Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 1a
The South Lhonak Lake (SLL) Glacial Lake Outburst Flood (GLOF) cascade event of 3-4 October 2023 triggered widespread devastation across Sikkim and the downstream region of Bangladesh, causing significant loss of lives and property. The post-disaster research shows that the GLOF event was triggered by a moraine failure, creating tsunami waves in the lake, eventually leading to the breach of the frontal moraine. Despite partial drainage of the lake in the 2023 event, the hazard potential of the lake needs further investigation. This makes it extremely important to continuously monitor the surrounding regions to identify unstable slopes that can potentially fail and impact the lake. The present study utilises a Sentinel-1 Small Baseline Subset (SBAS) workflow performed in the ASF OpenSARLab environment to analyse the condition of the moraines post-SLL disaster. Post-disaster analysis spanning October 2023 to September 2025 reveals continued moraine instability, characterised by an actively deforming zone along the right flank of the failed zone. This region shows a maximum LOS displacement rate of approximately -4 cm yr-1, with a maximum cumulative LOS displacement reaching around -6 cm in the ascending track and -5 cm in the descending track. The results indicate persistent post-failure deformation and ongoing slope instability in the moraines of South Lhonak. The study provides a critical insight into the temporal behaviour of moraine slopes. This study aimed at strengthening the disaster management strategies by integrating satellite-based deformation monitoring for early warning and risk reduction measures.
How to cite: Verma, U. and Sattar, A.: Monitoring post-GLOF moraine dynamics at South Lhonak lake using satellite radars, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11635, https://doi.org/10.5194/egusphere-egu26-11635, 2026.
Accelerated glacier retreat in the Western Himalaya has led to rapid expansion of glacial lakes and increasing concern over Glacial Lake Outburst Flood (GLOF) hazards. This study presents a basin-scale assessment of glacial lake evolution, potential future lake formation, and GLOF susceptibility in the Chenab Basin, integrating multi-temporal remote sensing, terrain analysis, and probabilistic exposure modelling. A decadal inventory of glacial lakes was developed for five time periods (1990, 2000, 2010, 2020 and 2025) using Landsat and Sentinel-2 imagery, combined with semi-automated extraction and geomorphological classification. Results reveal a consistent increase in both lake number and total area over the last three decades. Potential future glacial lakes were identified using various ice-thickness modeling approach applied to current glacier extents. This analysis presents an inventory of the future glacial lake in the entire basin giving special emphasis to determining the characteristics of the future lake including maximum extent of the future lakes and volume of the future glacial lakes. GLOF susceptibility of existing lakes was evaluated using a multi-criteria framework to identify critical lakes requiring priority monitoring. Downstream exposure was further assessed using the Monte Carlo Least Cost path approach, explicitly accounting for uncertainty in breach location and flood routing parameters to delineate probable impact corridors. The framework provides new insights into evolving cryospheric hazards in the Chenab Basin and demonstrates the utility of combining lake dynamics, future lake potential, susceptibility assessment, and probabilistic exposure analysis for improved GLOF risk prioritization in the Western Himalayas.
How to cite: Das, D. R. and Sattar, A.: Evolution of present and potential future glacial lakes and implications for GLOF hazard in the Chenab Basin, Western Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17889, https://doi.org/10.5194/egusphere-egu26-17889, 2026.
The Karakoram is known for its numerous surge glaciers and associated hazards from ice-dammed lake outburst floods. However, significant discrepancies persist in our understanding of surge trends and flood frequency. Therefore, this study aims to clarify the surge behaviour and related glacial lake outburst flood (GLOF) history for the Kumdan group of glaciers (Chong Kumdan, Kichik Kumdan, and Aktash). The study analysed historical archives, high-resolution satellite imagery, elevation changes derived from digital elevation models (DEMs), and glacier surface velocity from the ITS_LIVE dataset. Based on an in-depth review of historical records and cross-verified with multi-temporal satellite imagery, 16 GLOFs have been documented from this group since 1835, primarily originating from Chong Kumdan and Kichik Kumdan. The Aktash Glacier has surged several times but has not formed any ice-dammed lake due to efficient subglacial drainage, which prevents river blockages. Chong Kumdan and Aktash glaciers exhibit longer active phases (~7-10 years), whereas Kichik Kumdan Glacier shows shorter phases (~2 years). Out of all three Kumdan glaciers, the Chong Kumdan has produced the most devastating floods in 1835, 1926 and 1929. This glacier comprises two tributaries (a and b) and main trunk. Tributary ‘a’ follows a ~77-year surge cycle, and tributary ‘b’ and the main trunk exhibit asynchronous surge records. The surge cycle duration of Kichik Kumdan Glacier decreased from 33 years (1833–1866) to 27 years (1970–1997) due to climate warming. The last GLOFs from Chong Kumdan and Kichik Kumdan occurred in 1934 and 1903, respectively. DEM analysis from 2015 to 2022 reveals thickening in the reservoir areas of Chong Kumdan (~22 m) and Kichik Kumdan (~20 m), suggesting potential future surge but with a low probability of GLOF events. Overall, our study observed a decline in surge-generated GLOFs due to climate warming, reduced mass accumulation and weakening of ice dams. These insights will help downstream communities and risk management authorities better understand future risks and develop effective mitigation strategies.
