Distribution and Source Sites of Nonlinear Internal Waves Northeast of Hainan Island

The distribution and source sites of nonlinear internal waves (NLIWs) northeast of Hainan Island were investigated using satellite observations and a wavefront propagation model. Satellite observations show two types of NLIWs (here referred to as type-S and type-D waves). The type-S waves are spaced at a semidiurnal tidal period and the type-D waves are spaced at a diurnal tidal period. The spatial distribution of the two types of NLIWs displays a sandwich structure in which the middle region is influenced by both types of NLIWs, and the northern and southern regions are governed by the type-S and type-D waves, respectively. Solving the wavefront model yields good agreement between simulated and observed wavefronts from the Luzon Strait to Hainan Island. We conclude that the NLIWs originate from the Luzon Strait.

Internal solitary waves propagation speed estimation in the northern-part of Lombok Strait observed by Sentinel-1 SAR and Himawari-8 images

Remotely sensed data, both Synthetic Aperture Radar (SAR) and optical sensors, significantly contribute to the study and understanding internal solitary wave (ISW) dynamics in the ocean. Pairs of SAR and optical sensors were analyzed to estimate the ISW propagation speed in the northern-part of Lombok Strait. ISW propagation speed estimation used an image from Sentinel-1 SAR and three image pairs of Himawari-8 on 29 October 2018 with a time difference of 409 minutes. Sentinel-1 wide-swath imagery (250 km x 400 km) from two adjacent scenes can provide information on multiple ISW packets evolution in the northern-part of Lombok Strait. ISW propagation speed estimation on Sentinel-1 SAR image using the simple estimation by measuring the interpacket distance and dividing by the semidiurnal tidal period. The high temporal resolution of the optical sensor from Himawari-8 can estimate the ISW propagation speed using two different approaches. ISW propagation speed estimation using the semidiurnal tidal period from Sentinel-1 and Himawari-8 showed almost similar values. Sentinel-1 estimation results are 2.69 m.s−1 (Lombok Strait) and 1.30 m.s−1 (northern-part area), Himawari-8 results are 2.52 m.s−1 (Lombok Strait) and 1.27 m.s−1 (northern-part area). ISW propagation speed variability in the northern-part of the Lombok Strait shown in this study.

A tidally driven fjord-like strait close to an amphidromic region

The strait studied in this paper, “Sundalagið Norður”, is the northern part of a narrow body of seawater separating the two largest islands in the Faroe Islands (Faroes). It has shallow sills in both ends and considerably deeper waters in between. South of the southern end of the strait there is an amphidromic region for the semidiurnal tides so that the tidal range is much lower south of the strait than north of it. The resulting tidal forcing generates periodically varying inflow of seawater across the northern sill, but only a part of that manages to cross the narrow and shallow southern sill. Combined with a large input of freshwater, this gives the strait a fjord-like character. To investigate how this fjord-like character affects the circulation within the strait and its exchanges with outside waters, a pilot project was initiated to simulate the dynamics of the strait with a high-resolution ocean model for a month. The model simulations show clearly the dominance of tidal forcing over freshwater (estuarine) and wind on timescales up to a day. On longer timescales, the simulations indicate systematic variations in the net flows (averaged over a diurnal tidal period) through both the upper and deeper layers. These long-period variations of net flow in the model simulations are forced by sea level differences between both ends of the strait generated by the dominant fortnightly and monthly tidal constituents (Mf, MSf, Mm, MSm). Harmonic analysis of sea level records from two tide gauges located off each end of the strait demonstrates that this behaviour is not a model artefact and it has pronounced effects on the strait. Not only does it induce long-period (mainly fortnightly) variations in the net flow through the strait, but it also generates variations in the estuarine characteristics. According to the model simulations, periods with net southward flow, typically lasting a week, have a strait-like character with net southward flow almost everywhere. Periods with net northward flow, in contrast, have a more fjord-like character with stronger salinity stratification and a southward counter-flow in the deep layer. This also induces a large difference in renewal rate of the deep water between the two periods, which is important to consider for human utilization of the strait, especially the local aquaculture plant. The combination of topographic, freshwater, and tidal characteristics creating these long-period variations is rather unusual, and it is not known whether similar systems exist elsewhere, but the long-period variations tend to be masked by the stronger semidiurnal and diurnal variations and may easily be overlooked.

