LONG-TERM MONITORING ON THE SAND SPIT MORPHODYNAMICS
AT THE TENRYU RIVER MOUTH
Haijiang Liu1, Yoshimitsu Tajima1 and Shinji Sato1
In this study, long-term monitoring on the sand spit morphodynamics at the Tenryu River mouth was carried out basedon the time series of sand spit profiles extracted from the field camera recorded images between Apr 2007 and Dec
2009. Time stack images were applied to qualitatively characterize the variation of sand spit morphology at the
representative longshore location. Primarily analyses were conducted by focusing on the bulk statistical properties of
the recorded sand spit profile data. Detailed insight into the evolution process of the sand spit shoreline location was
estimated through the Empirical Orthogonal Function (EOF) analysis. Sand spit riverside morphodynamics presents a
short-term, but intensive variation pattern and is found to be closely related to the overtopping or flood events.
Modification on the sand spit shoreline location shows an asymmetric nearshore process. In the first two years, slow
beach accretion accompanying with the eastward longshore sand movement prevailed; whereas, rapid shoreline
erosion, together with the westward longshore sand movement, became predominant in the following half year.
Keywords: sand spit morphodynamics; image analysis; time stack image; bulk statistical analysis; EOF

INTRODUCTION

When investigating the sophisticated nearshore process, prediction of sediment transport andevaluation of beach morphology in this area are difficult tasks considering that they undergo
complicated interactions and feedback mechanisms between the acting force and system response,
which becomes more embarrassed in terms of a long time period coupled with a fairly wide spatial
range. Various beach monitoring programs have been deployed to characterize the correspondingnearshore morphological behaviors (Larson et al., 2003; Southgate et al., 2003).
Over the last two decades, it has been extensively proved that the shore-based video technique,such as the ARGUS system, is a powerful and cost-efficient tool to acquire large amounts of real-timenearshore information with high resolutions in time and space (Holman and Stanley, 2007). In general,
field cameras are installed at a fairly two-dimensional coastal site with a straight shoreline and shoreparallel bars where morphological change is mainly caused by wave actions (Aarninkhof, 2003). In
this study, a long-term field investigation, based on video imagery, was carried out by focusing on thesand spit morphodynamics. Taking into account the present study area, the estuarine morphology
process is rather dynamic and complex under the co-actions of daily waves, tidal current and riverdischarge, as well as the episodic typhoon and flood events. This process occurs along both cross-shore
and longshore directions, even on the riverside of the sand spit. By estimating the sand spit area fromrecorded images, Liu et al. (2008) studied a short-term morphodynamic process of the correspondingarea, which included the sand spit collapse and recovery process mainly ascribing to two successive
typhoon events in 2007.
In this paper, further analyses based on the sand spit outline extracted from the field recorded
image are conducted with respect to the long-term viewpoint. Long-term monitoring is implemented topresent the general evolution process of the sand spit morphodynamics, and to discover the mechanism
behind it. Discussions are carried out based on the qualitative description and quantitative analysis on
the field recorded images. Through this study, evaluation on the temporal and spatial deformation of
the sand spit is demonstrated. At the same time, the general morphodynamic scenario of the sediment
movement feature in the research area is proposed.
STUDY AREA AND REASEARCH APPROACH
Study Area

Figure 1 illustrates the target area in this study, the sand spit at the Tenryu River mouth, and thefield camera deployment together with the camera recording azimuth indicated using dashed lines. The
Tenryu-Enshunada fluvial system is located at the west of Shizuoka Prefecture, Japan. Detailed
descriptions on this region can be found in Liu et al. (2007). A highly dynamic sand spit extends fromthe west side of the river mouth, which shows significant spatiotemporal variation owing to thecomplex external forces. In order to reveal such sophisticated morphodynamic behavior, field cameras

1

Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

1

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COASTAL ENGINEERING 2010

were installed on both sides of the river mouth since Apr 2007, i.e., Seien camera on the west side andKaketsuka camera on the east side. Camera set-up, capacity and image recording were described indetail in Liu et al. (2008). In this study, the long-term investigation on the sand spit morphodynamics
covers a 2.5-year period, extending from Apr 2007 to Dec 2009. During this period, there were threesignificant typhoons attacking the corresponding area, i.e., T0704, T0709 and T0918, respectively.
Relevant physical characteristics of these three typhoon events are summarized in Table 1. Aftertyphoon T0704, sand spit collapse occurred owing to the intensive river flood. Afterwards, seaside
recovery of sand spit underwent by the daily wave actions. Wave overtopping during typhoon T0709
induced the riverside recovery of the collapsed area. Detailed analyses on the nearshore process related
to these two events can be found in Liu et al. (2008). In the following time period, the wave climate
was relatively calm until the visiting of typhoon T0918, which has a significant wave height more than
10 m. Landward migration of the sand spit was confirmed due to the wave overtopping (Takagawa etal., 2011). During the research period, the significant river discharge only occurred accompanying withtyphoon T0704, which offered a large amount of sands to the river mouth nearshore area. For the othertwo typhoons, the corresponding sediment supplies from the Tenryu River played a minor role withrespect to the nearshore morphodynamics.