How to cite: Halder, S. and Bhambri, R.: Impact of climatic warming on glacier surges and associated ice-dammed lake outburst floods in the Eastern Karakoram, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5669, https://doi.org/10.5194/egusphere-egu26-5669, 2026.
Machine learning (ML) remains one of the best approaches for long-term seasonal streamflow forecasting in cold regions owing to its capacity to capture nonlinearity between inputs and outputs, as well as its scalability across hydroclimatic regimes. ML’s main advantage lies in the generalizability of these models when applied to heavily glacierized catchments. In this data-driven study, we mainly utilize the Extreme Gradient Boosting (XGBoost) regression to train and test seasonal streamflow predictions using the LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland (LamaH-Ice). This new dataset for Iceland, published in 2024 consists of topographic, hydroclimatic, land cover, vegetation, soil, geological, and glaciological attributes that are essential for understanding cryosphere–hydrology processes in cold regions. For more than 100 basins, time series information on meteorological forcings and variables relevant to cold-region hydrology, such as MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover, glacier albedo are also available. The majority of gauged rivers in LamaH-Ice are reported to have minimal human disturbances, making the dataset particularly unique. The XGBoost model demonstrates strong predictive skill across the study basins, as indicated by Kling-Gupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE) metrics exceeding 0.98. Ultimately high-precision streamflow forecasting is needed to track hydrometeorological hazards and to aid our ability to manage water resources in cold regions, which are a source for irrigation and hydropower.
References
Helgason, Hordur Bragi, and Bart Nijssen. “LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland.” Earth System Science Data, vol. 16, no. 6, 13 June 2024, pp. 2741–2771, doi:10.5194/essd-16-2741-2024.
Strahlendorff, Mikko, et al. “Forestry Climate Adaptation with HarvesterSeasons Service—a Gradient Boosting Model to Forecast Soil Water Index SWI from a Comprehensive Set of Predictors in Destination Earth.” Frontiers in Remote Sensing, vol. 5, 20 Dec. 2024, doi:10.3389/frsen.2024.1360572.
How to cite: Prakasam, G., Strahlendorff, M., Kröger, A., and Gunnarsson, A.: Machine Learning based Seasonal Streamflow Forecasting in Cold-Region Catchments: Insights from LamaH-Ice dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20743, https://doi.org/10.5194/egusphere-egu26-20743, 2026.
Sea ice classification is often a crucial step to predict climatic insights and ensure safe marine navigation. In the last few decades, satellite information has been widely used to classify sea ice in broad areas for practical applications. However, common problems are:
1) Low resolution of satellite images to provide precise classification,
2) High computational need, and
3) Scarcity of general models to discover unknown patterns in the data, especially those that enable free selection of satellite sensors to fit the application at hand.
We propose an explainable unsupervised model to integrate ice-experts’ inputs to models so that the problem of having low-resolution data can be overcome. In other words, the results of the models, given as semantic maps, can be further refined using inputs from ice-experts.
Model explainability and visual interpretation of models serve as tools to talk to’ domain experts. The use of Explainable AI in such vital activities ensures trust and easy detection of error. We present an example from a sea ice classification with Sentinel-1 time-series in the scope of the Horizon 2020 project ExtremeEarth.
A further example from the Horizon Europe project dAIEdge demonstrates the use of these explainable models for ‘on-the-edge’ inference.
How to cite: Dumitru, C. O., Karmakar, C., and Wiehle, S.: Explainable Expert-in-the-loop sea-ice classification with statistical models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16248, https://doi.org/10.5194/egusphere-egu26-16248, 2026.
Dust in the Arctic is an emerging topic related to climate and environmental impacts. The United Nations (UN) General Assembles and the UN Coalition to Combat Desertification (UNCCD) have reiterated that the global frequency, intensity, and duration of Sand and Dust Storms (SDS) have increased in the last decade and that SDS have natural and human causes that can be exacerbated by desertification, land degradation, drought, biodiversity loss, and climate change. UNCCD and FAO have also highlighted that emerging SDS source areas have been associated with the warming of the Arctic and high latitude regions, the seasonal or permanent drying of inland waters and river deltas, or are following large-scale deforestation and wildfires, or even the ploughing of a single field. Loss of snow cover, retreat of glaciers, and increase in drought intensity due to climate change can lead to surface conditions that increase the likelihood of creation, continuation and expansion of SDS source areas.
