Marine Geology 406 (2018) 132–141
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Observations and analysis of giant sand wave ﬁelds on the Taiwan Banks, northern South China Sea ⁎
Jieqiong Zhoua, Ziyin Wub, , Xianglong Jina,b, Dineng Zhaob, Zhenyi Caoc, , Weibing Guanc a
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China Key Laboratory of Submarine Geosciences and Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China c State Key Laboratory of Satellite Ocean Environment Dynamics and Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Sand wave Giant sand wave Characteristics Bedform migration Sediment transport Taiwan Banks
Understanding sand wave dynamics is of importance in marine science and also has relevance in engineering. Giant sand wave ﬁelds (wave heights up to 10 m) are not common and thus have been relatively rarely studied. This paper presents the ﬁrst study on bedform changes related to giant sand wave geomorphology on the Taiwan Banks. Using data from three repeated multi-beam bathymetric surveys (2011, 2012 and 2013) and a spatial cross-correlation method, diﬀerent sand transport patterns are obtained. Classical crest-perpendicular migration is observed in the small sand waves with migration rates of 1–5 m/a, and migration is parallel with the current direction. However, the giant sand waves are immobile over the observed periods, and along-crest transport sand transport has been observed that is perpendicular to the predominant current direction. Statistical results support these ﬁndings and thus illustrate a type of sediment transport pattern that diﬀers from other sand wave ﬁelds around the world. It is concluded that the giant sand waves act as a bathymetric obstacle, thus changing the direction of bottom currents ﬂowing over them, resulting in a change in sediment transport pattern on the Taiwan Banks.
1. Introduction The bottom of many tide-dominated continental shelves is covered with regular large-scale patterns of elongated bedforms (Oﬀ, 1963; Liu et al., 1998; Liao et al., 2008; Wu et al., 2010). Their wavelengths range from several hundred meters to a few kilometers, with amplitudes in the order of meters (Allen, 1968). Their formation and migration are closely related to tidal currents, with sand ridges (sand banks) forming as rhythmic series oriented parallel with the current direction, while sand waves form perpendicular to the current direction (Oﬀ, 1963; Beldenson et al., 1982; Stride et al., 1982; Amos and King, 1984). Sand waves are dynamic, with the ability to migrate tens of meters per year, thus posing potential hazards to engineered coastal structures such as navigation channels, pipelines and wind farms (McCave, 1971; Berne et al., 1988; Dorst et al., 2011). Hence, sand wave behavior is of interest to marine scientists and engineers. There are two basic research approaches used when investigating sand wave dynamics: seabed observations and models. Models provide generic knowledge of sand waves and can be used to study the eﬀects of various hydrodynamic components (i.e., Hulscher, 1996; Németh et al., 2002; Besio et al., 2003; Németh et al., 2007; Campmans et al., 2018).
Observations provide more direct information on the actual bedform shapes and changes over time at a measured location (Terwindt, 1971; Langhorne, 1981; Williams, 1995; Anthony and Leth, 2002). With the increasing resolution and positioning accuracy of observational equipment in recent years, remote sensing of the seabed has become more reliable (Knaapen, 2005; Dorst et al., 2009; Van Landeghem et al., 2009). Sand wave migration is usually expressed as horizontal displacement over time, achieved by comparing repeated surveys in both plane (crest) and cross-sectional (proﬁle) shapes (i.e. Van Dijk and Kleinhans, 2005; Barnard et al., 2011). However, these lack comprehensive analysis of 3-D bathymetric data. To improve this methodology, researchers have adopted an eﬀective cross-correlation method to detect geodetic deformation of bedforms (Duﬀy, 2005). The migration vectors obtained can be overlain upon bathymetric data, thus providing more detailed characteristics of sand wave migration. This method has now been widely used and further developed with the use of observational data from Acoustic Doppler Current Proﬁler (ADCP) backscatter and remote sensing inversion (i.e., Buijsman and Ridderinkhof, 2008). More environmental parameters have also been introduced into this method (i.e., Damen et al., 2018). Many observational studies indicate that sand wave migration is more complex than simply crest-