Seien

Tenryu River

Shizuoka Pref.
Japan

C1
Research Area

River mouth Sand Spit

Enshunada Coast, Pacific Ocean

N

Kaketsuka
C2
200 m

Figure 1. Research area and field camera deployment.

Table 1. Physical characteristics of three typhoons between Apr 2007 and Dec 2009.
TyphoonDate

Significant wave
height H1/3 (m)Significant wave
period T1/3 (s)River discharge
3
Q (m /s)

T0704

Jul 14, 2007

7.1

11.2

8150

T0709

Sep 6, 2007

5.8

12.5

590

T0918

Oct 8, 2009

10.8

13.9

1050

Memo
Sand spit
collapse
Sand spit
recovery
Landward
migration

After field image recording, image analysis was performed to extract the useful information forfurther investigations. The 10 min-averaged time exposure images at zero tide level for each day wereselected to remove the individual image noise and to perform the inter-comparison for a long-term
period. Acquiring the accurate geo-referencing (the real world location) from the oblique video images
is important for the concrete discussion on the nearshore morphodynamics. Based on the image
rectification relationship presented by Holland et al. (1997), Liu et al. (2008) introduced the weightfunction to the ground control points which improves the accuracy of image rectification. By taking
into account the simultaneously recorded images from both sides of the river mouth, Figure 2 presents
an example of the merged, geo-rectified time exposure image recorded on Jul 9, 2007, which shows
clear sand spit characteristics. The spatial resolution of such image is within a few meters in thecamera-far field with respect to the sand spit area. In Figure 2, the coordinate system is based on theoriginal point setting at the Seien camera with going eastwardly/southwardly corresponding to thepositive longshore/cross-shore positions, respectively. The merged, geo-rectified time exposure images,
together with the specified coordinate system, will be employed in this study.

COASTAL ENGINEERING 2010

3

Cross-shore distance (m)

100
200
300

N

400
500
600
700
800
900
200

400

600

800

1000

1200

1400

1600

Longshore distance (m)Figure 2. A merged, geo-rectified time exposure image recorded at zero-tide level on Jul 9, 2007.

Methodology

Time stack images specified at a representative longshore transection position is applied for thequalitative description on the temporal variation of the sand spit configuration. Quantitative analysis isconducted based on the sand spit profiles extracted from the merged, geo-rectified images. Various
automated methods have been developed to pick up the shorelines or sand spit profile from images(Plant et al., 2007; Liu et al., 2008). Nevertheless, these approaches tend to be site specific or shortterm applicable and can not routinely apply to the present study area with comparable accuracy tomanually extracting the sand spit boundary. Therefore, similar to Fairley et al. (2009), sand spit
profiles were obtained manually from images using a graphical user interface. Although a little laborconsuming, this method affords the best accuracy in detecting the sand spit boundary considering therather hostile field weather conditions during the image recording.
Time exposure images were used which remove boundary discrepancies from individual wave
run-up and present a more reliable boundary estimation. Sand spit profiles were only extracted fromimages with good clarity. To minimize the influence from the wave set-up to the shoreline position,
recorded images with a significant wave height larger than 1.1 m have been excluded from the present
analysis. Owing to the episodic camera mechanical problems, image recording failed for one or both
cameras sometimes. This makes the sand spit profile extraction impossible for the relevant periods. As
a result, temporal spacing of sand spit profile extraction varies from daily to monthly. In total, 354
profiles were picked up between Apr 20, 2007 and Dec 9, 2009.
Preliminary investigation focuses on the simple bulk statistical analysis on the sand spit profilesuch as the mean, range, standard deviation and correlation coefficient to characterize the nearshore
evolution process. Although bulk statistics are quite useful in characterizing long-term beach dynamics,
more sophisticated approaches may provide additional insights into how the nearshore responds. In
case of sand spit response over large spatial and long temporal scales, Empirical Orthogonal Function(EOF) analysis was applied. EOF analysis, also commonly referred to as Principal Component
Analysis (PCA), has become a powerful tool in analyzing the successive coastal survey data sets toreveal the beach profile evolution process and to understand the regularity of the nearshore variability.
The application of EOF technique to coastal study starts from the mid 1970s. Pioneer works have been
conducted by Winant et al. (1975) to describe the beach profile changes and by Aubrey (1979) to study
the seasonal patterns of on/offshore sediment movement. These studies, focusing on the cross-shore
variability, relate the first three eigenfunctions to the variation in profile configuration representing themean beach profile, the seasonal winter bar and summer berm profiles and the low tide terrace.
Recently, EOF technique was applied to the assessment on the longshore shoreline variability (Miller
and Dean, 2007) and shoreline changes behind the detached breakwaters (Fairley et al., 2009), as wellas the variation and prediction of the high-energy shoreline (Hansen and Barnard, 2010).
EOF analysis operates by exploiting the properties of matrices to identify patterns of standing
oscillations within a data set which can then be used to represent the data in a more compact form. It
reduces the number of data variables required to represent a specified data set. This is achieved by

