Pub Date : 2026-02-01Epub Date: 2026-01-19DOI: 10.1016/j.ocemod.2026.102684
Zhiyong Peng , Jiehua Wu , Peng Wang
Storm surge represents a significant coastal hazard in Fujian Province, where intense wave activity can exacerbate inundation by imparting additional momentum and mass flux to coastal waters. Accurate simulation of storm surge events requires explicit consideration of wave-current interactions, particularly wave-enhanced bottom stress. In this study, the effects of wave-enhanced bottom stress on storm surge induced by Typhoon Doksuri (2023) are investigated using numerical simulations. Results demonstrate that wave-enhanced bottom stress contributes to a 4-8 % increase in nearshore storm surge by modifying volume transport through the strait. Moreover, alternative parameterizations of bottom stress produce substantial differences in simulated surge magnitude. These findings highlight the critical importance of appropriately representing wave-enhanced bottom stress in numerical models to improve the reliability of storm surge and coastal inundation forecasts.
{"title":"How does wave-enhanced bottom stress affect typhoon-induced storm surge","authors":"Zhiyong Peng , Jiehua Wu , Peng Wang","doi":"10.1016/j.ocemod.2026.102684","DOIUrl":"10.1016/j.ocemod.2026.102684","url":null,"abstract":"<div><div>Storm surge represents a significant coastal hazard in Fujian Province, where intense wave activity can exacerbate inundation by imparting additional momentum and mass flux to coastal waters. Accurate simulation of storm surge events requires explicit consideration of wave-current interactions, particularly wave-enhanced bottom stress. In this study, the effects of wave-enhanced bottom stress on storm surge induced by Typhoon Doksuri (2023) are investigated using numerical simulations. Results demonstrate that wave-enhanced bottom stress contributes to a 4-8 % increase in nearshore storm surge by modifying volume transport through the strait. Moreover, alternative parameterizations of bottom stress produce substantial differences in simulated surge magnitude. These findings highlight the critical importance of appropriately representing wave-enhanced bottom stress in numerical models to improve the reliability of storm surge and coastal inundation forecasts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102684"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-28DOI: 10.1016/j.ocemod.2025.102642
Hyungju Yoo , Haocheng Yu , Y. Joseph Zhang , Wenfan Wu , Fei Ye , Saeed Moghimi , Gregory Seroka , Zizang Yang , Edward Myers
Simulating Total Water Level (TWL) at continental scale is inherently challenging and it is often desirable to correct model bias a posteriori. Here we present a simple yet effective bias correction method for NOAA’s STOFS-3D (Three-Dimensional Surge and Tide Operational Forecast System) forecasting system. The method seeks to dynamically correct the model bias, calculated from the results from the previous 2 days, by compensating it with an adjusted non-tidal elevation boundary condition. The adjustment is spatially uniform but varies over each forecast cycle. We demonstrate that the existing 3D model bias is largely attributable to the model’s exclusion of the large-scale steric effect, and therefore the method can be effectively used to incorporate this effect into the 3D model. Assessment at over 140 NOAA stations in US east and Gulf coasts show significant reductions in biases and root-mean-square errors for the non-tidal elevation and TWL, while having a small impact on tides and surges during extreme conditions.
