NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-17-2041-2017Global ship accidents and ocean swell-related sea statesZhangZhiweihttps://orcid.org/0000-0001-6158-2962LiXiao-Minglixm@radi.ac.cnhttps://orcid.org/0000-0001-5009-5413East Sea Information Center, State Oceanic Administration,
Shanghai, ChinaCollege of Geography and Environment, Shandong Normal University,
Jinan, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, Beijing, ChinaHainan Key Laboratory of Earth Observation, Sanya, ChinaXiao-Ming Li (lixm@radi.ac.cn)28November201717112041205112April201726April20174September201716October2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/17/2041/2017/nhess-17-2041-2017.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/17/2041/2017/nhess-17-2041-2017.pdf
With the increased frequency of shipping activities, navigation
safety has become a major concern, especially when economic losses, human
casualties and environmental issues are considered. As a contributing factor,
the sea state plays a significant role in shipping safety. However, the types
of dangerous sea states that trigger serious shipping accidents are not well
understood. To address this issue, we analyzed the sea state characteristics
during ship accidents that occurred in poor weather or heavy seas based on a
10-year ship accident dataset. Sea state parameters of a numerical wave
model, i.e., significant wave height, mean wave period and mean wave
direction, were analyzed for the selected ship accident cases. The results
indicated that complex sea states with the co-occurrence of wind sea and
swell conditions represent threats to sailing vessels, especially when these
conditions include similar wave periods and oblique wave directions.
Introduction
The shipping industry delivers 90 % of all world trade. It is
currently a thriving business that has experienced increases in both the
number and size of ships. However, due to the frequency of shipping
activities, ship accidents have become a growing concern, as have the
associated destructive consequences, including casualties, economic losses
and various types of environmental pollution.
Investigations into the causes of shipping accidents show that over 30 %
of the accidents are caused by poor weather, and an additional 25 %
remain completely unexplained (Faulkner, 2004). Due to these dangerous
uncertainties, accidents that involve poor weather and severe sea states
should be further studied for shipping safety.
However, under changing weather conditions, the sea surface is too complex
to predict, especially on short timescales (Kharif et al., 2009). The sea
surface is composed of random waves of various heights, lengths and periods.
Meanwhile, different kinds of waves emerge frequently; among them, wind sea
and swells are the two main types of ocean waves classified by wave
generation mechanisms. Wind sea waves are directly generated by local winds,
and when wind-generated waves propagate without receiving further energy
from wind they transition into swells.
Meteorologists and oceanographers generally work with statistical parameters,
such as the significant wave height (Hs), wave period (T,
zero-crossing period) and wave direction (D), to represent a given sea
state. Additionally, the wave spectrum, i.e., the distribution of wave energy
among different wave frequencies (f, f=1/T), is analyzed in some
studies to better understand wave dynamics. Note that a typical ocean wave
spectrum with two peaks (e.g., one from distal swells and the other generated
by the local wind) is much more complicated and variable.
In terms of the sea state parameters, Hs is usually a practical
indicator of the sea state during marine activities. Indeed, some studies,
such as an analysis of ship accidents that occurred in the North Atlantic
region (Guedes et al., 2001), have shown that accident areas coincide with
the zones with the highest Hs. A high wave height is undoubtedly
a threat to ships, yet some ships wreck in sea states characterized by
relatively low wave heights and high wave steepness (Toffoli et al., 2005).
A sea state with a narrow wave spectrum was observed during several major
ship accidents, including the Voyager accident (Bertotti and Cavaleri,
2008), the Suwa Maru incident (Tamura et al., 2009), the Louis Majesty
accident (Cavaleri et al., 2012) and the Onomichi Maru incident (In et al.,
2009; Waseda et al., 2014). Studies have assumed that the narrowed wave
spectrum is primarily generated by the nonlinear coupling of swell and wind
sea (or swell and swell) (Bertotti and Cavaleri, 2008; Tamura et al., 2009;
Cavaleri et al., 2012; Waseda et al., 2012). During such wave couplings, the
wave energy from one wave system (wind sea or swell) is enhanced and
transferred to the other wave system (usually a swell) (Tamura et al., 2009;
Waseda et al., 2014). As a result, the wave energy transformation produces a
steep swell, with a high wave energy and extreme wave height (Bertotti and
Cavaleri, 2008).
The angle of obliquity between two waves is another important condition
involved in the interaction of wave systems. The traveling angles associated
with ship accidents have varied from 10∘ (Onorato et al., 2010) to
60∘ (Tamura et al., 2009). The features noted above emerge
individually or simultaneously during ship accidents or rare extreme sea
states when swells and wind seas co-occur. Indeed, the co-occurrence of wind
seas and swells can lead to dangerous seas, as demonstrated by the parametric
rolling experienced by the German research vessel Polarstern (Bruns et al.,
2011) despite the absence of extreme wave heights.
