Renato Mendes

This interdisciplinary work demonstrates the viability of coupling ocean models with in situ robotic sampling in a dynamic coastal region as a means to increase model skill and prediction. Model-driven exploration closes the sample-assimilate-predict-direct loop within a virtuous cycle to refine prediction for a range of applications in a region characterized by harsh conditions, high variability, and diverse physical forcings, including bathymetry and coastal topography. By exploring the feasibility of coupling high-resolution autonomous underwater vehicle (AUV) sampling and data assimilation with a geostatistical model in a continuous loop, we aim to provide a new approach to understanding coastal dynamics and processes, while using modest computational resources. The novelty of this effort is threefold: the demonstration of coupling models with AUV sampling, the importance of targeted sampling, and the impact of loop closure toward model prediction.

This work presents a method for exploring a dynamic river plume boundary using an autonomous underwater vehicle with an on-board lightweight boundless model. The boundless approach is achieved by not constraining the path evaluations or the Gaussian random field to a predefined geographical area. In-situ decision-making enables targeted sampling of the ocean–river plume interaction. The data-driven and adaptive approaches provide the capability and opportunity to fully utilize the operational window for the vehicle. The method was developed using a simulated plume and vehicle, and results from simulation studies and successful field trials from the Douro River plume outside Porto, Portugal, are presented. The vehicle adapts its path based on the underway real-time assimilation of measurements, seeking to gain new information while not straying away from the front. Owing to the unpredictable shape and size of the river front, a model-based boundless method for adaptive sampling was constructed, generating potential waypoints as a function of the vehicle's position and the accumulated knowledge of the plume. Not bounding the spatial or geographical extent of the method allows for greater variation in plume shape and size. The river plume's extent is defined here as the area within the sharpest spatial salinity gradient, containing less saline water than the surrounding ocean. In the method, the depth of the sharpest vertical salinity gradient, or plume depth, is estimated using a 2-D Gaussian Process, where the plume depth is estimated from a dive and ascent envelope of the robot traversing the ocean in an undulating fashion. Computational efficiency is gained from the resulting low number of inputs to the Gaussian process, compared to the number of salinity measurements, ensuring rapid on-board adaption. The next waypoint is chosen as the first waypoint in a path that maximizes the weighted sum of uncertainty, estimated plume depth, and the absolute value of the difference between the current plume depth and the estimated river plume depth along the path. This encourages traversal of the plume in a fashion that enables the extent of the plume to be resolved in high detail. The data-driven method was field verified in the Douro River, proving the ability to track the river plume to balance exploration and exploitation behavior to maximize the information value of the mission in real time onboard the vehicle.

Ocean drifters provide essential data for understanding surface currents. As part of a summer school on marine robotics, we developed a curriculum around a low-cost drifter platform. Using this curriculum, students explore oceanographic concepts and engineering principles by designing the drifter with buoyancy and drag considerations, deploying it, and analyzing data to understand the relationship between Lagrangian trajectories and Eulerian flow fields. They also investigate the influence of tides, wind, and bathymetry on surface currents, comparing their observations with other datasets. At the same time, they build foundational engineering skills, including waterproofing, circuit assembly, sensor integration, and using hand tools, gaining practical experience in applying these to ocean systems.

Coastal municipalities and industries often discharge poorly treated wastewater into proximate marine and estuarine environments. The urban and/or effluent input can lead to eutrophication and lower water quality, as it holds high concentrations of nutrients and pollutants. One widely applied tool to increase effluent dispersion and direct it away from coastal areas, thus causing fewer impacts on human activities, is sea outfall. In Aveiro, Portugal, the São Jacinto sea outfall construction was completed in 1998; however, limited literature regarding the sea outfall’s monitoring using satellite data is available. The methodology in this study involved collecting four years’ worth (2016–2019) of satellite data to investigate visible traces of the interaction between the S. Jacinto sewage water mass and the Ria de Aveiro lagoon ecosystem using ocean color and spectral analysis, and producing new qualitative data regarding sea outfall plume dispersion monitoring through high-resolution Sentinel-2 imagery. The results showed a clear spectral signature of the sewage water mass, and a seasonal pattern was observed in which the plume was more evident in winter and autumn. Additionally, the coastal longshore current and the Aveiro lagoon’s runoff were able to restrict the marine outfall’s dispersion superficially. Ocean color data were revealed to be a factual and cost-effective tool to monitor the plume water. Finally, an exchange between the marine outfall water mass and Ria de Aveiro lagoon could happen in high tide under northern wind conditions. Therefore, it is important to monitor the water quality to ensure the coastal ecosystem’s good environmental state.