{"title":"A bias correction method for total water level prediction at continental scale","authors":"Hyungju Yoo , Haocheng Yu , Y. Joseph Zhang , Wenfan Wu , Fei Ye , Saeed Moghimi , Gregory Seroka , Zizang Yang , Edward Myers","doi":"10.1016/j.ocemod.2025.102642","DOIUrl":"10.1016/j.ocemod.2025.102642","url":null,"abstract":"<div><div>Simulating Total Water Level (TWL) at continental scale is inherently challenging and it is often desirable to correct model bias <em>a posteriori</em>. Here we present a simple yet effective bias correction method for NOAA’s STOFS-3D (Three-Dimensional Surge and Tide Operational Forecast System) forecasting system. The method seeks to dynamically correct the model bias, calculated from the results from the previous 2 days, by compensating it with an adjusted non-tidal elevation boundary condition. The adjustment is spatially uniform but varies over each forecast cycle. We demonstrate that the existing 3D model bias is largely attributable to the model’s exclusion of the large-scale steric effect, and therefore the method can be effectively used to incorporate this effect into the 3D model. Assessment at over 140 NOAA stations in US east and Gulf coasts show significant reductions in biases and root-mean-square errors for the non-tidal elevation and TWL, while having a small impact on tides and surges during extreme conditions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102642"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-12DOI: 10.1016/j.ocemod.2025.102673
Francisco Pereira , Francisco López-Castejón , Félix Francés , Andrés Alcolea , Joaquín Jiménez-Martínez , João Miguel Dias , Javier Gilabert
Reproduction of hydrodynamic and hydrologic processes in complex coastal lagoons requires the development and calibration of linked numerical model implementations, that can show accuracy even in extreme weather scenarios. To achieve that, robust datasets for a wide variety of parameters are needed to validate the model. This study aimed to develop and validate a hydrodynamic model linked to a groundwater and a watershed model, for the microtidal Mar Menor coastal lagoon located in the southeastern Spain. Special concern was given to flash flood events, which, although infrequent, are proved to trigger mass mortality of species inside the lagoon. To achieve that, a ROMS numerical implementation was developed and linked to atmospheric (HARMONIE-AROME), groundwater (SUTRA), and watershed (TETIS) models. The model results were compared with a robust dataset with hydrodynamic, salinity, and water temperature data. Special attention was given to the September 2019 Cut-off Low (CoL) flash flood event. The model demonstrated high accuracy in reproducing the lagoon’s dynamics under normal conditions, including the currents in the narrow inlets connecting the lagoon with the Mediterranean Sea. After the CoL event, an extraordinary hydrological scenario developed — characterized by strong vertical stratification that persisted for over a month — explained by the lack of sufficient shear instability to overcome buoyancy forces induced by density gradients, despite the occurrence of a two-layer opposite direction flow. Runoff associated with the CoL event also led to a nearly 20 % reduction in the lagoon’s Water Renewal Time.
{"title":"Linking ROMS with watershed models for simulating hydrodynamics and thermohaline dynamics in a coastal lagoon affected by extreme weather events","authors":"Francisco Pereira , Francisco López-Castejón , Félix Francés , Andrés Alcolea , Joaquín Jiménez-Martínez , João Miguel Dias , Javier Gilabert","doi":"10.1016/j.ocemod.2025.102673","DOIUrl":"10.1016/j.ocemod.2025.102673","url":null,"abstract":"<div><div>Reproduction of hydrodynamic and hydrologic processes in complex coastal lagoons requires the development and calibration of linked numerical model implementations, that can show accuracy even in extreme weather scenarios. To achieve that, robust datasets for a wide variety of parameters are needed to validate the model. This study aimed to develop and validate a hydrodynamic model linked to a groundwater and a watershed model, for the microtidal Mar Menor coastal lagoon located in the southeastern Spain. Special concern was given to flash flood events, which, although infrequent, are proved to trigger mass mortality of species inside the lagoon. To achieve that, a ROMS numerical implementation was developed and linked to atmospheric (HARMONIE-AROME), groundwater (SUTRA), and watershed (TETIS) models. The model results were compared with a robust dataset with hydrodynamic, salinity, and water temperature data. Special attention was given to the September 2019 Cut-off Low (CoL) flash flood event. The model demonstrated high accuracy in reproducing the lagoon’s dynamics under normal conditions, including the currents in the narrow inlets connecting the lagoon with the Mediterranean Sea. After the CoL event, an extraordinary hydrological scenario developed — characterized by strong vertical stratification that persisted for over a month — explained by the lack of sufficient shear instability to overcome buoyancy forces induced by density gradients, despite the occurrence of a two-layer opposite direction flow. Runoff associated with the CoL event also led to a nearly 20 % reduction in the lagoon’s Water Renewal Time.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102673"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-29DOI: 10.1016/j.ocemod.2025.102676
Silje Christine Iversen , Ann Kristin Sperrevik , Kai Håkon Christensen
Infrared satellite sea surface temperature (SST) observations capture the strong warming signal at the surface of the ocean during diurnal warming events. Assimilating these observations into ocean forecast models might introduce forecast errors, as the models do not fully resolve the strong diurnal cycle captured by SST observations. A standard approach is to remove affected SST observations from the assimilated dataset using a simplistic filter. However, this approach also removes observations not affected by diurnal warming. For high northern latitudes, where diurnal warming events are less frequent than at lower latitudes, it is unclear whether observations affected by strong warming negatively impact the forecasts, which would justify applying the filter. Here, we explore the consequences of both assimilating diurnal warming-affected SSTs and applying the filter using an ocean forecast model covering the seas off Norway. Through data assimilation experiments assimilating synthetic SSTs, we find that the strong warming signal in observations impacts forecasts in undesirable ways. The surface warming spreads below the mixed layer, decreasing its depth, and changes made to the model below the mixed layer persist into subsequent forecasts. Applying the filter reduces assimilated observations by 46 % without degrading forecasts, suggesting a redundancy in satellite SSTs.
