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Climatic change-related impacts on coastal areas became an important issue in past decades and nowadays threaten many human settlements and activities. Coastal hazards are linked to flooding and erosion processes associated with sea level rise and the increased strength of hurricanes, cyclones and storms. The main aim of this work is the characterization of coastal storms in Cadiz (SW Spain) and the determination of their recurrence intervals and relationships with several regional cycles. Storm characterization was carried out using the Storm Power Index (Dolan and Davis, 1992) and five classes were obtained, from class I (weak events) to V (extreme events). Storm occurrence probability was 96% for class I (i.e. almost one event per year) to 3% for class V. The return period for class V was 25 years and ranged from 6 to 8 years for classes III and IV storms, e.g. significant and severe events. Classes I and II showed a period of recurrence ranging from 1 to 3 years. Stormy winter seasons were 2009/10 (12 events), 1995/6 and 2002/3 (with 10 events each) and 1993/4 (8 events). Approximately 40% of the change in monthly wave data and storminess indices was related to several teleconnection patterns, the most important drivers of change being the Arctic Oscillation (AO), 21.45%, and the North Atlantic Oscillation (NAO), 19.65%. It is interesting to note that a great number of storms, larger storm duration and higher values of Storm Power Index were only observed when neutral to strong negative NAO and AO phases occurred at the same time (89 storms and 3355 h) and/or when there was an abrupt change of NAO and AO phases, i.e. they moved from a positive to negative phase without passing through a neutral phase. The results obtained in this work have wider applications for ocean and coastal management. It is suggested that methodology used can be easily applied in different areas where wave buoy data are available. In the same way, information obtained with this kind of work constitutes the first step in the development of coastal protection plans to preserve socio-economic activities from the impact of severe storm events.
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INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 33: 2142 –2156 (2013)
Published online 30 August 2012 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3579
Winter wave climate, storms and regional cycles: the SW
Spanish Atlantic coast
N. Rangel-Buitrago* and G. Anfuso
Departamento de Ciencias de la Tierra, Facultad de Ciencias del Mar y Ambientales, Universidad de C´adiz, C´adiz, Spain
ABSTRACT: Climatic change-related impacts on coastal areas became an important issue in past decades and nowadays
threaten many human settlements and activities. Coastal hazards are linked to flooding and erosion processes associated
with sea level rise and the increased strength of hurricanes, cyclones and storms. The main aim of this work is the
characterization of coastal storms in Cadiz (SW Spain) and the determination of their recurrence intervals and relationships
with several regional cycles. Storm characterization was carried out using the Storm Power Index (Dolan and Davis, 1992)
and five classes were obtained, from class I (weak events) to V (extreme events). Storm occurrence probability was 96%
for class I (i.e. almost one event per year) to 3% for class V. The return period for class V was 25 years and ranged from
6 to 8 years for classes III and IV storms, e.g. significant and severe events. Classes I and II showed a period of recurrence
ranging from 1 to 3 years. Stormy winter seasons were 2009/10 (12 events), 1995/6 and 2002/3 (with 10 events each) and
1993/4 (8 events). Approximately 40% of the change in monthly wave data and storminess indices was related to several
teleconnection patterns, the most important drivers of change being the Arctic Oscillation (AO), 21.45%, and the North
Atlantic Oscillation (NAO), 19.65%. It is interesting to note that a great number of storms, larger storm duration and
higher values of Storm Power Index were only observed when neutral to strong negative NAO and AO phases occurred at
the same time (89 storms and 3355 h) and/or when there was an abrupt change of NAO and AO phases, i.e. they moved
from a positive to negative phase without passing through a neutral phase. The results obtained in this work have wider
applications for ocean and coastal management. It is suggested that methodology used can be easily applied in different
areas where wave buoy data are available. In the same way, information obtained with this kind of work constitutes the
first step in the development of coastal protection plans to preserve socio-economic activities from the impact of severe
storm events.
KEY WORDS waves; storm; power index; teleconnection patterns; Cadiz; Spain
Received 12 April 2012; Revised 30 July 2012; Accepted 30 July 2012
1. Introduction
The recent research on climate change effects on coastal
zone has been almost completely devoted to the impacts
of sea level rise as a result of the global warming (Komar
and Allan, 2008; Phillips and Crisp, 2010). Less attention
has been given to the knowledge and trend of wave
climate and storms. As suggested by Keim et al. (2004),
wave climate, occurrence and distribution of extreme
waves and storms are important issues in the occurrence
and amount of coastal erosion, the deterioration and (at
some places) complete disappearance of ecosystems and
the safety of shipping and offshore platforms.
Different researchers around the World have recog-
nized that the height of extreme waves, the number of
storms and their intensity have been increasing in last
decades (Bacon and Carter, 1991; Allan and Komar,
2000; Dupuis et al., 2006; Komar and Allan, 2008;
Soomere, 2008). In a scenario of rising sea levels and
* Correspondence to: N. Rangel-Buitrago, Departamento de Ciencias
de la Tierra, Facultad de Ciencias del Mar y Ambientales, Universidad
de C´
adiz, Pol´
ıgono R´
ıo San pedro s/n, 11510 Puerto Real, C´
adiz, Spain.
E-mail: nelson.rangelbuitrago@mail.uca.es
increasing wave heights, the coastline will suffer huge
impacts in terms of erosion and flooding especially with
respect to low-lying regions that may partly or entirely
disappear (Hanson and Larson, 2008).
In order to reduce the impacts of climate change, it
is important to provide realistic analyses of the natural
variability and trends associated with climatic events, in
this case storms. The homogeneity and quality of wave
data over long periods (more than 25 years) are essential
in any analysis of extreme wave heights, storms and their
respective probability of recurrence. Many approaches
have been used to document wave climate changes: visual
observations, wave buoys data, instrumented ships, satel-
lite altimeters, numerical and physical models (Dupuis
et al., 2006; Komar and Allan, 2008).
This work deals with the analysis of a 27 year long
wave buoy dataset recorded at Cadiz (SW Spain). The
analysis consisted of characterizing winter wave climate,
extreme waves and storms characteristics and distribution
and their relationships with regional cycles (Telecon-
nection Patterns) such as the North Atlantic Oscillation
(NAO) Index, the East Atlantic (EA) pattern and the Arc-
tic Oscillation (AO), among others. The methodology
2012 Royal Meteorological Society
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2143
used can be easily applied in different coastal areas
around the World where wave buoy data are available for
a monitoring period greater than 25 years. Information
obtained in this study constitutes a first step in developing
coastal response plans to storm impacts for the protection
of socio-economic activities, especially tourism. This will
be a major concern for coastal managers in future years
because storm impacts will be magnified by predicted
sea level rise linked to global climate change (Jones and
Phillips, 2011).
1.1. Study area
This study investigates the wave climate and storm char-
acteristics of an Atlantic Ocean coastal area facing the
city of Cadiz, in South-West Spain (Figure 1). The area
corresponds with a northwestsoutheast oriented coast-
line which is characterized by a diversity of coastal land-
forms and environments including sand spits, beaches,
dunes, saltmarshes, cliffs and rocky shore platforms.
It has semidiurnal tides and a mesotidal range, with
mean values of neap and spring tides of 1.0 and 3.5 m,
respectively, and it is affected by western and eastern
winds. Western winds are related to Atlantic low pressure
systems that can continue for several days and affect
large portions of the Iberian Peninsula. They blow from
WNW to WSW directions with a mean annual velocity
of 16 km h1and a frequency of 13%. Winds, blowing
from E to SE directions, with an annual frequency of
20% and a mean velocity of 28 km h1, are originally
formed in the Mediterranean Sea and greatly increase in
velocity due to channeling through the Gibraltar Strait.
Due to coastline orientation, western winds give rise to
both sea and swell waves, while easterly winds have no
significant fetch and mainly give rise to sea waves. The
main longshore drift flows south-eastward.