In previous studies of ship accidents, researchers focused on only one severe
accident when discussing the sea state dynamics in detail or based their
studies on ship accident data to perform statistical analyses of classical
sea state parameters (e.g., Hs and T). To thoroughly
investigate sea state parameters, we collected information on a large number
of ship accidents and created a database for analysis. Additionally, we
discussed the parameters in both wind sea and swell conditions. Statistical
analyses were performed on data obtained from the International Maritime
Organization (IMO). The data include 10 years of ship accidents (2001–2010)
and 755 cases caused by bad weather or heavy seas. Because swells with large
wave energies can represent a threat to maritime activities, 58 cases in
which swells were reported as an important factor in the ship accident were
selected. The detailed information discussed above is presented in Sect. 2.
Following an overview of the ship accidents (Sect. 3), an analysis of the
swell-related sea state conditions for these ship accident cases is presented
in Sect. 4. In Sect. 5, two cases are illustrated to demonstrate the dynamic
processes that ensue when wind sea and swell conditions occur during ship
accidents. Finally, a summary and discussion are provided (Sect. 6).
Data and methodsShip accident database
A 10-year (2001–2010) ship accident dataset was gathered from the Marine
Casualties and Incidents Reports issued by the IMO. The dataset includes
3648 ship accidents, and each accident in the report includes the occurrence
information, such as the accident time and coordinates, initial event,
summary, casualty type and ship type. Since the primary
information used in this study includes the accident time and coordinates,
events that failed to record these details were excluded, and 1561 cases with
exact geographical locations remained in the dataset.
According to the description of initial events, which provides clues
regarding the accident causes recorded in the reports, those 1561 valid cases
cover different kinds of cases triggered by natural factors and human
factors. Because we focus on the events that occurred in natural
weather-related conditions, cases with descriptions such as fire or
explosion, improper operations and lost persons were eliminated from the
1561 cases, while cases with keywords such as strong wind, gale or cyclone or
heavy seas or rough waves were kept. Although the proximal human factors
resulting in ship accidents recorded in the IMO reports may have been
indirectly related to dangerous seas or heavy weather, e.g., improper
operations by crews, it would be exceedingly difficult to analyze the
original factors case by case. Thus, distinguishing among trigger factors
based on initial event keywords represents an optimal way of filtering the
dataset. After this filtering, 755 weather-related accidents were obtained
for the further analysis. An overview of these 755 cases is presented in
Sect. 3.
Classification of the 755 weather-related ship accidents based on
initial events (a) and ship type (b). The accidents were
recorded in the International Maritime Organization (IMO) database.
Furthermore, this study focuses on the cases that occurred in swell-related
sea states. After examining all the summaries of the 755 cases, we retained
58 cases with clear descriptions of the swell motion during the ship
accidents for the analysis of the swell-related sea states. A detailed
analysis is presented in Sect. 4.
Numerical wave model data
The ERA-20C numerical wave model data were obtained from the European Center
for Medium-Range Weather Forecasts (ECMWF). The ECMWF uses atmosphere, land,
surface and ocean wave models and data to reanalyze the weather conditions
during the last century. The ERA-20C products describe the spatiotemporal
evolution of ocean waves for 25 frequencies and 12 directions. The accuracy
is improved by validation with ERA-40 data and operational archive results.
Compared to the ERA-Interim dataset (12 h), ERA-20C has longer reanalysis
coverage (24 h) for single-point data (Poli et al., 2013). The Ocean Wave
Daily data in the ERA-20C dataset are available from 1900 to 2010 every 3 h
at a grid size of 0.125∘. The data provide 33 reanalyzed ocean wave
parameters, and separate entries are included for swell and wind sea.
Overview of ship accidents
In the ship accident dataset, 755 weather-related cases were distinguished
and discussed in Sect. 2. Hereafter, we provide an overview of these
755 cases in terms of the initial events, ship types and spatial
distribution. The initial event in the IMO reports describes the triggering
behaviors of each accident. Based on these records, five types of initial
events were selected for classification, which are stranding/grounding, hull
damage, others, capsizing/listing and foundering/sinking, sorted from the
largest proportion to the smallest (Fig. 1a). The initial event labeled
others in the classification includes report keywords such as “machinery
damage due to heavy weather”, “cargoes shifting due to rough seas” and
“fatalities in heavy weather conditions”, which are all related to bad
weather. Note that the classification shown in Fig. 1a is based not on a
detailed trigger factor but on a general result. For instance, when the ship
accidents are classified as stranding/grounding or foundering/sinking, the
vessels may have suffered from various types of dangerous seas or bad
weather, including parametric rolling, extreme slamming, bending and
torsional stresses and/or green water on deck, all of which all can reduce a
ship's stability and consequently cause stranding/grounding or sinking.