{"title":"How does assimilating satellite SSTs affected by strong diurnal warming impact higher latitude ocean forecasts?","authors":"Silje Christine Iversen , Ann Kristin Sperrevik , Kai Håkon Christensen","doi":"10.1016/j.ocemod.2025.102676","DOIUrl":"10.1016/j.ocemod.2025.102676","url":null,"abstract":"<div><div>Infrared satellite sea surface temperature (SST) observations capture the strong warming signal at the surface of the ocean during diurnal warming events. Assimilating these observations into ocean forecast models might introduce forecast errors, as the models do not fully resolve the strong diurnal cycle captured by SST observations. A standard approach is to remove affected SST observations from the assimilated dataset using a simplistic filter. However, this approach also removes observations not affected by diurnal warming. For high northern latitudes, where diurnal warming events are less frequent than at lower latitudes, it is unclear whether observations affected by strong warming negatively impact the forecasts, which would justify applying the filter. Here, we explore the consequences of both assimilating diurnal warming-affected SSTs and applying the filter using an ocean forecast model covering the seas off Norway. Through data assimilation experiments assimilating synthetic SSTs, we find that the strong warming signal in observations impacts forecasts in undesirable ways. The surface warming spreads below the mixed layer, decreasing its depth, and changes made to the model below the mixed layer persist into subsequent forecasts. Applying the filter reduces assimilated observations by 46 % without degrading forecasts, suggesting a redundancy in satellite SSTs.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102676"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Significant wave height (WVHT) has been identified as a key influencing factor in the research fields of coastal engineering, naval architecture and ocean engineering, maritime management, and other related disciplines. The wave height sequences are always featured as nonlinear and non-stationary, thus seriously concerned in ship voyage planning and route selection. The refined WVHT prediction will support the ship speed optimization and energy efficiency management. A novel hybrid model based on Variational Mode Decomposition (VMD) and Group Method of Data Handling (GMDH) has been proposed. Intrinsic mode functions (IMFs) of WVHT sequence were obtained by VMD, which were subsequently adopted as model inputs of GMDH. The contribution of various input variables was explored through sensitivity analysis. The hybrid VMD-GMDH model was validated through field dataset of National Data Buoy Center, and evaluated with different metrics. Its performance was further compared with four other models, namely GMDH, EMD-GMDH, GRU and VMD-LSTM. The results highlight the importance of data preprocessing through VMD and the prediction accuracy is greatly improved. Specifically, the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) decrease by 29.1%, 15.8%, 18.6% and 15.8%, respectively. The correlation coefficient (R2) is improved by 3.32%. The novel hybrid VMD-GMDH model provides an effective tool for WVHT prediction and would support the intelligent oceanographic studies.
{"title":"Significant wave height prediction using a novel hybrid model of group method of data handling","authors":"Naiwen Mei , Zhonglian Jiang , Bingchang Weng , Zhen Yu , Shijun Chen","doi":"10.1016/j.ocemod.2025.102654","DOIUrl":"10.1016/j.ocemod.2025.102654","url":null,"abstract":"<div><div>Significant wave height (WVHT) has been identified as a key influencing factor in the research fields of coastal engineering, naval architecture and ocean engineering, maritime management, and other related disciplines. The wave height sequences are always featured as nonlinear and non-stationary, thus seriously concerned in ship voyage planning and route selection. The refined WVHT prediction will support the ship speed optimization and energy efficiency management. A novel hybrid model based on Variational Mode Decomposition (VMD) and Group Method of Data Handling (GMDH) has been proposed. Intrinsic mode functions (IMFs) of WVHT sequence were obtained by VMD, which were subsequently adopted as model inputs of GMDH. The contribution of various input variables was explored through sensitivity analysis. The hybrid VMD-GMDH model was validated through field dataset of National Data Buoy Center, and evaluated with different metrics. Its performance was further compared with four other models, namely GMDH, EMD-GMDH, GRU and VMD-LSTM. The results highlight the importance of data preprocessing through VMD and the prediction accuracy is greatly improved. Specifically, the Mean Squared Error (<em>MSE</em>), Root Mean Squared Error (<em>RMSE</em>), Mean Absolute Percentage Error (<em>MAPE</em>) and Mean Absolute Error (<em>MAE</em>) decrease by 29.1%, 15.8%, 18.6% and 15.8%, respectively. The correlation coefficient (<em>R</em><sup>2</sup>) is improved by 3.32%. The novel hybrid VMD-GMDH model provides an effective tool for WVHT prediction and would support the intelligent oceanographic studies.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102654"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-15DOI: 10.1016/j.ocemod.2025.102639