Corresponding authors. E-mail addresses: [email protected]
(Z. Wu), [email protected]
https://doi.org/10.1016/j.margeo.2018.09.015 Received 14 September 2017; Received in revised form 16 September 2018; Accepted 24 September 2018 Available online 25 September 2018 0025-3227/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
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3. Data and methods
perpendicular migration, with both oblique and rotated migration having been observed in complex bathymetries (Duﬀy, 2005; Barnard et al., 2006; Fenster et al., 2006). Sand waves with meter-scale wave heights are very common, but giant sand waves (wave heights up to 10 m) are very rare. Such highamplitude bedforms are known primarily from San Francisco Bay (Barnard et al., 2006) and Georges Bank (Jordan, 1962; Todd and Valentine, 2012). The giant sand wave ﬁeld on the Taiwan Banks was ﬁrst reported in the 1970s (Boggs, 1974), although subsequent studies developed slowly. This was likely related to limited data, resources and development of appropriate methods (Zhang, 1988; Lan et al., 1991; Liu et al., 1998; Cai et al., 2003; Hu et al., 2013). Remote sensing techniques are most eﬀective for sand wave studies on the Taiwan Banks because of the shallow water depths and complex topography (Fan et al., 2009; Yang et al., 2010; Zhang et al., 2014). Multi-beam bathymetric data from recent studies has revealed co-existing sand waves on two distinct spatial scales on the Taiwan Banks. Yu et al. (2015) discussed the characteristics and distribution of the large sand waves, while Bao et al. (2014) focused on classiﬁcation of the small sand waves. In terms of sand wave evolution, Du and Gao (2012) proposed a 1-D model, while other studies estimated sediment transport and sand migration rates empirically, with results in the 2–20 m/a range (Du et al., 2010; Lian and Li, 2011). Most studies on the Taiwan Banks lack repeated high-resolution bathymetric data, resulting in a lack of comprehensive coverage of sand wave behavior in the study area. The purpose of this study is to present analysis of the characteristics and migration patterns of the two types of sand waves present based on repeated observational data. This is done to investigate how sand waves and sediments behave and interrelate in the study area. This paper is organized as follows: We start with a brief description of the study area in Section 2. The bathymetric data and the methods used for analysis are introduced in Section 3. The results and interpretation are presented for the two types of sand waves in Section 4, and a discussion on sediment transport patterns of the co-existing giant and small sand waves is presented in Section 5. Finally, the conclusions are presented in Section 6.
3.1. Bathymetric data acquisition and model construction Three repeated multi-beam echo sounder surveys (R2Sonic 2024, 200–400 kHz range, with 256 beams per ping) were carried out in the study area in 2011, 2012 and 2013. The sounding accuracy of the surveys meets the requirements of the S-44 special order of the International Hydrographic Organization (IHO, 2008). The valid coverage width of the multi-beam swath reaches ~100 m, which is roughly three times the water depth in the study area. A Veripos DGPS (Veripos Ltd., Aberdeen, United Kingdom) was used for positioning, with a horizontal accuracy of ± 10 cm. The original multi-beam soundings were then processed using CARIS HIPS and SIPS software (Teledyne CARIS, Inc., 2014; Caris HIPS and SIPS, version 8.1.9; Laurel Technologies, Inc., 2013). This was achieved by: (a) applying a sound velocity correction; (b) applying a tide correction; (c) editing navigation data and altitude data; and (d) data cleaning. Ultimately, a digital bathymetric model (DBM) was constructed at an appropriate resolution (Zhao et al., 2015). Fig. 2 illustrates the distribution of the repeated multi-beam survey lines, wherein the “Line-west” represents