Articles | Volume 11, issue 6
https://doi.org/10.5194/esurf-11-1145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-11-1145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2023
Research article |
| 13 Nov 2023
| 13 Nov 2023
On the use of convolutional deep learning to predict shoreline change
Eduardo Gomez-de la Peña
CORRESPONDING AUTHOR
School of Environment, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Giovanni Coco
School of Environment, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Colin Whittaker
Department of Civil and Environmental Engineering, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Jennifer Montaño
Auckland Council – Air, Land, and Biodiversity Team, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Related authors
No articles found.
Wagner L. L. Costa, Karin R. Bryan, and Giovanni Coco
Nat. Hazards Earth Syst. Sci., 23, 3125–3146, https://doi.org/10.5194/nhess-23-3125-2023, https://doi.org/10.5194/nhess-23-3125-2023, 2023
Short summary
Short summary
For predicting flooding events at the coast, topo-bathymetric data are essential. However, elevation data can be unavailable. To tackle this issue, recent efforts have centred on the use of satellite-derived topography (SDT) and bathymetry (SDB). This work is aimed at evaluating their accuracy and use for flooding prediction in enclosed estuaries. Results show that the use of SDT and SDB in numerical modelling can produce similar predictions when compared to the surveyed elevation data.
Charline Dalinghaus, Giovanni Coco, and Pablo Higuera
Nat. Hazards Earth Syst. Sci., 23, 2157–2169, https://doi.org/10.5194/nhess-23-2157-2023, https://doi.org/10.5194/nhess-23-2157-2023, 2023
Short summary
Short summary
Wave setup is a critical component of coastal flooding. Consequently, understanding and being able to predict wave setup is vital to protect coastal resources and the population living near the shore. Here, we applied machine learning to improve the accuracy of present predictors of wave setup. The results show that the new predictors outperform existing formulas demonstrating the capability of machine learning models to provide a physically sound description of wave setup.
Matthew W. Hayward, Emily M. Lane, Colin N. Whittaker, Graham S. Leonard, and William L. Power
Nat. Hazards Earth Syst. Sci., 23, 955–971, https://doi.org/10.5194/nhess-23-955-2023, https://doi.org/10.5194/nhess-23-955-2023, 2023
Short summary
Short summary
In this paper, 20 explosive volcanic eruption scenarios of differing location and magnitude are simulated to investigate tsunami generation in Lake Taupō, New Zealand. A non-hydrostatic multilayer numerical scheme resolves the highly dispersive generated wavefield. Inundation, hydrographic and related hazard outputs are produced, indicating that significant inundation around the lake shore begins above 5 on the volcanic explosivity index.
Matthew W. Hayward, Colin N. Whittaker, Emily M. Lane, William L. Power, Stéphane Popinet, and James D. L. White
Nat. Hazards Earth Syst. Sci., 22, 617–637, https://doi.org/10.5194/nhess-22-617-2022, https://doi.org/10.5194/nhess-22-617-2022, 2022
Short summary
Short summary
Volcanic eruptions can produce tsunamis through multiple mechanisms. We present validation cases for a numerical method used in simulating waves caused by submarine explosions: a laboratory flume experiment and waves generated by explosions at field scale. We then demonstrate the use of the scheme for simulating analogous volcanic eruptions, illustrating the resulting wavefield. We show that this scheme models such dispersive sources more proficiently than standard tsunami models.
Yizhang Wei, Yining Chen, Jufei Qiu, Zeng Zhou, Peng Yao, Qin Jiang, Zheng Gong, Giovanni Coco, Ian Townend, and Changkuan Zhang
Earth Surf. Dynam., 10, 65–80, https://doi.org/10.5194/esurf-10-65-2022, https://doi.org/10.5194/esurf-10-65-2022, 2022
Short summary
Short summary
The barrier tidal basin is increasingly altered by human activity and sea-level rise. These environmental changes probably lead to the emergence or disappearance of islands, yet the effect of rocky islands on the evolution of tidal basins remains poorly investigated. Using numerical experiments, we explore the evolution of tidal basins under varying numbers and locations of islands. This work provides insights for predicting the response of barrier tidal basins in a changing environment.
Giovanni Coco, Daniel Calvete, Francesca Ribas, Huib E. de Swart, and Albert Falqués
Earth Surf. Dynam., 8, 323–334, https://doi.org/10.5194/esurf-8-323-2020, https://doi.org/10.5194/esurf-8-323-2020, 2020
Short summary
Short summary
Sandbars are ubiquitous features of the surf zone. They are rarely straight and often develop crescentic shapes. Double sandbar systems are also common, but the possibility of feedback between inner and outer sandbars has not been fully explored. The presence of double sandbar systems affects wave transformation and can result in a variety of spatial patterns. Here we model the conditions, waves and initial bathymetry that lead to the emergence of different patterns.
Marinella Passarella, Evan B. Goldstein, Sandro De Muro, and Giovanni Coco
Nat. Hazards Earth Syst. Sci., 18, 599–611, https://doi.org/10.5194/nhess-18-599-2018, https://doi.org/10.5194/nhess-18-599-2018, 2018
Sarik Salim, Charitha Pattiaratchi, Rafael Tinoco, Giovanni Coco, Yasha Hetzel, Sarath Wijeratne, and Ravindra Jayaratne
Earth Surf. Dynam., 5, 399–415, https://doi.org/10.5194/esurf-5-399-2017, https://doi.org/10.5194/esurf-5-399-2017, 2017
Short summary
Short summary
The aim of this paper was to verify the existence of a mean critical velocity concept in terms of turbulent bursting phenomena. Laboratory experiments were undertaken in a unidirectional current flume where an acoustic Doppler velocimeter was used. Results in the laboratory conditions both above and below the measured mean critical velocity highlighted the need to re-evaluate the accuracy of a single time-averaged critical velocity for the initiation of sediment entrainment.
