Oceanic waves transformation and beach morphology
evolution associated to complex submarine topography
Dissertation Prospectus – Doctoral Program in Geological Sciences
Summer 2014
JUAN FELIPE PANIAGUA-ARROYAVE
Geomorphology Lab
Department of Geological Sciences
120 Williamson Hall
University of Florida
Gainesville FL 32611-2120
Oceanic waves transformation and beach morphology
evolution associated to complex submarine topography
Dissertation Prospectus – Doctoral Program in Geological Sciences
Major: GEOLOGICAL SCIENCES
Minor: COASTAL AND OCEANOGRAPHIC ENGINEERING
JUAN FELIPE PANIAGUA-ARROYAVE
__________________________________________________
Dr. Peter N. Adams
Assistant Professor, Committee Chair
Department of Geological Sciences
__________________________________________________
Dr. Ray M. Russo
Associate Professor, Graduate Coordinator
Department of Geological Sciences
University of Florida
Gainesville, FL 32611 USA
Summer of 2014
1. Introduction
Global Change in coastal zones
Natural and human systems within coastal zones form a complex social-ecological
system [Hopkins et al., 2012]. Present morphological features and ecosystems define the natural
framework providing a variety of services of the most importance for human well-being. Human
socioeconomic systems evolve to the natural environment, human activities (e.g. tourism), and
formal and informal regulations (e.g. policies, laws) [Nicholls et al., 2008]. Within this context,
coastal zones are subject to intense environmental change with feedback effects between natural
and human systems, encompassing nonlinear shifts in ecosystems services [Turner, 2000;
Carpenter et al., 2007].
Global change affects coastal zones but the distinction between climatic- and
anthropogenic-related drivers is difficult to identify [Slaymaker et al., 2009]. For instance, major
processes such as global sea level rise are attributable to both to climate change and human
reduction of sediment supply to deltas [Syvitski et al., 2009; Cazenave and Llovel, 2010].
Furthermore, considering that ~44% of world’s population inhabits coastal zones with 8 of 10
largest cities being coastal [United Nations, 2014], understanding global change within coastal
zones is imperative for a scientific based vulnerability assessment towards the formulation of
adaptation-related policies for the already threatened coastal communities around the world
[Wong et al., 2014].
Waves transformation in a Global Change context
Waves (i.e. wave climate) are considered to be a climate-related factor affecting coastal
zones, with temporal variations and extreme events impacting human and natural settings
through erosion which threatens infrastructure [Reguero et al., 2013]. Although it is well-known
how oceanic waves are related to coastal processes and morphologic evolution via longshore
sediment transport [Komar and Inman, 1970; Kamphuis, 1991; van Rijn, 2014], beach cusps
formation [Guza and Inman, 1975; Werner and Fink, 1993], and surfzone sand bar migration
[King and Williams, 1949; Hoefel and Elgar, 2003; Garnier et al., 2006], it remains an open
question how climate-induced change in storm patterns and associated modification of sea-state
may lead to a redistribution in the energy flux through modifications in local wave propagation
[Casas-Prat and Sierra, 2012].
A particular situation of wave transformation disturbances and potential associated
shoreline planform changes is related to human-induced topographic changes caused by dredging
operations performed within the continental shelf topography [Bender and Dean, 2003b; 2004;
2005]. The Florida East coast inner continental shelf is a classic example of a suitable location
for marine sand mining for beach nourishment projects [Finkl and Andrews, 2008; Finkl and
Hobbs, 2009], with recent dredging operations conducted in the offshore sand ridges near Cape
Canaveral. In this particular case, specific impacts related to a modification in local wave
transformation such as exacerbation of already severe sediment relocation or loss of habitat
suitability for benthic species are still not identified [Bender and Dean, 2003a; Diaz et al., 2004;
Jaeger et al., 2010].
This proposal deals with the analysis of surface gravity waves transformation due to
propagation over complex topography and its relationship with both topographic evolution and
energy flux in the littoral fringe. Recent dredging operations conducted near Cape Canaveral will
be used for case study purposes. To do so, oceanographic data will be collected within a sandy
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shoal near Cape Canaveral using moored Acoustic Doppler Currents Profilers (ADCPs) in order
to analyze both temporal (pre- and post- dredging) and spatial (within dredge site and to the
north in a control site) evolution of wave field. Moreover, several monthly GPS surveys carried
on Kennedy Space Center near Cape Canaveral will be analyzed coupling them with in situ wave
measurements, buoys measurements, and numerical modeling results in order to gain an
assessment of phenomena as complete as possible.