the three repeated survey lines (2011, 2012, 2013), being 120 km in length and 120 m in width. The “Line-east” represents two repeated survey lines (2012, 2013), being 100 km in length and 120 m in width. DBMs with 1 × 1 m2 gridsize resolution were then constructed for the 2011, 2012, and 2013 bathymetric data of Line-west and Line-east. 3.2. Cross-correlation technique Sand wave migration is characterized not only by a shift in sand wave crest-lines but also by seabed deformation. Following Duﬀy (2005) and Buijsman and Ridderinkhof (2008), we use an eﬃcient cross-correlation technique to determine migration of sand waves on the Taiwan Banks. Given that there are two scatter datasets f(x,y), g(x,y) in space (Fig. 3a), a search window matrix is set with the size Wx × Wy. The cross-correlation technique calculates a correlation coeﬃcient within the window, and the normalized correlation coeﬃcient is given as:
2. Study area
Rk , l =
The Taiwan Banks is a shallow continental shelf feature located in the south Taiwan Strait, between mainland China and the island of Taiwan, covering an area of 13,000 km2. The average water depth of the Taiwan Banks is shallow (~20 m), and deepens gradually northward to the central Taiwan Strait. It deepens dramatically seaward to the continental slope (Fig. 1). It is a tectonically formed platform situated on a long-term uplifted plate (Ma and Liu, 1994), the top of which is covered by sediment. The sediments contain well-sorted medium to coarse sands, mixed with various shell debris and gravels (Lan et al., 1991; Cai et al., 2003). Winds over the Taiwan Banks are dominated by the East Asia monsoon, and tropical cyclones in summer and autumn aﬀect the weather signiﬁcantly. Maximum wave heights of 4.7 m (winter) and 6.5 m (summer) have been recorded. Several typhoons pass through the Taiwan Banks every year, causing signiﬁcant damage to the seabed patterns (Bao et al., 2014). Seasonal variations in the circulation are controlled by the China Coastal Currents, the northward South China Sea Current, and a branch of the Kuroshio Current (Jan and Chao, 2003; Wang et al., 2003; Hong et al., 2009) (Fig. 1). The tides on the Taiwan Banks are primarily irregular semidiurnal, and the M2 tide is a predominant constituent with a range of 0.8–1.0 m/s (Liu et al., 1998; Du et al., 2010). The clockwise tidal currents of the southern branch and the anticlockwise tidal currents of the northern branch of the M2 tide propagate from the Paciﬁc Ocean into the Taiwan Strait, and meet north of the Taiwan Banks.
∑x =x 0 ∑y =y 0 [f (x , y ) − f ][g (x − k , y − l) − gk, l ] W
∑x =x 0 ∑y =y 0 [f (x , y ) − f ]2 ∑x =x 0 ∑y =y 0 [g (x − k , y − l) − gk, l ]2 (1)
(−M ≤ k ≤ M , k ∈ Z ,−N ≤ l ≤ N , l ∈ Z ) where M and N are search parameters along the x- and y-axis, respectively. The search window is then moved along the x- and y-axis within the range of the search parameters to obtain a correlation coeﬃcient matrix R with size (2 M + 1) × (2 N + 1). Finally, the maximum correlation coeﬃcient within the matrix R is found, to locate two data points with “maximum correlation” within the two datasets. In this paper, the repeated DBMs in diﬀerent years are considered to be different datasets. The two data points with “maximum correlation” become the starting and end points of a vector (termed “migration vector”) that indicates the sediment transport pathway between two observed periods. Some pre-analysis of the cross-correlation analyzing ﬁelds are necessary so that suitable values for search window size and search parameters can be chosen. Two types of sand waves at distinct spatial scales are developed in the study area: giant sand waves (with a length of ~750 m), and small sand waves (with lengths of 30–100 m). Preanalysis indicates that the former are almost immobile, thus, the crosscorrelation technique is mainly used to detect migration of the active small sand waves. For quality control of the results, the sensitivity of the correlation technique has been studied for window sizes of 30, 60, 133
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Fig. 1. Location and elevation/depth data of the study area on the Taiwan Banks, northern South China Sea.
the x- and y-axis, respectively, were selected.