During the past 40 years, sea level at the Cadiz coast
did not show a single clear trend (Marcos and Tsimplis,
2008; Marcos et al., 2011) and winter storms represent
the main coastal hazard in the investigated littoral since
they largely affect tourism activities, e.g. the sun, sea
and sand (3S) market. They result in major damage
to recreational and protection structures causing sand
starvation and associated reduction in beach width and
scenic value (Anfuso and Gracia, 2005; Meyer-Arendt,
2011).
The area is characterized by extensive sandy beaches
that, in the past decade, have undergone erosion with
locally recorded values greater than 1 m year1, essen-
tially associated with the impact of storm events (Mu˜
noz
and Enriquez, 1998; Reyes et al., 1999; Benavente et al.,
2002; Anfuso et al., 2007; Rangel-Buitrago and Anfuso,
2011a). In order to balance coastal retreat, in the past
three decades, more than 600 fills and refills have been
Figure 1. Study area with location of the REDCOS buoy n°1316 operated by Puertos del Estado (Spanish Ministry of Public Works).
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2144 N. RANGEL-BUITRAGO AND G. ANFUSO
Table I. Characteristics of the five storm classes (modified from Rangel-Buitrago and Anfuso 2011a): range, frequency (number
of cases and percentages), mean values (X) of significant wave height and period, storm duration and storm power index per
class.
CLASS Range (m2h) Frequency Wave height Period Duration Storm power
N% X (m) X (s) X (h) X (m2h)
I–Weak <515 74 57 3.46 7.02 20 242.4
II – Moderate 5161225 41 31 4.46 7.52 40.2 785.3
III – Significant 1226–2537 11 8 4.56 7.5 89.6 1850
IV – Severe 2538–5167 2 3 5.06 8.6 139.3 3311
V – Extreme >5167 1 1 7.8 9.7 89 5414
performed along the Spanish coast. Investment in beach
nourishment during the 1990s along the Atlantic coast of
Andalusia was US $37M, representing the cost of inject-
ing circa 13 ×106m3of sediment (Mu˜
noz et al., 2001).
2. Methodology
2.1. Wave Data
Real-time wave measurements along the Spanish lit-
toral are available from the coastal buoy network oper-
ated by Puertos del Estado (Spanish Ministry of Pub-
lic Works). Specifically, this work was based on the
analysis of data obtained from the scalar buoy n°
1316 (36.50 °N; 6.33 °W), a waverider-datawell instru-
ment which is located at a water deep of 21 m, in front
of Cadiz city (Figure 1). Data were logged at 1 h inter-
vals and included a time series of 163 237 wave height
and period records acquired between 1983 and 2010. In
this study, average annual and monthly winter values of
significant wave height (Hs)andthe99
th percentile of
Hs(Hs99) were used for the characterization of wave cli-
mate and definition and description of storms. The Hs99
was used because it is associated with high energy events
(Almeida et al., 2011; BACC Author Team, 2008).
According to previous studies carried out by Anfuso
et al. (2007) and Rangel-Buitrago and Anfuso (2011a),
winter season was defined as the October to March
period. The investigated dataset presented a length of
27 years that, according to Komar and Allan (2008),
represents a sufficient time period to analyse wave height
trend, presence of climate-controlled cycles and wave
height annual variations due to climate events.
2.2. Storm definition and characterization
Storminess is a key issue in coastal erosion and climate
change studies (Li et al., 2011). Coastal engineers nor-
mally use the number of storm events or the amount
of hours during which a certain wave height thresh-
old is exceed (Goda, 1988; Lemm, et al., 1999; Barr,
2004). Meteorologists use the number of storm sys-
tems, characterized by wind speed values grater than
a certain threshold or central air pressure values lower
than 1000 hPa (Schmith et al., 1998; Zielinski, 2002;
Keim et al., 2004). Other researchers use the number of
events or the number of hours during which a specific
water level threshold is exceed (Eliot and Clarke, 1986;
Zhang et al., 2000; Bromirski et al., 2003; Pattiaratchi
and Elliot, 2008; Phillips, 2008).
In this work, a storm is defined as a climatic event
during which the significant wave height (Hs) exceeds a
threshold over a minimum, specific time, so the following
three criteria have to meet:
Wave height Hs2.5 m. This threshold reflects the
deep-water wave height at which erosion affected
Cadiz beaches (Plomaritis et al., 2009, 2010; Rangel-
Buitrago and Anfuso, 2011a).
The minimum storm duration was set at 12 h, in this
way the storm affected the coast at least during a
complete tidal cycle.
The inter-storm period was set at 1 d in order to create
de-clustered, independent sets of storms (Morton et al.,
1997; Dorsch et al., 2008).
The energy content for each storm was calculated
according to the Storm Power Index (Dolan and Davis,
1992), which is a valuable indicator of storm strength and
associated shoreline erosion (Li et al., 2011), using the
formula:
Hs2td(1)
Where Hsis the maximum significant wave height
in meters and tdis the storm duration in hours. Once
storms were recognized, they were categorized by means
of the natural breaks function analysis (Jenks and Caspall,
1971), into five classes from class I (weak) to class V
(extreme events, limits presented in Table I).
2.3. Storm recurrence period
Frequency analysis was applied to estimate extreme
waves and storm power recurrence periods by means of
the Generalized Extreme Value (GEV) distribution; it is a
family of continuous probability distributions developed
within extreme value theory to combine the Gumbel,
Fr´
echet and Weibull families also known as types I, II
and III extreme value distributions (Coles, 2001). The
GEV distribution was calculated according to:
F(x) =exp{− exp[(x u)/α]1/k}(2)
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 2156 (2013)
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2145
Where xis the random variable and u,αand k
are location, scale and shape parameters, respectively,
that should be estimated for each sample. The equation
reduces to type I (or Gumbel, for k=0), type III (or
Weibull, for k>0) or type II (for k<0) distribution.
The equation for the type I (Gumbel) distribution is:
F(x) =exp{− exp[(x u)/α]}(3)
The methods of parameter estimation for each distri-
bution are also discussed in detail in Rao and Hamed
(2000). In this study, the method of moments and maxi-
mum likelihood was applied to estimate the distribution
of investigated parameters. The root mean square error
was then used to select the appropriate distribution. To
investigate the recurrence period, wave and storm data
were plotted with the Gringorten (1963) plotting position
formula and the Gumbel distribution was fitted to the
data. The distribution function is given by the formula:
TR =N+0.12/m0.44 (4)
Where Nis the number of annual maximum obser-
vations and mis the rank of extreme waves or storm
power values from the lowest to the highest observa-
tion. According to the Gumbel distribution, the expected
significant values for a selected return period can be esti-
mated as follows (reduced value):
TRp =−ln[ln(11/TR)](5)
2.4. Northern Hemisphere teleconnection patterns
The term Regional Cycle and Teleconnection Pattern
are terms referred to a recurring and persistent, large
scale pattern of pressure and circulation anomalies that
span vast geographical areas (Hatzaki et al., 2006).
Teleconnection patterns are also referred to as preferred
modes of low-frequency (or long time scale) variability
of the atmospheric circulation with geographically fixed
centres of action.
The NOAA Climate Dynamics Research Centre has
shown the existence of several teleconnection patterns
influencing the European region (including the area
investigated in this study):
North Atlantic Oscillation – NAO (Wallace and Gut-
zler, 1981; Hurrel, 1995; Barnston and Livezey, 1987).
East Atlantic Pattern – EA (Esbensen, 1984; Wallace
and Gutzler, 1981).
East Atlantic/Western Russia – EA/WR (Barnston and
Livezey, 1987)
Scandinavia – SCAND (Barnston and Livezey, 1987).
Polar/Eurasia – POL (Barnston and Livezey, 1987).
East Pacific – North Pacific Pattern – EPNP (Bell
and Janowiak, 1995).
Pacific/North American Pattern – PNA (Barnston and
Livezey, 1987; Chen and van den Doo, 2003).
Arctic Oscillation – AO (Zhou et al., 2001; Higgins
et al., 2002).