2. General research questions
1. How do the dredging-induced bathymetric changes affect the shoaling
patterns of ocean waves across the Florida inner-shelf near Cape Canaveral?
2. What are the beach morphology evolution patterns associated with wave
field transformations near Cape Canaveral?
3. General hypotheses
Incoming deep-water oceanic wave fields are transformed across the Florida inner-shelf
in complex patterns. This is particularly reflected on the variations in wave energy flux delivered
to the shoreline depending on the shift in the deep-water attack angle and refraction over
submarine features [Kline, 2013].
Wave fields traveling through shallower water will develop and maintain heterogeneities
affecting the wave statistics reflected in the coherency of wave signals [Janssen et al., 2008].
Moreover, shoaling over the continental shelf produce variations in wave statistics related to
coherent wave interference patterns [Smit and Janssen, 2013].
The utilization of a more general transport model will improve the prediction of wavestatistics of shoaling ocean waves across the inner-shelf near Cape Canaveral versus a model
based on the Radiative Transfer Equation solution alone [Smit and Janssen, 2013].
4. Goals
Main goal: To conduct high-quality research activities within the UF Geological Sciences
Department, in collaboration with colleagues of Coastal and Oceanographic Engineering
Department.
Specific goals:
1. Gain expertise in conducting individual research within a collaborative group,
in order to prepare myself for an already opened Faculty position at EAFIT
University, Colombia.
2. Publish at least three manuscripts in peer-reviewed journals of relevance.
5. Materials and methods
Hydrostatic data were collected from September 23rd, 2014 to December 2nd, 2014,
within the Florida inner-shelf near Cape Canaveral using pressure sensors mounted on three
moored Acoustic Doppler Current Profilers (ADCPs); one Nortek Acoustic WAves and Currents
instrument (AWAC) one Nortek Aquadopp, and one RDI Workhorse Sentinel. The AWAC
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pressure values were sampled at 2 Hz frequency in bursts of 2,048 samples separated 1,920 sec
(32 min); the Aquadopp dataset was collected using bursts of 1,200 samples at 2 Hz separated by
3,600 sec (1 hour); and the Workhorse pressure values were collected in bursts of 2,400 samples
separated by 1,200 min, i.e. one burst each 2 hours (See Table 1 for a summary and Figure 1 for
location).
Prior to quantification of spectra, hydrostatic pressure data measured (dbar) was
considered equal to water column meters data (m), given that a dbar is roughly equal to 1 m
water depth with an error ~2% [Emery and Thomson, 2001]. Additionally, wave data from
moored instruments is compared with an offshore buoy in order to gain understanding of
offshore wave conditions and transformation of wave field through inner continental shelf.
6. Preliminary Results
Offshore wave conditions
Available wave buoy data was extracted from the National Buoy Data Center (NOAA)
for buoy 41009. This data is useful to compare the offshore wave conditions to wave field
measured by the moored instruments (Figure 2 and Figure 3).
In situ spectrograms
A power spectrum was quantified for each burst within the time span of collection as a
proxy of wave field energy, associating each spectrum to the time average for each burst
measurement. This approach allows construction of a spectrogram for the total time span of data
collection for each one of the instruments (Figure 4, Figure 5, and Figure 6). Each spectrum was
calculated based on a time series length of 211 (2,048), to fulfill the Fast Fourier Transform (FFT)
input requirements. Thus, each Aquadopp burst data sub-set of 1,200 samples was padded with
zeros at the end to complete 211 points (Emery & Thomson, 2001). In this case, the FFT is
defined by:
1
FFT = N ∑N−1
t=0 f(t) e
−2πint⁄
N
(1)
For both instruments, power spectra (PS) were computed as the square of the absolute
value of the complex FFT of the water level time series, the latter quantified as the detrended,
demeaned water column height data. 15 windows of 28 (256) values each were used for a total of
15 (211 over 27, or half of 28, minus 1) segments for spectral computing with 60 degrees of
freedom (dof). For the segments after the first, the beginning is located in the mid part of
preceding segment, v.gr. 256 over 2 plus 1, to 128 plus 256 for the beginning and end of second
window respectively. Each water level segment of data was tapered using a Hanning window
defined by Equation 2, which requires a scaling of the amplitudes of the Fourier transform by
multiplying them by the square root of 8 over 3 [Emery and Thomson, 2001]:
w = 0.5 [1 −
cos(2πn)
N
]
(2)
Here, n varies from 0 to an N value equal to the segment (or block) size, 28 in this
particular case. A total of 15 spectra of 28 values each were computed. At each calculated
frequency, the spectral values were averaged to produce only one spectrum for each burst. Also,
Page 5 of 14
since the frequency vector was calculated from 0 to the maximum frequency from Equation 2
considering a Δf according to Equation 3 and a Δt of 0.5 sec, only 27+2 values were taken into
account from mean spectra for each of the bursts. These spectral values have a statistical
significance which depends on the degrees of freedom and follow a chi-squared inverse
cumulative distribution, with upper (cul) and lower (cll) limits calculated by Equations 4 and 5
respectively.
