Session NH6.6

[Programme]

NH6.6 | Assessing coastal and estuarine resilience to multi-hazards: from driving processes to remote sensing approach

Assessing coastal and estuarine resilience to multi-hazards: from driving processes to remote sensing approach

Convener: Emma Imen Turki | Co-conveners: Charlotte Lyddon, Gabriela Medellín, E. Tonatiuh Mendoza, Timothy Price, Christian Schwarz

Orals

| Wed, 17 Apr, 16:15–18:00 (CEST)

Room 1.14

Posters on site

| Attendance Thu, 18 Apr, 10:45–12:30 (CEST) | Display Thu, 18 Apr, 08:30–12:30

Hall X4

Posters virtual

| Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00

vHall X4

With the global context of climate change and the increasing human pressure, coasts and estuaries are becoming more vulnerable to environmental hazards and are currently facing an intensification of natural hazards including sea level rise and severe climate events.
These hazards have intensified in the last decade due to the overexpansion of urbanization and infrastructure that these areas are facing, together with the climate change effects, such as sea-level rise and the increase in storminess and droughts. Such drivers have often degraded coastal ecosystems triggering a larger exposure to hazards, consequently increasing the associated risk to coastal populations and reducing their natural resilience.
The assessment of multi-time scale dynamics within coastal zones and their corresponding resilience can be effectively conducted through a diverse array of remote sensing techniques. The use of such techniques depends on the spatial and temporal scales of interest (shoreline, morphological systems, wetlands, vegetation cover, estuaries and reefs), the physical process which could be resolved, and also the availability of measurements in the area of interest. The study on the interaction between several processes requires a coupling between different techniques to overcome the limitations exposed by each technique used separately.
The main objective of this session is to highlight the relevance of remote sensing for the assessment of resilience of coastal and estuarine systems exposed to various external and internal drivers and controlled by different physical and anthropogenic processes. This session particularly invites contributions aimed at the monitoring of coastal and estuarine resilience using approaches that focus on:

(1) Identifying the key variables that allow to assess the coastal resilience;
(2) Building openly accessible coastal and estuarine observation datasets from the use of different satellite missions and compiling them.
(3) Developing new approaches based on physics-based algorithms and/or artificial intelligence for compiling Remote Sensing dataset for the evaluation of resilience.
(4) Assessing the quality of remote sensing datasets in the different environments and their use at different time and spatial scales; and investigating the relevance of their combination with numerical models to evaluate the multi-timescale dynamics of coastal areas and their resilience.

Orals: Wed, 17 Apr | Room 1.14

Chairpersons: Emma Imen Turki, Charlotte Lyddon, Gabriela Medellín

16:15–16:20

5-minute convener introduction

16:20–16:40

EGU24-17509

ECS

solicited

On-site presentation

Assessing Coastal Resilience from Space

Arjen Luijendijk, Etienne Kras, and Floris Calkoen

Satellite imagery proves to be a promising data source to gain insights in historic shoreline behavior over the last 4 decades on a global scale. To enable the use of such a large amount of satellite data, image processing techniques are introduced to interpret such large datasets. Furthermore, Machine Learning (ML) algorithms allow for an extra in-depth understanding of the shoreline dynamics, while growing computational power and standardization of ML packages, opens possibilities for studying shoreline dynamics and their drivers on a global scale.

In this way, human drivers, such as nourishments, ports, coastal structures, and natural drivers, such as relative sea level rise, inlet systems, and storms, can be identified across the globe. The high spatial and temporal resolution of this information yields more comprehensive understanding of our coasts and their resilience to cope with a changing climate. This is not only of great added value in data-poor environments, but it will also allow for more cost-effective coastal monitoring in data rich environments as the necessity of in-situ measurements will reduce in future. Furthermore, information on the governing drivers for local coastal change is one of the key elements required for shoreline predictions.

Providing such a prediction for future shoreline positions is just one example of a climate service. To prepare coastal zones for a changing climate in the future, coastal managers are demanding various other climate services to efficiently access and use state-of-the-art data on projections related to flooding, erosion, subsidence, vulnerability of assets and adaptation measures. The CoCliCo platform will be presented that fulfils the stakeholder needs by providing climate services at a pan-European scale.

How to cite: Luijendijk, A., Kras, E., and Calkoen, F.: Assessing Coastal Resilience from Space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17509, https://doi.org/10.5194/egusphere-egu24-17509, 2024.

16:40–16:50

EGU24-17886

ECS

On-site presentation

Unlocking the potential of observations in shoreline modelling through data assimilation

Moisés Álvarez-Cuesta, Alexandra Toimil, and Iñigo Losada

Analyzing the coastal response is a complex problem that usually requires the use of numerical modelling in combination with observations (Alvarez-Cuesta et al., 2023). To this end, data assimilation is a useful tool to blend observational data and models to produce more accurate forecasts.

Here, the performance of different data assimilation algorithms in predicting multiscale shoreline dynamics is studied. Two statistical algorithms based on the Kalman filter (Alvarez-Cuesta et al., 2021) and one variational algorithm named 4DVar (LeDimet, F-X. & Talagrand, O., 1986) are employed together with an equilibrium cross-shore model and a one-line longshore model. A twin experiments procedure is performed to obtain the observation requirements for the different assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needs and the ability of the different assimilation methods to track the system non-stationarity are evaluated under synthetic scenarios.

With noisy observations, the Kalman filter variants outperform the 4DVar. However, the 4DVar is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. Results are demonstrated at two real beaches governed by different processes with different data sources used for calibration and stress the need for assimilating shoreline observations to produce robust forecasts.

REFERENCES

Alvarez-Cuesta, M., Losada, I. J. & Toimil, A. (2023). A nearshore evolution model for sandy coasts: IH-LANSloc. Environmental Modelling and Software, 169, 105827

Alvarez-Cuesta, M., Toimil, A., & Losada, I. J. (2021). Modelling long-term shoreline evolution in highly anthropized coastal areas. Part 1: Model description and validation. Coastal Engineering, 169(July), 103960.

LeDimet, F-X. & Talagrand, O. (1986). Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus a 38.2: 97-110.

How to cite: Álvarez-Cuesta, M., Toimil, A., and Losada, I.: Unlocking the potential of observations in shoreline modelling through data assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17886, https://doi.org/10.5194/egusphere-egu24-17886, 2024.