M. Jiménez, S. Castanedo, Z. Zhou, G. Coco, R. Medina, and I. Rodriguez-Iturbe
Adv. Geosci., 39, 69–73, https://doi.org/10.5194/adgeo-39-69-2014, https://doi.org/10.5194/adgeo-39-69-2014, 2014
Z. Zhou, L. Stefanon, M. Olabarrieta, A. D'Alpaos, L. Carniello, and G. Coco
Earth Surf. Dynam., 2, 105–116, https://doi.org/10.5194/esurf-2-105-2014, https://doi.org/10.5194/esurf-2-105-2014, 2014
R. O. Tinoco and G. Coco
Earth Surf. Dynam., 2, 83–96, https://doi.org/10.5194/esurf-2-83-2014, https://doi.org/10.5194/esurf-2-83-2014, 2014
E. B. Goldstein, G. Coco, A. B. Murray, and M. O. Green
Earth Surf. Dynam., 2, 67–82, https://doi.org/10.5194/esurf-2-67-2014, https://doi.org/10.5194/esurf-2-67-2014, 2014
Related subject area
Physical: Geomorphology (including all aspects of fluvial, coastal, aeolian, hillslope and glacial geomorphology)
Pliocene shorelines and the epeirogenic motion of continental margins: a target dataset for dynamic topography models
Decadal-scale decay of landslide-derived fluvial suspended sediment after Typhoon Morakot
Role of the forcing sources in morphodynamic modelling of an embayed beach
A machine learning approach to the geomorphometric detection of ribbed moraines in Norway
Stream hydrology controls on ice cliff evolution and survival on debris-covered glaciers
Time-varying drainage basin development and erosion on volcanic edifices
Geomorphic risk maps for river migration using probabilistic modeling – a framework
Evolution of submarine canyons and hanging-wall fans: insights from geomorphic experiments and morphodynamic models
Riverine sediment response to deforestation in the Amazon basin
Physical modeling of ice-sheet-induced salt movements using the example of northern Germany
Downstream rounding rate of pebbles in the Himalaya
A physics-based model for fluvial valley width
Implications for the resilience of modern coastal systems derived from mesoscale barrier dynamics at Fire Island, New York
Quantifying the migration rate of drainage divides from high-resolution topographic data
Long-term monitoring (1953–2019) of geomorphologically active sections of Little Ice Age lateral moraines in the context of changing meteorological conditions
Coevolving edge rounding and shape of glacial erratics: the case of Shap granite, UK
Geomorphic indices for unveiling fault segmentation and tectono-geomorphic evolution with insights into the impact of inherited topography, Ulsan Fault Zone, Korea
How water, temperature and seismicity control the preparation of massive rock slope failure (Hochvogel, DE/AT)
Dimensionless argument: a narrow grain size range near 2 mm plays a special role in river sediment transport and morphodynamics
Path length and sediment transport estimation from DEMs of difference: a signal processing approach
The impact of bedrock meander cutoffs on 50 ka-year-scale incision rates, San Juan River, Utah
Influence of cohesive clay on wave–current ripple dynamics captured in a 3D phase diagram
Statistical characterization of erosion and sediment transport mechanics in shallow tidal environments – Part 1: Erosion dynamics
Statistical characterization of erosion and sediment transport mechanics in shallow tidal environments – Part 2: Suspended sediment dynamics
Geomorphological and hydrological controls on sediment export in earthquake-affected catchments in the Nepal Himalaya
Optimization of passive acoustic bedload monitoring in rivers by signal inversion
River suspended-sand flux computation with uncertainty estimation, using water samples and high-resolution ADCP measurements
Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis
Field monitoring of pore water pressure in fully and partly saturated debris flows at Ohya landslide scar, Japan
Analysis of autogenic bifurcation processes resulting in river avulsion
Bedload transport fluctuations, flow conditions, and disequilibrium ratio at the Swiss Erlenbach stream: results from 27 years of high-resolution temporal measurements
Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection
Coexistence of two dune scales in a lowland river
Alpine hillslope failure in the western US: insights from the Chaos Canyon landslide, Rocky Mountain National Park, USA
Barchan swarm dynamics from a Two-Flank Agent-Based Model
Using repeat UAV-based laser scanning and multispectral imagery to explore eco-geomorphic feedbacks along a river corridor
Numerical modelling of the evolution of a river reach with a complex morphology to help define future sustainable restoration decisions
Method to evaluate large-wood behavior in terms of the convection equation associated with sediment erosion and deposition
Effects of seasonal variations in vegetation and precipitation on catchment erosion rates along a climate and ecological gradient: insights from numerical modeling
On the use of packing models for the prediction of fluvial sediment porosity
Automated riverbed composition analysis using deep learning on underwater images
Marsh-induced backwater: the influence of non-fluvial sedimentation on a delta's channel morphology and kinematics
Spatial and temporal variations in rockwall erosion rates derived from cosmogenic 10Be in medial moraines at five valley glaciers around Pigne d'Arolla, Switzerland
Building a bimodal landscape: bedrock lithology and bed thickness controls on the morphology of Last Chance Canyon, New Mexico, USA
Geotechnical controls on erodibility in fluvial impact erosion
Linear-stability analysis of plane beds under flows with suspended loads
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
Sediment source and sink identification using Sentinel-2 and a small network of turbidimeters on the Vjosa River
Spatiotemporal bedload transport patterns over two-dimensional bedforms
Ice-buttressing-controlled rock slope failure on a cirque headwall, Lake District, UK
Andrew Hollyday, Maureen E. Raymo, Jacqueline Austermann, Fred Richards, Mark Hoggard, and Alessio Rovere
Earth Surf. Dynam., 12, 883–905, https://doi.org/10.5194/esurf-12-883-2024, https://doi.org/10.5194/esurf-12-883-2024, 2024
Short summary
Short summary
Sea level was significantly higher during the Pliocene epoch, around 3 million years ago. The present-day elevations of shorelines that formed in the past provide a data constraint on the extent of ice sheet melt and the global sea level response under warm Pliocene conditions. In this study, we identify 10 escarpments that formed from wave-cut erosion during Pliocene times and compare their elevations with model predictions of solid Earth deformation processes to estimate past sea level.