Previous teleconnection patterns were recognized as
the most important characterizing parameters of the
European climate system, the NAO apparently being
the most important. A link between these teleconnection
patterns and seasonal, interannual and decadal variations
in weather, waves and storminess for the European
Atlantic coastlines was established by different authors
(Wang et al., 2008; Lozano et al., 2004; Hurrel and
Deser, 2009; Dodet et al., 2010). Likewise, changes in
coastal evolution during the second half of the 20th
century were also connected to modulations of the NAO
Index (Lozano et al., 2004; Esteves et al., 2011; Thomas
et al., 2011; O’Connor et al., 2011).
In this study, distributions of average winter monthly
and annual values of NAO, EA, EATL/WRUS, SCAND,
Polar/Eurasia, EP-NP, PNA and AO from 1983 to 2010
were compared with winter wave height data and storm
classes by means of multiple and independent regression
analysis.
3. Results
3.1. Wave climate
Data showed a clear pattern of cyclic variations of aver-
age monthly values of significant wave height (Hs)and
99th percentile of Hs(Hs99 – Figure 2). Waves were usu-
ally low (Hs<0.8m,Hs99 <1.8 m) in May to August
period (late spring to summer), reaching minimum val-
ues in August (Hs=0.6m,Hs99 =1.22 m). During the
winter season, waves rapidly increased in height, reaching
peak values (Hs=1.2mandHs99 =3.1) in December
to January period.
Trends of average monthly and annual values of Hs
and Hs99 during the investigated period (19832010)
and associated linear regression analyses, were plotted
in Figure 2; data showed a small decrease in monthly
(Avg: 1; r2:0.018; P: 0.118; trend: 0.0011) and annual
(Avg: 1; r2:0.085; P: 0.14; trend: 0.008) Hsvalues,
whereas Hs99 remained constant. However, low recorded
values of Pearson coefficient revealed that these trends
are not statistically significant (Pmonthly: 0.118; P
annual: 0.14). Similar results were obtained using the
MannKendall trend test and the Wilcoxon rank-sum
test commonly used in such kind of studies (Carter and
Draper, 1988; Bacon and Carter, 1991; Allan and Komar,
2000).
Considering the 27 investigated winters, a great vari-
ability of average monthly wave height was observed.
Low Hsvalues (<0.8 m) were recorded in winter sea-
sons within the 1990/11994/5, 1998/9– 2001/2 and
2004/52007/8 periods; high Hsvalues (1.3m)were
recorded during the 1995/6, 1996/7, 1997/8, 2002/3,
2003/4 and 2009/10 winter season periods. Concerning
extreme wave conditions, the highest monthly value was
7.8 m (recorded December 1989) and the average value
of extreme wave height was 4.6 m.
Figure 2 evidenced a quasi-periodic 3–4 year beha-
viour in the recurrence of high wave height values. A
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2146 N. RANGEL-BUITRAGO AND G. ANFUSO
Figure 2. Winter monthly and annual Hsand Hs99 for the 1983– 2009 period.
spectral analysis of time series of extreme waves, based
on the Fourier transformation (Boashash, 2003), indicated
a cyclic trend of 3 years. The probability density function
for the period of recurrence and occurrence probability
of maximum annual Hswas obtained (Figure 3) applying
the GEV proposed by Jenkinson (1955). The probability
of occurrence of 3.03.5 m waves ranged from the 86 to
96%, meanwhile the probability of occurrence of waves
higher than 6 m did not reach 6%. The recurrence periods
for 4 and 8 m waves were 5 and 50 years respectively.
3.2. Storm characteristics and trend
One hundred and nineteen storms were recorded during
the investigated 27 year time span. Classes I (weak) and
II (moderate) accounted for 55 and 33%, respectively, of
records. These values were very close to those obtained
by Dolan and Davis (1992), Moritz and Moritz (2006),
Mendoza and Jimenez (2008) and Rangel-Buitrago and
Anfuso (2011a) in United States and Spain. Class III
(significant), constituted 8% of the records and classes
IV (severe) and V (extreme) accounted for 3 and 1%,
respectively (Table I).
Associated average wave height and storm duration
values presented important variations (Table I) and aver-
age wave period ranged from 7.0 (class I) to 9.7 s
(class V). Storm power values were larger than the
ones proposed by Dolan and Davis (1992) because of
the major threshold of storm wave height selected, and
apparent longer duration of investigated storms. Dealing
with monthly distribution, classes I and II events were
observed over the entire winter season, class III from
November to March and classes IV and V only in Decem-
ber and January, January being the stormiest month.
The distribution of the most commonly used storminess
indices over the 27 years is shown in Figure 4. Consid-
ering the number of storms per year (Figure 4), stormy
winter seasons were 2009/10 (12 events), 1995/6 and
2002/3 (with 10 events each) and 1993/4 (8 events); no
storms at all were recorded during 1994/5. Despite some
anomalous peaks, the 1983/41986/7, 1990/1– 1992/3,
1998/92001/2 and 2003/4– 2008/9 winter periods pre-
sented the lowest average number of storms (Figure 4);
the 3 year moving average did not show a clear trend in
storm occurrence whereas indicated a 67 years cyclic
behaviour with storm peaks observed in 1989/90, 1995/6,
2002/3 and 2009/10 winter seasons.
Patterns of variability of storms duration and Storm
Power Index were very similar to that found for the
number of storms (Figure 4). This was because, in
general, stormy winter seasons presented a great number
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 2156 (2013)
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2147
Figure 3. Wave Recurrence and probability. Annual maximum wave height plotted versus the reduced value from the Gumbel distribution using
the Gringoten Plotting position and wave occurrence probability plotted versus the annual maximum wave height value.
of storms which gave many hours under storm conditions
and therefore an elevated storm power.
Linear regressions analysis, MannKendall trend tests
and Wilcoxon rank-sum tests did not show a clear
trend of storminess indices (i.e. number of storms: Avg:
4.85; r2:0.040; trend: 0.082) although they indicated a
67 year period (return periods: r2: 0.97 and occurrence
probability: r2: 0.95 – Figures 4 and 5). In order to
further investigate storm periodicity, the return period and
occurrence probability of each storm class were estimated
using the method of the maximum likelihood based
on the Gumbel distribution of annual maximum storm
power values. The probability plot for each storm power
value was represented in Figure 5 and derived using the
Gringoten (1963) plotting position analysis suggested by
Cook (2004) and Goel et al. (2004).
4. Discussion
4.1. Wave climate and storm trend
Results obtained confirmed the clear seasonal nature of
wave height in Cadiz area as previously observed by
Men´
endez et al. (2004), Anfuso and Gracia (2005) and
Rangel-Buitrago and Anfuso (2011a).
The time series of average and extreme monthly values
of wave height presented similar behaviour. The lack
of a clear trend of both features disappointed with
observations carried out by several authors who suggested
an increase in mean significant wave height in many
Oceans around the World and, more closely, on the
Northern part of the North Atlantic Ocean. Nevertheless,
results obtained confirmed observations carried out at
the mid and southern latitudes of the North Atlantic
Ocean, by Dolan et al. (1989) and WASA (1998), and
at regional scale, e.g. Cadiz Gulf, by Men´
endez et al.
(2004), Ferreira et al. (2009) and Almeida et al. (2011).
Concerning the distribution of stormy years, it was
quite close to the one presented at local and regional
scales by previously mentioned authors. Specifically,
Rodriguez et al. (2003) used wind records and individu-
ated eight stormy years in Huelva during the 19621999
period; two out of three of the recorded storminess sea-
sons after 1983 (e.g. 1989/90 and 1995/6) corresponded
with the two years that recorded the highest values of
Storm Power Index.