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COASTAL ENGINEERING 2010

linearly decomposing a data set in terms of orthogonal functions both in space and time (Aubery, 1979;
Miller and Dean, 2007),
n

n

k =1

k =1

y ( x, t ) = ∑ yk ( x, t ) = ∑ ak ck (t )ek ( x)(1)where y ( x, t ) is the original sand spit profile data recorded at any longshore point x for any time t
in which each row contains a series of temporal observations at a specified location and each column
includes the spatial observations at a specified time. yk ( x, t ) is the k-th combined eigenfunction pairwhich consists of the k-th spatial eigenfunction, ek ( x ) , and the k-th temporal eigenfunction, ck (t ) .
Parameter n is equal to the minimum of nx and nt , where nx and nt are the number of spatial andtemporal samples respectively. The normalizing factors ak are determined by,

ak = λk nx nt(2)

in which λk is the eigenvalue associated with the k-th eigenfunction. The spatial eigenfunctions,ek ( x) , are simply the eigenfunctions (eigenvactors) associated with the spatial covariance matrix andcan be decided from the equation,

Aek ( x) = λk ek ( x)(3)where the symmetric correlation matrix A is formed by,A=

1( YYT )
nx nt

(4)

in which matrix Y is made up of the original profile data y ( x, t ) and T is the matrix transpose
operator. Similarly, the temporal eigenfunctions, ck (t ) , are found by the equations,

Bck (t ) = λk ck (t )B=

1
YT Y )
(
nx nt

(5)

The matrices A and B have the same non-trivial eigenvalues. The eigenfunction associated with thelargest eigenvalue represents the bulk of the variability in the data set, or the data best in a least squares
sense; whereas each subsequent eigenfunction accounts for the majority of the remaining variability.
Therefore, the original data set can be efficiently represented by a few empirical functions (the lower
order modes) which extract the dominant meaningful signals from the background noise described by
the higher order modes.

RESULTS AND INTERPRETATIONSTaking into account the flied recorded image data set, qualitative description and quantitative
analysis on the long-term sand spit morphodynamics are presented in this section. Considering thedominant external forces are different between the seaside and riverside of the sand spit where riverflows and daily waves play their roles on two sides’ deformation respectively, the whole sand spit
morphodynamics is investigated with respect to these two regions.
Qualitative Description

Qualitative analysis on the sand spit morphodynamics is based on the time stack image to reveal
the general variation tendency. Figure 3 presents a time stack image at the 600 m longshore position asshown in Figure 2 highlighted by a red line. This image covers a period from Apr 20, 2007 to Dec 9,
2009. The dark regions correspond to the days when technical problems occur for image recording or

COASTAL ENGINEERING 20105

there is no zero tide level during the daytime. Considering the riverside, it shows the event-related
modification process in Figure 3. Before Jul 2007, there is no significant change on the riversideboundary. During typhoon T0704, the sand spit collapsed at this location due to the river flood which
resulted in sudden erosion on the riverside. In the following two months, the recovery process occurred
starting from the seaside by the daily normal waves and finally to the riverside as a result from thewave overtopping ascribed to typhoon T0709 (Liu, et al., 2008). In the succedent two years, the sandspit riverside experiences a fairly stable evolution phase since there is no significant river flood orsevere overtopping events during this period. On Oct 8, 2009, one powerful typhoon event, T0918,
with a significant wave height exceeding 10 m, attacked the river mouth, which migrated the sand spit
to the landward direction induced by the sediment movement after the wave overtopping (Takagawa, etal., 2011). In Figure 3, it is confirmed that the riverside boundary shifts riverward remarkably after
typhoon T0918. In case of the seaside morphodynamics, different from the riverside, a relatively
gradual and long-term variation process is observed in Figure 3. There is no distinct change on the
shoreline location after going through typhoons T0704 and T0709. Subsequently, a seasonal migrationof the shoreline position is detected. At this location, accretion process occurs during spring andsummer (Apr to Sep); whereas, beach erosion (shoreline recession) undergoes in autumn and winter(Sep to Mar).

T0704 T0709
River

300
Cross-shore Position (m)

T0918

200

accretion

erosionaccretion

stable evolution

400

500

accretion

erosion accretion

600

Sea

erosion

700
07Jul

07Oct

08Jan

08Apr

08Jul

08Oct

09Jan

09Apr

09Jul

09Oct

Date

Figure 3. The time stack image at 600 m longshore position.