Gregory A. Ruetenik, Ken L. Ferrier, and Odin Marc
Earth Surf. Dynam., 12, 863–881, https://doi.org/10.5194/esurf-12-863-2024, https://doi.org/10.5194/esurf-12-863-2024, 2024
Short summary
Short summary
Fluvial sediment fluxes increased dramatically in Taiwan during Typhoon Morakot in 2009, which produced some of the heaviest landsliding on record. We analyzed fluvial discharge and suspended sediment concentration data at 87 gauging stations across Taiwan to quantify fluvial sediment responses since Morakot. In basins heavily impacted by landsliding, rating curve coefficients sharply increased during Morakot and then declined exponentially with a characteristic decay time of <10 years.
Nil Carrion-Bertran, Albert Falqués, Francesca Ribas, Daniel Calvete, Rinse de Swart, Ruth Durán, Candela Marco-Peretó, Marta Marcos, Angel Amores, Tim Toomey, Àngels Fernández-Mora, and Jorge Guillén
Earth Surf. Dynam., 12, 819–839, https://doi.org/10.5194/esurf-12-819-2024, https://doi.org/10.5194/esurf-12-819-2024, 2024
Short summary
Short summary
The sensitivity to the wave and sea-level forcing sources in predicting a 6-month embayed beach evolution is assessed using two different morphodynamic models. After a successful model calibration using in situ data, other sources are applied. The wave source choice is critical: hindcast data provide wrong results due to an angle bias, whilst the correct dynamics are recovered with the wave conditions from an offshore buoy. The use of different sea-level sources gives no significant differences.
Thomas J. Barnes, Thomas V. Schuler, Simon Filhol, and Karianne S. Lilleøren
Earth Surf. Dynam., 12, 801–818, https://doi.org/10.5194/esurf-12-801-2024, https://doi.org/10.5194/esurf-12-801-2024, 2024
Short summary
Short summary
In this paper, we use machine learning to automatically outline landforms based on their characteristics. We test several methods to identify the most accurate and then proceed to develop the most accurate to improve its accuracy further. We manage to outline landforms with 65 %–75 % accuracy, at a resolution of 10 m, thanks to high-quality/high-resolution elevation data. We find that it is possible to run this method at a country scale to quickly produce landform inventories for future studies.
Eric Petersen, Regine Hock, and Michael G. Loso
Earth Surf. Dynam., 12, 727–745, https://doi.org/10.5194/esurf-12-727-2024, https://doi.org/10.5194/esurf-12-727-2024, 2024
Short summary
Short summary
Ice cliffs are melt hot spots that increase melt rates on debris-covered glaciers which otherwise see a reduction in melt rates. In this study, we show how surface runoff streams contribute to the generation, evolution, and survival of ice cliffs by carving into the glacier and transporting rocky debris. On Kennicott Glacier, Alaska, 33 % of ice cliffs are actively influenced by streams, while nearly half are within 10 m of streams.
Daniel O'Hara, Liran Goren, Roos M. J. van Wees, Benjamin Campforts, Pablo Grosse, Pierre Lahitte, Gabor Kereszturi, and Matthieu Kervyn
Earth Surf. Dynam., 12, 709–726, https://doi.org/10.5194/esurf-12-709-2024, https://doi.org/10.5194/esurf-12-709-2024, 2024
Short summary
Short summary
Understanding how volcanic edifices develop drainage basins remains unexplored in landscape evolution. Using digital evolution models of volcanoes with varying ages, we quantify the geometries of their edifices and associated drainage basins through time. We find that these metrics correlate with edifice age and are thus useful indicators of a volcano’s history. We then develop a generalized model for how volcano basins develop and compare our results to basin evolution in other settings.
Brayden Noh, Omar Wani, Kieran B. J. Dunne, and Michael P. Lamb
Earth Surf. Dynam., 12, 691–708, https://doi.org/10.5194/esurf-12-691-2024, https://doi.org/10.5194/esurf-12-691-2024, 2024
Short summary
Short summary
In this paper, we propose a framework for generating risk maps that provide the probabilities of erosion due to river migration. This framework uses concepts from probability theory to learn the river migration model's parameter values from satellite data while taking into account parameter uncertainty. Our analysis shows that such geomorphic risk estimation is more reliable than models that do not explicitly consider various sources of variability and uncertainty.
Steven Y. J. Lai, David Amblas, Aaron Micallef, and Hervé Capart
Earth Surf. Dynam., 12, 621–640, https://doi.org/10.5194/esurf-12-621-2024, https://doi.org/10.5194/esurf-12-621-2024, 2024
Short summary
Short summary
This study explores the creation of submarine canyons and hanging-wall fans on active faults, which can be defined by gravity-dominated breaching and underflow-dominated diffusion processes. The study reveals the self-similarity in canyon–fan long profiles, uncovers Hack’s scaling relationship and proposes a formula to estimate fan volume using canyon length. This is validated by global data from source-to-sink systems, providing insights into deep-water sedimentary processes.