90, 120, 150 and 180 m. Following Buijsman and Ridderinkhof (2008), the sensitivity is indicated by standard deviations (σu,σv) of migration vectors along the x- and y- axis. The percentage of migration vectors with a correlation coeﬃcient < 0.9 is plotted as a function of window size (Fig. 3b). Such a plot shows a decrease in the standard deviations and the percentage of “bad ﬁt” correlation with larger window size. Conceptually, the window size must be large enough to just encompass a unique area of seabed (Duﬀy, 2005). Therefore, the window size of 30 m is not able to track the large bedforms correctly, while window sizes of 60 m or 90 m are much better. On the other hand, the window sizes must be small enough to track smaller bedforms (Buijsman and Ridderinkhof, 2008); thus a window size of 60 m was chosen. The search parameter is determined by the migration rates over the study area (1–5 m/a, see Section 4.2.2). Consequently, a window size (Wx × Wy) of 60 × 60 m and search parameters (M, N) of ± 10 m along
3.3. Numerical simulation of tidal currents To evaluate the relationship between tidal currents and sand wave migration on the Taiwan Banks, the M2 tide was simulated in the MIKE 21 HD Model (DHI, 2014; MIKE 21, Release 2014; DHI China, 2014). The MIKE 21 HD Model is a 2-D numerical model constructed using the ﬁnite-diﬀerence method. The controlling equations of this model include the continuity equation and the momentum equation, on which the numerical calculations of ﬂow ﬁeld and water-level variation over the model area can be carried out. The open boundary of the model was extracted from the Global Ocean Tide Mode provided by DTU Space (http://www.space.dtu.dk/English/Research/Scientiﬁc_data_and_ models/Global_Ocean_Tide_Model.aspx) to set initial tides for the 134
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Fig. 2. Details of the study area showing (a) division into North and South subareas, separated by the giant sand wave Orientation Changing Limit (OCL); (b) and (c) shows a comparison of sand wave crest-lines (orange lines) extracted from satellite images (base map) with those extracted from high-resolution multi-beam bathymetry. Sand wave crest-lines extracted from satellite images coincide well with the giant sand wave crest-lines from multi-beam bathymetry, while the small sand wave crest-lines cannot be detected because of limited resolution of the satellite images. The data of the extracted crest-lines from HJ-1A/1B sun glitter images are from the study of Zhang et al. (2014). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)
model. The drag coeﬃcient at the bottom is given by a 2-D Manning map that varies with water depth, and the eddy viscosity is a default value (Cs = 0.28).
depth at crest (d) ratio of ~1/1.
Based on satellite images and DBMs, sand wave crest-lines were extracted to study the orientation of the sand waves (Fig. 2a). In this paper, we use the extracted crest-lines of Zhang et al. (2014) from sun glitter images (HJ-1A/1B satellite). The crest-lines (orange) extracted from satellite images (gray base map) were overlain with the multibeam bathymetric data (Fig. 2b, c) to validate the possibility of using satellite images to study sand wave characteristics. The crest-lines extracted from satellite images coincide well with the giant sand wave
4.1. Orientation change of giant sand waves
High-resolution DBMs constructed in Section 3.1 reveal a detailed morphology of two types of co-existing sand waves on the Taiwan Banks: the small sand waves (~1.5 m in height, ~50 m in length) have developed in the troughs of the giant sand waves (~15 m in height, ~750 m in length). One of the most remarkable characteristics of the giant sand waves is their scale, with a sand wave height (H) to water 135
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Fig. 3. (a) Sketch of the cross-correlation technique workﬂow, where Wx, Wy represent window size, M, N are search parameters along the x- and y-axis, respectively, and R is a calculated correlation coeﬃcient matrix (compiled from Duﬀy, 2005); (b) Graph showing sensitivity analysis of the cross-correlation technique with the parameter of window size, where σu, σv are standard deviations of migration vectors along the x- and y- axis, respectively.
2015). (2) Sand waves are immobile within the observed period: the migration direction is determined by the asymmetry of sand wave proﬁles, given that sand waves tend to migrate in the direction faced by the steeper (lee) slope (McCave, 1971; Bartholdy et al., 2002).
crest-lines in the multi-beam bathymetric data, although the small sand wave crest-lines cannot be detected because of the limited resolution of the satellite imagery. Consequently, the satellite images were used only to study the characteristics of the giant sand waves. Fig. 2a illustrates the distribution of the extracted crest-lines from a total length of 16,000 km of giant sand waves developed in the study area. From north to south, the giant sand wave orientation changes from NW-SE (average direction of 132°) to W–E (average direction of 083°). A NE–SW giant sand wave orientation changing limit (OCL) is evident on the central Taiwan Banks; thus we divided our study area into two subareas: the North Sub-area (NS) and the South Sub-area (SS).