Climatic feedback mechanisms and ecosystem impacts related to dust in the Arctic include direct radiative forcing (absorption and scattering), indirect radiative forcing (via clouds and cryosphere), semi-direct effects of dust on meteorological parameters, effects on atmospheric chemistry, as well as impacts on terrestrial, marine, freshwater, and cryosphere ecosystems. Here we give an overview of our recent understanding on dust emissions and their long-range transport routes, deposition, and ecosystem effects in the Arctic as presented in Meinander et al. (2025), part of the series of review papers of the Arctic Council Working Group AMAP (Arctic Monitoring and Assessment Program) and CAFF (Conservation of Arctic Flora and Fauna), where the target audience is the scientific community focusing on the Arctic. Additional audiences include policy advisers and other staff in environmental-related ministries.
We conclude that the multiple mechanisms related to dust emissions, transport and deposition both cool and warm the climate system, with an uncertain net effect. Dust plays a significant role in terrestrial and aquatic ecosystems, e.g., by providing nutrients, and with impacts on the availability of light and water. Due to Arctic warming, HLD dust emissions can be expected to increase. The contributions of LLD and HLD complicates the interpretation of how much different sources contribute to the dust loadings and corresponding temporal and spatial deposition patterns. Another challenge is that low latitude dust source emissions of road and agricultural dust is barely characterized.
Reference:
Meinander O, Uppstu A, Dagsson-Waldhauserova P, Groot Zwaaftink C, Juncher Jørgensen C, Baklanov A, Kristensson A, Massling A and Sofiev M (2025). Dust in the arctic: a brief review of feedbacks and interactions between climate change, aeolian dust and ecosystems. Front. Environ. Sci. Sec. Interdisciplinary Climate Studies, Volume 13 – 2025. doi: 10.3389/fenvs.2025.1536395. CAFF-special issue.
How to cite: Meinander, O., Uppstu, A., Dagsson-Waldhauserova, P., Groot-Zwaaftink, C., Juncher Jørgensen, C., Baklanov, A., Christenson, A., Massling, A., and Sofiev, M.: Dust in the Arctic: feedbacks and interactions between climate change, aeolian dust and ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16935, https://doi.org/10.5194/egusphere-egu26-16935, 2026.
Near-surface water ice in Phlegra Montes, Mars, could support human exploration and settlement. Orbital sounding radar provides strong evidence for the existence of this ice, as does morphology consistent with debris-covered glaciers. Impact excavation of these glacier-like features has exposed ice, visible in HiRISE images, but the distribution and quantity of this ice is uncertain, necessitating further evaluation for its potential to support human exploration. We are developing the Prototype Radar Sounding Experiment for Unveiling the Subsurface (PERSEUS) instrument to study debris-covered glaciers on Earth and Mars, and we propose D-PERSEUS, a mission to study a debris-covered glacier in Phlegra Montes using a drone-based Ground Penetrating Radar like this one. This mission could verify the presence of water ice in-situ and improve characterization of water ice resources, which could serve as exploratory work ahead of a potential Mars Life Explorer mission.
How to cite: Spurling, R., Nerozzi, S., and Aguilar, R.: D-PERSEUS: A Drone Radar Mission to Study a Debris-Covered Glacier on Mars, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21809, https://doi.org/10.5194/egusphere-egu26-21809, 2026.
Arctic sea ice plays a critical role in regulating global climate and marine primary production, yet long-term records documenting its natural variability remain sparse in the Pacific sector of the Arctic Ocean. This limitation hampers our ability to establish a regionally coherent understanding of how sea ice responds to climatic and oceanographic forcing on centennial to millennial timescales. Here, we present a new biomarker-based reconstruction of Holocene sea ice and environmental change from the southern Chukchi Sea, north of the Bering Strait.
A 519-cm sediment core (SKQ-VC29) was recovered using a vibracorer and spans the last ~8.6 kyr, based on 17 AMS radiocarbon dates from shells and terrestrial macrofossils. Downcore concentrations of highly branched isoprenoids (HBIs) and sterols were quantified to reconstruct sea-ice conditions, marine productivity, and terrestrial organic matter (OM) inputs. Seasonal sea ice presence is inferred from IP25, a mono-unsaturated HBI produced by sea-ice diatoms, while open-water conditions and phytoplankton productivity are tracked using HBI III, brassicasterol, and dinosterol. These proxies are combined using the PIP25 index to provide a semi-quantitative reconstruction of sea-ice cover. Terrestrial inputs are assessed using vascular-plant sterols (campesterol and β-sitosterol), alongside bulk δ¹³C and C:N ratios.
The record indicates predominantly open-water conditions during the early to mid-Holocene, followed by the reappearance of seasonal sea ice at ~2.5 kyr BP—substantially later than in more northerly Arctic records. This delayed signal suggests that Neoglacial sea-ice expansion in the Pacific Arctic was spatially heterogeneous. Bulk OM proxies and declining β-sitosterol concentrations indicate a progressive reduction in terrestrial OM delivery through the Holocene, while marine productivity remains relatively stable. A pronounced shift at ~4 ka BP marks reduced organic carbon accumulation and broader environmental reorganization.