16:50–17:00

EGU24-12667

On-site presentation

Satellite optical imagery: towards fully integrated measurements of the coastal zone.

Erwin Bergsma, Stephanie Artigues, Rafael Almar, Adrien Klotz, and Thierry Garlan

Space-based coastal observations are emerging as satellite imagery becomes more freely available through large-scale programs such as Landsat and Copernicus. Remote sensing techniques enable large-scale, even global, studies with sufficient spatio-temporal resolution, given that, for example, optical satellite imagery from the Sentinel-2 program has a global coastal coverage of up to 20 km offshore and a revisit of 2 to 5 days. The availability of data combined with accessible tools has enabled an explosion in space-based observations, even if these observations now go beyond the research phase alone and can support large-scale decision-making (see, for example, the Space Climate Observatory). However, most spaceborne applications for the coastal zone focus on useful but indirect proxy indicators such as waterline estimation. Submerged bathymetry and emergent topography are often not taken into account, even though they are essential for the usage and forecasting with of process-based models. Here, we present current work by the French Space Agency in collaboration with LEGOS and Shom on the future of space-based coastal observations: total, fully integrated monitoring of the coastal zone from space. This includes simultaneous measurements of bathymetry, coastline and topography at multiple spatial and temporal scales. Sentinel-2's large-scale bathymetry estimation and coastline detection, complemented by 3D topography using very high-resolution Pleiades images, offer a solution for monitoring the coastal zone from space over large regional scales. All these components of coastal monitoring are open-source, such as the CNES CARS routines for DEM 3D topography estimation, the CNES-IRD-SHOM S2SHORES bathymetry estimation. While focusing on current capabilities, we will also present prospects for future Earth observation missions, such as CO3D, and new capabilities for obtaining fully integrated measurements in a single satellite pass.

How to cite: Bergsma, E., Artigues, S., Almar, R., Klotz, A., and Garlan, T.: Satellite optical imagery: towards fully integrated measurements of the coastal zone., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12667, https://doi.org/10.5194/egusphere-egu24-12667, 2024.

17:00–17:10

EGU24-19824

ECS

On-site presentation

Advanced Insights into Coastal and Estuarine Environments: Key Fine Targets Analysis through Automated 2D and 3D Techniques

Jianru Yang, Kai Tan, Shuai Liu, Ruotong Zhou, Yuekai Hu, and Weiguo Zhang

Coastal inshore areas, recognized as invaluable yet vulnerable, are experiencing shifts between various states due to gradual environmental changes and artificial disturbance. These transitions, however, are often imperceptible with large-scale mapping or through on regional in situ surveying when using traditional techniques. Advanced 2D and 3D technologies, particularly high-resolution remote sensing (HRRS) and LiDAR, offer novel perspectives that unveil fine details and precise vertical 3D structure of coastal ingredients. These technologies enable early, rapid, and accurate identification of significant transient or persistent patterns. Additionally, machine learning (ML), encompassing parametrized algorithms, ensemble learning (EL), and deep learning (DL), provides a unique advantage for automated observation.

This work aims to advance the observation of key fine components in coastal inshore areas by designing automated methods and frameworks. It considers both natural and human-made sources as targets. with the focus of Poaceae and marine debris.

First, an automated 3D recognition of stalks and leaves for Poaceae in coastal mudflats. Poaceae species (Giant reed and reed) in coastal mudflats hold ecological importance and serve as indicators. However, obtaining their phenotypic parameters like stalks and leaves is challenging. Our new automated, parametrized algorithm recognizes stalks and leaves of individual Poaceae plants in coastal wetlands using terrestrial LiDAR point clouds, leveraging radiometric and geometric features.

Second, a new framework for comprehensive surveying of coastal Fairy Circles (FCs). FCs, predominantly formed by Poaceae, are self-organized patterns linked to recovery processes and salt-marsh resilience. Our new framework aims for automated surveying of coastal FCs, utilizing ML methods (which includes state-of-the-art foundation model, EL, and DL methods) on 2D and 3D data (satellite-borne and airborne). It is grounded in clear principles of FCs' definition and dynamics, potentially revolutionizing our understanding of coastal FCs behavior.

Third, an automated method for 2D and 3D recognition of marine debris across complex scenarios. Marine debris in coastal environments poses significant ecological and environmental issues and has garnered widespread concern. Our new method detects and extracts marine debris from terrestrial LiDAR point clouds or UAV HRRS imagery, combining calibrated radiometric data with geometric features.

Fourth, we have developed a series of mathematical models for instrumentation and data processing to achieve these goals. We proposed generalized rigorous model to mathematically correct the density variation in terrestrial LiDAR point clouds, the novel distribution pattern features, and a model to eliminate the specular effect on UAV LiDAR point cloud intensity.

How to cite: Yang, J., Tan, K., Liu, S., Zhou, R., Hu, Y., and Zhang, W.: Advanced Insights into Coastal and Estuarine Environments: Key Fine Targets Analysis through Automated 2D and 3D Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19824, https://doi.org/10.5194/egusphere-egu24-19824, 2024.

17:10–17:20

EGU24-19590

ECS

On-site presentation

Water surface temperature and salinity estimation from EO satellites for estuarine dynamics assessment in the Mediterranean and Black Sea

Regine Anne Faelga, Giogia Verri, and Sonia Silvestri

Estuaries are known as transition zones which modulate the freshwater inputs into the sea, with ocean salt water entering the river mouth and merging with the zero-salinity river streamflow. Understanding their dynamics is important for several purposes including the estimate of the salinization of inland waters and the effects in the thermohaline variability of the shelf to the open sea. The Copernicus Service Evolution Project EstuarIO proposes a low-to-high complexity modeling of the estuaries by merging 1D box and 3D unstructured modeling approaches. The final aim is to better represent the river release (in terms of runoff, temperature and salinity) within the Copernicus forecasting Centres over the Southern European Seas. A source of uncertainty is that most estuaries are poorly monitored, river discharge measurements are taken far from river outlets, and salinity and temperature at the river mouths are mostly unknown. One of the EstuarIO objectives is to strengthen the calibration and validation of the estuarine models applied to target sites (Rhone, Po, Ebre and Danube deltas), using water temperature and salinity data derived from EO satellites. Landsat 8 and 9, along with other data sources such as MODIS are used as preliminary data sources for the riverine and coastal surface temperature (ST). The Landsat scenes used in the study were the L1TP (calibrated top-of-atmosphere reflectance and brightness temperature) data, with a combined repeat coverage of 8 days and spatial resolution of 30 m for the Operational Land Imager (OLI) multispectral bands and 100 m resampled to 30 m for the Thermal Infrared Sensors (TIRS) bands. Atmospheric correction and cloud masking were applied before retrieving the ST values. Current results suggest that the Landsat 8 and 9 imageries can be utilized to obtain high-resolution riverine and coastal ST data. A multilayer perceptron neural network based (MPNN) model is under testing in the target estuaries to estimate SSS values with in situ observations as benchmark to judge this innovative approach. Preliminary results on SSS extraction will be presented as well.

