Andres Payo

Soft cliffed coasts are particularly vulnerable to erosion and their retreat depends on environmental, geological and geomechanical factors. In this work, we use a mathematical modeling approach to understand effects of future global sea level rise on these coasts over multiple centuries. We develop a simple mass conservation model, equating material eroded from a cliff to the sediment required for foreshore adaptation to sea level rise. The model considers geological differences between cliff and beach sediment, variable and uncertain rates of sea level rise, and the complex inland topography a cliff may erode through. The model is solved analytically, and long‐term behaviors discussed, including conditions for an equilibrium recession rate or a change in coastal typology. Methods for obtaining estimates of model parameters are detailed and an application of the depth of closure concept to multi‐century timescales proposed. The model is applied to the Holderness coast, United Kingdom. It predicts an acceleration of cliff retreat due to rising sea levels over the next centuries, though local topographical features may cause temporary deceleration. No long‐term equilibrium behavior is identified. The study also estimates that vulnerable locations along this coast will experience a change in coastal typology within the next few hundred years. These findings highlight the importance of inland topography in determining an eroding cliffed coast's resilience to sea level rise. We propose that the relative sea level rise corresponding to typology change should be used as an indicator of cliff resilience to sea level rise, instead of current cliff height.

Seismic ambient noise is a ubiquitous and constant resource, ideal for non-invasive investigations of the solid earth. Coastlines around the world are handling an increase in coastal erosion due to sea level rise and more energetic storms. Monitoring this is becoming an increasingly necessary task to protect coastal settlements. Using Distributed Acoustic Sensing in seismic monitoring has already shown incredible potential and offers the advantage of dense measurements. Our project seeks to identify the efficacy of Distributed Acoustic Sensing for monitoring subsurface changes which precede cliff failure. We present early findings from the first long-term deployment of a fibre optic cable along the coastline - North Sea, Norfolk, UK. We investigate differences in signal characteristics between conventional seismometers and Distributed Acoustic Sensing in this setting, and interpret the seismic signatures of key sources in the area. This deployment was recording for 22 months, allowing us to monitor both short-term and seasonal changes. We identify the frequency ranges excited by storm events (0.2 - 1 Hz), the dominance of short-period secondary microseismic activity, and the importance of local sea state and weather on influencing higher frequency signals. We also discuss limitations of Distributed Acoustic Sensing and the sources it can not reliably capture when compared to broadband seismometers and nodal geophones. We conclude by discussing how this noise analysis affects the use of ambient noise tomography for seismic velocity monitoring. Future research will test the efficacy of such applications, with the hope of providing better estimates of coastal recession and identifying hazardous areas on a metre-scale.

Soft rock cliffs are common coastal features made of erodible material, such as clay and glacial deposits. The erosion of soft-cliffed coasts threatens communities around the world, destroying homes and habitats. Here we present a numerical model suited for estimating the evolution of these coastal systems over centuries in response to sea level rise (SLR) alone. To achieve computational efficiency, we propose a simple conceptual model, based on a mass- conservation approach. In this model we have relaxed the conditions used by Wolinsky and Murray (2009) when solving the Exner equation for cliffed coastlines. In particular, the assumptions of constant SLR and simplified inland topography. In this work we remove these restrictions, and obtain a simple behavioural equation for soft-cliffed coasts for which an analytical solution exists. This allows study of the conditions for existence of an equilibrium rate and the dynamics of equilibrium approach.

The high frequency acquisition and broad area covered by new satellite missions (e.g. Sentinel-1 and Sentinel-2) provide high amount of information that allows for the monitorization of coastal processes in several time and spatial scales. In the past few years, the potential of using satellite-derived shorelines obtained from optical sensors to detect coastal changes has been deeply explored (Vos et al., 2023). A collaborative work between SAR experts and end- users was developed with the aim of enhancing the understanding of SAR-SL from Sentinel-1 and to assess their potential application to detect coastal change. The results from such a collaboration are presented here through three pilot case studies with different environmental characteristics (e.g. different backshore type and met- ocean conditions). All three sites are located in tidal beaches of the European Atlantic coast, where the probability of cloud cover is high. This allowed to assess the skill of SAR-SL in different environments and in sites that represent a challenge for shoreline detection from optical satellite sensors.