Quantitative Analysis

Attention was firstly paid to the sand spit riverside morphodynamics. Figure 4 shows the temporalvariation of riverside boundaries at three representative locations, i.e., 600 m, 800 m, and 1000 m, as
well as the average location of the sand spit riverside boundary over the longshore range of 450 - 1050
m. Considering that the east part of the sand spit is highly dynamic owing to the multiple external
forces, no sand spit exists east of the longshore location 1050 m at certain times during the image
recording period (Liu et al., 2008). Therefore, quantitative analyses were carried out up to thelongshore location of 1050 m. In Figure 4, it further confirms that distinct changes on the riverside
morphology occur accompanying with the overtopping or flood events. On the other hand, riverside
profile modification is rather insignificant under the normal conditions (after T0709 until T0918).
Modification on the riverside boundary location is also not spatially uniform mainly ascribed to thedifferent sand spit elevation. Comparing the riverside positions before T0704 and after T0709, about
45 m erosion happens at 600 m location. It is about 30 m at 800 m location. Further going east at 1000m location, it is found that erosion caused by typhoon T0704 is fully recovered after typhoon T0709
and there is no clear change on the riverside profile pre and post these two events. After typhoon
T0918, landward migration occurred with a magnitude of 65 m, 20 m and 40 m with respect to thesethree longshore locations. Further focusing on the temporal variation of the mean riverside profile

6

COASTAL ENGINEERING 2010

location in Figure 4, it is seen that the distribution of mean position is quite similar to the case of the
800 m longshore location. Pre-T0704 and post-T0709, the mean riverside suffered 30 m erosion;
whereas, pre-/post-T0918, another 30 m accretion occurred. In between, the mean position is rather
stable at cross-shore location of 350 m. Accordingly, there is no change on the mean riverside position
within the present research period, i.e., the 320 m cross-shore location.

200

T0704
Cross-shore Position (m)

250

300

T0709

600 m
800 m
1000 m
mean

T0918

350

400

450

500
07Jul 07Oct 08Jan 08Apr 08Jul 08Oct 09Jan 09Apr 09Jul 09Oct

Date
Figure 4. Temporal variation of riverside sand spit boundaries at three specified locations, i.e., 600 m, 800 m
and 1000m, together with the mean position of the sand spit riverside boundary.

Hereafter, interpretations focus on the sand spit seaside morphodynamics by scrutinizing the sandspit shoreline movement during this period. Figure 5 illustrates the bulk statistical analysis results on
the time series of shoreline recordings to characterize the shoreline response, which include the mean,
range, standard deviation and correlation coefficient over the time period between Jun 2007 - Dec 2009and longshore range of 300 - 1050 m. In Figure 5(a), the mean shoreline position extends southwardfrom west to east. As indicated by the range, the recorded shoreline position varies by about ±50 m at
most locations, with the smallest bias around 900 m and the largest excursion at the east end where thespatiotemporal variation of sand spit is sophisticated. Standard deviation of the shoreline position timeseries is presented in the middle panel of Figure 5. Large standard deviations are observed around 600
m and at the east end. In which, longshore position of 600 m corresponds to the sand spit collapse
location during typhoon T0704. Although there is no clear difference in terms of the shoreline range
shown in Figure 5(a), this location demonstrates a relatively fragile shoreline feature within the
research period. Small standard deviation is obtained around 900 m longshore location which isconsistent with the range distribution. Spatial distribution of the correlation coefficient between timeseries of shoreline locations at specified four longshore locations is presented in Figure 5(c). The
maximum correlation (a value of one) is estimated when taking into account the self variation. West of
the longshore location of 900 m, there are positive correlations between relevant two locations, e.g.,
results from 300 m, 600 m and 800 m, which means the shoreline migration in this area accompanies
with each other or in the same phase. However, considering the correlation between the west and theeast of 900 m, negative correlations are confirmed which illuminates that the shoreline variation is outof phase between these two parts. In other words, shoreline accretion at the west side goes along withshoreline erosion at the east side, or vice versa. This may indicate the nearshore dynamics induced by
the longshore sediment transport.

COASTAL ENGINEERING 20107

Shoreline (m)

350

(a)

400
450
500
550

Standard Deviation (m)30Correlation Coefficient

600
300

1

400

500

600

700

800

900

1000

(b)
25

20

15
300

400

500

600

700

800

900

(c)

0.5
0
-0.5
-1
300

1000

400

500

600

700

800

300m
600m
800m
1050m
900

1000

Longshore Position (m)Figure 5. (a) Mean (blue line) and range (red dot line) of the sand spit shoreline data set. (b) Standard
deviation of the sand spit shoreline data set. (c) Correlation coefficient between time series of shoreline
locations at selected four longshore locations, i.e., 300 m, 600m, 800m and 1050 m.