How to cite: Faelga, R. A., Verri, G., and Silvestri, S.: Water surface temperature and salinity estimation from EO satellites for estuarine dynamics assessment in the Mediterranean and Black Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19590, https://doi.org/10.5194/egusphere-egu24-19590, 2024.

17:20–17:25

discussions Part 1

17:25–17:35

EGU24-1664

Highlight

On-site presentation

The Increasing Impact of Climate Change on coastal-fluvial Extremes and Severity of Compound Flood Events in UK Estuaries

Pete Robins, Charlotte Lyddon, Chien Nguyen, Grigorios Vasilopoulos, Mirko Barada, Andrew Barkwith, Gemma Coxon, Laura Devitt, and Thomas Coulthard

Estuarine flooding is driven by extreme sea-levels and river discharge, either occurring independently or at the same time, or in close succession to exacerbate the hazard, known as compound events. Understanding compound flooding in the face of climate change is crucial for anticipating and mitigating heightened risks. Rising sea levels, increased storm intensity, and changing precipitation patterns can amplify the simultaneous occurrence of extreme storm surges and river flows. It is necessary to assess changing patterns of timing and intensity in extreme storm-driven compound events to inform future incident and hazard management strategies. Understanding whether these events will intensify or diminish is crucial for adapting and developing effective mitigation measures.

This research represents the first time that projections of future sea-level, storm surge, and river discharge to assess changes in the magnitude and timing of storm-driven compound events in an estuary particularly vulnerable to compound flooding (Dyfi, west Wales). Sub-daily projections of river discharge from a hydrological model and sea level and residual surge from a shelf sea model are assessed independently to identify changes in their magnitude and return periods. Projections are then assessed in combination to identify future extreme dependence and timing of compound events. The analysis provides forcing conditions representative of a 1 in 20-year and 1 in 50-year event to simulate the impacts of future return periods in the Dyfi Estuary.

The research shows that more extreme river discharge and storm surges will occur up to 2100, and the severity of a 1 in 1-year to a 1 in 5-year event will become more severe into the future. There is a stronger likelihood of an extreme river discharge occurring at the same time as an extreme skew surge in the future, more often per storm season, and with greater dependence. Further to this, as storm-driven compound events become more prevalent in the future, the associated flood impacts are anticipated extend over larger areas and occur with increased severity.

This research presents a scalable methodology for comprehensive assessment and analysis of the future likelihood and impacts of storm-driven compound events, that can be applied worldwide where sub-daily river and sea level projection forced by the same global climate model are available.

How to cite: Robins, P., Lyddon, C., Nguyen, C., Vasilopoulos, G., Barada, M., Barkwith, A., Coxon, G., Devitt, L., and Coulthard, T.: The Increasing Impact of Climate Change on coastal-fluvial Extremes and Severity of Compound Flood Events in UK Estuaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1664, https://doi.org/10.5194/egusphere-egu24-1664, 2024.

17:35–17:45

EGU24-6436

ECS

On-site presentation

Alternative nature-based solutions for flood protection in a macrotidal estuary under a changing climate.

Marta Payo Payo, Constantinos Matsoukis, Xiaorong Li, Elina Apine, Amani Becker, Sara Kaffashi, Marta Meschini, Francisco Calafat, Claire Evans, Kenisha Garnett, Stephen Jay, Simon Jude, Andy Plater, Leonie Robinson, Joana Zawadzka, Richard Dunning, Anil Graves, Tim Stojanovic, Jenny Brown, and Laurent Amoudry

Estuaries are complex and dynamic systems where physical and biological processes overlap with social and economic activities. Increasing coastal hazards and human pressure threaten the fragile equilibrium of these ecosystems. The combination of fluvial and coastal processes increases the probability of flooding in estuaries. Expanding urban development in these low-lying areas increases their exposure (i.e. the population, and the number and value of the coastal assets), which impacts the vulnerability of coastal communities and businesses. Traditionally, hard-engineered structures (i.e. grey defences) have been used to protect the coast from flooding risk. Nature-based solutions (i.e. green solutions) are now increasingly promoted to help remediate the growing costs and long-lasting impacts of grey defences, and to address the need for solutions that can balance the benefits for both nature and society and bridge social and economic interests. These green solutions have the potential to deliver both flood risk reduction, and other co-benefits such as habitat provision, spaces for recreation, or climate regulation. However, the shift towards green solutions is hindered by social and political resistance, by the difficulty to assess the co-benefits they offer, and by knowledge gaps in the level of flood protection they can provide under a changing climate.

Here, we explore management options that can mitigate or worsen flood hazards both now and in the future for the Ribble estuary, a macrotidal estuary in North West England. The Ribble estuary includes Hesketh Out Marsh, one of England’s most important estuarine bird habitats and one of the biggest (322ha) completed managed realignment schemes in the UK. We will present coastal inundation modelling results using the SFINCS (Super-Fast Inundation of CoastS, Deltares) model under a series of ‘what-if’ scenarios including alternative interventions, and future sea level rise. We tailored the experimental design by considering the coupled human-environment estuarine system. The chosen scenarios encompass a range of plausible interventions focusing on the managed realignment site of Hesketh Bank (e.g. no intervention vs managed realignment alternatives). We reviewed historic events with strong local narrative and legacy (e.g. near-miss event for storm Desmond in 2015). We chose the events so that they cover a range of compound fluvial and coastal hazards. We built on these historic events to explore mid to long term flooding risk under changing climate. For each scenario, we propagated nearshore the offshore conditions with the Delft3d model (Deltares). We then used the Delft3d outputs as inputs to the SFINCS model. The outcome is a library of flood maps, which can be overlapped with vulnerability or exposure data. This evidence can support coastal managers both on present day coastal management and on adaptation planning for environmental resilience.