Almeida et al. (2011) used HIPOCAS and offshore
wave buoy data recorded in Faro, in the Algarve coast
(Southern Portugal), and affirmed that the storminess
years were 1987, 1989, 1995, 1996, 1997, 2002, 2003
and 2009. All these except one, i.e. 2003, confirmed
this study’s observations since they corresponded with
years characterized by high Storm Power Index values, a
great number of storms and high values of storm duration
(Figure 4). Similarities to previous studies indicate a
general homogeneity of wave climate in the Cadiz Gulf. It
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2148 N. RANGEL-BUITRAGO AND G. ANFUSO
(a)
(b)
(c)
Figure 4. Distribution of the number of storms, duration and sum of storm power per winter year with their respective linear regression and the
3 year moving average. Average values (Avg), Root mean square (r2) and trends also presented.
is broadly exposed to storms approaching from the third
and fourth quadrants, as observed by Rangel-Buitrago
and Anfuso (2011b), who analysed HIPOCAS datasets
at five locations along Andalusia’s Atlantic littoral.
Comparing obtained data in this study with observa-
tions carried out in Northern Europe, a certain correspon-
dence was evidenced with data presented by O’Connor
et al. (2011). They analysed gale-day frequency in North-
ern Ireland finding a peak in storm generation in the
early 1990s, before an apparently decreasing until 2009.
The most energetic conditions recorded in this work
(1995/6, 1996/7, 2002/3 and 2009/10) matched the
extreme weather conditions recorded over the same peri-
ods in Wales (Phillips, 2008; Phillips and Crisp, 2010;
Thomas et al., 2011), Lithuania (Dailidiene et al., 2011;
Kelpsaite et al., 2011), Estonia and Southern Gulf of Fin-
land (Suursaar, 2010). These authors reported significant
changes in wind direction, high wind speeds, mildest tem-
perature records and an increase in damage to coastal
structures (because of storm impacts). In addition, fair
weather conditions recorded in Cadiz area during 1998
were similar to the observations carried out in Eng-
land and reported by Environmental Scientist (2000) that
observed as 1998 and the 1990s respectively were the
third warmest year and the warmest decade on records in
the UK.
Concerning storm occurrence probability in the inves-
tigated area, it was 96% for class I (i.e. almost one
event per year) to 3% for class V. The return period
for class V was 25 years and ranged from 6 to 8 years
for classes III and IV storms, e.g. significant and severe
events (return periods: r2: 0.97 and occurrence proba-
bility: r2: 0.95 – Figure 5). Such periodicity was simi-
lar to the 67 year periodicity proposed for Cadiz and
Huelva respectively by Mu˜
noz and Enriquez (1998) and
Rodriguez et al. (2003) and to the 78 year periodic-
ity recorded by Ferreira et al. (2009) and Almeida et al.
(2011) in Faro (Southern Portugal) and in the southern
part of North Atlantic by WASA (1998) and Matulla et al.
(2007).
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 2156 (2013)
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2149
Figure 5. Storm recurrence and probability for the different storm classes in Cadiz littoral. Annual maximum storm power plotted versus the
reduced value from the Gumbel distribution using the Gringoten plotting position and storm occurrence probability plotted versus the annual
maximum storm power.
Lastly, classes I and II showed a period of recurrence
ranging from 1 to 3 years, similar to the 2 3 year
recurrence period for minor storm events detected in
Cadiz and Huelva respectively by Mu˜
noz and Enriquez
(1998) and Rodriguez et al. (2003).
4.2. Storms and Northern Hemisphere teleconnection
patterns
A series of linear regression analyses and Pearson corre-
lation tests were performed considering the annual and
monthly wave data (Hsand Hs99), storminess indices
and teleconnection patterns. The analysis of annual and
monthly datasets gave very different result, the latter pre-
senting clearer trends (Table II).
Multiple linear regression analysis between wave data,
storminess indices and the teleconnection patterns indi-
cated that approximately 40% of the change in monthly
wave data and all used storminess indices related to
changes in the eight teleconnection patterns used in this
work. Drivers of change being the AO, with 21.45%, and
NAO, with the 19.65% (Table II); both being important
controllers on short- and long-term climatic variability
in North Atlantic Ocean, Europe and the Mediterranean
basin (T¨
urke¸s and Erlat, 2008).
Specifically, the AO is an atmospheric circulation
pattern reflecting the non-seasonal sea level pressure
variations north of 20 °N latitude and varies over time
with no particular periodicity; it is characterized by
pressure anomalies of opposite sign, one located in
the Arctic and the other centred at 3745 °N latitude
(Thompson and Wallace, 1998). The AO is believed to
be causally related to, and thus partially predictive of,
weather patterns in locations many thousands of miles
away, including many major population centres of Europe
and North America.
In the same way, negative AO values play an important
role in determining extreme conditions such as frozen
precipitations, strong winds and extreme weather events
in general over the Northern Hemisphere and in particular
over the North Eastern United States, the Mediterranean
area and China (Thompson and Wallace, 2000; Higgins
et al., 2002; Wettstein and Mearns, 2002; Xoplaki, 2002;
T¨
urke¸s and Erlat, 2008; Mao et al., 2011).
The NAO is defined as the difference in normalized
sea level pressure computed between a station in the
Azores (Ponta Delgada) or Southern Europe (e.g., Gibral-
tar, Lisbon) and another station in Iceland (Stykkishol-
mur). Negative NAO values give rise to changes in the
surface westerly winds across the North Atlantic that
eventually affects The Azores and the western European
coasts (Andrade et al., 2008). In this respect, severity of
winters in northern and western Europe (WMO, 1995;
Esteves et al., 2011; Thomas et al., 2011), temperature
anomalies (Hurrell, 1995; Trigo et al., 2002), intensity
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2150 N. RANGEL-BUITRAGO AND G. ANFUSO
Table II. Pearson correlation values for winter annual (a) and monthly (b) data.
a) Annual data correlations
Teleconnection
pattern
Hs
mean
monthly
Hs99
percentile
Number
of
storms
Duration
of
storms
Avg
duration
Max
duration
Sum
SPI
Avg
SPI
Max
SPI
North Atlantic Correlation 0.48 0.59 0.51 0.50 0.33 0.52 0.42 0.25 0.23
Oscillation P-value 0.01 0.00 0.01 0.01 0.09 0.01 0.03 0.21 0.25
(NAO) r20.23 0.35 0.26 0.25 0.11 0.27 0.17 0.06 0.05
East Atlantic Correlation 0.33 0.34 0.32 0.31 0.25 0.24 0.32 0.31 0.30
(EA) P-value 0.09 0.08 0.11 0.11 0.20 0.22 0.10 0.12 0.13
r20.11 0.12 0.10 0.10 0.06 0.06 0.10 0.10 0.09
East Correlation 0.13 0.10 0.13 0.15 0.11 0.20 0.11 0.08 0.11
Atlantic/Western P-value 0.52 0.62 0.53 0.47 0.59 0.33 0.59 0.70 0.59
Russia (EA/WR) r20.02 0.01 0.02 0.02 0.01 0.04 0.01 0.01 0.01
Scandinavia Correlation 0.45 0.48 0.41 0.38 0.35 0.44 0.23 0.13 0.06
(SCAND) P-value 0.02 0.01 0.03 0.05 0.07 0.02 0.26 0.53 0.78
r20.21 0.23 0.17 0.14 0.13 0.20 0.05 0.02 0.00
Polar/Eurasia Correlation 0.11 0.06 0.08 0.10 0.09 0.06 0.07 0.07 0.09
Pattern P value 0.58 0.76 0.70 0.63 0.67 0.78 0.72 0.72 0.64
(POL) r20.01 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.01
Arctic Correlation 0.43 0.47 0.52 0.51 0.34 0.46 0.39 0.22 0.16
Oscillation P-value 0.03 0.01 0.01 0.01 0.09 0.02 0.04 0.26 0.42
(AO) r20.18 0.22 0.27 0.26 0.11 0.21 0.15 0.05 0.03
Pacific/North Correlation 0.09 0.13 0.11 0.03 0.16 0.12 0.06 0.14 0.19
American P-value 0.67 0.52 0.57 0.89 0.44 0.54 0.77 0.48 0.33
Pattern (PNA) r20.01 0.02 0.01 0.00 0.02 0.01 0.00 0.02 0.04
East Pacific/ Correlation 0.07 0.12 0.11 0.03 0.16 0.12 0.07 0.16 0.22
North Pacific P-value 0.72 0.56 0.60 0.88 0.42 0.55 0.72 0.43 0.28
Pattern (EP/NP) r20.01 0.01 0.01 0.00 0.03 0.01 0.01 0.03 0.05