To further identify and characterize the sand spit shoreline morphodynamics, EOF analysis wasapplied to evaluate the dominant modes of variability contained in the shoreline data set. In this study,
considering that most of the shoreline variability is represented by the first two modes of the EOF
analysis, the first two modes’ results will be discussed in detail below.
First mode results are presented in Figure 6, the upper panel shows the spatial eigenfunction e1(x)distribution and the lower panel shows the temporal eigenfunction c1(t) variation. In Figure 6, e1(x)exhibits positive values within the whole considered area with gradual increase from west to east. In
general, e1(x) displays a strong resemblance to the statistical mean shoreline as shown in Figure 5(a)(being aware that the vertical axis is reversed in Figure 5a). Miller and Dean (2007) referred to e1(x) as
the mean shoreline function. Figure 6 demonstrates the shoreline movement indicated from the first
spatial eigenfunction is in phase among the entire longshore locations, and is more reflective of thetypical shoreline response (advances or retreats) due to the cross-shore processes. Miller and Dean(2007) and Fairley et al. (2009) also linked the first mode variation of the EOF analysis to the crossshore movement. A large e1(x) value at east implicates the cross-shore variation is more significant
over there. The first temporal eigenfunction also presents all positive values with a gradual increase
from Jun 2007 to Jul 2009 followed by a rapid decrease up to Dec 2009. At the same time, different
from the result revealed in the time stack image at 600 m longshore location (Figure 3), c1(t), whichcontains the whole sand spit shoreline dynamics, does not present a clear seasonal variation pattern. In
order to emphasize the interdependence of the temporal and spatial eigenfunctions, the combined
eigenfunction pair for the first mode, y1(x,t)=a1c1(t)e1(x), is demonstrated in Figure 7. Large values
correspond to the seaward shoreline positions as illustrated in Figure 2. Figure 7 implicates that within
the research period, the sand spit shoreline undergoes a long-term, gradual, but asymmetricmodification process with respect to the cross-shore sediment movement. Before Jul 2009, a gradually
seaward migration (accretion process) occurs with the y1(x,t) value increased for the first two years;
whereas, a rapid landward recession (beach erosion) happened in the following time duration. Suchaccretion/erosion process is consistent with the variation of the temporal eigenfunction in Figure 6.
Nevertheless, there is almost no change on the sand spit mean shoreline location comparing the
beginning and ending stages of the present research period, which can be further confirmed from the8COASTAL ENGINEERING 2010

temporal eigenfunction since c1(t) approaches 0.05 at both ends of the target period. This suggests thesand spit went through a cyclic cross-shore movement in these two and half years.

0.21
0.2

e1(x)

0.19
0.18
0.17
0.16
0.15
300

400

500

600

700

800

900

1000

Longshore Position (m)

0.057
0.056
0.055

c1(t)

0.054
0.053
0.052
0.051
0.05
0.049

07Jul

07Oct

08Jan

08Apr

08Jul

08Oct

09Jan

09Apr

09Jul

09Oct

Date

Figure 6. First mode results of the sand spit shoreline variation based on the EOF analysis. Upper panel:
spatial eigenfunction, e1(x); Lower panel: temporal eigenfunction, c1(t).

560
09Oct
540
09Jul
520

09Apr

Date

09Jan

500

08Oct
480
08Jul
460
08Apr

440

08Jan

07Oct
420
07Jul
300

400

500

600

700

800

900

1000

Longshore Position (m)Figure 7. The combined eigenfunction pair, y1(x,t), of the first mode based on the EOF analysis.

COASTAL ENGINEERING 2010

9

Figure 8 presents the second mode results of the shoreline variation from the EOF analysis. In theupper panel of Figure 8, one nodal point is observed at 775 m longshore location. Nodal points inspatial eigenfunctions typically separate adjacent areas of erosion and accretion. Therefore, shorelinechanges are often referred to as being out of phase across a node (Miller and Dean, 2007). Taking into
account the shoreline variability, nodal points are regarded as an indicator of the importance of thelongshore sediment processes. Hence, the second mode results generally represent the longshore sandmovement. Similar to the result from the spatial distribution of correlation coefficients in Figure 5(c),longshore sediment transport pattern is expected along the sand spit which causes the rotation ofshoreline location. Further looking at the temporal eigenfunction of the second mode as shown in thelower panel of Figure 8, c2(t) shows a positive trend through the research period. The temporaleigenfunction changes its sign from negative to positive around Jan 2009, which indicates the change
in the longshore transport pattern/direction at that time. On the other hand, regular seasonal variationof c2(t) is still hard to be identified. Figure 9 illustrates the combined eigenfunction pair for the second
mode, y2(x,t), after considering the contributions from both e2(x) and c2(t). The longshore transport
pattern can be easily identified in Figure 9. It is found before Jan 2009, y2(x,t) presents
negative/positive values on the west/east sand spit, which means contribution from the second mode isto transport sands from the west to the east in this period. Such eastward longshore transport featureinduces shoreline recession on the west with the simultaneous beach progradation on the east andcauses a clockwise rotation of the shoreline centered at the nodal point, i.e., 775 m longshore location.
However, longshore transport changed to the westward direction after Jan 2009. In Figure 9, this is
illuminated with positive/negative values on the west/east sand spit, which implicates a westward
longshore transport pattern. An anti-clockwise rotation of the shoreline experienced within this period.
Such modification on the longshore transport pattern is also confirmed from field surveys. In 2008,
sand spit extended significantly to the east, and the sand spit tip was even out of the camera recording
range; whereas, it shrunk rapidly to the west after Mar 2009.