Together, these results improve spatial coverage of Holocene sea-ice reconstructions in the Pacific Arctic and highlight the complex, regionally variable nature of sea-ice evolution in a climatically sensitive gateway region.
How to cite: Jin, K., de Vernal, A., Pickart, R. S., Chen, M., Otiniano, G., and Porter, T.: Holocene Sea Ice and Organic Matter Dynamics in the Southern Chukchi Sea Revealed by Lipid Biomarkers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15143, https://doi.org/10.5194/egusphere-egu26-15143, 2026.
Ocean salinity serves as a key indicator of the global water cycle and exerts important controls on oceanic circulation, sea level, and stratification, thereby playing a critical role in marine thermodynamic and dynamic processes. In recent years, salinity variability in the tropical Indian Ocean, particularly its dynamic mechanisms and climatic effects, has attracted growing scientific interest. Using 31 years of satellite observations, in-situ data sets, and model reanalysis data, this study investigates the decadal variability and formation mechanisms of the low salinity tongue in the South Indian Ocean between the equator and 20°S. The results indicate that both the volume and mean salinity of the low-salinity tongue exhibit a quasi-12-year oscillation, which is primarily associated with the Interdecadal Pacific Oscillation (IPO). Further analysis reveals that on decadal timescales, variability in the volume of the upper 50 m low-salinity tongue is mainly driven by local precipitation. Through anomalous atmospheric circulation, sea surface temperature anomalies in the tropical Pacific lead to multi-year precipitation anomalies in the southeastern Indian Ocean, which subsequently alter the westward extension of the surface low-salinity tongue and ultimately govern its volume variability in the upper 50 m. However, in the subsurface layer (50 to 200 m), variability in the volume and average salinity of the low salinity tongue is dominated by freshwater transport associated with the Indonesian Throughflow (ITF). During negative IPO phases, wind anomalies over the tropical Pacific trigger oceanic wave adjustments, which enhance the ITF salinity transport. This process subsequently leads to an expansion of the low salinity tongue and a decrease in its average salinity in the southeastern Indian Ocean. Based on the three-dimensional variability of the low salinity tongue, this study reveals the relationships between the volume and average salinity of the tongue at different depths and local freshwater forcing, as well as salinity transport by the ITF, thereby contributing to an improved understanding of how regional water mass changes respond to long-term climate variability.
How to cite: yanran, P., sun, Q., zhang, Y., zhang, Y., chi, J., and du, Y.: Analysis of the mechanisms underlying the low-frequency variability of the low-salinity tongue in the southeastern Indian Ocean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10227, https://doi.org/10.5194/egusphere-egu26-10227, 2026.
Induced diffusion (ID), an important mechanism of spectral energy transfer due to interacting internal gravity waves (IGWs), plays a significant role in driving turbulent dissipation in the ocean interior. In this study, we revisit the ID mechanism to elucidate its directionality and role in ocean mixing under varying IGW spectral forms, with particular attention to deviations from the standard Garrett-Munk spectrum. The original interpretation of ID as an action diffusion process, as proposed by McComas et al., suggests that ID is inherently bidirectional, with its direction governed by the vertical-wavenumber spectral slope σ of the IGW action spectrum, n ~ mσ. However, through the direct evaluation of the wave kinetic equation, we reveal a more complete depiction of ID, comprising both a diffusive and a scale-separated transfer rooted in the energy conservation within wave triads. Although the action diffusion may reverse direction depending on the sign of σ (i.e., red or blue spectra), the net transfer consistently leads to a forward energy cascade at the dissipation scale, contributing positively to turbulent dissipation. This supports the viewpoint of ID as a dissipative mechanism in physical oceanography. This study presents a physically grounded overview of ID and offers insights into the specific types of wave-wave interactions responsible for turbulent dissipation.
How to cite: Wu, Y. C. and Pan, Y.: Induced Diffusion of Interacting Internal Gravity Waves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-221, https://doi.org/10.5194/egusphere-egu26-221, 2026.
The Gulf of Panama is a semi-enclosed tropical basin where coastal processes are driven by a multimodal wave climate with pronounced interannual-to-decadal variability (Vallarino-Castillo, 2026). Offshore wave conditions were characterized at three spectral locations near the Gulf entrance using GLOSWAC-5 spectral data (Portilla-Yandún and Bidlot, 2025), revealing dominant wave systems with distinct directional origins and seasonal variability. A persistent Southern Ocean swell dominates year-round from the south–southwest, while northerly wind-seas associated with the Panama Low-Level Jet prevail during the dry season (December–April). Their opposing directions lead to frequent crossing-sea conditions, particularly along the western Gulf entrance, where partial blocking by the Azuero Peninsula enhances directional spreading. In contrast, more exposed central-eastern locations exhibit consistently multimodal spectra, whereas sheltered eastern areas show reduced northerly wind-sea influence and narrower directional ranges. During the wet season (May–November), additional southerly swell components linked to subtropical trade winds and the Chocó Low-Level Jet reinforce low-frequency energy, while episodic North Pacific swell incursions further increase spectral complexity. Building on these offshore patterns, we analyze how wave systems transform as they propagate across the Gulf’s complex basin geometry.
