Water masses distribution offshore the Sabrina Coast (East Antarctica)

Current glacier melt rates in West Antarctica substantially exceed those around the East Antarctic margin. The exception is Wilkes Land, where for example Totten Glacier underwent significant retreat between 2000 and 2012, underlining its sensitivity to climate change. This process is strongly influenced by ocean dynamics, which in turn changes in accordance with the evolution of the ice caps. Here, we present new oceanographic data (temperature, salinity, and dissolved oxygen) collected during austral summer 2017 offshore the Sabrina Coast (East Antarctica) from the continental shelf break to ca 3000 m depth. This area is characterized by very few oceanographic in situ observations. The main water masses of the study area, identified by analysing thermohaline properties, are the Antarctic Surface Water with potential temperature θ>-1.5 ∘C and salinity S<34.2 (σθ<27.55 kg m−3), the Winter Water with -1.92<θ<-1.75 ∘C and 34.0<S<34.5 (potential density, 27.55<σθ<27.7 kg m−3), the modified Circumpolar Deep Water with θ>0 ∘C and S>34.5 (σθ>27.7 kg m−3), and Antarctic Bottom Water with -0.50<θ<0 ∘C and 34.63<S<34.67 (27.83<σθ<27.85; neutral density γn>28.30 kg m−3). The latter is a mixture of dense waters from the Ross Sea and Adélie Land continental shelves. Such waters are influenced by the mixing processes they undergo as they move westward along the Antarctic margin, also interacting with the warmer Circumpolar Deep Water. The spatial distribution of water masses offshore the Sabrina Coast also appears to be strongly linked with the complex morpho-bathymetry of the slope and rise area, supporting the hypothesis that downslope processes contribute to shaping the architecture of the distal portion of the continental margin. Oceanographic data presented here can be downloaded from https://doi.org/10.25919/yyex-t381 (CSIRO; Van Graas, 2021).

How can BCO-DMO help with your oceanographic data?

BCO-DMO curates a database of research-ready data spanning the full range of marine ecosystem related measurements including in-situ and remotely sensed observations, experimental and model results, and synthesis products. We work closely with investigators to publish data and information from research projects supported by the National Science Foundation (NSF), as well as those supported by state, private, and other funding sources. BCO-DMO supports all phases of the data life cycle and ensures open access of well-curated project data and information. We employ F.A.I.R. Principles that comprise a set of values intended to guide data producers and publishers in establishing good data management practices that will enable effective reuse.

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

Fishing Ground Mapping of Demersal Fish in The Riau Islands Province Waters Related to the Oceanographic Factors

Research on the potential fishing ground for demersal fishing is a way to determine the areas that have the potential for fishing activities. Potential fishing ground mapping can be done by observing the dynamics of oceanographic parameters. The use of satellite imagery helps in providing oceanographic data in order to study the variability of oceanographic parameter. The aim of the study was to analyse the relationship between oceanographic parameter and demersal fish catch in order to validate potential fishing grounds for demersal fish. This research has been conducted in the waters of the Riau islands. Field observations and data collection including surveys of fishing grounds and identification of fish catches were carried out during March to August 2020. To support the analysis, Aqua-Modis Level-3 satellite data was used to observe the oceanographic variations. The data used in the analysis consisted of fishing grounds coordinate information, catches, sea surface temperature, chlorophyll-a, water depth, and salinity. The results showed that during the period 2010-2020, oceanographic dynamics did not experience significant changes or tended to be stable. Most of the research areas indicated appropriate categories for fishing activities based on the research parameters analysis. The correlation of the research parameters described a significant effect on fishing activities.

Topo-bathymetric and oceanographic datasets for coastal flooding risk assessment: French Flooding Prevention Action Program of Saint-Malo

The French Flooding Prevention Action Program of Saint-Malo requires assessment of coastal flooding risks. The first prerequisite is a knowledge of the topography and bathymetry of the bay of Saint-Malo. In addition to existing topo-bathymetric data, the acquisition of new multibeam bathymetric data is performed. The combination of these datasets allows the generation of two high resolution topo-bathymetric digital terrain models. Then, to understand the hydrodynamic conditions which cause coastal flooding, a dense and extensive oceanographic field experiment is conducted. Oceanographic data were acquired using a network of 22 moorings with 37 sensors, during winter 2018–2019. The network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers and 4 acoustic wave-current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset provides an overview of hydrodynamics in Saint-Malo bay and wave processes leading to coastal flooding. The combination of high-resolution topo-bathymetric and oceanographic datasets provides a unique capability for model validation and process studies. The topo-bathymetric and oceanographic datasets are available freely at doi : https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84, and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO.

A statistical approach for modeling shallow (<200 m) temperature profiles in the Pacific Ocean off northwestern Mexico

Temperature is perhaps the most important seawater property. It is a measure of the energy content in the ocean and it affects the metabolic rates, distribution, and abundance of species that are important from the economic and ecological points of view. Satellite-derived oceanographic data have been widely used to assess spatiotemporal variations of sea surface temperature on broad scales; satellites, however, are unable to reach subsurface levels, and obtaining reliable subsurface water temperature data is achieved by either numerical modeling or direct observations, the latter representing a very high-cost alternative. In this paper, a method for modeling temperature profiles is presented. A generalized additive mixed model (GAMM) with a gamma error distribution and an inverse link function was used to model shallow (200 m) temperature profiles in the Pacific Ocean off northwestern Mexico. The dataset included 656 profiles that were linearly interpolated at depth, which resulted in 127,595 observations. The database covered an area from 18.5º to 25.8ºN and from –114.5º to –105.9ºW in a time span from June 2007 to November 2016. The model included temperature as response variable; depth, surface dynamic topography, wind stress curl, latitude, longitude, and the Oceanic Niño Index as covariates; and month as random effect. The final model explained 86% of the total deviance of the dataset used to fit the GAMM. Although important deviations between the observations and the predictions of the model were observed, the results of the validation process and of predictions made on an independent dataset (correlation of observed vs. predicted temperature, 0.93; root-mean-square error, 1.5 ºC) were comparable to the results obtained with more complex modeling techniques, suggesting that this statistical approach is a valuable tool for modeling oceanographic data.

Cormorants Are Helping Characterize Coastal Ocean Environments

The Cormorant Oceanography Project is using sensors deployed on diving marine birds to collect broadly distributed oceanographic data in coastal regions around the world.

Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.