fend =
0.5∗28 +1
(3)
28 ∗∆t
1
∆f = 28 ∗∆t
cul = χ2
cll = χ2
(4)
dof
(5)
0.95,dof
dof
(6)
0.05,dof
The bulk of the energy, located around 10 sec period waves is related to a swelldominated environment. These results are in agreement with previous work done in the zone
regarding the Cape Canaveral wave climate [Jaeger et al., 2010]. In addition, some of the highfrequency energy can be associated with locally-generated wind waves which are known to have
periods ~4 sec (frequencies ~0.25, or ~10–0.6 Hz) [Masselink and Hughes, 2003]. Moreover,
lower- and higher- frequency wave energy are present when higher energy events are also
present, it is more likely that they are associated with non-linear leakage of energy to higher and
lower frequencies due to wave field transformations. The latter may be mostly nonlinearlydriven cross-spectral transfers of energy and phase modifications due to wave train shoaling
across the shelf [Elgar et al., 1993].
7. Plan for future activities
Future activities would include.
 Continuing the development of good research questions guiding further activities.
 Cross-spectral analysis between three locations.
 Including wave directional spectra for three sites.
 Analysis of data collected between May and June, 2014.
 Analysis of overlapping time between waves data and lunar-monthly topographic
surveys conducted at Cape Canaveral using an RTK GPS [Jaeger et al., 2010].
 Additional data: future instruments deployment programmed for: AugustSeptember 2014, January-February 2015, May 2015, and July 2015.
 Submission of first draft to Journal of Geophysical Research on January of 2015.
8. Timeline for coursework and research activities
Please, refer to Table 2.
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9. References
Bender, C. J., and R. G. Dean (2003a), Wave field modification by bathymetric anomalies and resulting
shoreline changes: a review with recent results, Coastal Engineering, 49(1-2), 125-153, doi:10.1016/S03783839(03)00061-9.
Bender, C. J., and R. G. Dean (2003b), Wave transformation by two-dimensional bathymetric anomalies
with sloped transitions, Coastal Engineering, 50(1-2), 61-84, doi:10.1016/j.coastaleng.2003.08.002.
Bender, C. J., and R. G. Dean (2004), Potential shoreline changes induced by three-dimensional
bathymetric anomalies with gradual transitions in depth, Coastal Engineering, 51(11-12), 1143-1161,
doi:10.1016/j.coastaleng.2004.07.023.
Bender, C. J., and R. G. Dean (2005), Wave transformation by axisymmetric three-dimensional
bathymetric anomalies with gradual transitions in depth, Coastal Engineering, 52(4), 331-351,
doi:10.1016/j.coastaleng.2004.12.005.
Carpenter, S. R., R. DeFries, T. Dietz, H. A. Mooney, S. Polasky, W. V. Reid, and R. J. Scholes (2007),
Millennium Ecosystem Assessment: Research Needs, Science, 314, 257-258.
Casas-Prat, M., and J. P. Sierra (2012), Trend analysis of wave direction and associated impacts on the
Catalan coast, Climatic Change, 115(3-4), 667-691, doi:10.1007/s10584-012-0466-9.
Cazenave, A., and W. Llovel (2010), Contemporary Sea Level Rise, Annual Review of Marine Science,
2(1), 145-173, doi:10.1146/annurev-marine-120308-081105.
Diaz, R. J., G. R. Cutter Jr., and C. H. Hobbs III (2004), Potential Impacts of Sand Mining Offshore of
Maryland and Delaware: Part 2: Biological Considerations, Journal of Coastal Research, 20(1), 61-69.
Elgar, S., R. T. Guza, and M. H. Freilich (1993), Observations of Nonlinear Interactions in Directionally
Spread Shoaling Surface Gravity Waves, Journal of Geophysical Research, 98(C11), 20299-20305.
Emery, W. J., and R. E. Thomson (2001), Data Analysis Methods in Physical Oceanography, 2nd Edition,
Elsevier.
Finkl, C. W., and J. L. Andrews (2008), Shelf Geomorphology along the Southeast Florida Atlantic
Continental Platform: Barrier Coral Reefs, Nearshore Bedrock, and Morphosedimentary Features, Journal of Coastal
Research, 244, 823-849, doi:10.2112/08A-0001.1.