How to cite: Payo Payo, M., Matsoukis, C., Li, X., Apine, E., Becker, A., Kaffashi, S., Meschini, M., Calafat, F., Evans, C., Garnett, K., Jay, S., Jude, S., Plater, A., Robinson, L., Zawadzka, J., Dunning, R., Graves, A., Stojanovic, T., Brown, J., and Amoudry, L.: Alternative nature-based solutions for flood protection in a macrotidal estuary under a changing climate., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6436, https://doi.org/10.5194/egusphere-egu24-6436, 2024.

17:45–17:55

EGU24-3136

ECS

On-site presentation

Influence of groundwater in compound flooding in UK estuaries.

Ankita Bhattacharya, Andrew Barkwith, and Peter Robins

Low-lying estuaries, deltas, and bays are especially prone to flooding from multiple sources of high river discharge, coastal flooding from waves and storm surges, and pluvial flooding from intense rainfall – with groundwater levels a lesser researched flood driver. When these drivers occur simultaneously or sequentially, they create a greater impact, and are referred to as Compound Flooding. Recent compound events such as Hurricane Katrina (New Orleans in 2005), Cyclone Nargis (Myanmar in 2008) or Storm Xynthia (French Atlantic coast in 2010) have been shown to result in significant loss of lives and properties in coastal lowlands. Globally, 2.15 billion people reside in near-coastal areas, with 898 million in low-elevation coastal zones.

The UK has a long history of estuarine flooding from compound events. UK climate projections indicate that there will be hotter and drier summers and prolonged wet winter periods, with an increase in the frequency and intensity of extreme storm surge and rainfall events that are also more likely to co-occur. Climate projections also indicate sea level rise at most locations around the UK which will make the coastal areas increasingly vulnerable. Groundwater is an important and dynamic component of the coastal environment. Coastal aquifers are vital fresh groundwater resources that are frequently subjected to coastal flooding due to increased runoff, storm surge and sea-level rise. Despite its lesser volumetric contribution in comparison with fluvial inputs, recent studies have found the presence and movement of groundwater may be both volumetrically and chemically important in river dominated coastal environments and requires future attention in view of climate change. Through our study we aim to investigate the different drivers influencing compound flooding in UK estuaries.

Our focus is on the Conwy estuary in North Wales, which is a flashy catchment that floods several times per season. A serious recent compound flood event was due to Storm Ciara (February 2020) where river gauges hit record levels and combined with intense rainfall and high storm tide, impacting 172 properties. River Conwy drains a catchment of nearly 600 km² and includes large mountains with high annual precipitation of around 1700mm per year and a baseflow contribution of 27%. Baseflow, which is the contribution of groundwater to surface water components, is notably influenced by topography, geology, vegetation, land use, and climatic factors. In this study we will develop a coupled catchment and groundwater model in Caesar Lisflood to understand how groundwater processes in the form of the baseflow can influence compound flood events in the estuary. Model simulations are calibrated against past fluvial and tidal flows to show how the river discharge, groundwater and associated drivers are likely to influence the magnitude, behaviour, and timings of compound flooding in the future.

How to cite: Bhattacharya, A., Barkwith, A., and Robins, P.: Influence of groundwater in compound flooding in UK estuaries. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3136, https://doi.org/10.5194/egusphere-egu24-3136, 2024.

17:55–18:00

Discussions Part 2

Posters on site: Thu, 18 Apr, 10:45–12:30 | Hall X4

Display time: Thu, 18 Apr, 08:30–Thu, 18 Apr, 12:30

Chairpersons: E. Tonatiuh Mendoza, Timothy Price, Christian Schwarz

X4.149

EGU24-1860

ECS

Site-Specific Thresholds for Storm-Driven Compound Flooding in UK Estuaries

Charlotte Lyddon, Nguyen Chien, Grigorios Vasilopoulos, Michael Ridgill, Sogol Moradian, Indiana Olbert, Thomas Coulthard, Andrew Barkwith, and Peter Robins

Estuarine flooding is driven by extreme sea-levels and river discharge, either occurring independently or at the same time, or in close succession to exacerbate the hazard, known as compound events. Estuaries have their own dynamics, and different behavior means flooding will occur under different conditions. Recent UK storms, including Storm Desmond (2015) and Ciara (2020), have highlighted the vulnerability of mountainous Atlantic-facing catchments to the impacts of compound flooding including risk to life and short- and long-term socioeconomic damages. There is a need to identify site-specific thresholds for flooding in estuaries, which represent the magnitude of key drivers over which flooding occurs, to improve prediction and early-warning of compound flooding.

In this study, observational data and numerical modelling were used to reconstruct the historic flood record of an estuary particularly vulnerable to compound flooding (Conwy, North Wales). The record was used to develop a method for identifying combined sea level and river discharge thresholds for flooding using idealised simulations and joint-probability analyses. Only 6 records of known flooding are identified in the official record. The key limitation of using historic records of flooding is that not all flooding events have been documented, and there are gaps in the record. Therefore, this research also identified the top 50 extreme sea-level and river discharge events in the historic gauge measurements in the estuary, and cross-checked these against online sources using web scraping to establish if these additional 100 extreme events also led to flooding. A more comprehensive historic record of flooding allows more accurate thresholds for flooding to set in each estuary.

Caesar-LISFLOOD, a hydrodynamic flow and morphological evolution model, is used in a sensitivity test to simulate inundation under different idealized sea-level and river discharge conditions to further isolate accurate thresholds. The variation in flooded area from a baseline scenario is used to capture flood magnitude associated with each scenario. The results show how flooding extent responds to increasing total water level and river discharge, with notable amplification in flood extent due to the compounding drivers in some circumstances, and sensitivity due to a 3-hour time-lag between the drivers. Joint probability analysis is important for establishing compound flood risk behaviour. Elsewhere in the estuary, either sea state (lower-estuary) or river flow (upper-estuary) dominated the hazard, and single value probability analysis is sufficient. These methods can be applied to estuaries worldwide to identify site-specific thresholds for flooding to support emergency response and long-term coastal management plans.