b) Monthly data correlations
North Atlantic Correlation 0.43 0.46 0.49 0.40 0.36 0.38 0.35 0.33 0.33
Oscillation P-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
(NAO) r20.18 0.22 0.24 0.16 0.13 0.14 0.12 0.11 0.11
East Correlation 0.14 0.07 0.06 0.08 0.13 0.11 0.09 0.17 0.16
Atlantic P-value 0.11 0.42 0.49 0.35 0.13 0.21 0.29 0.06 0.07
(EA) r20.02 0.00 0.00 0.01 0.02 0.01 0.01 0.03 0.02
East Atlantic/ Correlation 0.16 0.07 0.07 0.01 0.00 0.01 0.02 0.00 0.00
Western Russia P-value 0.06 0.44 0.41 0.94 0.96 0.91 0.82 0.99 0.96
(EA/WR) r20.03 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00
Scandinavia Correlation 0.22 0.20 0.18 0.21 0.17 0.22 0.17 0.17 0.12
(SCAND) P-value 0.01 0.02 0.03 0.02 0.05 0.01 0.04 0.04 0.18
r20.05 0.04 0.03 0.04 0.03 0.05 0.03 0.03 0.01
Polar/Eurasia Correlation 0.00 0.03 0.04 0.07 0.06 0.03 0.05 0.02 0.03
Pattern P-value 0.96 0.73 0.68 0.42 0.50 0.70 0.53 0.81 0.76
(POL) r20.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Arctic Correlation 0.47 0.43 0.51 0.48 0.43 0.44 0.40 0.39 0.37
Oscillation P-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
(AO) r20.22 0.19 0.26 0.23 0.18 0.20 0.16 0.15 0.14
Pacific/North Correlation 0.07 0.04 0.11 0.05 0.01 0.02 0.05 0.05 0.07
American P-value 0.40 0.63 0.23 0.58 0.94 0.81 0.57 0.59 0.41
Pattern (PNA) r20.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01
East Pacific/ Correlation 0.00 0.00 0.06 0.01 0.05 0.03 0.00 0.00 0.03
North Pacific P-value 0.98 0.98 0.46 0.95 0.60 0.70 0.98 0.96 0.76
Pattern (EP/NP) r20.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
In bold significant correlation values.
and frequency of winter daily precipitation and storms
over the Iberian Peninsula (Mu˜
noz-D´
ıaz and Rodrigo
(2003); Gallego et al., 2005; Almeida et al., 2011) are
partially influenced by this teleconnection pattern.
The relative importance of the NAO index in the devel-
opment of extreme events in the Northern Hemisphere
was confirmed in this study and it was in agreement
with several authors such as: Matulla et al. (2007) that
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 2156 (2013)
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2151
Figure 6. Monthly values of number of storms, storm durations, North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). The dashed
polygons highlight ‘stormy’ years. NAO phases: SN, strong negative; N, negative; NT, neutral; P, positive; SP, strong positive.
showed as the NAO index was not particularly help-
ful in the determination of storm conditions in central
Europe and Andrade et al. (2008) that explained the ‘par-
tial responsibility’ of the NAO index over storminess and
their temporal distribution over the Azores region from
1865 to 2009. Previous assumptions are in accordance
with the observations of Allan et al. (2009), which found
a poor correlation between the ‘Gibraltar – SW Iceland
NAO’ and the distribution of severe winter storms over
the British Isles and Almeida et al. (2011), which indi-
cated that NAO index variations were not exclusively
responsible of winter wave and storm distribution along
the South coast of Portugal.
Considering that the AO and the NAO were the
principal teleconnection patterns involved in changes of
storminess patterns over the study area (Table II), their
relationships with number and duration of storms and
five step moving average were investigated (Figure 6). In
order to facilitate the analysis, NAO data were divided
in five phases (strong negative, negative, neutral, positive
and strong positive) according to Pinto et al. (2009) and
Donat et al. (2010). Negative phases of NAO and AO
presented a certain level of correlation with the different
storminess indices, and as an example the number (C:
0.49, r2: 0.24) and duration of storms (C: 0.40,
r2: 0.16) were presented in Figure 6 and Table II. It is
interesting to remark that a great number of storms, larger
storm durations and higher values of Storm Power Index
were only observed when neutral to strong negative NAO
and AO phases occurred at the same time (89 storms
and 3355 h – Figure 6) and/or when occurred an abrupt
change of NAO and AO phases, i.e. they moved from
a positive phase to a negative phase without passing
through a neutral phase. The behaviour of NAO and
AO indexes was described by Thompson and Wallace
(1998) who observed as the AO resembles the NAO in
numerous respects. However, the AO’s primary centre
of action covers more of the Arctic, giving it a more
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2152 N. RANGEL-BUITRAGO AND G. ANFUSO
Figure 7. Pressure and Geopotential height anomalies at 500 mb over the 2009/10, 1995/96 and 1963/64 winter seasons (from NCEP-NCAR
reanalysis). Great storminess and extremely coldest conditions were recorded during the aforementioned winter seasons.
zonal symmetrical appearance. Hence, although the AO
and the NAO patterns resemble each other, there is a clear
distinction that could play a guiding role in determining
the physical mechanisms that control the variability of the
Northern Hemisphere climate (Wallace, 2000). Rogers
and McHugh (2002) examined whether the NAO and
the AO were inseparable spatial modes of atmospheric
circulation in the Northern Hemisphere: the analysis
of the spring, summer and autumn sea level pressure
fields revealed that the NAO and the AO-liken patterns
occurred as separate regional teleconnections forming the
first and second principal components, respectively.
Specifically, in the investigated area, 1995/1996 was
one of the most storminess years over past decades
(Figure 6) and the great number of recorded storms
gave rise to important economic losses and damages in
the Gulf of Cadiz (Ballesta et al., 1998; Reyes et al.,
1999; Anfuso and Gracia, 2005). The 2009/10 winter
season was the stormiest season during the investigated
time span, with 12 storm events equivalent to 614 h
(26 d). The exceptional nature of the 2009/2010 winter
under different climatic aspects was confirmed by several
studies. In this sense, Cattiaux et al. (2010) observed as
it was one of the coldest winter seasons since 1949 and
Cohen et al. (2010) recorded extreme cold temperatures,
snow precipitations and storms in most of the major
population centres of the industrialized countries of the
Northern Hemisphere.
Climatic anomalies were associated with an extreme
persistence of negative phases of AO and NAO indices
and an exceptional Northern Hemisphere mean atmo-
spheric circulation episode (Wang et al. 2010) reflected
by the distribution of the geopotential height anoma-
lies at 500 mb (Figure 7). This parameter exhibited a
strong zonal hemispheric pattern, with anomalously high
pressures over the pole during 2009/2010 (Figure 7).
Such a distribution favoured the development of neg-
ative phases for the AO and NAO at the same time
(Thompson and Wallace, 1998; L’Heureux et al., 2010).
Similar results were recorded by Cohen et al. (2010) and
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 2156 (2013)
WINTER WAVE CLIMATE, STORMS AND REGIONAL CYCLES 2153
1980-1981
1981-1982
1982-1983
1983-1984
1984-1985
1985-1986
1986-1987
1987-1988
1988-1989
1989-1990
1990-1991
1991-1992
1992-1993
1993-1994
1994-1995
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
YEAR
Avg: 4.37 R2:0.040 Trend: 0.010
Avg: 156.1 R2:0.028 Trend: 6.70 Avg: 185.2 R2:0.006 Trend: 4.5 Avg: 176 R2:0.36 Trend: 41.2
Avg: 4.6 R2:0.30 Trend: 0.06 Avg: 5.5 R2:0.10 Trend: 0.51
0
4
8
12
Number of Storms
0
0
1
1
2
S.P
P
N.T
N
S.N
2
200
400
600
Storm Duration (hr)
North Atlantic
Oscillation (NAO)
0
1
1
2
2
Artic Oscillation (AO)
Linear Regression
3 Step-Moving Average
Figure 8. Yearly values of number of storms, storm duration, North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) phases. Average
(Avg), Root mean square (r2), lineal regression and the three step moving average also presented. NAO phases: SN, strong negative; N, negative;
NT, neutral; P, positive; SP, strong positive.