Anuska Narayanan, Sagy Cohen, and John R. Gardner
Earth Surf. Dynam., 12, 581–599, https://doi.org/10.5194/esurf-12-581-2024, https://doi.org/10.5194/esurf-12-581-2024, 2024
Short summary
Short summary
This study investigates the profound impact of deforestation in the Amazon on sediment dynamics. Novel remote sensing data and statistical analyses reveal significant changes, especially in heavily deforested regions, with rapid effects within a year. In less disturbed areas, a 1- to 2-year lag occurs, influenced by natural sediment shifts and human activities. These findings highlight the need to understand the consequences of human activity for our planet's future.
Jacob Hardt, Tim P. Dooley, and Michael R. Hudec
Earth Surf. Dynam., 12, 559–579, https://doi.org/10.5194/esurf-12-559-2024, https://doi.org/10.5194/esurf-12-559-2024, 2024
Short summary
Short summary
We investigate the reaction of salt structures on ice sheet transgressions. We used a series of sandbox models that enabled us to experiment with scaled-down versions of salt bodies from northern Germany. The strongest reactions occurred when large salt pillows were partly covered by the ice load. Subsurface salt structures may play an important role in the energy transition, e.g., as energy storage. Thus, it is important to understand all processes that affect their stability.
Prakash Pokhrel, Mikael Attal, Hugh D. Sinclair, Simon M. Mudd, and Mark Naylor
Earth Surf. Dynam., 12, 515–536, https://doi.org/10.5194/esurf-12-515-2024, https://doi.org/10.5194/esurf-12-515-2024, 2024
Short summary
Short summary
Pebbles become increasingly rounded during downstream transport in rivers due to abrasion. This study quantifies pebble roundness along the length of two Himalayan rivers. We demonstrate that roundness increases with downstream distance and that the rates are dependent on rock type. We apply this to reconstructing travel distances and hence the size of ancient Himalaya. Results show that the ancient river network was larger than the modern one, indicating that there has been river capture.
Jens Martin Turowski, Aaron Bufe, and Stefanie Tofelde
Earth Surf. Dynam., 12, 493–514, https://doi.org/10.5194/esurf-12-493-2024, https://doi.org/10.5194/esurf-12-493-2024, 2024
Short summary
Short summary
Fluvial valleys are ubiquitous landforms, and understanding their formation and evolution affects a wide range of disciplines from archaeology and geology to fish biology. Here, we develop a model to predict the width of fluvial valleys for a wide range of geographic conditions. In the model, fluvial valley width is controlled by the two competing factors of lateral channel mobility and uplift. The model complies with available data and yields a broad range of quantitative predictions.
Daniel J. Ciarletta, Jennifer L. Miselis, Julie C. Bernier, and Arnell S. Forde
Earth Surf. Dynam., 12, 449–475, https://doi.org/10.5194/esurf-12-449-2024, https://doi.org/10.5194/esurf-12-449-2024, 2024
Short summary
Short summary
We reconstructed the evolution of Fire Island, a barrier island in New York, USA, to identify drivers of landscape change. Results reveal Fire Island was once divided into multiple inlet-separated islands with distinct features. Later, inlets closed, and Fire Island’s landscape became more uniform as human activities intensified. The island is now less mobile and less likely to resist and recover from storm impacts and sea level rise. This vulnerability may exist for other stabilized barriers.
Chao Zhou, Xibin Tan, Yiduo Liu, and Feng Shi
Earth Surf. Dynam., 12, 433–448, https://doi.org/10.5194/esurf-12-433-2024, https://doi.org/10.5194/esurf-12-433-2024, 2024
Short summary
Short summary
The drainage-divide stability provides new insights into both the river network evolution and the tectonic and/or climatic changes. Several methods have been proposed to determine the direction of drainage-divide migration. However, how to quantify the migration rate of drainage divides remains challenging. In this paper, we propose a new method to calculate the migration rate of drainage divides from high-resolution topographic data.
Moritz Altmann, Madlene Pfeiffer, Florian Haas, Jakob Rom, Fabian Fleischer, Tobias Heckmann, Livia Piermattei, Michael Wimmer, Lukas Braun, Manuel Stark, Sarah Betz-Nutz, and Michael Becht
Earth Surf. Dynam., 12, 399–431, https://doi.org/10.5194/esurf-12-399-2024, https://doi.org/10.5194/esurf-12-399-2024, 2024
Short summary
Short summary
We show a long-term erosion monitoring of several sections on Little Ice Age lateral moraines with derived sediment yield from historical and current digital elevation modelling (DEM)-based differences. The first study period shows a clearly higher range of variability of sediment yield within the sites than the later periods. In most cases, a decreasing trend of geomorphic activity was observed.
Paul A. Carling
Earth Surf. Dynam., 12, 381–397, https://doi.org/10.5194/esurf-12-381-2024, https://doi.org/10.5194/esurf-12-381-2024, 2024
Short summary
Short summary
Edge rounding in Shap granite glacial erratics is an irregular function of distance from the source outcrop in northern England, UK. Block shape is conservative, evolving according to block fracture mechanics – stochastic and silver ratio models – towards either of two attractor states. Progressive reduction in size occurs for blocks transported at the sole of the ice mass where the blocks are subject to compressive and tensile forces of the ice acting against a bedrock or till surface.