How to cite: Lyddon, C., Chien, N., Vasilopoulos, G., Ridgill, M., Moradian, S., Olbert, I., Coulthard, T., Barkwith, A., and Robins, P.: Site-Specific Thresholds for Storm-Driven Compound Flooding in UK Estuaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1860, https://doi.org/10.5194/egusphere-egu24-1860, 2024.

X4.150

EGU24-11728

ECS

Dynamics of Coastal Extremes: Unravelling Estuarine Processes through Numerical Modelling

Aaron Furnish, Peter Robins, and Simon Neill

Exploring the intricate relationships between land and marine processes is essential for a comprehensive understanding of climate dynamics. While contemporary coupled climate models have made significant progress in capturing various interactions, the explicit resolution of estuarine and intertidal processes remains a challenge. Building upon the foundation laid by the UKC3 UK national climate model, we present a novel perspective by incorporating a high-resolution (<20 m) flexible mesh model, Delft-3D, to specifically address intertidal and estuary regions.

Our study focuses on the dynamic eastern Irish Sea, marked by hyper-tidal conditions and hosting eight estuaries alongside a significant intertidal zone. Employing a comprehensive comparison between the Delft model and the UKC3 model, we emphasize the simulation of extreme water heights during the winter storm season of 2013-2014. The outcomes provide valuable insights into the capabilities of both models in capturing high-water levels, paving the way for future investigations.

Looking ahead, our research extends to incorporate the latest UKCP18 climate scenarios into the refined Delft model. This expansion allows us to explore potential variations in climate patterns and their implications for estuarine and coastal regions. The anticipated analysis aims to offer valuable insights into the impact of future climate change on these vital areas.

As a final objective, I plan to parameterize estuarine processes within the UKC3 coupled system using an estuarine box model. This simplified approach holds promise in resolving coastal extremes and fluxes for impact studies, marking a crucial step towards enhancing the overall accuracy of climate models in portraying estuarine dynamics.

How to cite: Furnish, A., Robins, P., and Neill, S.: Dynamics of Coastal Extremes: Unravelling Estuarine Processes through Numerical Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11728, https://doi.org/10.5194/egusphere-egu24-11728, 2024.

X4.151

EGU24-11761

ECS

Statistical properties of water level extremes along the St. Lawrence fluvial estuary

Silvia Innocenti, Pascal Matte, Remi Gosselin, Mouna Doghri, Caroline Sevigny, Olivier Champoux, and Jean Morin

The governmental Flood Hazard Identification and Mapping Program (FHIMP) seeks to update standards for flood mapping and risk area definition in Canada. Within this initiative, Environment and Climate Change Canada (ECCC) has been mandated to provide 2D simulations of water levels in the St. Lawrence fluvial estuary to estimate return periods of extreme water levels under historical and future conditions. Long-term fine-scale hydrodynamic simulations are necessary to reproduce accurately the complex interplay of hydrological, meteorological and tidal processes responsible for extreme water levels in this system. However, the substantial computational resources and time needed to run the hydrodynamic numerical models constrain the feasibility of producing numerous long-term simulations with a wide range of potential flood-generating conditions. Consequently, this study considers a complementary statistical framework to assess the extreme characteristics and drivers from historical data to prepare input scenarios for climatic projections.

Event-based analyses of water level records are conducted at 18 stations across the St. Lawrence system using univariate and multivariate techniques to characterize the observed extreme dynamics and flood events. Specifically, univariate frequency analysis is applied at each station to quantify local flood risk based on approximately 400 extreme events observed in the Estuary between 1972 and 2022. Multivariate investigations based on a non-stationary tidal harmonic regression tool (NS Tide) are then used to study the system dynamics involved in major observed events and reconstruct the extreme water level series using a set of hydrological, meteorological, and astronomical covariates. Finally, multivariate spatial analyses are performed on the identified extreme events and NS Tide continuous reconstructions. The goal is to assess the characteristics of high water-level events (e.g., duration, seasonality, and probability distribution) and extreme drivers at the local and regional scales.

How to cite: Innocenti, S., Matte, P., Gosselin, R., Doghri, M., Sevigny, C., Champoux, O., and Morin, J.: Statistical properties of water level extremes along the St. Lawrence fluvial estuary, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11761, https://doi.org/10.5194/egusphere-egu24-11761, 2024.

X4.152

EGU24-13318

ECS

Use of Sentinel-1 SAR data in assessing the accuracy of LISFLOOD-FP in modelling (compound) flooding in estuaries

Mirko Barada, Peter Robins, Martin Skov, and Matthew Lewis

Estuaries are among most vulnerable parts of our planet in terms of flood risk because they are constantly exposed to flood sources from at least two directions. While different flood modelling tools are helping us to be better prepared for flood events, state-of-the-art space technologies are providing useful high resolution (temporal and spatial) data to quantify and monitor real flood impacts and consequences, regardless of night and cloudiness.

In this study we are: a) applying LISFLOOD-FP hydrodynamic model for modelling compound flood event in the Dyfi estuary, Wales (UK) and b) using Sentinel-1 SAR data to map flood event from the same period and to validate flood inundation model. The selected flood event (> 300 m³/s and around 70 cm surge) caused large flooding along the estuary, particularly in the upper parts. Modelled results are shown as water surface elevation and water depth classified raster maps, which are used later for comparison with the SAR image.

Raw Sentinel-1 SAR (GRD) image downloaded from Copernicus Browser had to be pre-processed in ArcGIS PRO (The Synthetic Aperture Radar toolset) to remove unwanted noise and correct distortions. Further, RGB color composite was produced from the calibrated SAR image and used for extracting water bodies/flooded areas. It was achieved by applying deep learning tools integrated in ArcGIS PRO which classify pixels (wet/dry) based on a previously trained sample. Resulting raster was then compared with the modelled flood extent, quantifying the differences. Matching was very good in the upper parts where major flooding was recorded (> 80 % agreement), while the model was slightly less accurate in the lower estuary and along the salt marsh zone due to larger DEM uncertainty in those areas. However, when selected shallow areas (e.g. 0-1 cm or 0-2 cm class) were removed from the model, matching between modelled and observed flood extent was higher.