L’Heureux et al. (2010) which highlighted that the nega-
tive AO phase of December 2009 was a record result of
an unusual occurrence of two tropospherestratosphere
coupling events (NAOAO) that occurred more rapidly
than usual and in quick succession. Similar patterns
of circulation and AONAO values (Figures 6 and 7)
were recorded during the 2005/06, 1995/96, 1987/88
and 1963/64 winter seasons. These years were character-
ized by a high number of storms, great storm durations
and a great amount of reported coastal damage (Rangel-
Buitrago and Anfuso, 2011a).
Lastly, considering the decadal behaviour of annual
values of AO and NAO indices, positive values were
recorded during the past three decades (Figure 8). The
1980s were characterized by the general predominance
of positive NAO phases, which specifically ranged from
neutral to strong positive phases. The AO presented clear
negative phases from 1980 to 1987 and positive ones
from 1987 to 1994. Considering storminess, the 1980s
can be classified as a period of ‘calm’ due to the low
recorded condition in wave heights and storminess (35
storms).
In the 1990s, NAO and AO indices presented the
predominance of positive phases, 1995 being the only
year that recorded strong negative phases. During the
1990s, the teleconnection behaviour patterns and gen-
erally low storm activity, coincided with analyses per-
formed by WASA (1998) in the northeast Atlantic coast,
2012 Royal Meteorological Society Int. J. Climatol. 33: 2142 –2156 (2013)
2154 N. RANGEL-BUITRAGO AND G. ANFUSO
Ferreira et al. (2009) and Almeida et al. (2011) in Portu-
gal and Rangel-Buitrago and Anfuso (2011a) in the Gulf
of Cadiz. Finally, 20002009 was characterized by cyclic
behaviour of both NAO and AO indices, as well as wave
climates and storminess that showed extreme conditions
between 2002 and 2003 and low energy conditions from
2004 to 2007.
5. Conclusions
Coastal erosion and flooding, increased by climatic
change-related processes, represent a great threat to
human activities and settlements developed along the
World’s coastlines especially because human pressure
and occupation in such environments have been grow-
ing considerably in recent decades. The Cadiz coastal
area includes different human settlements and natural
environments, essentially large sandy beaches of great
economic interest because of tourism-related activities.
In past decades, beach reduction and loss of attractive-
ness were related to elevated coastal retreat rates basically
linked to the impact of winter storms since sea level varia-
tions did not show a clear trend. Large amounts of money
were spent counteracting coastal retreat by using beach
nourishment works and hard engineering structures.
An important step towards reducing and preventing
beach erosion and infrastructure damage is to understand
and characterize wave climate and coastal processes, e.g.
past and future trends. Understanding seasonal and annual
wave climate behaviour is crucial in erosion prevention,
as well as assessing beach response, cyclic variance and
evolution. These are key issues in distinguishing real
erosive trends from natural alternating erosion/accretion
phases. Consequently, the applied methodology enables
the characterization of wave climate, e.g. seasonal and
annual periodicity, and storm events according to energy.
This makes it simple and objective, while data can be
easily compared with similar datasets from elsewhere.
In particular, wave climate and storm classes’ distri-
bution evidenced a clear seasonal trend in Cadiz area.
Classes I and II (weak and moderate events) were
observed along the entire winter season which covered
the October to March period, class III events character-
ized the November to March period and classes IV and
V (severe and extreme) events were recorded only in
December and January, with January being the stormiest
month. Wave climate and storminess during the 27 year
period did not show a clear trend. This contradicted the
forecast general increase in the number and intensity of
storms in many oceans around the World and specifi-
cally the Northern Hemisphere, due to climate change.
Storminess presented a cyclic behaviour which occur-
rence probability varied from 96% for class I to 3% for
class V. The return period for class V was 25 years and
ranged from 6 to 8 year for storms belonging to classes III
and IV, e.g. significant and severe events. Observations
broadly confirmed results recorded at mid and low lati-
tudes of the Northern Hemisphere and at other locations
of the Cadiz Gulf, in this sense pointing to a broad homo-
geneity of regional wave climate.
Efforts in the direction of storm characterization and
prediction should also point to the determination of
the general climatic conditions for their formation, e.g.
the comprehension of general atmospheric circulation
systems linked to the teleconnection patterns. In the
Cadiz area, storm distribution appeared linked to negative
phases of AO and NAO indices. Even if further investiga-
tion is needed, it was highlighted that a great number of
storms, larger storm duration and higher values of Storm
Power Index were only observed when neutral to strong
negative AO and NAO phases there were at the same
time and/or when occurred an abrupt change of AO and
NAO phases, i.e. they moved from a positive phase to a
negative phase without passing through a neutral phase.
Hence, accurate short-term predictions of AO and NAO
indices might represent valuable predictive techniques to
forecast storm events, e.g. establishing early and warn-
ing systems in order to reduce the impacts of energetic
erosive events.
Acknowledgements
This work is a contribution to the RESISTE Research
Project (CGL2008-00458/BTE, supported by the Spanish
Ministry of Science and Technology and by European
Funds for Regional Development F.E.D.E.R.) and to
the Andalusia P.A.I. Research Group no. RNM-328.
Thanks to Puerto del Estado (Spanish Ministry of Public
Works) for offshore wave data records. This work has
been partially developed at the Centro Andaluz de
Ciencia y Tecnolog´
ıa Marinas (CACYTMAR), Puerto
Real (Cadiz, Spain).
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... Dans l'analyse des processus responsables de l'érosion côtière, il convient de distinguer la notion de tempête et celle d'évènement extrême. Selon Météo France, une tempête correspond à la force 10 de l' Anfuso, 2013 ;Anfuso et al., 2016 ;Amarouche et al., 2020). Les auteurs déterminent la durée d'une tempête en fonction de la hauteur significative des vagues. ...
... On observe également quelques années sans tempêtes : 1985, 1991, 1997, 2001, 2004, 2005, 2007, 2012, 2015 et 2021. Depuis quelques années, certains auteurs tentent d'expliquer l'activité morphogène des côtes en s'appuyant sur l'analyse d'indices climatiques (Rangel-Buitrago et Anfuso, 2013 ;Anfuso et al., 2016 ;Castelle et al., 2017 ;Stéphan et al., 2018). Les travaux menés par Castelle et al. (2017) tendent à démontrer la relative influence de l'Oscillation Nord Atlantique dont le gradient de pression est mesuré entre Lisbonne (Portugal) et Reykjavik (Irlande), sur les conditions de houle dans le Golfe de Gascogne par rapport à celle de l'indice WEPA basé sur le gradient de pression entre les stations Valentia (Irlande) et Santa Cruz de Tenerife (îles Canaries). ...
... -Identifier les concomitances entre les variables météorologiques et les niveaux d'eau et d'en estimer la période de retour Notre difficulté à anticiper l'impact d'évènements extrêmes pourrait faire l'objet d'une nouvelle démarche en région. Depuis quelques années, certains auteurs tentent d'expliquer la mobilité des littoraux sableux en étudiant les indices climatiques (Rangel-Buitrago et Anfuso, 2013 ;Anfuso et al., 2016 ;Castelle et al., 2017 ;Stéphan et al., 2018). Les travaux menés par Castelle et al. (2017) et présentés au sein du Chapitre 2 (Partie 1), tendent à démontrer l'influence marquée de l'indice WEPA au regard de l'ONA, sur les hauteurs de houles significatives. ...