Dongliang Shen, Xiaofeng Li
The Oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean are simulated and investigated by the Regional Ocean Modeling System (ROMS) integrated with a Machine Learning (ML) based ocean vertical mixing parameterization (OVMP) scheme. Traditional OVMP schemes, such as MY25 and KPP, underestimate the ocean vertical mixing processes under typhoon condition. To address this limitation, vertical eddy viscosity (Km) data were generated under Typhoon Bolaven using the high-resolution Parallelized Large Eddy Simulation Model (PALM) and used to train a XGBoost-based ML model. This XGBoost model is used to form a ML-based OVMP scheme and integrated into ROMS model via Forpy coupler. The results indicate that ROMS-ML coupled model can significantly improve the simulations of sea surface temperature (SST) cooling and subsurface thermal structure compared to traditional OVMP schemes. The ML-based OVMP scheme estimates stronger ocean vertical mixing under Typhoon Bolaven, enhancing the upper-oean heat redistribution and aligning more closely with the satellite and in-situ observations. Thermodynamic analyses reveal that the temperature cooling in the upper ocean is primarily driven by strong ocean vertical mixing, latent heat loss, and vertical advection. Notably, the structure of the North Pacific Subtropical Mode Water (STMW) was altered by Typhoon Bolaven, with reductions in its area and thickness, suggesting a weakened heat reservoir and potential impact on regional climate buffering. Momentum energy analyses confirm that vertical viscosity is the dominant contributor to oceanic energy input during Typhoon Bolaven, promoting local eddy generation and associated cooling. Moreover, additional diagnostics under Typhoon Haikui (2023) indicate that while the ML-based OVMP scheme captures localized cooling more accurately than traditional schemes, it tends to overestimate vertical mixing in regions with complex circulation and steep bathymetry. Overall, this study highlights the potential of physics-informed ML approaches in improving the accuracy of ocean simulations under extreme weather events, offering a promising pathway for improving coupled atmosphere–ocean prediction systems under climate change with more frequent super typhoons.
{"title":"Simulating oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean using a numerical model coupled with machine learning-based ocean vertical mixing parameterization","authors":"Dongliang Shen, Xiaofeng Li","doi":"10.1016/j.ocemod.2025.102639","DOIUrl":"10.1016/j.ocemod.2025.102639","url":null,"abstract":"<div><div>The Oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean are simulated and investigated by the Regional Ocean Modeling System (ROMS) integrated with a Machine Learning (ML) based ocean vertical mixing parameterization (OVMP) scheme. Traditional OVMP schemes, such as MY25 and KPP, underestimate the ocean vertical mixing processes under typhoon condition. To address this limitation, vertical eddy viscosity (Km) data were generated under Typhoon Bolaven using the high-resolution Parallelized Large Eddy Simulation Model (PALM) and used to train a XGBoost-based ML model. This XGBoost model is used to form a ML-based OVMP scheme and integrated into ROMS model via Forpy coupler. The results indicate that ROMS-ML coupled model can significantly improve the simulations of sea surface temperature (SST) cooling and subsurface thermal structure compared to traditional OVMP schemes. The ML-based OVMP scheme estimates stronger ocean vertical mixing under Typhoon Bolaven, enhancing the upper-oean heat redistribution and aligning more closely with the satellite and in-situ observations. Thermodynamic analyses reveal that the temperature cooling in the upper ocean is primarily driven by strong ocean vertical mixing, latent heat loss, and vertical advection. Notably, the structure of the North Pacific Subtropical Mode Water (STMW) was altered by Typhoon Bolaven, with reductions in its area and thickness, suggesting a weakened heat reservoir and potential impact on regional climate buffering. Momentum energy analyses confirm that vertical viscosity is the dominant contributor to oceanic energy input during Typhoon Bolaven, promoting local eddy generation and associated cooling. Moreover, additional diagnostics under Typhoon Haikui (2023) indicate that while the ML-based OVMP scheme captures localized cooling more accurately than traditional schemes, it tends to overestimate vertical mixing in regions with complex circulation and steep bathymetry. Overall, this study highlights the potential of physics-informed ML approaches in improving the accuracy of ocean simulations under extreme weather events, offering a promising pathway for improving coupled atmosphere–ocean prediction systems under climate change with more frequent super typhoons.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102639"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-03DOI: 10.1016/j.ocemod.2026.102678