How to cite: Barada, M., Robins, P., Skov, M., and Lewis, M.: Use of Sentinel-1 SAR data in assessing the accuracy of LISFLOOD-FP in modelling (compound) flooding in estuaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13318, https://doi.org/10.5194/egusphere-egu24-13318, 2024.

X4.153

EGU24-6937

ECS

Spatial-temporal variation of estuarine acidification in the southeastern Yellow Sea, Korea

Yujeong Choi, Hyebin Kim Kim, and Tae-Hoon Kim

Geographically, an estuary is a transition zone where river water and seawater mix. In estuaries, where river water and sea water meet, acidification can occur due to carbon dioxide (CO₂) changes due to strong horizontal stratification, long residence time, eutrophication, and weak acid-base buffering capacity.

Despite the potential consequences, studies on acidification in Korean estuaries are notably scarce. This research focuses on the seasonal variations in aragonite saturation in the Han River estuary (an open estuary), and the Geum River and Yeongsan River estuaries (constrained by estuary dams) to assess the status of estuary acidification.

Seasonal changes in aragonite saturation (Ωarg) recorded values of 1.5±0.5, 1.8±0.8, and 2.1±0.4 at the mouths of the Han River (HRE), Geum River (GRE), and Yeongsan River estuaries (YRE), respectively. Acidification was weak at the YRE, where dissolved inorganic carbon and total alkalinity were high. Conversely, acidification was pronounced at the HRE, where dissolved inorganic carbon (DIC) and total alkalinity (ALK) were low. Remarkably, downstream areas of the estuary, particularly those near large cities like Seoul, exhibited heightened vulnerability to acidification.

In all three estuaries, aragonite saturation was lower in the upper reaches, influenced by river water with weaker acid-base buffering capacity than in the lower reaches. This underscores the potential for estuarine acidification to either worsen or alleviate based on future changes in the river's carbonate system, nutrient supply rates, and biological communities.

Should estuary acidification intensify, the buffering capacity of estuaries will be compromised, potentially leading to the transfer of acidification to the ocean. This research sheds light on the intricate dynamics of estuarine acidification and emphasizes the need for continued monitoring and understanding of these crucial ecosystems.

How to cite: Choi, Y., Kim, H. K., and Kim, T.-H.: Spatial-temporal variation of estuarine acidification in the southeastern Yellow Sea, Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6937, https://doi.org/10.5194/egusphere-egu24-6937, 2024.

X4.154

EGU24-2010

ECS

EU-Funded LIFE Projects Influence in Land Use/Land Cover Changes in Insular Ecosystems: The Case-Study of São Miguel Island (Azores)

Rafaela Tiengo, Silvia Merino-De-Miguel, Alicia Palacios-Orueta, Jéssica Uchôa, and Artur Gil

Small oceanic islands, like São Miguel Island (Azores), show high vulnerability to climate change impacts, biological invasions, and land-use/land-cover changes that threaten their biodiversity and affect their ecosystem functions and services. Organized and long-term nature conservation actions and projects such as those funded by the EU LIFE Programme have been fundamental to mitigating biodiversity loss in the eastern part of São Miguel Island since 2003. The use of remote sensing-based approaches may constitute a cost-effective way to support the management, monitoring, and control of these LIFE projects. In this work, a land-use/land-cover change detection approach focusing on the 2003-2022 LIFE Projects intervention areas was applied by using the RAO’s Q diversity index, which holds significant potential for monitoring the proliferation of invasive plant species and alterations in land use patterns. Using the ASTER, Landsat 8, and Sentinel-2 images from the Google Earth Engine on Google Colab and Python as the programming language, the average distribution of RAO’s Q diversity index values in the intervention areas was analyzed. The Normalized Difference Vegetation Index was calculated for the different years within the LIFE projects. The Classic Rao was calculated, giving the ability of this methodology to identify and evaluate diversity, making it possible to determine areas in which changes occurred in the project areas and the period in which these areas underwent interventions. By evaluating the effectiveness of conservation initiatives on small oceanic islands and archipelagos, we can gain insights into the ecological responses and long-term sustainability of these projects. This knowledge can inform future conservation strategies, contribute to the broader field of island conservation, and enhance our understanding of the unique dynamics and challenges associated with protecting biodiversity in insular environments.

How to cite: Tiengo, R., Merino-De-Miguel, S., Palacios-Orueta, A., Uchôa, J., and Gil, A.: EU-Funded LIFE Projects Influence in Land Use/Land Cover Changes in Insular Ecosystems: The Case-Study of São Miguel Island (Azores), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2010, https://doi.org/10.5194/egusphere-egu24-2010, 2024.

X4.155

EGU24-2112

Reasons of changes sediment movement near the Rioni river estuary

Manoni Kodua, Ivane Saghinadze, and Mari Tebidze

Rioni river, which joins the Black Sea in the territory of Western Georgia, has undergone hydrological changes many times as a result of artificial intervention. Close to its estuary to the south is the port of the city of Poti, whose breakwater was extended by 1.8 km and approached closer to the estuary of the river Rioni.

The article discusses the influence of the construction of a breakwater at the mouth of the northern channel of the river Rioni on the movement of sediments along the shore.

The constancy of sediment mass balance condition was used to study the sediment transport rates in the coastal zone and the change in the topography of the seabed, based on this the equation of water depth change was obtained. The finite element method and Crank-Nicolson schemes are used to solve the developed equations. The seacoast near the Rioni Nabada delta is taken as the research area.

Based on the resulting equations, the numerical experiments are conducted using the values of the known hydrological and hydrometric parameters of the Rioni river and the sea coast.

The overbank and bank-directed sediment transport rates are determined. The amount of beach-forming sediment imported by the Rioni river is about 4 million m³per year.

Numerical analysis shows that after the construction of the new port breakwater, the impact of southwesterly waves will be weakened to the north, the movement of sediment in the southern direction will be completely blocked, and 0.85 mln m³ volume of solid sediment will begin to settle in the north of the new Breakwater. In the case of the current hydrological and hydrometric parameters of the Rioni River, the accumulation of a large amount of sediment over time will lead to the blocking of the southern branch of the Nabada channel. Accordingly, the total flow of water from the channel will be shifted to the northern branch and the formation of a new delta will begin there.