Different types of ships respond differently when they encounter potentially
dangerous sea conditions because of their different structures and functions.
Among the 755 cases, general cargo vessel types experienced the highest
proportion of accidents (32.3 %) in rough weather and severe sea states,
followed by bulkers and fishing vessels. Collectively, these data highlight
the types of ships that may require more attention during shipping activities
(Fig. 1b).
Figure 2 presents the spatial distribution of the 755 cases in terms of
occurrence density. To construct the ship accident density graph, the
research area was divided into 188 325 raster cells with a cell size of
50 km × 50 km. Then, a circle area with a radius of 500 km was
defined as the region around each cell center. The number of ship accidents
that fell within each region was summed and divided by the area of the
region, which provided the ship accident density. Additionally, 58 ship
accidents that occurred in swell sea states have been superimposed as blue
dots. The areas of deeper colors in the map reflect a higher density of ship
accidents. Clearly, these accidents are densely distributed in the North
Atlantic Ocean, the North Indian Ocean and the west Pacific Ocean, which
represent areas that coincide with the major shipping routes of Asian,
European and North American countries.
Figure 2 shows that few accidents occurred in the open sea, although this
result may have been related to the limited data recorded in the IMO database
on severe open sea accidents that occurred from 2001 to 2010.
Geographical distribution of the ship accident density according to
the 755 weather-related cases. The superimposed blue dots indicate the ship
accidents (58 cases) that occurred in a swell sea state.
Analysis of the sea state during ship accidents
As discussed in the Introduction, the co-occurrence of wind sea and
swell conditions is considered a potential causal factor that leads to
dangerous sea states for ships. In this section, we focus on 58 swell-related
cases to discuss the sea state characteristics associated with wind sea and
swell conditions. The sea states are described by three parameters:
significant wave height, wave period and wave direction. Sea wind does have a
significant impact on shipping safety, and in many cases the high waves
induced by wind can cause serious ship casualties. However, in this study, we
focus on the impacts of sea state on shipping safety when both wind sea and
swell are present. Swells are long waves propagating far from their
generation sources and are therefore no longer affected by the original sea
wind. Consequently, in this study, the relation between sea wind and ship
accidents was not considered.
Significant wave height
In terms of swell-related cases, both the total sea wave height and swell
wave height (Hsw) were analyzed. Moreover, the percentage of
swell wave energy relative to the total sea energy was used in the analysis.
Figure 3a shows the distribution of these values. The bar chart indicates
that almost half of the cases occurred in an Hs range of
0–3 m, which is not high enough
to warrant a rough-sea warning. However, the proportion of the swell wave
energy (i.e., the line in the graph) within this range is greater than
50 %; thus, when ships sail in relatively low sea states, the increased
contribution of swells may lead to dangerous sea states that threaten
shipping safety. Along with an increase in Hs, the proportion of
swell wave energy relative to the total sea energy remains at approximately
30 %, which reflects the increasing contribution of wind sea to worsening
sea conditions when Hs is greater than 3 m. In general, almost
half of the swell-related cases occurred at Hs values smaller
than 3 m, which suggests that high wave height is not the only critical
factor that triggers ship accidents. Indeed, other parameters may also play
pivotal causal roles in these accidents. Therefore, additional wave
parameters, including the wave period and wave direction, are subsequently
examined for these accidents.
Incidence rate of ship accidents: (a) at different
significant wave heights (Hs, bar chart) and the proportional change
in swell energy in the total sea (polygonal line); (b) with wave
period differences (ΔT, bar chart) and the mean wave period (T,
polygonal line); (c) with wave direction differences (ΔD)
and ΔT; and (d) with ΔD and the value of the
wave steepness of the total sea (S).
Mean wave period
Figure 3b depicts the relationship between the occurrence of specific ship
accidents and wave period differences (bar charts) between swells and wind
sea (ΔT, i.e., the mean period of the swell minus the mean period of
the wind sea). Furthermore, the mean wave period of the total sea (T, solid
line) is also plotted in the graph. Approximately two-thirds of the cases
occurred in sea states where ΔT was less than 3 s and the value of
T approached 7 s, which represents a close wave period for swell and wind
sea conditions in most cases. In other cases, the value of T was larger
than 8 s when ΔT was larger than 3 s. On the whole, an upward trend
can be observed with an increase in ΔT, except for a slight
fluctuation between 4 and 5 s. Overall, a close mean wave period (ΔT < 3 s) between swell and wind sea in a co-occurring sea state
is an important factor for shipping accidents.