0.5

e2(x)

0.25

0

-0.25

-0.5
300

400

500

600

700

800

900

1000

Longshore Position (m)

0.15
0.1

c2(t)

0.05
0
-0.05
-0.1
-0.15
-0.2

07Jul

07Oct

08Jan

08Apr

08Jul

08Oct

09Jan

09Apr

09Jul

09Oct

Date

Figure 8. Second mode results of the sand spit shoreline variation based on the EOF analysis. Upper panel:
spatial eigenfunction, e2(x); Lower panel: temporal eigenfunction, c2(t).

10COASTAL ENGINEERING 2010
60
09Oct
40

09Jul

09Apr
20

Date

09Jan

08Oct

0

08Jul
-20
08Apr

08Jan
-40
07Oct
-60

07Jul
300

400

500

600

700

800

900

1000

Longshore Position (m)Figure 9. The combined eigenfunction pair, y2(x,t), of the second mode based on the EOF analysis.

After knowing the overall characteristics of the nearshore sand spit morphodynamics, intercomparison among the original shoreline data, the first mode results and the first plus second mode
results derived from the EOF analysis was performed at three representative locations, i.e., 500 m (thewest region point), 775 m (the central point) and 1000 m (the east region point). The results arepresented in Figure 10. Taking into account the first two modes, the EOF analyzing results canreproduce the original shoreline variation satisfactorily since they represent the dominant shorelinevariability. As for the first mode results (red lines in Figure 10), it is found that at these three locations,shoreline data gradually increase by a magnitude of 60 m between Jun 2007 and Jul 2009.
Subsequently, rapid reduction of the first mode results is observed. This corresponds to theaccretion/erosion processes along the cross-shore direction specified by the EOF analysis as indicatedin Figures 6 and 7. Considering the contribution from the second mode results by comparing the red
and green lines in Figure 10, sediment longshore transport pattern can be characterized. First, paying
attention to the 775 m longshore location which is the nodal point specified by the second spatial
eigenfunction, e2(x), in Figure 8, there is no visible difference between the first mode and the first plus
second mode results in Figure 10(b). This is ascribed to the fact that this location is a pivot point andshows stability in terms of the longshore movement. Therefore, contribution from the second mode
EOF analysis is rather insignificant. The fist mode result, or the cross-shore movement, is predominantat this location. The small difference between the original data and the first plus second mode results
implicates the shoreline variability contributed from the higher order modes of the EOF analysis, e.g.,
the third mode contribution which will be further discussed in the following context. On the other hand,
it is found that in the west region (500 m longshore location) and before Jan 2009, the second mode
contributes the shoreline to the landward direction (red line to green line as shown in Figure 10a).
Nevertheless, during the same time period, the second mode moves the shoreline to the seaward
direction in the east region (1000 m longshore location as shown in Figure 10c). This indicates thenearshore sediment is transported from the west to the east in this period. After Jan 2009, an opposite
scenario occurred. The second mode contributes shoreline to the seaward/landward direction in thewest/east region, which corresponds to a westward longshore transport feature. Such longshore
sediment movement pattern is in agreement with the interpretation on the longshore variability
revealed in Figures 8 and 9.

COASTAL ENGINEERING 2010
480

Cross-shore Position (m)Cross-shore Position (m)

420

440

460

480

500

520

11

(a) 500 m

540
07Jul 07Oct 08Jan 08Apr 08Jul 08Oct 09Jan 09Apr 09Jul 09Oct

Date

500

520

540

560

(b) 775 m
580
07Jul 07Oct 08Jan 08Apr 08Jul 08Oct 09Jan 09Apr 09Jul 09Oct

Date

Cross-shore Position (m)

460

480

(c) 1000 m

500

Original shoreline data

520

First mode result

540

First mode + second mode
560

580
07Jul 07Oct 08Jan 08Apr 08Jul 08Oct 09Jan 09Apr 09Jul 09Oct

Date
Figure 10. Inter-comparison among the original shoreline data, the first mode results and the first plus
second mode results derived from the EOF analysis at three representative longshore locations, i.e., 500 m,
775 m and 1000 m.

