Exchange of Plankton, Pollutants, and Particles Across the Nearshore Region

Exchange of material across the nearshore region, extending from the shoreline to a few kilometers offshore, determines the concentrations of pathogens and nutrients near the coast and the transport of larvae, whose cross-shore positions influence dispersal and recruitment. Here, we describe a framework for estimating the relative importance of cross-shore exchange mechanisms, including winds, Stokes drift, rip currents, internal waves, and diurnal heating and cooling. For each mechanism, we define an exchange velocity as a function of environmental conditions. The exchange velocity applies for organisms that keep a particular depth due to swimming or buoyancy. A related exchange diffusivity quantifies horizontal spreading of particles without enough vertical swimming speed or buoyancy to counteract turbulent velocities. This framework provides a way to determinewhich processes are important for cross-shore exchange for a particular study site, time period, and particle behavior.

Automatic Shoreline Position and Intertidal Foreshore Slope Detection from X-Band Radar Images using Modified Temporal Waterline Method with Corrected Wave Run-up

Automatic and accurate shoreline position and intertidal foreshore slope detection are challenging and significantly important for coastal dynamics. In the present study, a time series shoreline position and intertidal foreshore slope have been automatically detected using modified Temporal Waterline Method (mTWM) from time-averaged X-band radar images captured throughout the course of two-week tidal cycle variation over an area spanning 5.6 km on the Hasaki coast between 12 April 2005 and 31 December 2008. The methodology is based on the correlation map between the pixel intensity variation of the time-averaged X-band radar images and the binary signal of the tide level ranging from −0.8 m to 0.8 m. In order to ensure the binary signal represented each of the water levels in the intertidal shore profile, determining the water level direction-wise bottom elevation is considered as the modification. Random gaps were detected in the captured images owing to the unclear or oversaturation of the waterline signal. A horizontal shift in the detected shoreline positions was observed compared to the survey data previously collected at Hasaki Oceanographical Research Station (HORS). This horizontal shift can be attributed to wave breaking and high wave conditions. Wave set-up and run-up are the effects of wave breaking and high wave conditions, respectively. The correction of the wave set-up and run-up is considered to allow the upward shift of the water level position, as well as shoreline position, to the landward direction. The findings indicate that the shoreline positions derived by mTWM with the corrected wave run-up reasonably agree with the survey data. The mean absolute bias (MAB) between the survey data and the shoreline positions detected using mTWM with the corrected wave run-up is approximately 5.9 m, which is theoretically smaller than the spatial resolution of the radar measurements. The random gaps in the mTWM-derived shoreline positions are filled by Garcia’s data filling algorithm which is a Penalized Least Squares regression method by means of the Discrete Cosine Transform (PLS-DCT). The MAB between survey data and the gap filled shoreline positions detected using TWM with corrected wave run-up is approximately 5.9 m. The obtained results indicate the accuracy of the mTWM with corrected wave run-up integrated with Garcia’s method compared to the survey observations. The executed approach in this study is considered as an efficient and robust tool to automatically detect shoreline positions and intertidal foreshore slopes extracted from X-band radar images with the consideration of wave run-up correction.