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OCR for page 98
Oceanography in 2025: Proceedings of a Workshop
Future of Nearshore Processes Research
Rob Holman*
BACKGROUND
The nearshore, generally defined as depths less than 10 m, is an energetic, wave-forced region whose dynamics are driven by the propagation of a random wave field over a shoaling bathymetry. The bathymetry, in turn, responds to these overlying wave motions, introducing a strong feedback and resulting rich system behavior such as complex sand bar systems. Predictions can be partitioned by time scale. Nowcasts, for which bathymetry is unchanging, are a physical oceanography problem with the mobility of the sediments introducing only small boundary layer effects. Predictions of the short-term system evolution of a specific bathymetry, akin to short-term weather forecasts for the atmosphere, can be carried out using coupled models of fluid and sediment response. Predictions for time scales beyond the prediction horizon (perhaps weeks), akin to the climate case, are not simply achievable through integration of weather models.
COMPLICATING FACTORS
Progress has been slowed by several characteristics of the nearshore problem:
*
College of Oceanic and Atmospheric Sciences, Oregon State University
OCR for page 99
Oceanography in 2025: Proceedings of a Workshop
Time scales of important processes span about ten orders of magnitude from interannual to breaking- or bottom-induced high frequency turbulence.
The location of the bottom, a sensitive boundary condition for wave dynamics, changes at O(1) on time scales of days.
Feedbacks between fluid motions and bathymetry are strong, driving the formation of patterns ranging from bottom ripples to rips channels and complex sand bars.
The response time scale of sand bars, days to weeks, is somewhat longer than those of external wave forcing so that the system is constantly in dynamic pursuit of equilibrium.
Depth goes to zero within the domain creating a singularity by definition.
In situ sampling in the nearshore is difficult due to the harsh climate and rapidly changing bathymetry.
DIRECTIONS OF PROGRESS
For time scales shorter than the prediction horizon, progress will involve improvements in measurement capabilities, in dynamics and in data assimilation procedures.
Due to the harsh nature of the environment and the rapid evolution of variables such as bathymetry, remote sensing will play a growing role in both research and applications. A renewed focus on the physics of electromagnetic scattering from the surface and interior will allow us to exploit previously empirical relationships between remote sensing signatures and geophysical variables, some of which will have no in situ measurement analog. For example, research into the dynamics of breaking-induced bubble populations and their signatures to optical, infrared and radar polarimetric sensors will allow estimation and understanding of nearshore radiation stress gradients, the primary driver of nearshore flows. Multi-sensor methods will be developed that exploit variations of response among sensors to improve measurement capabilities. For example, breaking waves, foam and a non-breaking sea surfaces all yield different signals at optical, infrared and radar frequencies with additional differences depending on polarization.
With the explosive growth of unmanned aerial vehicles (UAVs), there will be a proliferation of available platforms for overhead remote sensing. Improvements in small navigation systems, in miniaturized sensors and in light-weight computing will make UAV-based imaging very powerful once air traffic control and image co-registration issues are solved. Research methods developed for fixed camera systems like Argus for remotely measuring currents, wave spectra and evolving bathymetry
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Oceanography in 2025: Proceedings of a Workshop
will become operational for mobile platforms like UAVs and will be key to operational predictions.
The rapid commercial sector improvements in computing power, particularly in small packages with powerful object-oriented toolboxes, will allow substantial improvements in intelligent instrumentation. Imaging sensors will become smart and situationally aware, automating many of the tedious details such as distortion, gain correction, georeferencing and the calculation of derivative image products such as polarimetry images. Networks of sensors will be integrated easily.
Increasing computational power will also benefit in situ instruments. However, the logistics of deploying and maintaining instruments in the nearshore will always be daunting and we will likely see a growth in the use of small, cheap Lagrangian sensors that could measure surface waves and flow, bottom boundary physics and potentially depth. Water column tracer use will continue to expand and we will continue to learn more from infrared signatures.
The explosive growth in computing power will have obvious payoffs to nearshore modeling work. Previously parameterized processes will be increasingly resolvable and run-time reductions will allow greater use of ensemble-based methods. Recognizing that the limitations in nearshore predictive capability lies more with limited data and nonlinear feedback behavior than with limitations in understanding (excepting the dynamics of wave breaking), there will be major progress in data assimilation methods, particularly those that work with the remote sensing data that is increasingly available. Methods should be developed that explicitly exploit non-traditional measurements such as the width of the surf zone.
Hopefully we will discover simplifying principles to some of the vexing components of the nearshore problem. For example, bottom bed roughness may respond to overlying flows according to some macroscopic principle that simplifies bottom stress calculations (akin to turbulence principles). However, unlike turbulence, our models will need to recognize that time-variations in forcing mean that we are always in pursuit of equilibrium (if equilibrium states even exist). Overall, our largest problem is learning to deal with coupled feedback systems and their resulting complex behavior. We will need to discover appropriate statistical variables, for example to represent complex sand bars simply, and we will need to determine to what extent variability is a consequences of the basic feedbacks and is robust rather than sensitive to details in the physics.