Mean wave direction
As noted in the Introduction, previous theoretical studies and ship accident
analyses have indicated that crossing sea states (particularly crossing swell
and wind sea states) may induce high waves and generate dangerous sea
conditions. To investigate this issue further, the mean wave direction
differences between the swell and wind sea (ΔD, i.e., the absolute
value of the mean swell direction minus the mean wind sea direction) for all
the swell-related ship accidents have been analyzed. Approximately half of
the cases (55 %) exhibited ΔD values less than 30∘
(Fig. 3c), and the values of ΔT (indicated by the solid line
superimposed on the bars) within this range were approximately 3 s before
decreasing to 1.8 s at ΔD values ranging from 30 to 40∘.
During swell and wind sea interactions, the rate of change in swell energy
under the influence of wind sea energy (Tamura et al., 2009) reaches a
maximum at approximately 40∘ (Masson, 1993). The ΔD range of
30 to 40∘ for the lowest value of ΔT (1.8 s) demonstrates
strong coupling between two waves. However, 45 % of the accidents were
associated with ΔD values larger than 30∘. An angle of
30∘ appears to be a critical point for ship accidents because a
rising trend in the ΔT line begins at this point. As the angle
increases, the ΔT values decrease to below 3 s, and the sea state
can be more easily transformed into a crossing sea state (Li, 2016; Onorato
et al., 2010), which could pose a risk for ships.
Figure 3d is identical to Fig. 3c except that the wave steepness of the total
sea (S) has been added to the bar chart. In the present study, the wave
steepness of the total sea is calculated via S=2πHs/gT2. Along with an increase in ΔD, a rising trend in
wave steepness can be observed, although a slight fluctuation appears from
40–50∘. Wave
steepness appears to be positively correlated with ΔD, particularly
when ΔD of approximately 50∘. This condition is associated
with a crossing sea state with a close wave period. Overall, large direction
angle and wave steepness values appear to generate dangerous sea state
conditions.
Time series of sea surface wind and sea state at the ship accident
location over 24 h for the case occurred at 20:30 UTC on 24 February 2009.
At the top of the figure, wind vanes and numbers indicate wind direction and
wind speed at the accident location. Three polylines that represent the significant wave height of total sea, wind sea and
swell, respectively, in grey, green and
blue. The numbers in green and blue are the mean wave
period of wind sea and swell. At the bottom of the figure, the arrows that in
green and blue indicate the mean wave direction of wind sea and swell,
respectively.
The sea state in the vicinity of the case that occurred on
24 February 2009. Panels (a), (c), (e) and (b),
(d), (f) are the model results at 18:00 and 21:00 UTC,
respectively; panels
(a) and (b) are Hs, (c) and
(d) are ΔT and (e) and (f) are ΔD. The accident location is marked with a black star. Arrows in
the plots of the third row represent the wave directions of the swell (black)
and wind sea (light grey).
Sea states of typical cases
Based on the statistical analysis of the sea state characteristics presented
above, we preliminarily conclude that close wave periods and oblique angles
between co-occurring wind sea and swell conditions play important causal
roles in ship accidents. In this section, two cases are presented to reveal
the dynamic processes underlying co-occurring wind sea and swell conditions
during ship accidents. One case occurred in a relatively low sea state,
while the other case occurred in a high sea state.
The first ship accident case occurred at approximately 20:30 UTC on
24 February 2009. The Korean tug Chong jin capsized at 34∘8′ N,
124∘131′ E. To thoroughly investigate the possible cause of this
accident, the sea state is analyzed in detail. Figure 4 shows the time series
of the sea state and sea surface wind at the accident location over 24 h. At
the top of the graph, wind vanes and numbers represent the sea surface wind
direction and wind speed. The lines in the middle of graph represent
significant wave heights of the swell (blue), wind sea (green) and total sea
(grey). The mean wave period of the swell and wind sea are
annotated in the same colors as the wave height. The wave directions of the
swell and wind sea are also presented at the bottom of the graph.
The sea surface wind fields at 18:00 (a) and 21:00 UTC
(b) of the first case that occurred at 20:30 UTC on
24 February 2009. The accident location is marked with a black star. The
arrows represent the sea surface wind directions.