Remote sensing plays a vital role in understanding and managing the oceans. This technology is used to observe and monitor the ocean's physical, chemical, and biological properties, allowing scientists to detect large-scale changes in the marine environment, such as currents, sea surface temperature, and marine life populations. This data can then be utilized to track changes in the marine environment, assess the ocean’s health, and identify areas that require conservation efforts. In oceanography, a front is a boundary between two distinct water masses with different properties, such as temperature, salinity, and density. These fronts are critical scientific phenomena and have a cascade of events of significant importance to the fishing, marine biology, shipping, and logistics industries. For example, upwelling fronts are typically sites of strong vertical movements that bring cold, nutrient-rich water to the euphotic zone. This phenomenon is a primary factor controlling phytoplankton growth, which is the foundation of the marine food chain. It can also influence the concentration of floating marine litter, plastic, and other human-made objects. Our work comprised the search, revision, and implementation of three algorithms to detect oceanic fronts through the model and satellite sea surface temperature (SST) data. The chosen algorithms, Canny, Belkin O’Reilly, and Cayula-Cornillon, use SST data to provide historical frontal probability maps and near-real-time daily fronts identification. These algorithms were aggregated, simplified, and adapted for use in the Python programming language. Establishing free and open repositories helps to spur research, innovation, and development. That’s why we have created the following public repository (https://github.com/CoLAB-ATLANTIC/JUNO), which includes a set of notebooks outlining the step-by-step process for obtaining frontal probability or daily fronts maps using each of the three algorithms. The method consists of downloading the data (MUR or CMEMS), applying the algorithms, and saving the results in a NetCDF file. This repository will help scientists, researchers, and business people understand the ocean’s dynamics and make front detection more accessible. Through this repository, our work is making strides to advance the oceanography field and make ocean research more efficient and available to everyone.

The precise evaluation of the conservation status of salt marshes, the understanding of the mechanisms behind loss and degradation and the anticipation of future threats are fundamental to assist management solutions able to reverse the declining trend observed in these environments. Previous methods combining remote sensing and hydrodynamic modelling have been revealed to be effective in assessing degradation in restricted marshes, but unable to characterise dynamics of marshes at the estuarine scale. This study proposes an alternative methodology to identify priority areas for conservation by combining LANDSAT satellite remote sensing and numerical modelling. Using the Ria de Aveiro as case‐study, the main objectives of this work are to: (1) identify vegetation loss and degradation in estuarine marshes; (2) determine the mechanisms behind marsh deterioration; (3) anticipate future threats posed by climate‐induced mean sea level rise and; (4) provide management solutions aiming to protect and recover degraded salt marshes. LANDSAT imagery reveals that the most deteriorated salt marshes are located on the central and upper lagoon regions, and the numerical results support that marsh loss and degradation result from the increased tidal action triggered by the deepening of the lagoon channels. The increase in tidal currents promoted the erosion of marsh shoreline, whereas the changes in the inundation patterns over the intertidal areas triggered interior drowning. Climate‐induced mean sea level rise will likely exacerbate marsh deterioration, increasing the urgency to implement a management plan aiming to protect marshes from erosion and promote sedimentation in submerged marshes, to ensure their sustainability.

Internal waves (IWs) in the ocean span across a wide range of time and spatial scales and are now acknowledged as important sources of turbulence and mixing, with the largest observations having 200 m in amplitude and vertical velocities close to 0.5 m s⁻¹. Their origin is mostly tidal, but an increasing number of non-tidal generation mechanisms have also been observed. For instance, river plumes provide horizontally propagating density fronts, which were observed to generate IWs when transitioning from supercritical to subcritical flow. In this study, satellite imagery and autonomous underwater measurements are combined with numerical modeling to investigate IW generation from an initial subcritical density front originating at the Douro River plume (western Iberian coast). These unprecedented results may have important implications in near-shore dynamics since that suggest that rivers of moderate flow may play an important role in IW generation between fresh riverine and coastal waters.