Thesis
Les côtes sableuses sont des espaces particulièrement mobiles, à la fois dans le temps et dans l’espace. Leur sensibilité face aux aléas côtiers, qu’ils soient naturels ou anthropiques, incite à étudier leur fonctionnement ainsi que leur réponse face aux changements climatiques et ce, à court, moyen et long termes. Ce sont essentiellement les évènements extrêmes survenus depuis la fin des années 90, et plus particulièrement la tempête Xynthia en février 2010, qui ont éveillé les consciences quant à la nécessité d’accentuer les recherches sur l’aléa érosion et les enjeux associés.Dans un contexte où les politiques publiques s’inscrivent de plus en plus dans une gestion intégrée du trait de côte, ces travaux visent à renforcer les connaissances relatives à l’aléa érosion, ainsi qu’à son impact sur les côtes sableuses et les enjeux côtiers. Ce processus d’érosion côtière est ainsi étudié dans ses diverses dimensions temporelles. L’analyse porte sur ses dynamiques passées ; d’une part à long terme (depuis 1950), d’autre part à court terme à travers la mesure d’impacts d’évènements extrêmes tels que Lothar et Martin (1999) ou encore Xynthia (2010). La dimension prospective est également abordée via la projection du trait de côte, avec un intérêt particulier pour l’estimation des incertitudes induites par la démarche. Un indice de vulnérabilité côtière est également réalisé, remobilisant l’ensemble des résultats issus des démarches précédemment évoquées, et ce afin d’identifier les secteurs vulnérables en région et de mesurer l’exposition des enjeux face à l’aléa érosion.
... This storm occurred in a phase of transition from neap to spring tides, which probably significantly reduced its impact on wave propagation over the rocky platform, and, consequently, on boulder mobility. The storm wave threshold, which corresponds to the minimum wave height under favorable high-tide conditions (especially during spring tides) liable to produce significant morphological changes on beaches and dunes, has been defined in terms of a significant wave height (Hs) value of about 2.5 m at the southwest Spanish Atlantic coast [60], and of 2.5-4.5 m along the Portuguese coast [61], which is relatively similar to the Moroccan Atlantic coast in terms of exposure to Atlantic swells. The storm wave threshold values can vary from one coastline to another depending on initial morphology and the degree to which significant morphological change is considered. ...
... The storms of 2013-2014 significantly impacted the Atlantic coast of Europe [60,67] but also the Moroccan coast [62]. The recurrence of this type of strong storm over the last 20 years is remarkable, as between 1998 and 2015 there were up to 36 storm events of Hs > 7 m, while only 2 were recorded between 1956 and 1998 [62]. ...
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The detachment and mobilization of boulders from rocky shore platforms by waves involves complex geomorphic and hydrodynamic processes. Understanding these processes requires precise information on the rates and patterns of movement of these megaclasts scaled against the wave conditions that generate boulder mobility. Repeat photogrammetry and structure-from-motion (SfM) models commonly used in geomorphic analyses are an interesting option for monitoring boulder dynamics. In this study, we used unmanned aerial vehicle (UAV)-based digital photogrammetry and SfM differential models to identify recent boulder movements over a rocky shore platform in Laghdira, Morocco. Combining these results with data on storm occurrence in the study area allowed us to identify storm waves as the unique driver of the dislodged and mobilized boulders. The identified storm event had a significant wave height of 5.2 m. The UAV models were built from imagery captured in September and December 2019 using a DJI MAVIC PRO PLATINUM, and we used QGIS to produce 2D and 3D model outputs. The exploitation of the 2D model differentials allowed us to appreciate the response of the boulders to the storm waves and to determine platform volumetric changes and, therefore, boulder mobility. The 3D models were valuable in determining the mode of transport of the boulders. Mobility patterns included sliding, overturning with no further mobility, and rotation and saltation, as well as boulder breakup. Storm waves did not have a preferential impact on any particular boulder shape, size category, or position at the outer edge of the platform. These results highlight the utility of combining UAV surveys with identified storm events, which are much more frequent than tsunamis, in determining observed boulder initiation and mobility.
... The first step in this direction was the determination of a wave height threshold value, i.e., the height at which erosion starts to affect the considered coastal area. Average annual and monthly winter values (i.e., from November to February) of significant wave height (H s ) and the 99 th percentile of H s (H s99 ) were used for the characterization of wave climate and the definition and description of storms by different authors [79,80]. In this paper, the H s99 was used, as it is associated with the highest energetic events [19,79]. ...
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High coastal sectors constitute the most widespread coastal environment and, under the present accelerated sea-level rise scenario, are suffering huge impacts in terms of erosion. The aim of this paper is the proposal of a new methodological approach for the assessment of their susceptibility to erosive processes. The method is based on the combination of two matrices, i.e., a matrix considering the main physical elements (essentially morphological and geotechnical characteristics) that determine the proneness to erosion of a specific high coastal sector, and a forcing matrix, which describes the forcing agents affecting the considered sector. Firstly, several variables were selected to construct each one of the two matrices according to existing studies and, in a second step, they were interpolated to obtain the susceptibility matrix (CSIx). The approach was applied to Procida Island and Cilento promontory, both located in southern Italy. Results obtained were validated by comparing them with cliff retreat data obtained by means of aerial photographs and satellite images. The analysis shows that the greater part of the analyzed high coastal sectors belongs to the high-susceptibility class due to the combination of adverse morphological, geotechnical and forcing characteristics. Such sectors can be considered “hotspots” that require an increase in monitoring programs and, at places, urgent protective actions.
... Todavia, os estudos que avaliam os impactos associados a esse fenômeno muitas vezes não consideram condições locais e, portanto, não são eficientes, haja vista que impactos serão sentidos apenas na franja próxima ao mar, principalmente nas áreas de baixas elevações. Além desse fenômeno, destacam-se ainda o aumento na frequência e intensidade de eventos extremos, como marés e ondas de tempestades (Phillips & Crisp, 2010;Rangel-Buitrago & Anfuso, 2013), que podem ocasionar inundações costeiras (Mcgranahan et al., 2007;Nicholls, 2004) e diversos episódios de alteração no balanço sedimentar (Esteves & Finkl, 1998). Todos os fenômenos previamente citados exigem medidas específicas de gestão (Williams et al., 2018), uma vez que alguns lugares são mais susceptíveis que outros a esses eventos (Birkmann, 2007;Bonetti & Woodroffe, 2017). ...
... However, oceanic coasts are exposed to a wide variety of energy regimes and may receive very high waves associated to sporadic strong storms, a situation that will not occur at Gallocanta Lake. In the case of the Mazagón coast, high-energy storms (classified as Class V by Rangel-Buitrago and Anfuso, 2013) are linked to large-scale atmospheric phenomena (Ribera et al., 2011;Almeida et al., 2012) and are characterised by an average wave height of 6.9 m, a peak period of 9.4 s, and 93 h duration. Their frequency is quite low (only one event recorded in the period 1952-2009, according to Anfuso et al., 2015). ...
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... Exceptionally intense storm wave events in recent years have increased awareness of the frequency, impact, and roles of storms in the European Union (Cooper et al., 2004;Ciavola et al., 2011;Rangel-Buitrago and Anfuso, 2013;Loureiro et al., 2014;Fig. 1. Location of the studied Asturian dune fields (black circles) and the riverine fluvial sources of sediments.1) ...