Cho-Hee Lee, Yeong Bae Seong, John Weber, Sangmin Ha, Dong-Eun Kim, and Byung Yong Yu
EGUsphere, https://doi.org/10.5194/egusphere-2024-198, https://doi.org/10.5194/egusphere-2024-198, 2024
Short summary
Short summary
Geomorphic indices were used to understand topographic changes in response to tectonic activity. We applied indices to evaluate the relative tectonic intensity of Ulsan Fault Zone, one of the most active fault zones in Korea. We divided the UFZ into five segments based on spatial variation in intensity. We modelled the landscape evolution of study area and interpreted tectono-geomorphic history that the northern part of the UFZ experienced asymmetric uplift, while the southern part did not.
Johannes Leinauer, Michael Dietze, Sibylle Knapp, Riccardo Scandroglio, Maximilian Jokel, and Michael Krautblatter
EGUsphere, https://doi.org/10.5194/egusphere-2024-231, https://doi.org/10.5194/egusphere-2024-231, 2024
Short summary
Short summary
Massive rock slope failures are a significant alpine hazard and change the earth´s surface. Therefore, we must understand what controls the preparation of such events. By correlating four years of slope displacements with meteorological and seismic data, we found that water from rain and snowmelt is the most important driver. Our approach is applicable to similar sites and indicates, where future climatic changes, e.g. in rain intensity and frequency, may alter the preparation of slope failure.
Gary Parker, Chenge An, Michael P. Lamb, Marcelo H. Garcia, Elizabeth H. Dingle, and Jeremy G. Venditti
Earth Surf. Dynam., 12, 367–380, https://doi.org/10.5194/esurf-12-367-2024, https://doi.org/10.5194/esurf-12-367-2024, 2024
Short summary
Short summary
River morphology has traditionally been divided by the size 2 mm. We use dimensionless arguments to show that particles in the 1–5 mm range (i) are the finest range not easily suspended by alluvial flood flows, (ii) are transported preferentially over coarser gravel, and (iii), within limits, are also transported preferentially over sand. We show how fluid viscosity mediates the special status of sediment in this range.
Lindsay Marie Capito, Enrico Pandrin, Walter Bertoldi, Nicola Surian, and Simone Bizzi
Earth Surf. Dynam., 12, 321–345, https://doi.org/10.5194/esurf-12-321-2024, https://doi.org/10.5194/esurf-12-321-2024, 2024
Short summary
Short summary
We propose that the pattern of erosion and deposition from repeat topographic surveys can be a proxy for path length in gravel-bed rivers. With laboratory and field data, we applied tools from signal processing to quantify this periodicity and used these path length estimates to calculate sediment transport using the morphological method. Our results highlight the potential to expand the use of the morphological method using only remotely sensed data as well as its limitations.
Aaron T. Steelquist, Gustav B. Seixas, Mary L. Gillam, Sourav Saha, Seulgi Moon, and George E. Hilley
EGUsphere, https://doi.org/10.5194/egusphere-2024-71, https://doi.org/10.5194/egusphere-2024-71, 2024
Short summary
Short summary
The rates at which rivers erode their bed can be used to interpret the geologic history of a region. However, these rates depend significantly on the time window over which you measure. We use multiple dating methods to determine an incision rate on the San Juan River and compare it to regional rates with longer timescales. We demonstrate how specific geologic events, such as cutoffs of bedrock meander bends, are likely to preserve material we can date but also bias the rates we measure.
Xuxu Wu, Jonathan Malarkey, Roberto Fernández, Jaco H. Baas, Ellen Pollard, and Daniel R. Parsons
Earth Surf. Dynam., 12, 231–247, https://doi.org/10.5194/esurf-12-231-2024, https://doi.org/10.5194/esurf-12-231-2024, 2024
Short summary
Short summary
The seabed changes from flat to rippled in response to the frictional influence of waves and currents. This experimental study has shown that the speed of this change, the size of ripples that result and even whether ripples appear also depend on the amount of sticky mud present. This new classification on the basis of initial mud content should lead to improvements in models of seabed change in present environments by engineers and the interpretation of past environments by geologists.
Andrea D'Alpaos, Davide Tognin, Laura Tommasini, Luigi D'Alpaos, Andrea Rinaldo, and Luca Carniello
Earth Surf. Dynam., 12, 181–199, https://doi.org/10.5194/esurf-12-181-2024, https://doi.org/10.5194/esurf-12-181-2024, 2024
Short summary
Short summary
Sediment erosion induced by wind waves is one of the main drivers of the morphological evolution of shallow tidal environments. However, a reliable description of erosion events for the long-term morphodynamic modelling of tidal systems is still lacking. By statistically characterizing sediment erosion dynamics in the Venice Lagoon over the last 4 centuries, we set up a novel framework for a synthetic, yet reliable, description of erosion events in tidal systems.
Davide Tognin, Andrea D'Alpaos, Luigi D'Alpaos, Andrea Rinaldo, and Luca Carniello
Earth Surf. Dynam., 12, 201–218, https://doi.org/10.5194/esurf-12-201-2024, https://doi.org/10.5194/esurf-12-201-2024, 2024
Short summary
Short summary
Reliable quantification of sediment transport processes is necessary to understand the fate of shallow tidal environments. Here we present a framework for the description of suspended sediment dynamics to quantify deposition in the long-term modelling of shallow tidal systems. This characterization, together with that of erosion events, allows one to set up synthetic, yet reliable, models for the long-term evolution of tidal landscapes.
