Marine plastic pollution poses a growing threat to island systems, where interactions between large-scale ocean circulation and local coastal dynamics control debris transport and retention. Identifying persistent accumulation hotspots and their driving mechanisms is essential to support targeted mitigation and monitoring strategies in vulnerable insular environments. This study combines numerical modelling and field observations to investigate transport pathways and concentration hotspots of floating plastic debris around Tenerife, Canary Islands. Particle transport was simulated using the MOHID-Lagrangian model, forced with high-resolution hydrodynamic, wind, and wave data from the Copernicus Marine Service. Model outputs were evaluated against 25 beach clean-up surveys conducted between 2024 and 2025 across seven coastal sites. Results reveal there are pronounced seasonal contrasts in accumulation patterns. Winter circulation promotes widespread nearshore retention across the archipelago, favouring the persistence of locally sourced debris. In contrast, intensified summer advection associated with the Canary Current reduces overall retention but generates spatially focused accumulation hotspots, particularly along Tenerife's eastern coast. The southwestern coast exhibits year-round accumulation sustained by local retention processes. Field observations independently corroborate the modelled accumulation patterns. These findings demonstrate the critical role of seasonal circulation variability in shaping plastic accumulation in insular systems and provide a transferable modelling–observation framework to support prioritised monitoring and evidence-based coastal management strategies.

Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and evaluate a spatio-temporal machine-learning framework to predict harmful algal bloom (HAB) occurrence using exclusively satellite-derived predictors under realistic forecasting constraints. We characterised environmental and biological variability across shellfish production zones (L1-L9) using 5,882 observations, providing system-wide context. Predictive models were developed for zones L1-L2, a hotspot for Pseudo-nitzschia and domoic acid events, using a decade-long dataset (2013-2023; 1,440 observations; more than 1,000 satellite-based predictors including sea surface temperature, an upwelling index, chlorophyll-a, and plankton functional types). Sampling locations were partitioned into ecologically meaningful sub-regions using a river-aware spatial clustering scheme. A stringent spatio-temporal cross-validation strategy that simultaneously withholds entire years and spatial clusters prevents leakage and closely mimics real-world forecasting conditions. HAB occurrence proved moderately predictable across model classes and feature configurations. Ensemble tree-based methods achieved the strongest discrimination: Random Forest reached 0.74 +/- 0.05 with environmental predictors; Extra Trees reached 0.77 +/- 0.06 with biological variables added. Feature-importance analyses revealed that seasonal structure, spatial context, and lagged environmental conditions dominate model decisions, while biological indicators refine bloom likelihood within physically favourable periods. The framework demonstrates operationally relevant skill for satellite-supported HAB early-warning systems along eastern boundary upwelling coasts.

Microplastics (MPs; < 5 mm) are a well-established environmental problem. Thus, this study aimed to address critical gaps related to MP in Brazilian rivers through a systematic review and mathematical modeling to assess the dispersion and accumulation zones of floating particles along the Santa Catarina state coast. 35 studies were reviewed, considering water, sediment, and biota. Sinos River, Rio Grande do Sul state, had the highest concentration of MPs in water (> 330 × 103 items/m3). Most studies reported a variation between 1 and 100 items/m3 in water. In sediments, the Tietê River, São Paulo state, had the highest level (∼190 × 103 items/kg). Blue fibers and PE and PP polymers were the predominant characteristics. Fish were the most studied organisms, but freshwater shrimp and stingrays also had MPs internally. The application of Lagrangian tracers and hydrodynamic model, considering four river sources, revealed that floating MPs tend to accumulate in the central-north region of the Santa Catarina coast. Due to oceanographic conditions and river flow, the Itajaí-Açu River can influence the spatial distribution of MPs along the entire coast. Rivers can generate critical accumulation points near their mouths. Thus, these results offer important insights to be considered in combating MPs pollution, from river to marine environments.

Portugal is the country in Europe where the death rate in winter and summer has the highest correlation with outdoor temperatures. The Portuguese National Institute of Public Health Ricardo Jorge has developed a national warning system for heat waves called ICARO, which has been in place since 1999 (and is the oldest in Europe). However, it presents some limitations, namely, the low spatial resolution (five regions in Portugal’s mainland), the low temporal forecasting period (one day), and the fact that it was only accessible to health authorities until very recently. This work describes the development of a new public dashboard that uses a new early warning index for extreme weather events, the CLIMAEXTREMO index, which extends the current warning system by improving the current forecasting models for risk by integrating new sources of public data and increasing the spatial and time resolution of the warnings to the municipality or the parish level. The new index is a combination of a new model to estimate the relative mortality increase (updating the model used in ICARO) together with a model of the indoor temperature of building archetypes for all municipalities and a vulnerability index that considers socio-demographic economic indicators. This work discusses the results of the new risk indicator for the heat waves that occurred in Portugal at the end of June and mid-August 2023, and it shows that the index was able to indicate a high risk for the municipalities that had an increase in the number of deaths during that period.