At 12:00 UTC on 24 February, approximately 8 h before the ship accident,
the sea was low with an Hs of 0.7 m. The dominant wave was
a swell
as it moved to the northwest, and the wind sea was fairly mild. At 15:00 UTC,
the wind sea began to develop rapidly due to sudden changes in the wind field
(the wind direction changed from east to northwest, and the wind speed
increased from 3.2 to 8.4 m s-1).
Along with the continual growth of the wind sea, the difference between
Tsw and Tws decreased. The wave directions of the
wind sea and swell at this moment were almost opposite. Soon thereafter, at
18:00 UTC, the wind speed rose continuously and reached 12.7 m s-1,
while the wind direction tended to the north. The Hws reached
1.4 m, while the Hsw was still less than 1 m. When the accident
occurred (close to 21:00 UTC), the swell direction was distinctly different
from that at 18:00 UTC, as it had shifted from southeasterly to
northwesterly. As the sea became rough, both Hs and
Hws increased rapidly by 2 m. Similar growth occurred in the
wave periods, specifically, from 5.7 to 6.2 s for the swell and from 4.3 to
5.4 s for the wind sea. As the wave period of the wind sea became close to
the swell, the difference between them (ΔT) decreased to 1 s.
Time series of sea surface wind and sea state at the ship accident
location over 24 h for the case that occurred at 14:45 UTC on
10 January 2010. The lines, symbols and numbers in the figure have the same
meanings as those presented in Fig. 4.
The sea state in the vicinity of the case that occurred on
10 January 2010. Panels (a), (c), (e) and (b),
(d), (f) are the model results at 18:00 and 21:00 UTC,
respectively; panels
(a) and (b) show Hs, (c) and
(d) show ΔT and (e) and (f) show ΔD. The accident location is marked with a black star. Arrows in
the plots of the third row represent the wave directions of the swell (black)
and the wind sea (light grey).
Figure 4 shows variations in the sea state at the accident location, while
sea state in the vicinity of the accident is presented in Fig. 5. The
diagrams in the first column show the wave model results at 18:00 UTC, 3 h
before the accident, whereas the second column shows the results at 21:00 UTC,
close to the accident occurrence time. From top to bottom, the sea state
parameters are Hs, ΔT and ΔD. Within 3 h
of the ship accident occurrence, the Hs increased slightly by
approximately 0.5 m across a large area proximal to the accident location.
Due to the growth of the wind sea forced by local wind (refer to Fig. 6), the
wave period difference ΔT decreased by approximately 2 s in the
area. The most distinct variation in sea state in the area is ΔD. At
18:00 UTC, the swell direction in the area was southeasterly, propagating
from other areas to the ship accident location. However, after 3 h,
the swell direction changed to northwesterly, the opposite of the direction 3 h before. Based on the wave direction of the wind sea in the area, we
find that the northwesterly swell system (at 21:00 UTC) likely transformed
from a fully developed local wind sea after the wind direction turned to the
north at 15:00 UTC. Consequently, ΔD narrowed greatly from 187 to
59∘ at 21:00 UTC, further decreasing to 30∘ at 03:00 UTC on
25 February.
The change in the wind is considered a key factor in this accident. The
turning point appeared at 15:00 UTC on the 24 February, when the sea wind changed significantly in terms of both magnitude
and direction. Afterwards, the continuous force of the sea wind induced the
growth of the wind sea. The “old” wind sea was transformed into a young
swell, which led to a marked decrease in ΔD. The closer wave period
and narrower direction angle between the wind sea and the swell produced a
resonance effect. Some experimental studies suggest that a swell with the
same direction as the wind will play a role in suppressing the growth of a
wind sea (Philips and Banner, 1974; DoneJan, 1987). As the swell direction
tends to be the same as the wind direction, the development of a wind sea is
suppressed. Meanwhile, the lower ΔD and ΔT provided
conditions for wave energy transformations from the wind sea to the swell
(Masson, 1993). Closer wave periods and narrower wave spectrum provide ideal
conditions for the transformation of wave energy, and the resulting
energy-enhanced swell represents a great threat to shipping safety.
Based on the analysis presented above, we found that the crossing sea state
of swell and wind sea may have triggered the accident. Moreover, the swell
that had a significant impact on the ship accident was transferred from the
local wind sea instead of the “old” one that propagated from a distant
storm. In the numerical wave modeling, discrimination of “swell” and
“wind sea” occurs in the post-processing step through wave spectral
partitioning. This spectral partitioning arbitrarily divides the
two-dimensional wave spectrum into wind sea and swell components (Gerling,
1992; Hanson and Phillips, 2000) based on some criteria, and the integrated
wave parameters of the corresponding wind sea and swell are subsequently
derived. These swell and wind sea values are useful for depicting the trend
of a sea state and can significantly contribute to many applications, such
as forecast and analysis of surface wave conditions in shipping lanes and
coastal areas, as in the statistical analysis and the case study presented
above. However, spectral partitioning may have trouble distinguishing among
the essential attributes of a wave field when both wind sea and swell or
multiple swell systems are present (Hanson and Phillips, 2000).