Emma L. S. Graf, Hugh D. Sinclair, Mikaël Attal, Boris Gailleton, Basanta Raj Adhikari, and Bishnu Raj Baral
Earth Surf. Dynam., 12, 135–161, https://doi.org/10.5194/esurf-12-135-2024, https://doi.org/10.5194/esurf-12-135-2024, 2024
Short summary
Short summary
Using satellite images, we show that, unlike other examples of earthquake-affected rivers, the rivers of central Nepal experienced little increase in sedimentation following the 2015 Gorkha earthquake. Instead, a catastrophic flood occurred in 2021 that buried towns and agricultural land under up to 10 m of sediment. We show that intense storms remobilised glacial sediment from high elevations causing much a greater impact than flushing of earthquake-induced landslides.
Mohamad Nasr, Adele Johannot, Thomas Geay, Sebastien Zanker, Jules Le Guern, and Alain Recking
Earth Surf. Dynam., 12, 117–134, https://doi.org/10.5194/esurf-12-117-2024, https://doi.org/10.5194/esurf-12-117-2024, 2024
Short summary
Short summary
Hydrophones are used to monitor sediment transport in the river by listening to the acoustic noise generated by particle impacts on the riverbed. However, this acoustic noise is modified by the river flow and can cause misleading information about sediment transport. This article proposes a model that corrects the measured acoustic signal. Testing the model showed that the corrected signal is better correlated with bedload flux in the river.
Jessica Laible, Guillaume Dramais, Jérôme Le Coz, Blaise Calmel, Benoît Camenen, David J. Topping, William Santini, Gilles Pierrefeu, and François Lauters
EGUsphere, https://doi.org/10.5194/egusphere-2023-2348, https://doi.org/10.5194/egusphere-2023-2348, 2024
Short summary
Short summary
Suspended-sand fluxes in rivers vary with time and space, complicating their measurement. The proposed method captures the vertical and lateral variations of suspended-sand concentration throughout a river cross section. It merges water samples taken at various positions throughout the cross section with high-resolution acoustic velocity and discharge measurements. The method also determines the sand flux uncertainty and can be easily applied to other sites using the available open-source code.
Byungho Kang, Rusty A. Feagin, Thomas Huff, and Orencio Durán Vinent
Earth Surf. Dynam., 12, 105–115, https://doi.org/10.5194/esurf-12-105-2024, https://doi.org/10.5194/esurf-12-105-2024, 2024
Short summary
Short summary
We provide a detailed characterization of the frequency, intensity and duration of flooding events at a site along the Texas coast. Our analysis demonstrates the suitability of relatively simple wave run-up models to estimate the frequency and intensity of coastal flooding. Our results validate and expand a probabilistic model of coastal flooding driven by wave run-up that can then be used in coastal risk management in response to sea level rise.
Shunsuke Oya, Fumitoshi Imaizumi, and Shoki Takayama
Earth Surf. Dynam., 12, 67–86, https://doi.org/10.5194/esurf-12-67-2024, https://doi.org/10.5194/esurf-12-67-2024, 2024
Short summary
Short summary
The monitoring of pore water pressure in fully and partly saturated debris flows was performed at Ohya landslide scar, central Japan. The pore water pressure in some partly saturated flows greatly exceeded the hydrostatic pressure. The depth gradient of the pore water pressure in the lower part of the flow was generally higher than the upper part of the flow. We conclude that excess pore water pressure is present in many debris flow surges and is an important mechanism in debris flow behavior.
Gabriele Barile, Marco Redolfi, and Marco Tubino
Earth Surf. Dynam., 12, 87–103, https://doi.org/10.5194/esurf-12-87-2024, https://doi.org/10.5194/esurf-12-87-2024, 2024
Short summary
Short summary
River bifurcations often show the closure of one branch (avulsion), whose causes are still poorly understood. Our model shows that when one branch stops transporting sediments, the other considerably erodes and captures much more flow, resulting in a self-sustaining process. This phenomenon intensifies when increasing the length of the branches, eventually leading to branch closure. This work may help to understand when avulsions occur and thus to design sustainable river restoration projects.
Dieter Rickenmann
Earth Surf. Dynam., 12, 11–34, https://doi.org/10.5194/esurf-12-11-2024, https://doi.org/10.5194/esurf-12-11-2024, 2024
Short summary
Short summary
Field measurements of the bedload flux with a high temporal resolution in a steep mountain stream were used to analyse the transport fluctuations as a function of the flow conditions. The disequilibrium ratio, a proxy for the solid particle concentration in the flow, was found to influence the sediment transport behaviour, and above-average disequilibrium conditions – associated with a larger sediment availability on the streambed – substantially affect subsequent transport conditions.
Byungho Kang, Rusty A. Feagin, Thomas Huff, and Orencio Durán Vinent
Earth Surf. Dynam., 12, 1–10, https://doi.org/10.5194/esurf-12-1-2024, https://doi.org/10.5194/esurf-12-1-2024, 2024
Short summary
Short summary
Coastal flooding can cause significant damage to coastal ecosystems, infrastructure, and communities and is expected to increase in frequency with the acceleration of sea level rise. In order to respond to it, it is crucial to measure and model their frequency and intensity. Here, we show deep-learning techniques can be successfully used to automatically detect flooding events from complex coastal imagery, opening the way to real-time monitoring and data acquisition for model development.
Judith Y. Zomer, Bart Vermeulen, and Antonius J. F. Hoitink
Earth Surf. Dynam., 11, 1283–1298, https://doi.org/10.5194/esurf-11-1283-2023, https://doi.org/10.5194/esurf-11-1283-2023, 2023
Short summary
Short summary
Secondary bedforms that are superimposed on large, primary dunes likely play a large role in fluvial systems. This study demonstrates that they can be omnipresent. Especially during peak flows, they grow large and can have steep slopes, likely affecting flood risk and sediment transport dynamics. Primary dune morphology determines whether they continuously or intermittently migrate. During discharge peaks, the secondary bedforms can become the dominant dune scale.