Considering the diﬀerences in their activities and behavior, the migration of giant and small sand waves is discussed separately.
4.2.1. Giant sand wave migration In the 2011–2013 period, giant sand waves were immobile. Moreover, the migration vectors around the giant sand waves suggest sediment migration parallel to the giant sand wave crests, indicating that proximal sediments tend to be transported along the crest-lines. Fig. 4 shows the distribution of migration vectors around the giant sand waves in a typical area of the NS in 2011–2012 and 2012–2013. The location of this area is shown in Fig. 2a. The crest-lines of the giant sand waves appear in a 3-D shape, with a mean NW–SE orientation. No small sand waves have been observed in this area. Small sand ripples developed oblique to the crest-lines on the slopes of the giant sand waves, consistent with along-crest transport of the sediments here. More results
4.2. Sand wave migration (2011−2013) Sand wave migration in the study area is determined and described based on the following two criteria: (1) Sand wave migrations are perceptible: the migration rate and direction of sand waves are determined by combining migration vectors calculated using the cross-correlation technique and shift of automatically extracted sand wave crest-lines in space (Zhou et al.,
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Fig. 4. Distribution of migration vectors around the giant sand waves in a typical area of the NS, calculated with the cross-correlation technique for 2011–2012 (top), and 2012–2013 (bottom) (see location in Fig. 2a). The base map is multi-beam bathymetric map in 2012. No small sand waves are developed in this area, and the sediments tend to migrate along the crest-lines of the giant sand waves.
Fig. 5. Transition of the giant sand wave proﬁles from north (Proﬁle AA′, steep northern slope) to south (Proﬁle BB′, steep southern slope) around the OCL (see location in Fig. 2a). The base map is multi-beam bathymetric map in 2012.
controlled by the local topography. When the small sand waves migrate forwards and meet the slopes of the giant sand waves, the migration vectors decrease and then change direction to along-crest of the giant sand waves (Figs. 6 and 7). In other words, sediment is not able to migrate over the crest of the giant sand wave but rather turns to become transported along-crest of the giant sand wave, which is consistent with the results of along-crest sediment transport described in Section 4.1.
of along-crest migration vectors of the giant sand waves are shown in Figs. 6 and 7 and are discussed in the following section. Fig. 5 shows the transition of giant sand wave proﬁles near the OCL. From north to south, it is evident that the steeper slopes of the giant sand wave proﬁles change from facing north (Proﬁle AA′ in Fig. 5) to facing south (Proﬁle BB’ in Fig. 5). It is indicated that, to the north of the OCL, the giant sand waves tend to migrate northwards, while to the south, they tend to migrate southwards. Therefore, the OCL not only divides giant sand waves based on characteristics but also divides them based on their migratory behavior.
4.2.3. Sediment transport of the bedforms Statistics of the migration vectors are presented in rose plots (Fig. 9) that reﬂect the characteristics of sediment transport directions at different locations. Each petal of the rose represents the proportion of migration vectors in each direction, and the magnitudes of migration vectors are shown in diﬀerent colors that correspond to velocities of 0–1, 1–2, 2–3, 3–4 and 4–5 m/a. In the NS, the predominant direction of migration vectors is NW–SE, with only minor proportions in other directions. Thus, the main sediment transport pathway tends to coincide with the crest-lines of the giant sand waves. In the SS, the major migration vectors are W–E and NE–SW. However, the proportion of vectors in the major direction decreases when those in other directions increase. Similar to the NS, one of the main sediment transport pathways in the SS coincides with the predominantly W–E orientation of giant sand waves. Additionally, the proportion of sediment transport increases in other directions, especially southward, reﬂecting the southward migration of the small sand waves in this area. Generally, migration of the giant sand waves has not been observed, and using the cross-correlation technique shows that nearby sediment is transported along the crest-lines of the giant sand waves within the observed periods over an annual timescale. The small sand waves migrate between two giant sand waves, northwards in the north and