Freshwater Composition and Connectivity of the Connecticut River Plume During Ambient Flood Tides

The Connecticut River plume interacts with the strong tidal currents of the ambient receiving waters in eastern Long Island Sound. The plume formed during ambient flood tides is studied as an example of tidal river plumes entering into energetic ambient tidal environments in estuaries or continental shelves. Conservative passive freshwater tracers within a high-resolution nested hydrodynamic model are applied to determine how source waters from different parts of the tidal cycle contribute to plume composition and interact with bounding plume fronts. The connection to source waters can be cut off only under low-discharge conditions, when tides reverse surface flow through the mouth after max ambient flood. Upstream plume extent is limited because ambient tidal currents arrest the opposing plume propagation, as the tidal internal Froude number exceeds one. The downstream extent of the tidal plume always is within 20 km from the mouth, which is less than twice the ambient tidal excursion. Freshwaters in the river during the preceding ambient ebb are the oldest found in the new flood plume. Connectivity with source waters and plume fronts exhibits a strong upstream-to-downstream asymmetry. The arrested upstream front has high connectivity, as all freshwaters exiting the mouth immediately interact with this boundary. The downstream plume front has the lowest overall connectivity, as interaction is limited to the oldest waters since younger interior waters do not overtake this front. The offshore front and inshore boundary exhibit a downstream progression from younger to older waters and decreasing overall connectivity with source waters. Plume-averaged freshwater tracer concentrations and variances both exhibit an initial growth period followed by a longer decay period for the remainder of the tidal period. The plume-averaged tracer variance is increased by mouth inputs, decreased by entrainment, and destroyed by internal mixing. Peak entrainment velocities for younger waters are higher than values for older waters, indicating stronger entrainment closer to the mouth. Entrainment and mixing time scales (1–4 h at max ambient flood) are both shorter than half a tidal period, indicating entrainment and mixing are vigorous enough to rapidly diminish tracer variance within the plume.

On a new formulation for energy transfer between convection and fast tides with application to giant planets and solar type stars

All the studies of the interaction between tides and a convective flow assume that the large scale tides can be described as a mean shear flow which is damped by small scale fluctuating convective eddies. The convective Reynolds stress is calculated using mixing length theory, accounting for a sharp suppression of dissipation when the turnover timescale is larger than the tidal period. This yields tidal dissipation rates several orders of magnitude too small to account for the circularization periods of late–type binaries or the tidal dissipation factor of giant planets. Here, we argue that the above description is inconsistent, because fluctuations and mean flow should be identified based on the timescale, not on the spatial scale, on which they vary. Therefore, the standard picture should be reversed, with the fluctuations being the tidal oscillations and the mean shear flow provided by the largest convective eddies. We assume that energy is locally transferred from the tides to the convective flow. Using this assumption, we obtain values for the tidal Q factor of Jupiter and Saturn and for the circularization periods of PMS binaries in good agreement with observations. The timescales obtained with the equilibrium tide approximation are however still 40 times too large to account for the circularization periods of late–type binaries. For these systems, shear in the tachocline or at the base of the convective zone may be the main cause of tidal dissipation.

Build Internet of Thing Based Sea Level Detection System

Tides can be defined as waves generated by the interaction between the earth, sun and moon. The peak of the wave is called the high tide (High Water/HW) and the tide is called low tide (Low Water/LW). The tidal period is the time between the crest or trough of a wave to the crest or tide of the next wave. The time of the tidal period varies from 12 hours 25 minutes to 24 hours 50 minutes. The results of preliminary observations to the desa nelayan in Kuala Langsa found that most of the outskirt fishermen and anglers always look for fish and other marine animals in high tide conditions. Nevertheless, they do not know for sure when the sea water will rise. They detect sea level rise using only a simple tool such as a stick attached to the river and from their house poles under the water. This condition can be detrimental to fishermen both time and economically. The author also began to think about how to create a detection tool that can provide accurate information on sea level conditions at any time to fishermen and anglers via smartphones. The author finally got the best idea, namely creating a "Sea Level Detection System in Kuala Langsa for Fishermen and Anglers Using NodeMCU and Internet of Thing (IoT) Based Waterlevel Sensors and Telegram Messenger. This research has been successfully carried out, where the IoT-based sea level detection tool can work well during experiments in the laboratory and during implementation at the Kuala Langsa estuary, Langsa City.

Tide-Surge-Wave Interaction in the Taiwan Strait during Typhoons Soudelor (2015) and Dujuan (2015)

Typhoons Soudelor (2015) and Dujuan (2015) were two of the strongest storms to affect the Taiwan Strait in 2015. This study investigated the response of the waters on the western bank of the Taiwan Strait to the passage of Soudelor and Dujuan. This included an investigation of the resonant coupling between the tide and storm surge, typhoon wave variation caused by the storm tide, and wave-induced water level rise. Analyses conducted using numerical model simulations and observations from tidal stations and buoys, obtained during the passage of both Soudelor and Dujuan, revealed that resonant coupling between the astronomical tide and storm surge in the Taiwan Strait was prominent, which resulted in tidal period oscillation on the storm surge and reduced tidal range. The tide wave arrived earlier than the predicted astronomical tide because of the existence of the storm surge, which was attributable to acceleration of the tidal wave caused by the water level rise. Wave height observations showed that the storm tide predominantly affected the waves, which resulted in wave heights that oscillated within the tidal period. Numerical experiments indicated that both the current and the water level affected wave height. Waves were affected mainly by the current in the middle of the Taiwan Strait, but mostly by water level when the water level was comparable with water depth. Wave setup simulations revealed that wave setup also oscillated within the tidal period, and that local bathymetry was the most important influencing factor of wave setup distribution.

