53] CENAPRED. (2005a). Características e Impacto
Socioeconómico del huracán ‘Emily’ en Quintana Roo,
Yucatán, Tamaulipas y Nuevo León en Julio de 2005.
[54] Frausto Martínez, O., Vázquez Sosa, A.B., Colin
Olivares, O., Hernández Aguilar, M.L., Arroyo Arcos, L.,
David Velázquez Torres, D. (2018). Monitoreo de
indicadores de resiliencia urbana ante huracanes para las
ciudades costeras: Playa del Carmen, caribe mexicano.
En Esteves Juan Manuel Rodríguez y Rodríguez, C. M.
W., & Aguilar, M. L. R. Riesgo de desastres en México:
eventos hidrometeorológicos y climáticos, pp. 29-55.
https://www.imta.gob.mx/biblioteca/libros_html/desastr
159
es-mexico/Libro-Riesgo-desastres-en-Mexico.pdf.
[55] Palacio-Aponte, G. (2010). Amenaza por huracanes.
Interciencia, 35: 400-406.
https://www.interciencia.net/wp-
content/uploads/2018/01/400-PALACIOS-7.pdf.
[56] Rey, W., Salles, P., Torres-Freyermuth, A., Ruíz-
Salcines, P., Teng, Y.C., Appendini, CM., Quintero-
Ibáñez, J. (2019). Spatiotemporal Storm Impact on the
Northern Yucatan Coast during Hurricanes and Central
American Cold Surge Events. Journal of Marine Science
and Engineering, 8(1): 2.
https://doi.org/10.3390/jmse8010002
[57] Ruiz-Salcines, P., Appendini, C. M., Salles, P., Rey, W.,
Vigh, J.L. (2020). On the use of synthetic tropical
cyclones and hypothetical events for storm surge
assessment under climate change. Natural Hazards,
105(1): 431-459. https://doi.org/10.1007/s11069-020-
04318-9
[58] IBTrACS. (2020). Browse IBTrACS.
http://ibtracs.unca.edu/index.php?name=browse-year-
basin.
[59] CONAGUA. (2020). Consulta de historial y resumenes
de ciclones tropicales. Información Historica.
https://smn.conagua.gob.mx/es/ciclones-
tropicales/informacion-historica.
[60] CENAPRED. (2005). Características e impacto
socioeconómico de los huracanes ‘Stan’ y ‘Wilma’ en la
República Mexicana en 2005. p. 322, 2006.
[61] Rodríguez Esteves, J.M., Welsh Rodríguez, C.M., Romo
Aguilar, M.L., Travieso Bello, A.C. (Coords). (2018).
Riesgo de desastres en México: eventos
hidrometeorológicos y climáticos. Instituto Mexicano de
Tecnología del Agua.
https://www.imta.gob.mx/biblioteca/libros_html/desastr
es-mexico/Libro-Riesgo-desastres-en-Mexico.pdf.
[62] Tun, J., Perdigón, T., Chalé, G., Gil, C. (2015). Impacto
Socioeconómico del Huracán Wilma en la Localidad de
Playa del Carmen. Observatorio Urbano de la Riviera
Maya. Observatorio Urbano Local de Playa del Carmen.
[63] Frausto-Martínez, O., Aguilar-Becerra, C.D., Colín-
Olivares, O., Sánchez-Rivera, G., Hafsi, A., Contreras-
Tax, A.F., Uhu-Yam, W.D. (2020). COVID-19, Storms,
and Floods: Impacts of Tropical Storm Cristobal in the
Western Sector of the Yucatan Peninsula, Mexico.
Sustainability, 12(23): 9925.
https://doi.org/10.3390/su12239925
NOMENCLATURE
km/h
Kilometer/hour
mb
Millibars
160