To resolve coastal wave conditions efficiently, we applied a hybrid spectral downscaling framework across the Gulf. Remote swell was reconstructed using BinWaves (Cagigal et al., 2024), which disaggregates each offshore spectrum into frequency–direction bins and propagates them individually with SWAN, assuming linear wave superposition over the nearshore of the Gulf of Panama, such that nonlinear wave–wave interactions are neglected during propagation. Nearshore spectra are then reassembled using precomputed propagation coefficients that account for coastal geometry. Locally generated seas were reconstructed with HyXSeaSpec, which extracts dominant atmospheric modes via multivariate dimensionality reduction, projects SWAN spectra onto a reduced EOF/PCA space and learns the nonlinear mapping between atmospheric modes and spectral coefficients using radial basis functions (RBFs). During prediction, new wind fields are projected into the reduced space to recover full directional spectra through inverse transforms. The hybrid workflow generates a 3-hourly directional wave spectrum hindcast (1969–2023) that combines remote swell and locally generated wind-sea contributions throughout the basin.
The ongoing nearshore analysis uses the reconstructed spectra to identify dominant variability patterns and coherent wave regimes, assessing how energy is redistributed within the gulf and how nearshore conditions respond to seasonal and interannual atmospheric forcing.
References:
Vallarino-Castillo R, Antolínez JAA, Negro-Valdecantos V, Portilla-Yandún J (2026). “Beyond understanding the role of far-field climate in the Gulf of Panama coastal dynamics: an analysis of long-term and seasonal variability of wave systems”. Climate Dynamics. https://doi.org/10.1007/s00382-025-08007-w
Portilla-Yandún J, Bidlot J-R (2025). “A global ocean spectral wave climate based on ERA-5 data: GLOSWAC-5”. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2025JC022629
Cagigal, L., Méndez, F.J., Ricondo, A., Gutiérrez-Barceló, D. & Bosserelle, C. (2024). “BinWaves: An additive hybrid method to downscale directional wave spectra to near-shore areas” en Ocean Modelling. 84, 102346.
How to cite: Vallarino-Castillo, R., Bellido, G., Cagigal, L., Negro-Valdecantos, V., Portilla-Yandún, J., Méndez, F., and A. A. Antolínez, J.: Hybrid spectral downscaling and climate-driven variability of multimodal wave systems in the Gulf of Panama, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3616, https://doi.org/10.5194/egusphere-egu26-3616, 2026.
The enhanced spatial resolution enables a more realistic simulation of key coastal processes, including tidal dynamics, shelf currents, and nearshore circulation features. Model performance is evaluated against available in situ observations and Copernicus Marine Environment Monitoring Service (CMEMS) model products, demonstrating a substantial improvement in the representation of coastal hydrodynamics compared to lower-resolution configurations.
The developed forecasting modeling framework provides a robust tool for investigating physical processes on the Tunisian shelf and offers a valuable foundation for coastal management, environmental monitoring, and hazard assessment (e.g., storm surges and coastal flooding).
How to cite: Bouzaiene, M. and Menna, M.: Development of a Fine-Scale (1/648°) Nested Ocean Forecasting Model for the Tunisian Shelf, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8164, https://doi.org/10.5194/egusphere-egu26-8164, 2026.
Estuaries represent complex morphodynamic systems where interactions between tides, waves, and sediment processes control coastal stability and its ecological resilience. One such estuary, located along the bank of the Purna River in Navsari District, Gujarat, India, is currently experiencing severe erosion, with nearly two-thirds of the estuarine coastline affected. Understanding spatio-temporal evolution of key coastal features is essential, including tidal flats, salt marshes, mangrove cover, and anthropogenic infrastructures within the study region. In this study, the coastal features segmentation is performed using the Random Forest on derived Landsat satellite imagery spectral indices spanning 2005–2024. The results indicate that over the past two decades, mangrove cover has increased by more than twofold, particularly near the estuary mouth. In contrast, tidal flat areas exhibited significant spatial variability, while salt marshes showed a considerable decline.
Shoreline change analysis shows extensive coastal erosion with the Net Shoreline Movement (NSM) exceeding 150 m in certain stretches, while the End Point Rate (EPR) ranged from 1.5 to 17 m/year (mean: 9.5 m/year). The analysis further indicates significant accretion in the estuaryward region and pronounced erosion along the seaward coast near its mouth. Further the coupled tide-wave numerical modelling was carried to attribute the observed changes. Overall, the findings highlight the complex interplay between natural coastal processes and anthropogenic pressures in this dynamic estuarine coastal system and provide valuable baseline information for coastal zone management and conservation planning.
Keywords: Estuary Dynamics, Random Forest, Shoreline changes, Tide Modelling, Wave Modelling, Remote Sensing.