In the second case, a bulk carrier with a gross tonnage of 36 546 sailed
from Davant, United States, to Hamburg, Germany, on 10 January 2010 and at
14:45 UTC encountered extremely poor weather, with westerly winds of more
than 20 m s-1 and southwest waves of more than 9 m. As a result, the
ship was seriously damaged at 46∘14′ N, 41∘29′ W. The
time series of the sea surface wind and sea state over 24 h for this case
are presented in Fig. 7. The lines, symbols and numbers in the figure have
the same meanings as those presented in Fig. 4.
Based on the sea surface wind field on 10 and 11 January over a large area in
the vicinity of the ship accident (not shown here), the area was experiencing
an extratropical cyclone. The Hs (over 8 m) and sea surface
wind speed (higher than 20 m s-1) presented in Figs. 7 and 8 also
reveal the bad weather situation when the ship accident occurred. From
03:00 UTC until the approximate time of the ship accident, the wind sea grew
under the force of the continuously increasing sea surface wind speed, as
evidenced by the increases in Hws and Tws. The time
at which the ship accident occurred, i.e., approximately 15:00 UTC, likely
corresponds to a turning point in the wind sea growth. Before then, the
Hws continuously increased from 3.7 m at 03:00 UTC to 8.2 m at
15:00 UTC. Simultaneously, the Tws increased from 8 to 11 s.
After the turning point, both the wave height and wave period of the wind sea
started to decrease gradually. More interesting is the variation in the swell
in the area. At the ship accident location, both Hsw and
Tsw gradually increased from 03:00 UTC to 21:00 UTC on
10 January. However, the time series of the mean wave direction of swell
shown in Fig. 7 suggests that the swell situation was very complicated. At
03:00 and 06:00 UTC on 10 January, the easterly swell was the dominant swell
system. The mean wave direction of the swell subsequently gradually turned to
southerly and southwesterly, leading to a decrease in the ΔD from 107
(03:00 UTC) to 59∘ (15:00 UTC).
The sea state maps shown in Fig. 8 can better resolve the variations over the
course of a few hours. The Hs graph clearly shows that the higher
wave area increased in size within 3 h. The ΔT map suggests that the
swell and wind sea had a close mean wave period of less than 1 s in a large
area surrounding the ship accident location. In fact, the ΔT derived
from the time series presented in Fig. 7 suggests that it retained a quite
small value of 0–1 s for
approximately 15 h. The ΔD graphs show that at least two swell
systems existed in the area, with one being southerly/southwesterly and the
other being southeasterly. This situation led to a high ΔD area and a
low ΔD area. The boundary of the two ΔD areas at
50–60∘ was
close to the accident area. Based on both Figs. 7 and 8, the ΔD was
becoming smaller during the event, not only at the location of the ship
accident but also across a large area. The time series of the swell wave
direction may misleadingly suggest that the swell direction changed suddenly
within a few hours. However, this was probably not the true situation. As
stated in the analysis of the first case, discrimination of the swell and
wind sea in the wave model post-processing step is an arbitrary process. The
wave model product used in this study only provides one swell component,
which cannot represent the complete swell state in a complicated situation.
In this case, as mentioned above, the area was experiencing an extratropical
cyclone that not only featured a rotational wind field but also moved in a
certain direction, thereby generating swells propagating in various
directions. Thus, multiple swell components might have coexisted in certain
observation locations. However, spectral partitioning cannot resolve the
complete swell components if only one (dominant) swell system was retained in
the wave product, such as that used in this study. This situation can lead to
misunderstanding data, suggesting that the swell direction changed suddenly.
Nevertheless, in the present case, large waves (higher than 8 m) may have
been a factor that threatened shipping activities. The additional causes of
the accident were likely related to the decreasing wave period and wave
direction changes that led to co-occurring wind sea and swell.
Summary and discussion
The present study is motivated by a desire to thoroughly evaluate the sea
state conditions during ship accidents. It aims to establish more accurate
and effective maritime warning criteria and better understand the mechanisms
underlying extreme waves. To this end, 10 years of ship accidents that
occurred in rough weather or sea conditions were chosen from the IMO ship
accident database and then analyzed.