4.2.2. Small sand wave migration In the giant sand wave troughs, small sand waves migrated perpendicular to their crest-lines at a rate of 1–5 m/a during both 2011–2012 and 2012–2013. More speciﬁcally, small sand waves migrated northwards in the NS (to the north of the OCL), and they migrated southwards in the SS (to the south of the OCL). Fig. 6 shows the distribution of migration vectors in a typical area of the NS during 2011–2012 and 2012–2013, and it shows a group of sand waves developed in the trough of a giant sand wave. Both the migration vectors and the shift of small sand wave crest-lines indicate northward migration in 2011, 2012 and 2013, which also coincides with the steeper northern slopes of the small sand wave proﬁles (Proﬁle CC′ in Fig. 6). On the contrary, Figs. 7 and 8 show the distribution of migration vectors of two typical areas along the Line-west and Line-east of the SS, respectively (locations of the areas shown in Fig. 2a). Both the migration vectors and the shift of the small sand wave crest-lines within the observed period indicate southward migration, which also coincides with the steeper southern slopes in the small sand wave proﬁles (Proﬁles DD′ and EE′ in Figs. 7 and 8, respectively). In addition, small sand wave migration and sediment transport are 137
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Fig. 6. Distribution of migration vectors in a typical area of the NS during 2011–2012 (top) and 2012–2013 (middle) (location shown in Fig. 2a). The base maps are multi-beam bathymetric maps in 2012 and 2013. Note the group of small sand waves developed in the trough of a giant sand wave. Both the migration vectors and the shift of the crest-lines of the small sand waves in 2011 (blue), 2012 (orange), and 2013 (gray) indicate northward (crest-perpendicular) migration direction, which also coincides with the steeper northern slopes of the small sand wave proﬁles (Proﬁle CC’). Along-crest sediment transport is also evident around the giant sand wave. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)
contrast to the existing knowledge of sand transport in sand wave ﬁelds. What is the cause of this distinct sediment transport pattern on the Taiwan Banks? One of the most remarkable characteristics of the giant sand waves is their scale. As described earlier, the ratio of H to d is ~1/1, and a total length of 16,000 km of giant sand waves occur in the study area (Fig. 2a). As such, we suggest that these giant bedforms constitute a wide-ranging obstacle, resulting in sediment transport not conforming with the major axis of the dominant tidal current, but instead the bedforms cause a direction change in the bottom currents ﬂowing over them (Fig. 10). This is further consistent with the diﬀerences in bedform activity: the giant sand waves are relatively stable compared with the small sand waves, with migrations not observed over an annual timescale, compared with small sand waves that migrate at a rate of 1–5 m/a. As a result, the giant sand waves form a barrier to migration of small sand waves over them. The rose plots in Fig. 9 also bear this out, given that along-crest sand transport predominates across the entire study area; the eﬀect of the giant sand waves overpowers the expected tidal current inﬂuence on sand transport. This along-crest direction is dominant around the giant sand waves, where the bottom currents are forced to ﬂow along the giant sand wave crest-lines. Crest-perpendicular sand wave migration only occurs in the small sand waves distal to the giant sand waves (Fig. 10). Thus, the widely developed giant sand waves on the Taiwan Banks act as a stable framework that limits and controls small bedform dynamics.
southwards in the south, at a rate of 1–5 m/a. The roses of migration vectors indicate that one of the dominant sediment transport pathway tends to be along the crest-lines of the giant sand waves, rather than perpendicular.