Lisbon, the capital of Portugal, is located on the mouth of the Tagus River, where the current speed and direction are mainly governed by the local tides. The narrowest part of the river is located between Lisbon downtown and the western side of the city. This narrowing accelerates the water flow and makes it a potential site for a tidal energy system. A preliminary study based on numerical simulations using the software MOHID was conducted to assess potential energy yields throughout the estuary using freely available hindcast data. This allowed the selection of three potential sites for a tidal turbine in the Lisbon area based on yearly tidal and current energy density: off the coasts of Cacilhas, Bel´em, and Pa¸co de Arcos. However, even if the current model has been previously validated with experimental data, it was only done at two locations in the estuary that are far from the potential sites. Due to the complexity of the phenomena driving the current speed at these locations, additional validation is necessary before committing to a specific site. This paper presents the numerical analysis, the experimental campaign and the validation of the results at those three locations. Drifters with sails of 3.4m and 4.5m depth were released at least 8 times at each location and retrieved after 15 minutes of free drift. Each drifter was tracked with a GPS and the current speed and direction were derived from the drifters’ trajectory. The analysis of the experimental data shows good agreement with the model, even though an error of 0.3m/s is consistent throughout the tests. This paper concludes with a discussion on the model temporal and spatial discretisation and the forces included in the analysis that could be the source of the differences between the numerical and experimental data.

In ocean modelling systems, transfer of information is typically from regional domain (RD) to local domain (LD – one-way) and, where there is an advantage in upscaling the LD into the RD (two-way), both domains are run at the same time transferring information online, resulting in two-way nesting systems. This article develops an offline (relaxation-based) upscaling approach, enabling the upscaling of several LDs simultaneously, run by different institutions or modelling tools at the same time as the RD is improved with the best available local knowledge. The approach is applied to the Tagus Region of Freshwater Influence, Portugal, where incremental upscaling simulations are tested, compared to the traditional downscaling approach, and validated against field data. Results show smoother solutions with upscaling, particularly at the boundaries between domains, where a less restricted estuarine plume significantly improved the surface salinity patterns at both sides of the boundary, during an extreme precipitation event.

Knowledge about streamflow regimes and values is essential for different activities and situations in which justified decisions must be made. However, streamflow behavior is commonly assumed to be non-linear, being controlled by various mechanisms that act on different temporal and spatial scales, making its estimation challenging. An example is the construction and operation of infrastructures such as dams and reservoirs in rivers. The challenges faced by modelers to correctly describe the impact of dams on hydrological systems are considerable. In this study, an already implemented solution of the MOHID-Land (where MOHID stands for HYDrodinamic MOdel, or MOdelo HIDrodinâmico in Portuguese) model for a natural flow regime in the Ulla River basin was considered as a baseline. The watershed referred to includes three reservoirs. Outflow values were estimated considering a basic operation rule for two of them (run-of-the-river dams) and considering a data-driven model of a convolutional long short-term memory (CLSTM) type for the other (high-capacity dam). The outflow values obtained with the CLSTM model were imposed in the hydrological model, while the hydrological model fed the CLSTM model with the level and the inflow of the reservoir. This coupled system was evaluated daily using two hydrometric stations located downstream of the reservoirs, resulting in an improved performance compared with the baseline application. The analysis of the modeled values with and without reservoirs further demonstrated that considering dams' operations in the hydrological model resulted in an increase in the streamflow during the dry season and a decrease during the wet season but with no differences in the average streamflow. The coupled system is thus a promising solution for improving streamflow estimates in modified catchments.

Several papers from the 1970s to 1990s reported a persistent eastward current in the Ilha Grande channel, which used no longer than 30 days’ time series of measured data and numerical models limited to the technology available at the time. Until now, this current was attributed to the density differences in the interior of Ilha Grande Bay. In the present work, the analysis of a 15-month time series of current data associated with the evaluation of three-dimensional numerical experiments allowed the investigation of this current in greater detail. Thus, it was possible to verify that the eastward along-channel current inside the channel behaves as a permanent circulation. Unlike it was believed, this current is associated with a subsurface eastward coastal current that flows along the coast. This current, in turn, is caused by the adjustment of density fields over the adjacent shelf and oceanic region.