Matthew C. Morriss, Benjamin Lehmann, Benjamin Campforts, George Brencher, Brianna Rick, Leif S. Anderson, Alexander L. Handwerger, Irina Overeem, and Jeffrey Moore
Earth Surf. Dynam., 11, 1251–1274, https://doi.org/10.5194/esurf-11-1251-2023, https://doi.org/10.5194/esurf-11-1251-2023, 2023
Short summary
Short summary
In this paper, we investigate the 28 June 2022 collapse of the Chaos Canyon landslide in Rocky Mountain National Park, Colorado, USA. We find that the landslide was moving prior to its collapse and took place at peak spring snowmelt; temperature modeling indicates the potential presence of permafrost. We hypothesize that this landslide could be part of the broader landscape evolution changes to alpine terrain caused by a warming climate, leading to thawing alpine permafrost.
Dominic T. Robson and Andreas C. W. Baas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2900, https://doi.org/10.5194/egusphere-2023-2900, 2023
Short summary
Short summary
We present simulations of large populations (swarms) of a type of sand dune known as barchans. Our findings reveal that the rate at which sand moves inside an asymmetric barchan is vital to the behaviour of swarms and that many observed properties of the dunes can be explained by similar rates. We also show that different directions of the wind and the density of dunes added to swarms play important roles in shaping their evolution.
Christopher Tomsett and Julian Leyland
Earth Surf. Dynam., 11, 1223–1249, https://doi.org/10.5194/esurf-11-1223-2023, https://doi.org/10.5194/esurf-11-1223-2023, 2023
Short summary
Short summary
Vegetation influences how rivers change through time, yet the way in which we analyse vegetation is limited. Current methods collect detailed data at the individual plant level or determine dominant vegetation types across larger areas. Herein, we use UAVs to collect detailed vegetation datasets for a 1 km length of river and link vegetation properties to channel evolution occurring within the study site, providing a new method for investigating the influence of vegetation on river systems.
Rabab Yassine, Ludovic Cassan, Hélène Roux, Olivier Frysou, and François Pérès
Earth Surf. Dynam., 11, 1199–1221, https://doi.org/10.5194/esurf-11-1199-2023, https://doi.org/10.5194/esurf-11-1199-2023, 2023
Short summary
Short summary
Predicting river morphology evolution is very complicated, especially for mountain rivers with complex morphologies such as the Lac des Gaves reach in France. A 2D hydromorphological model was developed to reproduce the channel's evolution and provide reliable volumetric predictions while revealing the challenge of choosing adapted sediment transport and friction laws. Our model can provide decision-makers with reliable predictions to design suitable restoration measures for this reach.
Daisuke Harada and Shinji Egashira
Earth Surf. Dynam., 11, 1183–1197, https://doi.org/10.5194/esurf-11-1183-2023, https://doi.org/10.5194/esurf-11-1183-2023, 2023
Short summary
Short summary
This paper proposes a method for describing large-wood behavior in terms of the convection equation and the storage equation, which are associated with active sediment erosion and deposition. Compared to the existing Lagrangian method, the proposed method can easily simulate the behavior of large wood in the flow field with active sediment transport. The method is applied to the flood disaster in the Akatani River in 2017, and the 2-D flood flow computations are successfully performed.
Hemanti Sharma and Todd A. Ehlers
Earth Surf. Dynam., 11, 1161–1181, https://doi.org/10.5194/esurf-11-1161-2023, https://doi.org/10.5194/esurf-11-1161-2023, 2023
Short summary
Short summary
Seasonality in precipitation (P) and vegetation (V) influences catchment erosion (E), although which factor plays the dominant role is unclear. In this study, we performed a sensitivity analysis of E to P–V seasonality through numerical modeling. Our results suggest that P variations strongly influence seasonal variations in E, while the effect of seasonal V variations is secondary but significant. This is more pronounced in moderate and least pronounced in extreme environmental settings.
Christoph Rettinger, Mina Tabesh, Ulrich Rüde, Stefan Vollmer, and Roy M. Frings
Earth Surf. Dynam., 11, 1097–1115, https://doi.org/10.5194/esurf-11-1097-2023, https://doi.org/10.5194/esurf-11-1097-2023, 2023
Short summary
Short summary
Packing models promise efficient and accurate porosity predictions of fluvial sediment deposits. In this study, three packing models were reviewed, calibrated, and validated. Only two of the models were able to handle the continuous and large grain size distributions typically encountered in rivers. We showed that an extension by a cohesion model is necessary and developed guidelines for successful predictions in different rivers.
Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya
Earth Surf. Dynam., 11, 1061–1095, https://doi.org/10.5194/esurf-11-1061-2023, https://doi.org/10.5194/esurf-11-1061-2023, 2023
Short summary
Short summary
A novel, artificial-intelligence-based riverbed sediment analysis methodology is introduced that uses underwater images to identify the characteristic sediment classes. The main novelties of the procedure are as follows: underwater images are used, the method enables continuous mapping of the riverbed along the measurement vessel’s route contrary to conventional techniques, the method is cost-efficient, and the method works without scaling.
Kelly M. Sanks, John B. Shaw, Samuel M. Zapp, José Silvestre, Ripul Dutt, and Kyle M. Straub
Earth Surf. Dynam., 11, 1035–1060, https://doi.org/10.5194/esurf-11-1035-2023, https://doi.org/10.5194/esurf-11-1035-2023, 2023
Short summary
Short summary
River deltas encompass many depositional environments (like channels and wetlands) that interact to produce coastal environments that change through time. The processes leading to sedimentation in wetlands are often neglected from physical delta models. We show that wetland sedimentation constrains flow to the channels, changes sedimentation rates, and produces channels more akin to field-scale deltas. These results have implications for the management of these vulnerable coastal landscapes.