All movement of fine sediments in this direction will be stopped, and solid sediments will begin to settle to the north of the new breakwater after the new breakwater is built. Ultimately, this will lead to the blocking of the southern branch of the Nabada channel. The entire flow of water from the Nabada channel will be diverted to the northern arm, and the formation of a new delta will begin there. The canyon is currently in equilibrium. A reduction in the supply of sediment may cause it to move towards the shore, which will interfere with the normal operation of the harbor.

How to cite: Kodua, M., Saghinadze, I., and Tebidze, M.: Reasons of changes sediment movement near the Rioni river estuary, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2112, https://doi.org/10.5194/egusphere-egu24-2112, 2024.

X4.156

EGU24-4146

ECS

Mapping Tidal Flat Changes and Determining Drivers in China's Coastal Zones: An Efficient and Reproducible Remote Sensing Method

Yuekai Hu, Lin Yuan, Huifang Fan, Yuwen Pang, Yao Li, Qiannan Ding, Juntian Liu, Bo Tian, and Yunxuan Zhou

The dynamic coastal zones, marked by rich biodiversity and rapid transformations due to human activities, present a challenging environment for monitoring and management. Tidal flats, a key natural feature of these zones, are increasingly subjected to anthropogenic stress, rising sea levels, and various environmental pressures. This study addresses the critical need for a robust, large-scale remote sensing approach to monitor these changes over time, particularly under fluctuating tides and evolving coastal landscapes. Using the three decades of Landsat 5 and Landsat 8 data (1990-2020), we developed an innovative approach for the automatic acquisition of low-tide imagery. Our method, which incorporates knowledge-based of tidal flat extraction, achieved a classification accuracy of over 95%. This technique effectively mitigates the impact of clouds, fog, and waves on image analysis, enabling precise and rapid delineation of large-scale intertidal zones. As a result, we produced the most extensive dataset on tidal flat areas in China's coastal zone, updated at three-year intervals. The spatial analysis results showed the primary distribution of tidal flats in Liaoning, Shandong, Jiangsu, Zhejiang, and Guangxi, which collectively account for over 70% of China's tidal flat areas. We observed distinct patterns of tidal flat evolution, with rapid changes in regions like the Liaohe River Delta, Yellow River Delta, Yangtze River Delta, and the Jiangsu Coast. These changes are closely linked to increased reclamation activities and salt marsh vegetation expansion. In contrast, coastal areas like Tianjin and Zhejiang showed a swift expansion of intertidal zones initially, followed by a stabilization post-2010, constrained by limited development space. Our study's approach to rapid tidal flat extraction has shown promising applications in other global river deltas. The comprehensive tidal flat mapping and data generation presented here offer valuable insights and support for the monitoring, management, and sustainable development of coastal wetlands.

How to cite: Hu, Y., Yuan, L., Fan, H., Pang, Y., Li, Y., Ding, Q., Liu, J., Tian, B., and Zhou, Y.: Mapping Tidal Flat Changes and Determining Drivers in China's Coastal Zones: An Efficient and Reproducible Remote Sensing Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4146, https://doi.org/10.5194/egusphere-egu24-4146, 2024.

X4.157

EGU24-9306

ECS

Predicting Coastal Flooding in the Mediterranean with Remote Sensing and Machine Learning

Alice Re, Lorenzo Minola, Alessandro Pezzoli, and Gustau Camps-Valls

Due to its historically low tidal variations, the Mediterranean Sea basin has seen significant coastal urbanisation, exemplified in the densely populated Italian region Liguria. However, the region faces increased vulnerability to extreme sea level changes and coastal flooding due to potential climate change-induced storminess.

Machine learning has recently received increased attention in the literature as regards the ability of data-driven approaches to solve flood-related problems, including the identification of areas potentially susceptible to inundation in support of risk preparedness and resilience in coastal cities. This work explores the application of machine learning using widely available remote sensing datasets to predict the inundation extent for a modelled 100-year return period coastal flooding event in Liguria. Numerical simulations produced by local administrations in the context of the EU Floods Directive serve as ground truth due to the absence of post-event inundation maps. Various pre-processed remote sensing datasets are employed as predictors, including land cover data, spectral indices and high-resolution DEM.

The results highlight challenges in integrating diverse timescales and data types and can be used to assess the influence of predictors on coastal resilience. The study also addresses the benefits and drawbacks of different machine learning algorithms in evaluating coastal resilience within this approach.

-------------------------------------------------------------------------------------------------------------------------------------

References

Woznicki, S. A., Baynes, J., Panlasigui, S., Mehaffey, M. and Anne Neale. “Development of a spatially complete floodplain map of the conterminous United States using random forest.” Science of the Total Environment 647 (2019): 942-953.

Ireland, Gareth, Michele Volpi, and George P. Petropoulos. "Examining the capability of supervised machine learning classifiers in extracting flooded areas from Landsat TM imagery: a case study from a Mediterranean flood." Remote sensing 7.3 (2015): 3372-3399.

Fogarin, S., et al. "Combining remote sensing analysis with machine learning to evaluate short-term coastal evolution trend in the shoreline of Venice." Science of The Total Environment 859 (2023): 160293.

Tsiakos, Chrysovalantis-Antonios D., and Christos Chalkias. "Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature." Applied Sciences 13.5 (2023): 3268.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, F. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.

Camps-Valls, Gustau, et al., eds. Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. John Wiley & Sons, 2021.

How to cite: Re, A., Minola, L., Pezzoli, A., and Camps-Valls, G.: Predicting Coastal Flooding in the Mediterranean with Remote Sensing and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9306, https://doi.org/10.5194/egusphere-egu24-9306, 2024.

X4.158

EGU24-18225

CoastSnap community beach monitoring: new innovations in smartphone-based monitoring of the coast

Mitchell Harley, Fred Chaaya, and Michael Kinsela

CoastSnap is a low-cost citizen science beach monitoring program that empowers local communities to collect quantitative measurements of coastline change using their smartphones. Underpinning CoastSnap is a stainless-steel smartphone cradle that is installed overlooking a beach in a location easily accessible to the public. Using the cradle for image positioning, passers-by simply take a photo of the coast and upload it to a centralized database, which in turn provides a crowd-sourced record of coastline change over time.