Finkl, C. W., and C. H. Hobbs (2009), Mining Sand on the Continental Shelf of the Atlantic and Gulf
Coasts of the U.S., Marine Georesources & Geotechnology, 27(3), 230-253, doi:10.1080/10641190902906067.
Garnier, R., D. Calvete, A. Falques, and M. Caballeria (2006), Generation and nonlinear evolution of shore
-oblique/transverse sand bars, Journal of Fluid Mechanics, 567, 327-360, doi:10.1017/S0022112006002126.
Guza, R. T., and D. L. Inman (1975), Edge Waves and Beach Cusps, Journal of Geophysical Reseach,
80(21), 2997-3012.
Hoefel, F., and S. Elgar (2003), Wave-Induced Sediment Transport and Sandbar Migration, Science,
299(5614), 1885-1887, doi:10.1126/science.1081448.
Hopkins, T. S., D. Bailly, R. Elmgren, G. Glegg, A. Sandberg, and J. G. Støttrup (2012), A Systems
Approach Framework for the Transition to Sustainable Development: Potential Value Based on Coastal
Experiments, Ecology and Society, 17(3), doi:10.5751/ES-05266-170339.
Jaeger, J. M., P. N. Adams, and R. MacKenzie (2010), Establishing measures and proxies of shoreline
change at Cape Canaveral, Florida: Annual Report Phase 1, May 2009-May 2010, NASA/IHA Annual Report.
Janssen, T. T., T. H. C. Herbers, and J. A. Battjes (2008), Evolution of ocean wave statistics in shallow
water: Refraction and diffraction over seafloor topography, Journal of Geophysical Research, 113(C03024), 1-12,
doi:10.1029/2007jc004410.
Kamphuis, J. W. (1991), Alongshore Sediment Transport Rate, Journal of Waterway, Port, Coastal, and
Ocean Engineering, 117(6), 624-640.
Page 7 of 14
King, C. A. M., and W. W. Williams (1949), The Formation and Movement of Sand Bars by Wave Action,
The Geographical Journal, 113, 70-85.
Kline, S. W. (2013), influence of wave energy dissipation on the geomorphic behavior of rocky and sandy
coasts, Doctoral Dissertation, University of Florida, Department of Geological Sciences.
Komar, P. D., and D. L. Inman (1970), Longshore sand transport on beaches, Journal of Geophysical
Reseach, 75(30), 5914-5927.
Masselink, G., and M. G. Hughes (2003), Introduction to Coastal Processes & Geomorphology, Hodder
Arnold.
Nicholls, R. J., P. P. Wong, V. Burkett, C. D. Woodroffe, and J. Hay (2008), Climate change and coastal
vulnerability assessment: scenarios for integrated assessment, Sustainability Science, 3(1), 89-102,
doi:10.1007/s11625-008-0050-4.
World Health Organization (2005), Ecosystems and Human Well-Being (Health Synthesis) - Millennium
Ecosystem Assessment.
Reguero, B. G., F. J. Méndez, and I. J. Losada (2013), Variability of multivariate wave climate in Latin
America and the Caribbean, Global and Planetary Change, 100, 70-84, doi:10.1016/j.gloplacha.2012.09.005.
Slaymaker, O., T. Spencer, and C. Embleton-Hamann (2009), Geomorphology and Global Environmental
Change, Cambridge University Press.
Smit, P. B., and T. T. Janssen (2013), The Evolution of inhomogeneous Wave Statistics through a Variable
Medium, Journal of Physical Oceanography, 43, 1741-1758, doi:10.1175/jpo-d-13-046.1.
Syvitski, J. P. M. et al. (2009), Sinking deltas due to human activities, Nature Geoscience, 2(10), 681-686,
doi:10.1038/ngeo629.
Turner, R. K. (2000), Integrating natural and socio-economic science in coastal management, Journal of
Marine Systems, 25, 447-460.
United Nations (2014), United Nations Atlas of the Oceans, http://www.oceansatlas.org/, accessed on
2014-06-12.
Werner, B. T., and T. M. Fink (1993), Beach Cusps as Self-Organized Patterns, Science, 260, 968-971.
Wong, P. P. et al. (2014), Chapter 5. Coastal Systems and Low-Lying Areas, R. Nicholls and F. Santos;
edited by, Intergovernmental Panel on Climate Change Work Group II - Assessment Report 5.
van Rijn, L. C. (2014), A simple general expression for longshore transport of sand, gravel and shingle,
Coastal Engineering, 90, 23-39, doi:10.1016/j.coastaleng.2014.04.008.