Davide Grande , Roberto Buizza , Andrea Storto
This review examines recent advances in the application of machine learning to ocean data assimilation, covering contributions published between 2020 and 2025. We identify emerging trends, recurring limitations, and critical open questions, structuring the discussion around four scientific challenges: observation integration, boundary treatment, fine-scale process representation, and physical consistency. While convolutional neural networks remain widely used, particularly in bias correction and super-resolution tasks, recent studies increasingly employ multilayer perceptrons, long short-term memories, transformers and neural operators for error estimation, sequential bias correction, and latent-space assimilation. Despite this architectural diversity, most contributions remain confined to idealized configurations or offline modules, with limited evidence of generalization and integration into operational pipelines. We conclude that hybrid systems combining embedded physical knowledge with systematic validation across different oceanic regimes will be essential to unlock the full potential of machine learning-enhanced ocean data assimilation.
{"title":"Machine learning in ocean data assimilation: Advances, gaps and the road to operations","authors":"Davide Grande , Roberto Buizza , Andrea Storto","doi":"10.1016/j.ocemod.2026.102678","DOIUrl":"10.1016/j.ocemod.2026.102678","url":null,"abstract":"<div><div>This review examines recent advances in the application of machine learning to ocean data assimilation, covering contributions published between 2020 and 2025. We identify emerging trends, recurring limitations, and critical open questions, structuring the discussion around four scientific challenges: observation integration, boundary treatment, fine-scale process representation, and physical consistency. While convolutional neural networks remain widely used, particularly in bias correction and super-resolution tasks, recent studies increasingly employ multilayer perceptrons, long short-term memories, transformers and neural operators for error estimation, sequential bias correction, and latent-space assimilation. Despite this architectural diversity, most contributions remain confined to idealized configurations or offline modules, with limited evidence of generalization and integration into operational pipelines. We conclude that hybrid systems combining embedded physical knowledge with systematic validation across different oceanic regimes will be essential to unlock the full potential of machine learning-enhanced ocean data assimilation.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102678"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.ocemod.2026.102682
Zhor Benhafid , Elise Mayrand , Sid Ahmed Selouani
Ocean acidification poses a growing threat to marine ecosystems and aquaculture productivity, particularly in under-monitored coastal regions such as Eastern Canada. Existing pH prediction frameworks typically rely on multi-year records combining extensive carbonate chemistry, physical, and biological parameters. While these models can achieve high accuracy, their data requirements make them costly, complex, and challenging to implement for local, site-specific acidification forecasting in aquaculture contexts. To address this limitation, this study benchmarks several machine learning models for coastal pHSWS prediction using only three routinely measured environmental variables (temperature, salinity, sea level), from which we derived moving-average descriptors, local gradients, and two temporal indicators, resulting in a compact set of 11 input features. Six different models and a multivariate linear regression baseline were trained on one of the most complete and extended high-frequency datasets available (BSSS2018) and evaluated across four independent datasets: one from the same site but six months earlier (BSSS2017), and three from nearby bays in northeastern New Brunswick collected between 2017 and 2019. Among all tested models, XGBoost emerged as the most reliable and interpretable, achieving the best trade-off between sensitivity and precision at the operational acidification threshold (). Its performance remained acceptable within-site but declined across bays due to environmental and seasonal discrepancies, underscoring the importance of training data representativeness. SHAP-based explainability confirmed that Julian day was the dominant predictor, integrating the composite effects of seasonal environmental variability. Overall, this study demonstrates that using only low-cost, routinely measured features provides a promising foundation for short-term coastal pH forecasting, particularly for aquaculture monitoring needs. Despite limited inter-bay generalization, the proposed framework shows that interpretable machine learning models can deliver actionable early-warning insights under realistic data constraints. It constitutes one of the first data-driven benchmarks explicitly tested at aquaculture-relevant thresholds, highlighting a scalable and transparent approach toward operational acidification forecasting.