Upscaling methodologies, enforced on nested ocean modelling grids, have become a subject of more intense research due to their benefits in modelling highly dynamic coastal areas. In this paper, an upscaling algorithm is developed for the 3D-MOHID Water model, enabling TwoWay implementations, which considers the nudging of a child domain's velocities, temperature and salinity fields by a parent domain. The algorithm is validated for a schematic case, and then applied to Tagus Region of Freshwater Influence (TagusROFI, Portugal). Results for the schematic case show an improvement of the parent domain while generating minimal mass conservation loss. TagusROFI model domains under TwoWay nesting improved the salinity transition between domain boundaries when reached by the estuarine plume. This new 3D-MOHID Water model feature improves the study of extreme scenarios where OneWay downscaling produces discontinuities on the nested domain's open boundaries, and enables the implementation of higher resolution domains with smaller size grids.

[ ]recently, CoVs were not assumed to be highly pathogenic in humans (Zaki et al , 2012), but this perception changed after the recent Severe Acute Respiratory Syndrome CoV (SARS-CoV), Middle East Respiratory Syndrome CoV (MERS-CoV), and the current severe acute respiratory syndrome (SARS-CoV-2) outbreaks [ ]viable viruses were detected in feces, implicating these as a potential source of SARS-CoV-2 transmission by fecal contamination (Wang et al , 2020) SARS-CoV-2 was also recovered from nasal washes, saliva, urine, and feces of infected animals (ferrets) up to 8 days post-infection (Kim et al , 2020) [ ]while still poorly understood, the GI symptoms reported from the start of COVID-19 epidemic at least suggest that fecal–oral transmission of SARS-CoV-2 may occur (Yeo et al , 2020) Respiratory droplets are the main human-to-human mechanism of transmission, but fecal shedding with environmental contamination is increasingly seen as having an important role in viral spread (Bhowmick et al , 2020;Dona et al , 2020) [ ]the viability of SARS-CoV-2 in wastewater has not been proven (Senatore et al , 2021), and further studies are needed to investigate its fate in wastewater (Collivignarelli et al , 2020) and in natural water bodies receiving treated or untreated wastewater (Kumar et al , 2021)

Nowadays flood warning systems are extremely important since they can provide critical information that can protect property and save lives. These systems should alert about whether a flood should be expected, when it will occur and how severe it will be.A warning system can be based on the analysis of historical events and a good monitoring system or it can be based on the capacity of predict the channel flow in key locations. In the second case, these type of systems, known as forecast systems, consider the meteorological predictions as driving forces for a hydrological model which estimates the channel flow for the next few hours and days, considering the processes that take place in a watershed. A hydrological forecast can only be reliable when a good calibration and validation of watershed processes is performed.This study aims to calibrate and validate the channel flow in Ulla river watershed (Galicia, Spain) using MOHID-Land model considering a sensitivity analysis of some parameters and user’s options that can affect model results. MOHID-Land model is a physically based, fully distributed model that considers four compartments or mediums: atmosphere, porous media, soil surface and river network. Water dynamics is computed through the different mediums using mass and momentum conservations equations.The model was firstly implemented in the studied domain with a resolution of 500 m. Data inputs included the digital Global Digital Elevation Model from NASA with a resolution of 30 m; the Corine Land Cover map from 2012 with a resolution of 100m; the soil hydraulic properties from the multilayered European Soil Hydraulic Database with a resolution of 250 m; hourly meteorological data (precipitation, solar radiation, wind velocity, air temperature, surface pressure and dew point temperature) from ERA5-Reanalysis with a resolution of 31 km; and daily total outflow for three reservoirs present in this watershed.The sensitivity analysis was performed to test the impact of grid and elevation data source resolution, cross-sections geometry, soil parameters, vertical soil discretization, surface and channel Manning coefficients, the infiltration process and deactivation of different modules such as porous media and vegetation on streamflow. The results of these tests were compared with a reference simulation by the analysis of flow duration curves.The hydrological model was calibrated and validated in 4 hydrometric stations not influenced by reservoirs and the river flows considering the reservoirs operation were compared with measured values in 2 hydrometric stations. Four statistical parameters (R2, RMSE, PBIAS and NSE) were used to evaluate model performance at a daily scale which was considered good.

Aquaculture has become the fastest-growing sector of the food industry worldwide. The increase of intensive aquaculture practices, however, has been raising global concern about economic and social impacts, but mostly due to the associated potential environmental impacts. The aim of this report is to make a preliminary assessment of the impact of an intensive sea bass aquaculture (Dicentrarchus labrax, L. 1758) on surrounding coastal waters. The aquaculture site is located at the SW Iberian coast (Sines, Portugal), having 16 cages, each holding approximately 150,000 specimens at different stages of growth. We present a spatial and temporal description of environmental physical, chemical, and biological parameters taken in the course of four monitoring campaigns conducted between June 2018 and April 2019. All monitored parameters, except phosphate concentration in October only at one sampling station, showed values within the desirable ranges for marine finfish production and the natural range of Portuguese coastal waters. So far, results do not reveal any detrimental impact of the production units on local water quality, although more research is needed. The preliminary findings suggest that the lack of stress on the receiving waters may be attributed to the hydrodynamic regime in the production area, the feeding strategy, and the dimension of the production.