Behind this simple idea are advanced image processing algorithms that then enable the shoreline position (and other relevant coastal features) to be mapped from these community snapshots in a scientifically rigorous manner. First established in Sydney, Australia in May 2017, the network of CoastSnap stations has grown rapidly over the past seven years to now encompass over 350 monitoring locations in 31 countries. This growth of this global network now means that the CoastSnap project comprises the largest coordinated network of coastal monitoring worldwide.

The poster will provide a general overview of this unique global citizen science program to date and present latest developments regarding enhanced automation using AI, participation and new research outcomes.

How to cite: Harley, M., Chaaya, F., and Kinsela, M.: CoastSnap community beach monitoring: new innovations in smartphone-based monitoring of the coast, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18225, https://doi.org/10.5194/egusphere-egu24-18225, 2024.

X4.159

EGU24-9879

Enhancing Coastal Infrastructure Resilience: three decades of InSAR Analysis of Nice Côte d’Azur Airport Subsidence

Olivier Cavalié, Frédéric Cappa, and Béatrice Pinel-Puysségur

Coastal areas can be tremendously biodiverse and host a substantial part of the world’s population and critical infrastructure. However, there are often fragile environments that face various hazards such as flooding, coastal erosion, land salinization or pollution, earthquake-induced land motion, or anthropogenic processes. In this article, we investigate the stability of the Nice Côte d’Azur Airport, which has been built on reclaimed land in the Var River delta (French Riviera, France). This infrastructure, as well as the ongoing subsidence of the airport runways, has been a permanent concern since the partial collapse of the platform in 1979. Moreover, using InSAR data between 2003 and 2011, Cavalié et al. (2015) showed that parts of the airport platform were subsiding up to 10 mm/yr.

Understanding the mechanism and thus the evolution of sediment compaction is essential to evaluate the danger caused by the coastal subsidence. Therefore, in this study, we extended the observation period of InSAR measurements to better analyze the temporal evolution of the ground displacement on the Nice Côte d’Azur Airport platform in the hope of capturing the non-linear component of the deformation. Indeed, the relatively short period of observation (2003-2011) of the previous study (Cavalié et al., 2015) impeded the accurate detection of non-linearity in the surface displacement and thus to understand its dynamic. So, we used here the complete archive of SAR images acquired by ESA over a much longer period of time (28 years from 1992 to 2020).

Extending the observation window to study the long-term subsidence leads to substantial improvements in the understanding of the ongoing mechanisms along this coastal area. Indeed, the new analysis reveals a notable deceleration in the maximum downward motion rate, decreasing from 16 mm/yr in the 1990s to 8 mm/yr in the present day (for the fastest subsidence area). We then used a simple analytical Burgers creep model to constrain the mechanisms and rheology at play. The data are properly explained by the phases of primary and secondary creep, highlighting a slow viscoelastic deformation at multiyear timescales. Our study thus proves that the long-term InSAR data can improve our understanding of the surface processes and the subsurface material properties. Although the subsidence rate decelerates, at least for 28 years, our results show that the compaction of the sediment is still active and its future evolution is uncertain and still at stake. Indeed, if compaction bands are developing under the airport platform, creep processes could potentially lead accumulated material damage to failure.

This study underscores the critical role of remote monitoring in comprehending coastal land motion. We show here that employing advanced InSAR techniques offers a better understanding of actual hazards posed by the airport built on reclaimed lands. The findings advocate for ongoing monitoring initiatives to mitigate risks and enhance the resilience of coastal infrastructure.

How to cite: Cavalié, O., Cappa, F., and Pinel-Puysségur, B.: Enhancing Coastal Infrastructure Resilience: three decades of InSAR Analysis of Nice Côte d’Azur Airport Subsidence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9879, https://doi.org/10.5194/egusphere-egu24-9879, 2024.

Posters virtual: Thu, 18 Apr, 14:00–15:45 | vHall X4

Display time: Thu, 18 Apr, 08:30–Thu, 18 Apr, 18:00

Chairpersons: Emma Imen Turki, E. Tonatiuh Mendoza, Christian Schwarz

vX4.34

EGU24-17702

ECS

solicited

Assessing Shoreline Dynamics under Macro to Meso-tidal Conditions through Integrative low-cost Remote Sensing Technique

khurram riaz, Marion McAfee, and Salem Gharbia

Coastal erosion, a critical issue in shoreline management, arises from a combination of natural dynamics and anthropogenic influences. In macro to meso-tidal regions, this phenomenon is particularly pronounced due to the interplay of sea level fluctuations, erosive wave action, and sediment displacement. Significantly, this erosion poses a direct threat to the stability and integrity of coastal dunes, which are vital for protecting inland areas and maintaining ecological balance on these beaches. This study addresses this issue by analysing shoreline changes over the past decade at three unmanaged Northwest beaches of Ireland: Enniscrone, Streedagh, and Dunmoran. Utilising open-source satellite imagery, the research employed the CoastSat and DSAS tools to extract data on shoreline movement to tackle coastal erosion or accretion. Acknowledging the errors in satellite-derived shoreline data due to high tidal variations, the study further validates its findings with field data. This validation was performed using two contrasting technological approaches: a high-cost LiDAR-equipped drone (DJI Terra drone and DJI Zenmuse L2 Lidar) and a low-cost DJI Phantom 4 RTK drone with a standard camera. The comparison of data from these diverse sources reveals crucial insights. Firstly, the study validated shoreline changes detected by satellite imagery, ensuring the consistency and reliability of observed trends across different remote sensing platforms. Additionally, the comparison between high-cost and low-cost drone data was instrumental in assessing their respective efficacies in capturing coastal topography. The high-resolution LiDAR data offered detailed 3D models of the coastal landscape, allowing for precise measurements of dune morphology and erosion patterns. In contrast, the standard camera on the low-cost drone provided broader, less detailed views but was surprisingly effective in identifying larger-scale changes and erosion hotspots. The study highlights the potential of integrating various remote sensing techniques for coastal monitoring, which provides a cost-effective and accurate way of assessing the coastal erosion in Macro to Meso-tidal beaches.

How to cite: riaz, K., McAfee, M., and Gharbia, S.: Assessing Shoreline Dynamics under Macro to Meso-tidal Conditions through Integrative low-cost Remote Sensing Technique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17702, https://doi.org/10.5194/egusphere-egu24-17702, 2024.