Based on the selected 755 weather-related accident cases, an accident
occurrence density map was generated. The ship accidents presented a dense
distribution in the North Atlantic Ocean, the North Indian Ocean and the
west Pacific Ocean because of the associated severe weather and sea state
conditions, and the locations of these accidents coincided with major
shipping routes. In terms of ship type in the casualty reports, the most
frequent ships involved in these accidents included general cargo ships,
bulkers and fishing vessels. Of the reported initial events,
stranding/grounding and hull damage were the most prominent.
Strong winds and high waves can cause heavy sea states, which are indeed the
primary risk factors for maritime activities. However, the potential dangers
of swells with relatively low wave heights are generally underestimated.
Notably, our analysis of the 58 swell-related accidents indicated that
52 % of the cases occurred in relatively low sea state conditions with
Hs values smaller than 3 m and that swells provided the
dominant wave energy in these conditions. A further analysis of these
accidents suggested that co-occurring wind sea and swells, especially when
the differences in their mean wave periods and mean wave directions meet
certain conditions, may lead to hazardous seas and pose a risk to shipping
activities. Among the 58 swell-related ship accidents, approximately 62 %
of the cases have ΔT values of less than or equal to 3 s.
Interestingly, for all these cases, the averaged ΔT for different
ΔD is approximately 3 s and is smallest, i.e., 1.8 s, when ΔD is between 30 and 40∘. When ΔD is between 30 and
40∘, the crossing sea state has a high potential of being composed of
a wind sea and a swell transformed from the local wind sea, as was the
situation in the first case. Overall, the statistical analysis reveals that
ΔT values less than 3 s and ΔD values smaller than
60∘ are two important factors of crossing seas that can lead to wave
interaction between the wind sea and the swells, consequently generating
dangerous seas that threaten shipping safety. Therefore, this finding can
potentially be used as a warning criterion in forecasts for shipping lanes.
According to the report records, many ship accidents have occurred in
offshore areas yet few have occurred in open sea areas. Although the
accuracy of the model data is fairly high, the coastline resolution used in
the dataset is relatively coarse. As a result, a bias may exist in the
offshore areas. Therefore, the diagrams shown in the statistical analysis
appear to be somewhat noisy. However, even under the same sea
state, different ship types should respond differently. This phenomenon may
also lead to a variety of statistical results in a variety of situations.
Nevertheless, the statistical results still reveal noteworthy
characteristics of dangerous sea state conditions.
The sea states of the two case studies meet the general conditions of a
possible occurrence of dangerous waves based on the statistical analysis,
whereas they also presented different situations. In the first case, the
overall sea state was relatively low, at 2.0–2.5 m. However, the sea
surface wind direction changed significantly approximately 6 h before the
accident. The gradually enhanced northerly and northwesterly wind forced the
growth and development of a wind sea, which later transformed into a swell.
The “new” swell therefore had a markedly different direction from that
present approximately 6 h before. The freshly generated swell and wind sea
both had smaller ΔT and ΔD values, producing favorable
conditions for coupling between the swell and the wind sea and leading to
possible generation of waves dangerous to ship safety. In the second case,
the overall sea state was quite rough, with an Hs higher than
8 m. Although the sea surface wind speed increased gradually before the
accident occurred, the sea surface wind direction remained southwesterly. However, although ΔT remained quite small (approximately
1 s) for more than 12 h, ΔD exhibited significant variation,
decreasing from more than 100∘ at 9 h before to approximately
60∘ when the accident occurred. A plausible explanation is that the
area was experiencing an extratropical cyclone, which had a rotational sea
surface wind field and also moved continually. This cyclone therefore
generated swells that propagated to multiple directions. Detailed analysis of
the sea states associated with these two specific cases further demonstrates
that both oblique wave directions and similar wave periods between the wind
sea and the swell are two key factors of crossing seas that can lead to the
generation of sea state dangerous to shipping safety.
Finally, ship safety could be improved if the major contributors to
dangerous sea states are identified and monitored, especially along major
shipping lanes. In future work, the use of multi-source data will undoubtedly
provide a more complete description of these complex phenomena.
The global ship accident database and the ERA-20C Ocean Wave dataset
were accessed from the IMO Global Integrated Shipping Information System
(https://gisis.imo.org/Public/Default.aspx/) and the ECMWF Public Datasets
web portal
(http://apps.ecmwf.int/datasets/data/era20c-wave-daily/type=an/),
respectively. The data are publicly accessible, readers can download the data by the website we provided.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was partially supported by grants from the National Natural
Science Foundation of China (no. 41406198), Special Project of Chinese
High-Resolution Earth Observation System (no. 41-Y20A14-9001-15/16),the
Hainan Key S&T Programme (no. ZDKJ2016015) and the “Pioneered Hundred
Talents Program, Chinese Academy of Sciences”.