How to cite: Makhan, P. K., Goud Lakku, N. K., Behera, M. R., and Vijaya Kumar, S.: Coastal Features Segmentation and Assessing their dynamics Using Machine Learning: Random Forest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3596, https://doi.org/10.5194/egusphere-egu26-3596, 2026.
The rapid expansion of offshore wind energy infrastructure represents a major anthropogenic modification of coastal and marginal seas, yet the physical interactions between monopile foundations, hydrodynamics, and sediment transport remain insufficiently quantified. This study investigates the impact of monopile foundations at the Meerwind offshore wind farm (German Bight, North Sea) on local and regional coastal dynamics. Using a high-resolution coupled wave-current-sediment transport model, we analyze hydrodynamic and sediment processes with mesh refinement of ~2 m near the structures to capture turbulent wake effects.
Our results demonstrate that monopile arrays act as significant sinks for wave energy: monthly mean significant wave heights (Hs) and mid-depth velocities decrease by ~5%, while turbulent kinetic energy increases by up to 70% near the foundations. Dominant westerly wind-driven waves modulate tidal asymmetry on the leeward (eastern) side of the piles, generating asymmetric turbulent wakes and altering bottom shear stress patterns.
Reduced wave-induced bottom stress enhances localized sediment deposition, increasing surface suspended sediment concentration (SSC) while reducing near-bottom loads. On a regional scale, wave attenuation leads to a ~1% decrease in depth-averaged SSC over a 20 km east of the piles. In consequence, the presence of the wind farm reduces the net inflowing sediment flux by ~25% within a 5 km radius during March 2020, linked to a ~2 cm attenuation of Hs.
These findings highlight how large-scale offshore energy infrastructure can reorganize sediment budgets and coastal morphodynamics under changing human activities, providing critical insights for the sustainable management of multi-use ocean spaces. Further work, including additional wind farms and extended simulation periods, is planned to substantiate these initial findings and better quantify cumulative impacts, particularly in light of ongoing erosion challenges in the Wadden Sea under sea-level rise.
How to cite: Hosseini, S. T., Pein, J., Staneva, J., Stanev, E., and Zhang, Y. J.: Influence of Offshore Wind Farm Monopiles on Multi-Scale Hydrodynamics and Sediment Transport in a Wave-Current Environment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10444, https://doi.org/10.5194/egusphere-egu26-10444, 2026.
The variability in the circulation of the Northern Ionian Gyre (NIG) during 1988-2020 is assessed via dynamic-height fields in the upper layer (0-120 m and 0-398 m) derived from the monthly-averaged temperature and salinity fields of the Copernicus reanalysis data. The yearly-averaged dynamic-height fields agree with the corresponding fields of altimetric sea-surface topography used in previous studies that found, at the area of the NIG, a maximum-variability mode in the sea-surface topography of the Ionian Sea. In the present results, the NIG coincides with the area of a) the variability maxima of the dynamic-heights, existing on the standard-deviation (std) maps of the yearly-averaged dynamic heights during 1988-2020, b) the std maxima of the averaged density in the upper layer and c) the std maxima of the averaged salinity in the upper layer; the density-salinity correlation coefficients in the upper-layer within the NIG range from 0.87 to 0.74.
Moreover, the std maxima of the precipitation fluxes, which have the dominant role on the evaporation-minus-precipitation (E-P) budget, are also located on the NIG area. The 5-year running-averaged values of yearly E-P and salinity in the upper-layer of the NIG, which filter out the variability in less that ~5-6 years while they preserve the dominant variability in the periodicities (~8-10 years) of the NIG-circulation, have statistically significant correlations ranging from 0.53 for the period 1990-2018 to 0.73 for the period 1997-2018. After ~2005, the two timeseries resemble to each other even more. In the upper layer, the area to the east-southeast of the NIG has statistically significant correlations in salinity (correlation coefficients: ~0.68-0.8) with the NIG area. This area can feed its higher-salinity signal to the NIG via northward transfer during the cyclonic circulation mode of the NIG.
How to cite: Kontoyiannis, H., Tsiaras, K., Iona, A., and Ballas, D.: The role of the air-sea water fluxes and the lateral influence on salinity in the bimodal circulation variability of the Northern Ionian Gyre in the period 1988-2020, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5203, https://doi.org/10.5194/egusphere-egu26-5203, 2026.