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Coastal dunes are sensitive to both anthropic and natural processes of erosion. In this study, we analyse the geomorphic changes in 15 dune-fringed coastlines of Asturias (NW Spain) for the period 1992 to 2014 to determine specific drivers of erosion. Coastline migration patterns were obtained using geospatial analysis of vertical aerial images and field surveys. The annual and maximum wave energy, maximum significant wave height, duration and number of storm events were determined for each climatic year by using available wave data from both recorded buoy data (1997–2014) and the WAM numerical model (1956–2015). The study shows that since 2006, coastline retreat has significantly accelerated along the Asturias coast. In particular, severe erosion of sandy beaches and dunes occurred in 2013/14, reaching up to 40 m of coastline retreat and destroying numerous ports and seafront promenades. Increased frequency of unusually strong storm waves, with significant wave height (Hs) exceeding 9 m, together with an unusual 5° westward variation from the mode in offshore wave approach direction during these storms, appear to be the main catalysts in heightened erosion capacity of storm waves in the 2013/14 period. This resulted in the removal of small dune areas and a severe recession in the easternmost part of the largest (natural) dune fields in the region. Under these stormy periods, foredunes and climbing dunes developed notably pronounced escarpments and blowouts, with sand subsequently relocated into the foreshore and backshore zones. This study demonstrated that increased frequency in powerful storm events (Hs > 9 m) and an alteration in storm approach direction, can lead to significantly enhanced erosion of dune coastlines, even along those that are modally-attuned to high energy events.
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In past decades coastal, erosion related impacts on the world’s shorelines have been significantly growing due to ongoing coastal development and tourist occupation as well as to natural erosion/flooding events exacerbated by climatic change. Ocean coastlines are highly dynamic and changing environments since they show great temporal and spatial variability in response to the action of different and complex coastal processes: at an inter-annual time scales, related to seasonal wave climate variations due to temporal and spatial distributions of high latitude storms and tropical storms/hurricanes, or as a result of events with a large return period, such as the impact of very energetic storms and tsunamis, sea level rise, and variations in rivers’ sediment supplies. In order to prevent and reduce such impacts, coastal managers need to know the sensitivity of natural coastal sectors, which is related to wave energy, beach characteristics/evolution, and sea level trend as well as the potential vulnerability and economic value of the urbanized sectors.This paper shows coastal evolution and the impacts on it of coastal structures and the characterization and evolution of dune systems along the Mediterranean coast of Andalusia (Spain). For this purpose, an amount of 47 units were defined along the studied coast, and evolution rates (erosion/accretion/stability), for the period 1956-2016, quantified by using the DSAS extension of ArcGIS software. As a result, 9 units recorded accretion, 19 erosion and 19 stability and, concerning the beach surface balance, 17 units presented a positive balance and 28 a negative one and a net balance of -29,738.4 m2/yr. The analysis of coastal evolution evidenced the impact of hard structures: accretion was essentially observed up-drift of ports and groins and in correspondence of breakwaters; erosion was observed down-drift of ports and groins and in correspondence of seawalls and revetments, and at largest river deltas; and stability was observed at pocket beaches and coastal areas locally stabilized by protection structures and nourishment works. These results were used to determine the distribution of swash- and drift-aligned coastal sectors and main direction of sediment transport.Concerning the characterization and evolution of dune systems, they were mapped different type dunes’ systems as well as dune toe position and fragmentation, and human occupation and evolution from 1977 to 2001 and from 2001 to 2016. In total, they were delimited 53 dune systems along the Mediterranean coast of Andalusia, differentiating three types: Embryo and mobile dunes, grass-fixed dunes and stabilized dunes. It was observed a general decrease in dunes’ surfaces in the 1977-2001 period (-7.5 x 106 m2), linked to the increase of anthropic occupation (+2.3 x 106 m2), and dunes’ fragmentation, especially in Málaga and Almería provinces. During the 2001-2016 period, smaller changes in the level of fragmentation and in dunes’ surfaces were observed. An increase of dunes’ surfaces was only observed on stable or accreting beaches (4 out of 53 dune systems), both in natural and anthropic areas (usually up-drift of ports).
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Several studies have shown the effect of climatic oscillations on fisheries. Small pelagic fish are of special global economic importance and very sensitive to fluctuations in the physical environment in which they live. The main goal of this study was to explore the relationship between the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), and the Arctic Oscillation (AO) on the landings and first sale prices of the most representative small pelagic commercial species of the purse-seine fisheries in the Gulf of Cadiz (North East Atlantic), the European anchovy Engraulis encrasicolus and the European sardine Sardine pilchardus. Generalised linear models (GLMs) with different data transformations and distribution errors were generated to analyse these relationships. The best results of the models were obtained by applying a moving average of order 3 to the dataset with a double weighted median. Our results demonstrate relationships between NAO, AO, and EA and European anchovy and sardine landings. These cause an indirect effect on the first sale price in markets through catch variations, which affect the price according to the law of supply and demand. The limitations of this study and management implications are discussed.
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The small island areas oriented to coastal tourism are highly vulnerable to marine storms, given the fluctuation of this economic sector and the increase in its exposure due to the increasing anthropogenic presence on the coastline. In this context, the storms between 1958 and 2017 are studied here in the most important tourist enclaves of Tenerife and Gran Canaria, located in the municipalities of Arona and Adeje (SW of Tenerife) and in San Bartolomé de Tirajana and Mogán (S-SW of Gran Canaria). In order to perform the analysis , wave data from State Ports (Spain) (SIMAR-44 nodes, Ministry of Transport, Mobility and Urban Agenda of Spain) are used. A statistical study of the phenomena can help establish a threshold for the identification of a marine storm based on a significant wave height (H s) of 2.7 m in Arona and Adeje and 1.7 m in San Bartolomé de Tirajana and Mogán. The application of this threshold identified 144 episodes in the SW of Tenerife and 154 in the S-SW of Gran Canaria. Their behavior is examined from, on the one hand, the duration, seasonality, type of waves and degree of severity that characterizes them; and, on the other, from the state of the atmosphere that concurs in its genesis by means of the Wetterzentrale synoptic maps. Finally, a first assessment of the effects of these stormy phenomena is made using local press reports on drowning, damage to urban furniture and port infrastructures and loss of sand on the beaches.
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A statistically-rational method of extreme wave data analysis is presented. A combination of the Fisher-Tippett type I and the four Weibull distributions is proposed as the candidates of distribution functions. The least square method is used for data fitting. The best plotting position formula for each function is determined by the Monte Carlo method with 10,000 simulations per sample size. Confidence intervals of estimated extreme wave heights for given return periods are evaluated by simulations and expressed in the form of empirical formulas, for both the cases when the true distribution is known and unknown. An example of extreme wave data analysis is given.
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Greenland ice-core data have revealed large decadal climate variations over the North Atlantic that can be related to a major source of low-frequency variability, the North Atlantic Oscillation. Over the past decade, the Oscillation has remained in one extreme phase during the winters, contributing significantly to the recent wintertime warmth across Europe and to cold conditions in the northwest Atlantic. An evaluation of the atmospheric moisture budget reveals coherent large-scale changes since 1980 that are linked to recent dry conditions over southern Europe and the Mediterranean, whereas northern Europe and parts of Scandinavia have generally experienced wetter than normal conditions.
Book
Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory, techniques and algorithms used for the analysis and processing of non-stationary signals, as found in a wide range of applications including telecommunications, radar, and biomedical engineering. This book gives the university researcher and R&D engineer insights into how to use TFSAP methods to develop and implement the engineering application systems they require. New to this edition: New sections on Efficient and Fast Algorithms; a "Getting Started" chapter enabling readers to start using the algorithms on simulated and real examples with the TFSAP toolbox, compare the results with the ones presented in the book and then insert the algorithms in their own applications and adapt them as needed. Two new chapters and twenty three new sections, including updated references. New topics including: efficient algorithms for optimal TFDs (with source code), the enhanced spectrogram, time-frequency modelling, more mathematical foundations, the relationships between QTFDs and Wavelet Transforms, new advanced applications such as cognitive radio, watermarking, noise reduction in the time-frequency domain, algorithms for Time-Frequency Image Processing, and Time-Frequency applications in neuroscience (new chapter). A comprehensive tutorial introduction to Time-Frequency Signal Analysis and Processing (TFSAP), accessible to anyone who has taken a first course in signals. Key advances in theory, methodology and algorithms, are concisely presented by some of the leading authorities on the respective topics. Applications written by leading researchers showing how to use TFSAP methods.