{"title":"Explainable machine learning models for coastal pH forecasting at aquaculture-relevant thresholds in Eastern Canada","authors":"Zhor Benhafid , Elise Mayrand , Sid Ahmed Selouani","doi":"10.1016/j.ocemod.2026.102682","DOIUrl":"10.1016/j.ocemod.2026.102682","url":null,"abstract":"<div><div>Ocean acidification poses a growing threat to marine ecosystems and aquaculture productivity, particularly in under-monitored coastal regions such as Eastern Canada. Existing pH prediction frameworks typically rely on multi-year records combining extensive carbonate chemistry, physical, and biological parameters. While these models can achieve high accuracy, their data requirements make them costly, complex, and challenging to implement for local, site-specific acidification forecasting in aquaculture contexts. To address this limitation, this study benchmarks several machine learning models for coastal pH<sub>SWS</sub> prediction using only three routinely measured environmental variables (temperature, salinity, sea level), from which we derived moving-average descriptors, local gradients, and two temporal indicators, resulting in a compact set of 11 input features. Six different models and a multivariate linear regression baseline were trained on one of the most complete and extended high-frequency datasets available (BSSS2018) and evaluated across four independent datasets: one from the same site but six months earlier (BSSS2017), and three from nearby bays in northeastern New Brunswick collected between 2017 and 2019. Among all tested models, XGBoost emerged as the most reliable and interpretable, achieving the best trade-off between sensitivity and precision at the operational acidification threshold (<span><math><mrow><mi>p</mi><msub><mrow><mi>H</mi></mrow><mrow><mi>S</mi><mi>W</mi><mi>S</mi></mrow></msub><mo><</mo><mn>7</mn><mo>.</mo><mn>75</mn></mrow></math></span>). Its performance remained acceptable within-site but declined across bays due to environmental and seasonal discrepancies, underscoring the importance of training data representativeness. SHAP-based explainability confirmed that Julian day was the dominant predictor, integrating the composite effects of seasonal environmental variability. Overall, this study demonstrates that using only low-cost, routinely measured features provides a promising foundation for short-term coastal pH forecasting, particularly for aquaculture monitoring needs. Despite limited inter-bay generalization, the proposed framework shows that interpretable machine learning models can deliver actionable early-warning insights under realistic data constraints. It constitutes one of the first data-driven benchmarks explicitly tested at aquaculture-relevant thresholds, highlighting a scalable and transparent approach toward operational acidification forecasting.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102682"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-12DOI: 10.1016/j.ocemod.2026.102683
Hui Xu , Kai Yin , Sudong Xu , Pengju Han , Shangpeng Gong
Global warming has resulted in rising sea levels and an increased frequency of extreme weather events, intensifying the need for effective coastal defences against marine disasters. Nature-based solutions, which offer benefits such as wave dissipation, sediment deposition, ecological compatibility, and environmental adaptability, have emerged as a crucial strategy for coastal protection. Previous studies have predominantly modelled vegetation as simplified cylinders to examine its role in wave attenuation, often ignoring the potential influence of vegetation’s branching structure. Based on these, this research constructs reasonable flume experiments to analyse the discrepancy in wave-attenuation performance between typical branched flexible vegetation and non-branched flexible vegetation. The experiments involve varying water depths, wave heights, and wave periods to comprehensively evaluate the impact of vegetation structure on wave propagation. Comparative analysis results indicate that the branching structure significantly enhances the wave-absorbing capacity of flexible vegetation. Within the tested simulation parameters, the average wave attenuation effect in flexible branched vegetation zones (FBV) was approximately 47 % higher than in flexible unbranched vegetation zones (FUV), with this quantitative difference varying considerably under different hydrodynamic conditions. Furthermore, the findings suggest that the impact of vegetation on wave attenuation is more pronounced under conditions of greater wave heights, shallower water depths, and longer wave periods. On the other hand, the function of vegetation's branching structure in wave reduction becomes more significant as the water depth increases or the wave height decreases. Physical modelling experiments revealing the specific influence of branching structure on wave attenuation can provide a scientific foundation and practical guidance for designing more effective ecological coastal protection measures.