Drowning accidents followed by the disappearance of the body are particularly distressing events. When such tragedy strikes, Search and Rescue Operations are usually deployed to recover the body. The efficiency of such efforts can be enhanced by timely data and appropriate data integration tools, such as operational prediction systems relying on numerical models or other data sources. In this paper, we propose four stages for Search and Rescue Operations after drowning accidents and briefly address the critical role of ocean observations at each stage, as well as the relevancy of available computational resources. The potential of the combination of different data sources on the state of the sea to provide better insights is discussed. This work encourages oceanographers, data scientists and relevant marine stakeholders to produce knowledge and tools to support Search and Rescue Operations after drowning accidents.

Hydrological models are increasingly used for studying watershed behavior and its response to past and future events. The main objective of this study was to conduct a sensitivity analysis of the MOHID-Land model and identify the most relevant parameters/processes influencing river flow generation. MOHID-Land is a complex, physically based, three-dimensional model used for catchment-scale applications. A reference simulation was implemented in the Ulla River watershed, northwestern Spain. The sensitivity analysis focused on sixteen parameters/processes influencing water dynamics at that scale. River flow generation was influenced by the resolution of the simulation grid, soil water infiltration, and crop evapotranspiration. Baseflow was affected by soil hydraulic properties, the depth of the soil profile, and the dimensions of the river cross-sections. Peak flows were mostly constrained by Manning’s coefficient in the river network, as well as the dimensions of the river cross-sections. The MOHID-Land model was then used to simulate daily streamflow during a 10-year period (2008−2017). Model simulations were compared against measured data at four hydrometric stations characterizing the natural flow regime of the Ulla River, resulting in coefficients of determination (R2) from 0.56 to 0.85; ratios of the standard deviation of the root mean square error to observation (RSR) between 0.4 and 0.67, and Nash and Sutcliffe model efficiency (NSE) values ranging from 0.55 to 0.84. The MOHID-Land model thus has the capacity to reproduce watershed behavior at a daily scale with reliable accuracy, constituting a powerful tool to improve water governance at the watershed scale.

Toxins from harmful algae and certain food pathogens (Escherichia coli and Norovirus) found in shellfish can cause significant health problems to the public and have a negative impact on the economy. For the most part, these outbreaks cannot be prevented but, with the right technology and know-how, they can be predicted. These Early Warning Systems (EWS) require reliable data from multiple sources: satellite imagery, in situ data and numerical tools. The data is processed and analyzed and a short-term forecast is produced. Computational science is at the heart of any EWS. Current models and forecast systems are becoming increasingly sophisticated as more is known about the dynamics of an outbreak. This paper discusses the need, main components and future challenges of EWS.

An ecosystem model of the Minho estuary (NW coast of Iberian Peninsula) was implemented in AQUATOX 3.1 (US-EPA) to check whether the effects of temperature rise, dry years, rainy years and river flow decrease acting isolated (single stressor) or combined (multiple stressor) would induce the same type of response on macro-invertebrate biomass variation and to identify the type of stressor interactions. The model was parameterised with site-specific and species-specific data and accounts for a food web with primary producers, benthic invertebrates, fish and detritus, with 12 biotic groups, 4 groups of detritus and 12 forcing functions. Results showed that macroinvertebrate biomass responded differently to single stressor and multiple stressor scenarios and interactions among the tested stressors were antagonistic. During medium term simulations (similar to 6 years), the biomass of macroinvertebrate communities from the Minho estuary was maintained or slightly increased in single scenarios of temperature rise but the occurrence of antagonistic interactions between temperature rise, river flow decrease, dry years and rainy years counteracted the increasing tendency in multiple stressor scenarios. Overall, the present results highlight the strong dependence of the system's biomass and production on hydrodynamics and contribute to increase knowledge on the mechanistic processes behind this. Thus, we recommend an ecosystem-based management of the Minho River basin supported by models such as the present one.

Coastal zones have always been preferential areas for human settlement, mostly due to their natural resources. However, human occupation poses complex problems and requires proper management tools. Numerical models rank among those tools and offer a way to evaluate and anticipate the impact of human pressures on the environment. This work describes the implementation of a hydrodynamic 3-dimensional computational model for the coastal zone in Sines, Portugal. This implementation is done with the MOHID model which uses a finite volume approach and an Arakawa-C staggered grid for spatial equation discretization and a semi-implicit ADI algorithm for time discretization. Sines coastal area is under significant pressure from human activities, and the model implementation targets the location of a fish aquaculture. Validation of the model was done comparing model results with in situ data observations. The comparison shows relatively small differences between model and observations, indicating a good simulation of the hydrodynamics of this system.