The dynamic interaction between cliff, beach and shore-platform is key to assessing the sediment balance for coastal erosion risk assessments, but this is poorly understood. We present a dataset containing daily, 3D,colour LiDAR scans of a 450 m coastal section at Happisburgh, Norfolk, UK. This previously para-glaciated region comprises mixed sand-gravel sediments, which are less well-understood and well-studied than sandy beaches. From Apr-Dec 2019, 236 daily surveys were carried out. The dataset presented includes: survey areas, transects LiDAR scans, georeferenced orthophotos, meteorological- and oceanographical conditions during the Apr-Dec observation period. Full LiDAR point-clouds are available for 67 scans (Oct-Dec). Hourly time-series of offshore sea-state parameters (significant wave height, mean propagation direction, selected spectral periods) were obtained by downscaling the ERA5 global reanalysis data (global atmosphere, land surface and ocean waves) using the numerical model Simulating Waves Nearshore (SWAN). We indicate how to obtain hourly precipitation time-series by interpolating ERA5 data. This dataset is important for researchers understanding the interaction between cliff, beach and shore-platform in open-coast mixed-sand-gravel environments.

This study represents the first attempt to map the sediment thickness spatial distribution along the Andalusian coastal zone by integrating various publicly available datasets. While prior studies have presented bedform- and sediment-type syntheses, none have attempted to quantify sediment thickness at the scale and resolution performed in this study. The study area has been divided into 18 physiographic zones, and we have used BGS Groundhog Desktop v2.6 software for 3D modeling and sediment thickness model calculations. We present here the modeling workflow, model results, and the challenges that we have encountered, including discrepancies in geological maps, difficulty managing data input for grain size/consolidation, and the need for additional geological information. We have compared the modeled sediment fractions of the unconsolidated material with 4194 seabed samples distributed along the study area and found that the differences between the modeled versus the sampled emphasized the importance of incorporating river contributions, particularly from the Guadalquivir River, into the model for more accurate results. The model intermediate and final outputs and the software routines used to query the sediment thickness model are provided as publicly accessible datasets and tools. The modeled sediment thickness could contribute to making quantitative predictions of morphological change at a scale that is relevant to longer-term strategic coastal management in Andalusia. The methodology and tools used for this study are transferable to any study area.

Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide.

Geology is the last bulwark against coastal erosion and flooding

Climate change and economic growth are having a profound influence on the integrity of socio-economics and ecology of coastal Bangladesh. In the extreme, there are widespread expectations of inundation and coastal abandonment. However, results from our integrated assessment model (IAM) show that over the next 30 years, development choices might have a stronger influence on livelihoods and economic wellbeing than climate driven environmental change. The IAM simulates the economic development of rural areas by coupling physical models (driven by expectations of climate change) with economic models (informed by a series of policy decisions). This is done using substantial primary, secondary and stakeholder-derived biophysical and socio-economic datasets, together with shocks such as cyclones. The study analyses the future socio-ecological sensitivity to climate change and policy decisions and finds that well managed development is as important as adaptation to mitigate risks, reduce poverty and raise aggregate well-being. This analysis enables decision makers to identify appropriate development pathways that address current social-ecological vulnerability and develop a more resilient future to 2050 and beyond. These policy actions are complementary to climate adaptation and mitigation. Our IAM framework provides a valuable evidence-based tool to support sustainable coastal development and is transferable to other vulnerable delta regions and other coastal lowlands around the world.

The process of shoreline change is inherently complex and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from Deep Learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of Convolutional Neural Networks (CNNs) and hybrid CNN - Long Short-Term Memory networks. We compare our results with two established models, a shoreline equilibrium model, and a model that addresses time scales in shoreline drivers. Using a systematic search and different measures of fitness we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models.