{"title":"Effects of monopodial branching flexible vegetation on the wave attenuation by vegetation","authors":"Hui Xu , Kai Yin , Sudong Xu , Pengju Han , Shangpeng Gong","doi":"10.1016/j.ocemod.2026.102683","DOIUrl":"10.1016/j.ocemod.2026.102683","url":null,"abstract":"<div><div>Global warming has resulted in rising sea levels and an increased frequency of extreme weather events, intensifying the need for effective coastal defences against marine disasters. Nature-based solutions, which offer benefits such as wave dissipation, sediment deposition, ecological compatibility, and environmental adaptability, have emerged as a crucial strategy for coastal protection. Previous studies have predominantly modelled vegetation as simplified cylinders to examine its role in wave attenuation, often ignoring the potential influence of vegetation’s branching structure. Based on these, this research constructs reasonable flume experiments to analyse the discrepancy in wave-attenuation performance between typical branched flexible vegetation and non-branched flexible vegetation. The experiments involve varying water depths, wave heights, and wave periods to comprehensively evaluate the impact of vegetation structure on wave propagation. Comparative analysis results indicate that the branching structure significantly enhances the wave-absorbing capacity of flexible vegetation. Within the tested simulation parameters, the average wave attenuation effect in flexible branched vegetation zones (FBV) was approximately 47 % higher than in flexible unbranched vegetation zones (FUV), with this quantitative difference varying considerably under different hydrodynamic conditions. Furthermore, the findings suggest that the impact of vegetation on wave attenuation is more pronounced under conditions of greater wave heights, shallower water depths, and longer wave periods. On the other hand, the function of vegetation's branching structure in wave reduction becomes more significant as the water depth increases or the wave height decreases. Physical modelling experiments revealing the specific influence of branching structure on wave attenuation can provide a scientific foundation and practical guidance for designing more effective ecological coastal protection measures.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102683"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-09-01DOI: 10.1016/j.ocemod.2025.102599
Fraser William Goldsworth
In the sub-polar North Atlantic, the accumulation of fresh meltwaters from Greenland and the Arctic can impact the strength of the climatically important Atlantic Meridional Overturning Circulation. In this study I investigate and map out the processes that contribute to the accumulation of freshwater in four different regions around Greenland, quantifying horizontal transports of freshwater and the expansion and depletion of freshwater reservoirs by surface sources and interior mixing. Rather than using traditional freshwater budgets, whose flaws are well documented, I propose the novel use of the freshwater transformation framework and apply it to outputs from an eddy resolving coupled climate model (10 km atmosphere and 5 km ocean).
Analysing volume transports in salinity space we observe the salinification of the boundary currents surrounding Greenland as they flow from Fram Strait towards the Labrador Sea. Using the freshwater transformation framework we are able to link the salinification to mixing, sea-ice formation or the accumulation of freshwaters stored in the waters surrounding Greenland. The balance changes depending upon the region and season under question. The mixing of freshwaters is found to be stronger during wintertime than in summertime. Furthermore, mixing plays a more dominant role in the freshwater transformation budget off Southern Greenland, where sea-ice cover is low, than off Northern Greenland, where sea-ice cover is high.
{"title":"A novel framework for studying oceanic freshwater transports, and its application in discerning the modelled fate of freshwater around the coast of Greenland","authors":"Fraser William Goldsworth","doi":"10.1016/j.ocemod.2025.102599","DOIUrl":"10.1016/j.ocemod.2025.102599","url":null,"abstract":"<div><div>In the sub-polar North Atlantic, the accumulation of fresh meltwaters from Greenland and the Arctic can impact the strength of the climatically important Atlantic Meridional Overturning Circulation. In this study I investigate and map out the processes that contribute to the accumulation of freshwater in four different regions around Greenland, quantifying horizontal transports of freshwater and the expansion and depletion of freshwater reservoirs by surface sources and interior mixing. Rather than using traditional freshwater budgets, whose flaws are well documented, I propose the novel use of the freshwater transformation framework and apply it to outputs from an eddy resolving coupled climate model (10 km atmosphere and 5 km ocean).</div><div>Analysing volume transports in salinity space we observe the salinification of the boundary currents surrounding Greenland as they flow from Fram Strait towards the Labrador Sea. Using the freshwater transformation framework we are able to link the salinification to mixing, sea-ice formation or the accumulation of freshwaters stored in the waters surrounding Greenland. The balance changes depending upon the region and season under question. The mixing of freshwaters is found to be stronger during wintertime than in summertime. Furthermore, mixing plays a more dominant role in the freshwater transformation budget off Southern Greenland, where sea-ice cover is low, than off Northern Greenland, where sea-ice cover is high.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102599"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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