Page 8 of 14
10.Figures
Figure 1. Location of instruments during the September-December 2013 experiment performed near Cape
Canaveral.
Page 9 of 14
Figure 2. Wave related data for the NOAA buoy 41009: (top panel) significant wave height, H S in meters; (medium
panel) peak period, Tp in seconds; (low panel) mean wave direction, Dd in decimal degrees. Right panels correspond
to histograms regarding the same data in the left panels. Majority of waves exhibits significant wave heights near 1.0
m (0.5 to 1.5 m), 8 seconds periods (7 to 9 sec), and directions from 70 to 90 degrees from the North.
Page 10 of 14
Figure 3. Joint histograms for the wave data extracted from NOAA buoy 41009. (Upper panel) peak period versus
significant wave height; (medium panel) peak period versus mean wave direction; (lower panel) significant wave
height versus mean wave direction. Wave field is characterized by peak periods between 6 and 8 seconds, mainly of
wave groups coming from the East, exhibiting significant wave heights between 0.5 and 1.5 m. Data spans from
September 23rd to December 2nd, 2013.
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Figure 4. Spectrogram of pressure data collected from September 23 rd to December 2nd, 2013 within the dredge site
at Cape Canaveral using Nortek Aquadopp instrument. It can be noticed the bulk of the energy near the 0.1 Hz
frequency (10 sec period waves) characteristic of the inner-shelf near Cape Canaveral and coherent with offshore
buoy data. In addition, also notice the spreading in energy towards lower and higher frequencies, which is more
prominent during high energy events (i.e. bulks of red in the plot).
Figure 5. Spectrogram of pressure data collected from September 23rd to December 2nd, 2013 within the sand shoal
near Cape Canaveral using Nortek AWAC instrument. It can be noticed the bulk of the energy near the 0.1 Hz
frequency (10 sec period waves) characteristic of the inner-shelf near Cape Canaveral and coherent with offshore
buoy data. In addition, also notice the spreading in energy towards lower and higher frequencies, which is more
prominent during high energy events (i.e. bulks of red in the plot).
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Figure 6. Spectrogram of pressure data collected from September 23rd to November 15th, 2013 within the control site
North of Cape Canaveral using RDI Workhorse Sentinel. It can be noticed the bulk of the energy near the 0.1 Hz
frequency (10 sec period waves) characteristic of the inner-shelf near Cape Canaveral and coherent with offshore
buoy data. In addition, also notice the spreading in energy towards lower and higher frequencies, which is more
prominent during high energy events (i.e. bulks of red in the plot).
11.Tables
Table 1 Summary of characteristics of data collected using Nortek and RDI ADCP instruments.
Aspect
Instrument
Aquadopp
AWAC
Workhorse
Deployment Location
28˚ 23.998’ N
80˚ 26.347’ W
28˚ 23.651’ N
80˚ 25.411’ W
28˚ 32.882’ N
80˚ 28.952’ W
23/09/2013
10:00:00
23/09/2013
10:00:00
23/09/2013
13:00:00
DD/MM/YYYY HH:MM:SS
(GMT)
05/12/2013
15:01:01
02/12/2013
15:21:01
15/11/2013
01:17:03
Total time span (approx.)
2 months, 11 days
2 months, 9 days
1 month, 22 days
Waves intervala
Number of wave samplesb
Waves sampling ratec
3600 sec
1200
2 Hz
1920 sec
2048
2 Hz
7200 sec
2400
2 Hz
First measurement
DD/MM/YYYY HH:MM:SS
(GMT)
Last measurement
a
Time between successive waves measurements.
Number of times the instrument measure waves.
c Quantity of wave measurements per second.
b
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Table 2 Proposal milestones by term considering courses and research credits (hours per week) load and
publications.
Semester
Term
Course
Load
Research
Load
Total
1
Fall
2013
8
1
9
2
Spring
2014
7
2
9
3
Fall
2014
6
3
9
First draft to journal - JGR,
4
Spring
2015
4
5
9
First abstract for meeting AGU, December 2015
5
Fall
2015
4
5
9
Qualifying Exams
6
Spring
2016
4
5
9
Second draft to journal Geomorphology, Summer 2016
7
Fall
2016
3
6
9
Second abstract for meeting AGU, December 2016
8
Spring
2017
3
6
9
Second draft to journal Geomorphology, Summer 2017
9
Fall
2017
9
Third abstract for meeting AGU, December 2016
Dissertation to advisor
10
Spring
2018
Third draft to journal - JGR,
Summer 2018
Dissertation defense
TOTAL
3
6
3
6
9
45
45
90
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Considerations