Edited by: Thomas Glade
Reviewed by: Thomas Bruns and Weizeng Shao
ReferencesBertotti, L. and Cavaleri, L.: The predictability of the “Voyager”
accident, Nat. Hazards Earth Syst. Sci., 8, 533–537,
10.5194/nhess-8-533-2008, 2008.
Bruns, T., Lehner, S., Li, X.-M., Hessner, K., and Rosenthal, W.: Analysis of
an event of “Parametric Rolling” onboard RV “Polarstern” based on
shipborne wave radar and satellite data, IEEE J. Ocean. Eng., 36, 364–372,
2011. Cavaleri, L., Bertotti, L., Torrisi, L., Bitner-Gregersen, E., Serio, M., and
Onorato, M.: Rogue waves in crossing seas: the Louis Majesty accident, J.
Geophys. Res., 117, C00J10,
10.1029/2012JC007923, 2012.
DoneJan, M. A.: The effect of swell on the growth of wind waves, Johns
Hopkins APL Tech Dig, 8, 18–23, 1987.
Faulkner, D.: Shipping safety: a matter of concern, in: Proceedings-Institute
of Marine Engineering Science and Technology Part B Journal of Marine Design
and Operations, 5, 37–56, 2004.
Gerling, T. W.: Partitioning sequences and arrays of directional ocean wave
spectra into component wave system, J. Atmos. Ocean. Tech., 9, 444–458,
1992.Guedes, S. C., Bitner-Gregersen, E., and Antao, P.: Analysis of the frequency
of ship accidents under severe North Atlantic weather conditions, in:
Proceedings of the Conference on Design and Operation for Abnormal Conditions
II, RINA, London, UK, 221–230, 2001.
Hanson, J. L. and Phillips, O. M.: Automated analysis of ocean surface
directional wave spectra, J. Atmos. Ocean. Tech., 18, 277–293, 2001.
In, K., Waseda, T., Kiyomatsu, K., and Iyama, K.: Analysis of a marine
accident and freak wave prediction with an operational wave model, in:
Nineteenth International Offshore and Polar Engineering Conference.
International Society of Offshore and Polar Engineers, 21–26, 2009.
Kharif, C., Pelinovsky, E., and Slunyaev, A.: Introduction, in: Rogue Waves
in the Ocean, Springer, Berlin, Germany, 1–10, 2009.
Li, X.-M.: A new insight from space into swell propagation and crossing in
the global oceans, Geophys. Res. Lett., 43, 5202–5209, 2016.
Masson, D.: On the nonlinear coupling between swell and wind waves, J. Phys.
Oceanogr., 23, 1249–1258, 1993.
Onorato, M., Proment, D., and Toffoli, A.: Freak waves in crossing seas, Eur.
Phys. J.-Spec. Top., 185, 45–55, 2010.
Philips, O. M. and Banner, M. L.: Wave breaking in the presence of wind drift
and swell, J. Fluid Mech., 66, 625–640, 1974.Poli, P., Hersbach, H., Tan, D. G. H., Dee, D. P., Thepaut, J-J., Simmons,
A., Peubey, C., Laloyaux, P., Komori, T., Berrisford, P., Dragani, R.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.:
ERA report series No. 14: The data assimilation system and initial
performance evaluation of the ECMWF pilot reanalysis of the 20th-century
assimilating surface observations only (ERA-20C),
http://www.ecmwf.int/en/elibrary/11699,
(last access: 26 September 2016), 2013.Tamura, H., Waseda, T., and Miyazawa, Y.: Freakish sea state and
swell-windsea coupling: numerical study of the Suwa-Maru incident, Geophys.
Res. Lett., 36, L01607,
10.1029/2008GL036280, 2009.
Toffoli, A., Lefèvre, J. M., Bitner-Gregersen, E., and Monbaliu, J.:
Towards the identification of warning criteria: analysis of a ship accident
database, Appl. Ocean Res., 27, 281–291, 2005.
Waseda, T., Tamura, H., and Kinoshita, T.: Freakish sea index and sea states
during ship accidents, J. Mar. Sci. Technol., 17, 305–314, 2012.Waseda, T., In, K., Kiyomatsu, K., Tamura, H., Miyazawa, Y., and Iyama, K.:
Predicting freakish sea state with an operational third-generation wave
model, Nat. Hazards Earth Syst. Sci., 14, 945–957,
10.5194/nhess-14-945-2014, 2014.