Coastal ecosystems are highly vulnerable to nutrient-driven eutrophication from anthropogenic sources such as urbanization, wastewater discharge, and industrial development, among others, which alters their ecosystem services. In order to determine nitrogen sources, the nitrogen isotopic composition (δ15N) was analyzed in the macroalgae Boodleopsis verticillata and Bostrychia spp., collected between 2014 and 2016 at four localities with different degrees of anthropogenic disturbance: Valencia – Very Low Intervention (MBI-VA), Chucheros – Low Intervention (BAI-CHU), San Pedro – Moderate Intervention (MOI-SP), and Piangüita – High Intervention (ALI-PI) in the Colombian Pacific. The δ15N values ranged between 0.3 and 2.4‰ in MBI-VA, 1.8 and 3.4‰ in BAI-CHU, 2.3 and 5.5‰ in MOI-SP, and 2.3 and 10.16‰ in ALI-PI. Since the assumptions of normality and homogeneity of variances were not met (p < 0.05), a non-parametric Kruskal–Wallis test was applied, revealing significant differences in δ15N among localities (p < 0.0001). Dunn’s test indicated that MBI-VA and BAI-CHU differed significantly from MOI-SP and ALI-PI (p < 0.05). Three nitrogen sources were defined: atmospheric deposition, oceanic waters, and wastewater. Both species (B. verticillata andBostrychia spp.) showed a decreasing gradient of atmospheric deposition (87% ± 3% to 52% ± 7% and 82% ± 6% to 21% ± 11%, respectively) from MBI to ALI, in contrast to an increase in oceanic waters (8% ± 4% to 37% ± 13% and 12% ± 7% to 38% ± 21%) and wastewater contributions (5% ± 2% to 12% ± 6% and 7% ± 3% to 41% ± 12%). This pattern was more evident in Bostrychia spp., suggesting greater sensitivity to variations in nitrogen sources. Linear regression between δ15N and nitrate concentration yielded coefficients of determination of R2 = 0.71 for B. verticillata and R2 = 0.89for Bostrychia spp., indicating that isotopic variability was explained by nitrate. The potential of macroalgae as bioindicators of anthropogenic intervention in coastal ecosystems of the Colombian Pacific is suggested.
How to cite: Arce-Sánchez, R. S., Medina-Contreras, D., and Sánchez-González, A.: Use of δ15N and macroalgae as indicators of the level of anthropogenic intervention in the Colombian Pacific., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13664, https://doi.org/10.5194/egusphere-egu26-13664, 2026.
Litter pollution has grown to be the most prominent threat to the coastal ecosystems, affecting both the environment and the local communities. An important step towards the mitigation of coastal pollution is the effective monitoring of the issue. The rapid evolution of Remote Sensing has offered many new techniques for the detection of beach litter, and Unmanned Aerial Vehicles (UAVs), especially, have proven to be invaluable tools. In this study, different approaches of beach litter detection are evaluated in order to determine which ones yield the most promising results. The data used were collected in the area of Palio Faliro, Greece and included RGB and Multi-spectral images. For the detection of the litter from the UAV images, two Deep Learning (DL) models were utilized, namely the Mask R-CNN and the YOLOv3. The accuracy of these two DL models in beach litter detection and also explore the potential challenges that may arise while trying to monitor the coastal environment with UAV methods. Our study findings suggest that the combined use of DL methods and UAV imagery can provide a cost-effective and scalable solution in litter detection and can assist relevant decision-making actions. Future work will focus on evaluating different DL methods under other experimental settings as well which will help towards assessing the wider applicability of the combined use of drone imagery and DL approaches in litter detection in coastal areas.
KEYWORDS: Remote Sensing, coastal little, UAVs, drones, deep learning, ACCELERATE project
Acknowledgements
This study is financially supported by the ACCELERATE MSCA SE program of the European Union’s Horizon research and innovation program under grant agreement No. 101182930
How to cite: Mitsopoulou, C., Petropoulos, G. P., Detsikas, S. E., Lekka, C., Grigoriadis, K., Polychronos, V., Mamagiannou, E.-M., Gkotsikas, C., Chardavellas, K., and Katsou, E.: Litter detection and mapping from the combined use of multispectral UAV imagery and Deep Learning: A case study from Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3528, https://doi.org/10.5194/egusphere-egu26-3528, 2026.
Effective coastal monitoring and forecasting systems rely on the availability and timeliness of interoperable, standardized, and accessible marine data across observational, modelling and service layers. Fragmented data formats, legacy infrastructures, and non-standardized access mechanisms remain significant barriers to the seamless integration of ocean observations into operational monitoring and forecasting systems and downstream applications.
This study presents the development of a standards-based data workflow designed to enhance interoperability, scalability, and facilitate marine data integration, through the adoption of international standards and best practices. The proposed approach focuses on establishing robust data flows that transform, validate, and harmonize heterogeneous datasets (e.g., in situ near-real-time observations and numerical model outputs) into NetCDF format. Standardized and programmatic access to these datasets is enabled though the OGC API Environmental Data Retrieval protocol, implemented using the pygeoapi platform. By adopting open standards and service-oriented architectures, this framework enables efficient spatio-temporal querying of ocean variables, facilitating their assimilation into forecasting systems, decision-support tools, and customized applications. In parallel, geoportal interfaces were updated to integrate the new OGC API EDR services, ensuring that interoperable data access is available both through machine-to-machine interfaces and user-friendly graphical tools, supporting a broad range of user profiles and promoting citizen involvement and ocean literacy.