5. Discussion Why is sand transport perpendicular to the dominant tidal current? Application of the cross-correlation analysis to both the giant and small sand waves has revealed diﬀerent sand wave migration characteristics. Along-crest sediment transport has been observed in the giant sand waves (Fig. 4), and migration vectors change direction from crest-perpendicular to along-crest when they meet the suddenly shallow waters along the slopes of the giant sand waves (Figs. 6 and 7). Moreover, the roses of migration also show that along-crest sand transport constitutes the majority of migration vectors (Fig. 9). To investigate the mechanism of sediment transport in the study area, the migration vectors are correlated to the dominant M2 tide (Fig. 9). In the NS, the rectilinear M2 tidal current is distributed in a NE–SW direction, with an amplitude of 0.8–1.0 m/s. In the SS, the M2 tidal current ellipse is distributed in a N–S direction and has a greater amplitude (1.0–1.2 m/s) than in the NS. The minor axis of the M2 tidal current increases gradually compared with the NS. The orientation of the giant sand waves is perpendicular (in the NS) or nearly perpendicular (in the SS) to the M2 tidal current ellipses, as documented by their commonly transverse orientation. However, the statistical results of Fig. 9 indicate that sediments are transported predominantly alongcrest, which is perpendicular to the dominant tidal current. This is in 138
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Fig. 7. Distribution of migration vectors during 2012–2013 in a typical area of the Line-west of the SS, which coincides with the shift of crest-lines between 2012 (orange) and 2013 (gray). The base map is multi-beam bathymetric map in 2013. The asymmetry of their proﬁles (Proﬁle DD’) indicates crest-perpendicular southward migration (location shown in Fig. 2a). In addition, along-crest sediment transport can be observed in the giant sand wave. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)
trending giant sand waves, and to the south of the OCL (SS) the giant sand waves are mainly W–E-trending. (2) Based on three repeated multi-beam bathymetric surveys (2011, 2012, 2013) of the Taiwan Banks, sand wave behavior was investigated. Use of the cross-correlation technique reveals that, within the observed periods, small sand waves are mobile (migration rates of 1–5 m/a), with their crests orientated northwards in
(1) Two distinct spatial scales of sand waves co-exist on the Taiwan Banks. These are giant sand waves (~15 m in height, ~750 m in length) and small sand waves (~1.5 m in height, ~50 m in length). A NE–SW-trending sand wave OCL divides two distinct giant sand wave orientations. North of the OCL (NS) comprises mainly NW–SE-
Fig. 8. Distribution of migration vectors during 2012–2013 in a typical area of the Line-east of the SS, which coincides with the shift of crest-lines between 2012 (orange) and 2013 (gray). The base map is multi-beam bathymetric map in 2013. The asymmetry of their proﬁles (Proﬁle EE′) indicates southward migration (see location in Fig. 2a). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.) 139
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Fig. 9. Correlation of migration vectors with the M2 tide on the Taiwan Banks. The statistics of migration vectors are presented in rose plots that reﬂect the sediment transport pathway in diﬀerent locations of the study area. Each petal of the rose represents the proportion of migration vectors in each direction, and the migration velocities (0–1, 1–2, 2–3, 3–4 and 4–5 m/a) are shown in diﬀerent colors. The black arrows denote the major orientation of the giant sand waves, while the blue arrows show the major migration vector directions, and the red arrows indicate the orientations of the major axis of the dominant tidal ellipse. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)
the NS and southwards in the SS. Conversely, the giant sand waves are apparently immobile, and the sediment transport direction observed around them is along-crest. (3) Migration vectors correlated with the dominant tidal current indicate that sand transport is predominantly along-crest and perpendicular to the dominant tidal current. This contrasts distinctly from sand transport patterns reported from other sand wave ﬁelds around the world. It is likely that this occurs because the giant sand waves act as a bathymetric obstacle, causing a change in the direction of bottom currents as their ﬂow meets the large bedforms. As such, the giant, immobile bedforms strongly inﬂuence the sediment transport pathway, more so than the major tidal currents.
This study is supported by the National Natural Science Foundation of China, China (41476049, 41830540), the Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, China (SOEDZZ1802) and the China Scholarship Council, China. We thank Prof. Zhang Huaguo for collecting the HJ-1A/ 1B data from the China Centre for Resources Satellite Data and Application, and Pieter C. Roos for detailed and constructive comments. The data used in the paper are accessible by contacting the authors through the email address [email protected]
and describing the intended purpose of usage. We thank Warwick Hastie, PhD, from Liwen Fig. 10. Sediment transport patterns are inﬂuenced by the shift of bottom currents as they impact the giant sand waves. Alongcrest sediment transport (red arrows) is dominant near the giant sand waves, when the bottom currents are forced to ﬂow along the giant sand wave crest-lines. Crestperpendicular sand wave migration (blue arrows) only occurs by normal bottom current ﬂow across small sand waves distal to the giant sand waves. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)
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Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.
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