Quantile function based, optical characterisation of the Nelson River plume dispersion in Hudson Bay (Canada)

<p>This study hypothesizes, that sediment-based optical estimation of plume extent underestimates the dispersion limit of river discharge in marine waters. Optically active Colour Dissolved Organic Matter (CDOM) has been used as a tracer along with suspended sediments to validate the hypothesis, for the Nelson River (NR) plume in southwest Hudson Bay (HB). Remote sensing reflectance (R<sub>rs</sub>) band ratio (R<sub>rs </sub>678nm/488nm) was tuned in to retrieve CDOM absorbance at 412nm: a<sub>CDOM</sub> (412nm) from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Similarly, R<sub>rs </sub>(678nm) was used for TSS concentration retrieval. Plume dispersion characteristics were compared for spring and neap tidal periods during a high (2005) and a normal (2006) discharge year. Quantile function (QF) provides variable values (a<sub>CDOM</sub>, TSS concentration) for a given cumulative probability. It was assumed that 0.90 QF (10% of the data distribution) is a representative of the river plume function, while 0.05 QF (95% of the data distribution) represents the ocean end-member values. These threshold values were calculated for each of the cumulative areas of coastal waters of southwest HB with NR mouth as the origin, limited till 500 km radial distance. Thresholds averaged over 0 km to 50km was used as the reference for estimation of river plume dilution. A conditional approach of the plume extent limit was set to the point at which the QF dilution (0.90-0.50) equals to the QF dilution (0.90-0.05). This dilution conditionality was satisfied for CDOM but was never achieved for TSS, indicating the additional source of sediment influx. Plume discharge volume was proportional to the a<sub>CDOM</sub> (412nm) plume extent threshold for all tidal periods. This threshold value was observed at ~250km during neap ebb tide (NET) for both years. While a shorter extent (150 km) was observed during the 2005 spring ebb tide (SET) and ~300km for the same tidal period during 2006. The point of minimum variance within a<sub>CDOM</sub> (412nm) dilution (0.50 to 0.90 QF) was used as a representative of the freshwater-marine boundary. An extent of 400km (SET) and 350km (NET) for 2005 was recorded, with a 500km extent observed for both ebb tides in 2006.  A lower a<sub>CDOM </sub>(412nm) dispersion threshold was observed for the SET (0.39 m<sup>-1</sup>) than the NET (0.59 m<sup>-1</sup>) for 2005 and vice-versa for 2006. A weak, negative correlation of a<sub>CDOM </sub>(412nm) minimum variance threshold between 2005 and 2006 indicated a weaker influence of discharge beyond the maximum variance threshold. This dispersion is expected to be controlled by a cyclonic eddy in southwest HB. Modeled dilution slope of a<sub>CDOM </sub>(412nm), <200km showed a significant influence of discharge with higher slope values obtained for 2005 for both spring and neap ebb tide conditions. The minimum variance of TSS concentration dilution is limited within 200-250 km, while its maximum variance is limited within 100km. Thus, TSS underestimates the river plume dispersion extent, which is better represented by CDOM of terrestrial provenance.</p>

Shorebird feeding specialists differ in how environmental conditions alter their foraging time

Feeding specialization is a common cause of individual variation. Fitness payoffs of specialization vary with environmental conditions, but the underlying behavioral mechanisms are poorly understood. Such mechanistic knowledge, however, is crucial to reliably predict responses of heterogeneous populations to environmental change. We quantified spatiotemporal allocation of foraging behavior in wintering Eurasian oystercatchers (Haematopus ostralegus), a species in which feeding specialization can be inferred from bill shape. We combined global positioning system (GPS) and accelerometer data to quantify foraging time of 64 individuals for every tidal period in one or two winter seasons. Individuals varied widely in foraging time (3.7–6.5 h per tidal period) and individuals that spend more time foraging had lower inferred survival. Feeding specialization appeared a major determinant of individual variation in foraging time and its spatiotemporal allocation. Visually hunting worm specialists foraged more during day time and complemented intertidal foraging with grassland foraging when the exposure of intertidal flats was limited and nights were well illuminated. Shellfish specialists increased total foraging time in cold weather, whereas foraging time of worm specialists decreased as frosty grasslands became inaccessible. Our results imply that worm specialists may be most sensitive to cold snaps and daytime disturbance, whereas shellfish specialists are most sensitive to high water levels. These behavioral responses can be implemented in population models to predict the vulnerability of heterogeneous populations to environmental change and, thereby, provide a shortcut to long-term population studies that require fitness data across many years and conditions to make similar projections.