By addressing interoperability at the data, service, and user-interface levels, this work demonstrates how standardized data infrastructures are key enablers for improved, scalable, and sustainable coastal monitoring and forecasting capabilities.
How to cite: Dias, T., Videira, C., Lobo, V., Costa, A. C., and Lourenço Baptista, M.: Improving coastal monitoring and forecasting systems through interoperable OGC API EDR-based data services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11166, https://doi.org/10.5194/egusphere-egu26-11166, 2026.
The dataset with the most spatial coverage for data assimilation of biogeochemical models in operational systems is the satellite-derived data. Nevertheless, variables derived from Remote Sensing Reflectance (RSR), like the sea surface chlorophyll concentration, for regions like coastal areas, can reach big errors if compared with in situ measurements. For this reason, a suggestion with the aim of improving the assimilated results comes from the direct assimilation of Remote Sensing Reflectance, removing the error derived from inferring the biogeochemical variable before assimilating. In this work, we focus on a case study, using the Biogeochemical Flux Model (BFM) merged with a hydrological model, we study the effects of the direct and indirect assimilation of RSR in a region located in the Ligurian Basin of the northwestern Mediterranean Sea. For both assimilation experiments, the algorithm used was an Error Subspace Kalman Filter. To assess the results, we compared them with climatologies computed with in situ measurements, highlighting the advantages and disadvantages of both approaches.
How to cite: Soto Lopez, C. E., Lazzari, P., Anselmi, F., and Teruzzi, A.: Indirect assimilation of remote sensing reflectance: case study in the Liguria Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22059, https://doi.org/10.5194/egusphere-egu26-22059, 2026.
The boreal early-spring of 2024 witnessed unprecedented marine heatwaves across the tropical Atlantic, setting a satellite-era record for basin-averaged marine heatwave intensity. Based on observational and reanalysis datasets and a mixed layer heat budget analysis, we identify three region-specific drivers. In the north (20°N–3°N), the event began in fall 2023 and was maintained by sustained positive shortwave radiation anomalies due to reduced cloudiness. Equatorial warming (3°N–3°S) was primarily driven by wind-driven ocean wave processes, amplified by a shallower mixed layer. In the south (3°S–20°S), the key mechanism was wind-driven mixed layer shoaling. The reduced cloudiness over the northern tropical Atlantic is linked to remote El Niño forcing, and the wind anomalies over the equatorial and southern tropical Atlantic are partly attributable to the concurrent South Atlantic Subtropical Dipole. Our findings clarify the multifaceted origins of such extreme marine heatwaves, offering crucial insights for improving their seasonal prediction.
How to cite: Yang, J.-C., Li, S., Richter, I., Liu, Y., Zhang, Y., Li, Z., and Lin, X.: Primary Factors Driving Extreme 2024 Early-spring Marine Heatwaves in the Tropical Atlantic: Shortwave Radiation and Mixed Layer Depth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3225, https://doi.org/10.5194/egusphere-egu26-3225, 2026.
A permafrost probability index (PPI) based on rock glacier inventory and machine learning models, including random forest, support vector machine, artificial neural network, and logistic regression, was generated for Kinnaur district, Himachal Pradesh, India. Intact rock glaciers were considered the dependent variable, and elevation, slope, aspect, and potential incoming solar radiation were used as independent variables to generate a spatially distributed, high-resolution permafrost probability index. Daily weather station data and daily multitemporal MODIS satellite data were used to train a linear regression model to predict the annual 0℃ isotherm in the region for the period of 2023-24, aiming to understand potential degradation by overlaying the isotherm on permafrost distribution. The random forest technique produced the best results with an overall accuracy of 89.43%. Seven glacial lakes were identified as located in potentially permafrost-degraded slopes, and the Kashang glacial lake was selected for detailed downstream glacial lake outburst flood process chain modeling based on its size, moraine-dammed proglacial setting, and potential downstream impact. The volume of the lake was estimated to be 8.6 × 106 m3 by extrapolating the contours from overdeepening of the main glacier. Three sources of avalanches were identified based on permafrost degradation and slopes greater than 30 degrees. Subsequently, three scenario-based process chains for glacial lake outburst floods were modeled. We simulate avalanche initialization, displacement wave generation, overtopping, moraine erosion, and downstream flooding. The modelling results revealed that the potential GLOF can cause a peak discharge of 16,167 ms⁻¹, and floodwater can reach the Kashang, where a hydropower is located, within 16 minutes in the high-magnitude scenario. The findings can give important insights into GLOF hazard mitigation in the valley and can aid as preliminary data for various stakeholders working towards mitigating glacier-related hazards.
Keywords: Permafrost, GLOF, machine learning, r.avaflow, Himalaya
How to cite: Alangadan, A. and Sattar, A.: Glacial lakes in permafrost terrain and downstream hazards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1083, https://doi.org/10.5194/egusphere-egu26-1083, 2026.