Modes higher than the second explain an increasingly small amount of the shoreline variance andgenerally show increasing longshore variability in various shoreline topographic features. Figure 11
illustrates the sand spit shoreline variability contributed from the third mode of the EOF analysis.
There are two nodal points existing along the sand spit shoreline with respect to the third spatial
eigenfunction, e3(x), one at 540 m longshore location and the other at 895 m longshore location. Thisimplicates a more complex longshore transport feature than that revealed from the second spatial
eigenfunction which has only one nodal point as shown in the upper panel of Figure 8. Result of thethird temporal eigenfunction, c3(t), is shown in the lower panel of Figure 11. Influence from typhoon
events can be identified through the c3(t) distribution. After typhoons T0704 and T0709, c3(t) suddenly
changes its value from -0.13 to 0.02, which means the longshore transport pattern indicated from thethird mode changed its direction pre and post these typhoons. This can also be recognized from thethird combined eigenfunction pair distribution, y3(x,t), as shown in Figure 12. Before typhoons, thethird mode longshore transport moved sands from the middle to both the west and the east, a divergent
process from the middle; whereas, after typhoons, the longshore transport moved sands from two ends
to the middle, a convergent process to the middle. After typhoon T0918 on Oct 8, 2009, there is also a
rapid increase on c3(t) values which modifies the previous decreasing trend of the temporaleigenfunction. Besides the effect from the typhoon events, the general long-term process can also be
confirmed from the c3(t) distribution. After typhoon T0709, a slowly decreasing trend prevailed until
May 2009 with a zero-crossing point around Jul 2008. In Figure 12, this process is represented by aconvergent pattern between Oct 2007 and Jul 2008 and the divergent pattern between Jul 2008 and
May 2009. Nevertheless, between May and Aug 2009, a rapid increasing trend on c3(t) is observedwith a zero-crossing point around Jul 2009 which changed the longshore movement to the convergent
pattern again as demonstrated in Figure 12. After Aug 2009, c3(t) presents a decreasing trend. However,this process was interrupted and modified by typhoon T0918 as mentioned previously. Comparing theshoreline variation amplitude in Figures 9 and 12, it is further verified that the contribution to theshoreline variability from the third mode plays a minor role than the second mode.

12

COASTAL ENGINEERING 2010
0.5

e3(x)

0.25

0

-0.25

-0.5
300

400

500

600

700

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1000

Longshore Position (m)

0.15
0.1

c3(t)

0.05
0
-0.05
-0.1
-0.15
-0.2

07Jul

07Oct

08Jan

08Apr

08Jul

08Oct

09Jan

09Apr

09Jul

09Oct

Date

Figure 11. Third mode results of the sand spit shoreline variation based on the EOF analysis. Upper panel:
spatial eigenfunction, e3(x); Lower panel: temporal eigenfunction, c3(t).
20
09Oct
15
09Jul
10
09Apr

5

Date

09Jan

08Oct

0

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-5
08Apr
-10
08Jan
-15

07Oct

07Jul
300

-20
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Longshore Position (m)Figure 12. The combined eigenfunction pair, y3(x,t), of the third mode based on the EOF analysis.

DISCUSSIONSIn this section, a general sand spit morphodynamic scenario is drawn based on the observation andanalysis on the field recorded images over the two and half year period. As mentioned previously in
Table 1, a large amount of sediment was provided to the river mouth nearshore area owing to thesevere river flood accompanying with the typhoon T0704. There was no other such tremendous sand

COASTAL ENGINEERING 201013

supply from the river during the research period. Taking into account the unique sand spit collapse
phenomenon occurred in typhoon T0704, a significant extra sand discharge from the Tenryu River wasstored in front of the sand spit. Although the apparent recovery of the sand spit accomplished twomonths later after typhoon T0709 (Liu, et al., 2008), influence owing to the extra sand discharge to thenearshore morphodynamics persists for a much longer duration.

200(a): Image Extraction

300
400

20090308

500

Cross-shore position (m)

600
700
200

20071031
400

600

800

1000

1200

200

1400

1600

(b): GPS Record

300

20090729
400

20090302

500
600
700
200

400

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(c): Image Extraction

300

20091209

400
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20090703

600
700
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400

600

800

1000

1200

1400

1600

Longshore position (m)Figure 13. Sand spit profiles recorded at several specified dates during the research period. (a) Imageindicated sand spit profiles on Oct 31, 2007 and Mar 8, 2009. Shadowed area represents the sand spit
collapse location in typhoon T0704. (b) GPS-recorded sand spit profiles on Mar 2, 2009 and Jul 29, 2009. (c)Image-indicated sand spit profiles on Jul 3, 2009 and Dec 9, 2009.

Figure 13 illustrates the sand spit profiles recorded at several representative dates during the
research period. In Figure 13(a), the sand spit profile extracted from the field image recorded on Oct
31, 2007 represents a typical sand spit situation just after typhoon T0709. In the subsequent period, as
revealed from the first mode of EOF analysis, the sand spit migrated seaward along the cross-shore
direction which implicates the extra sand supply stored in front of the sand spit after typhoon T0704
was transported back to the shore. Such nearshore sand movement is straightforward since the extra
sand storage modified the nearshore bathymetry and also changed the corresponding wave field. At thesame time, the eastward longshore transport was prevailing as indicated from the second mode of EOF
analysis, which moves sands towards the sand spit tip. Significant eastward extension of the sand spit
was observed as shown by the sand spit profile recorded on Mar 8, 2009. Due to the limitation ofcamera recording azimuth, east end of the sand spit was not fully recorded in the Mar 8, 2009 images.
Comparing two sand spit profiles in Figure 13(a), accretion along the cross-shore direction and theeastward longshore transport are confirmed. During the research period, the sand spit profiles were14

COASTAL ENGINEERING 2010

also irregularly recorded using the Global Positioning System (GPS), which thoroughly covers thewhole sand spit area. Figure 13(b) demonstrates the sand spit profiles recorded from the GPS on Mar 2,
2009 and Jul 29, 2009. Comparing these two profiles, it is easy to know that a certain amount of sands
was lost from the east end of the sand spit within this period. These sands may be transported further
east or moved to the offshore region under the coactions of waves, river flows and tidal currents. Afterlosing sand from the east, the sand spit morphodynamics were also modified. In the following time
period, beach erosion occurred which migrated the sand spit landward, and the westward longshore
movement prevailed which rotated the sand spit in an anti-clockwise direction. These are demonstrated
in Figure 13(c) which presents the sand spit profiles recorded on Jul 3, 2009 and Dec 9, 2009.
After these analyses, a general picture of the sand spit evolution process within the research period
can be proposed that after typhoon T0704, a two and half year asymmetric cycle for sand spit
morphodynamics underwent. In which, the sand spit shoreline accretion, together with a eastward
longshore transport, was dominant for the first two years. Nevertheless, significant sand loss from theeast end of the sand spit occurred around Jun 2009, which changed the relevant nearshore sandmovement pattern. Subsequently, significant shoreline erosion, accompanying with a westward
longshore transport, became predominant in this area.

CONCLUSIONSLong-term monitoring on the sand spit morphodynamics at the Tenryu River mouth was
performed based on the sand spit profiles extracted from the field recorded images between Apr 2007
and Dec 2009. Qualitative description on the sand spit evolution process was conducted in terms of thetime stack image specified at the representative longshore transection position. To figure out internal
insights of the nearshore response, quantitative discussions were carried out through the bulk statisticalanalysis and the empirical orthogonal function technique.
The riverside morphodynamics reflects a short-term, intensive variation process which is closely
related to the extreme events, such as wave overtopping and river flood. During typhoon T0704, sandspit collapsed owing to the intensive river flood which induced significant erosion on the riversideboundary; whereas during typhoons T0709 and T0918, the river side sand spit migrated towards
landward due to the sediment movement induced by the wave overtopping.
In case of the seaside morphodynamics, the sand spit shoreline demonstrates a long-term, gradualmodification process. Based on the first mode of the EOF analysis, an asymmetric cross-shore
movement pattern is confirmed in which a slow shoreline accretion process prevailed from Jun 2007 to
Jul 2009, followed by a rapid erosion process in the subsequent several months. Nevertheless, there is
no change on the mean shoreline location with respect to the cross-shore movement. The longshore
sediment movement feature is mainly characterized through the second mode results of the EOF
analysis. The second spatial eigenfunction presents a nodal point at 775 m longshore location which isthe pivot of the shoreline rotation in terms of the longshore movement. Before Jan 2009, eastward
longshore transport with a clockwise sand spit rotation is predominant; whereas, westward longshoretransport accompanying with an anti-clockwise sand spit rotation is confirmed afterwards. Influence
from typhoon events can be identified through the third mode results of the EOF analysis, which also
show the increasing longshore variability with a minor degree.
Within the studied two and half year period, the sand spit morphodynamic scenario is proposed.
After an extra river flood-induced sediment supply from typhoon T0704, the sand spit underwent an
asymmetric cycle. In which, the extra sands stored in front of the sand spit were transported back to thesand spit (shoreline accretion) and moved to the eastward simultaneously (eastward longshoretransport). Around Jun 2009, a large amount of sand loss occurred at the east end of the sand spit
which modified the nearshore sediment movement pattern. Following it, shoreline erosion, together
with a westward longshore transport, became prevailed.

ACKNOWLEDGMENTS

We would like to thank Dr. T. Takagawa, the University of Tokyo, for his helps during the fieldsurvey. This study is a part of Tenryu-Enshunada Project, “Dynamic sediment management and coastal
disaster prevention by advanced technologies” supported by the Special Coordination Funds forpromoting Science and Technology of Ministry of Education, Culture, Sports, Science and Technology,
Japan.

COASTAL ENGINEERING 201015

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