Katharina Wetterauer and Dirk Scherler
Earth Surf. Dynam., 11, 1013–1033, https://doi.org/10.5194/esurf-11-1013-2023, https://doi.org/10.5194/esurf-11-1013-2023, 2023
Short summary
Short summary
In glacial landscapes, debris supply rates vary spatially and temporally. Rockwall erosion rates derived from cosmogenic 10Be concentrations in medial moraine debris at five Swiss glaciers around Pigne d'Arolla indicate an increase in erosion from the end of the Little Ice Age towards deglaciation but temporally more stable rates over the last ∼100 years. Rockwall erosion rates are higher where rockwalls are steep and north-facing, suggesting a potential slope and temperature control.
Sam Anderson, Nicole Gasparini, and Joel Johnson
Earth Surf. Dynam., 11, 995–1011, https://doi.org/10.5194/esurf-11-995-2023, https://doi.org/10.5194/esurf-11-995-2023, 2023
Short summary
Short summary
We measured rock strength and amount of fracturing in the two different rock types, sandstones and carbonates, in Last Chance Canyon, New Mexico, USA. Where there is more carbonate bedrock, hills and channels steepen in Last Chance Canyon. This is because the carbonate-type bedrock tends to be more thickly bedded, is less fractured, and is stronger. The carbonate bedrock produces larger boulders than the sandstone bedrock, which can protect the more fractured sandstone bedrock from erosion.
Jens M. Turowski, Gunnar Pruß, Anne Voigtländer, Andreas Ludwig, Angela Landgraf, Florian Kober, and Audrey Bonnelye
Earth Surf. Dynam., 11, 979–994, https://doi.org/10.5194/esurf-11-979-2023, https://doi.org/10.5194/esurf-11-979-2023, 2023
Short summary
Short summary
Rivers can cut into rocks, and their strength modulates the river's erosion rates. Yet, which properties of the rock control its response to erosive action is poorly understood. Here, we describe parallel experiments to measure rock erosion rates under fluvial impact erosion and the rock's geotechnical properties such as fracture strength, elasticity, and density. Erosion rates vary over a factor of a million between different rock types. We use the data to improve current theory.
Koji Ohata, Hajime Naruse, and Norihiro Izumi
Earth Surf. Dynam., 11, 961–977, https://doi.org/10.5194/esurf-11-961-2023, https://doi.org/10.5194/esurf-11-961-2023, 2023
Short summary
Short summary
We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.
Matan Ben-Asher, Florence Magnin, Sebastian Westermann, Josué Bock, Emmanuel Malet, Johan Berthet, Ludovic Ravanel, and Philip Deline
Earth Surf. Dynam., 11, 899–915, https://doi.org/10.5194/esurf-11-899-2023, https://doi.org/10.5194/esurf-11-899-2023, 2023
Short summary
Short summary
Quantitative knowledge of water availability on high mountain rock slopes is very limited. We use a numerical model and field measurements to estimate the water balance at a steep rock wall site. We show that snowmelt is the main source of water at elevations >3600 m and that snowpack hydrology and sublimation are key factors. The new information presented here can be used to improve the understanding of thermal, hydrogeological, and mechanical processes on steep mountain rock slopes.
Jessica Droujko, Srividya Hariharan Sudha, Gabriel Singer, and Peter Molnar
Earth Surf. Dynam., 11, 881–897, https://doi.org/10.5194/esurf-11-881-2023, https://doi.org/10.5194/esurf-11-881-2023, 2023
Short summary
Short summary
We combined data from satellite images with data measured from a kayak in order to understand the propagation of fine sediment in the Vjosa River. We were able to find some storm-activated and some permanent sources of sediment. We also estimated how much fine sediment is carried into the Adriatic Sea by the Vjosa River: approximately 2.5 Mt per year, which matches previous findings. With our work, we hope to show the potential of open-access satellite images.
Kate C. P. Leary, Leah Tevis, and Mark Schmeeckle
Earth Surf. Dynam., 11, 835–847, https://doi.org/10.5194/esurf-11-835-2023, https://doi.org/10.5194/esurf-11-835-2023, 2023
Short summary
Short summary
Despite the importance of bedforms (e.g., ripples, dunes) to sediment transport, the details of sediment transport on a sub-bedform scale are poorly understood. This paper investigates sediment transport in the downstream and cross-stream directions over bedforms with straight crests. We find that the patterns of bedload transport are highly variable on the sub-bedform scale, which is important for our understanding of the evolution of bedforms with complex crest geometries.
Paul A. Carling, John D. Jansen, Teng Su, Jane Lund Andersen, and Mads Faurschou Knudsen
Earth Surf. Dynam., 11, 817–833, https://doi.org/10.5194/esurf-11-817-2023, https://doi.org/10.5194/esurf-11-817-2023, 2023
Short summary
Short summary
Many steep glaciated rock walls collapsed when the Ice Age ended. How ice supports a steep rock wall until the ice decays is poorly understood. A collapsed rock wall was surveyed in the field and numerically modelled. Cosmogenic exposure dates show it collapsed and became ice-free ca. 18 ka ago. The model showed that the rock wall failed very slowly because ice was buttressing the slope. Dating other collapsed rock walls can improve understanding of how and when the last Ice Age ended.
Cited articles
Antolínez, J. A., Méndez, F. J., Anderson, D., Ruggiero, P., and Kaminsky, G. M.: Predicting climate-driven coastlines with a simple and efficient multiscale model, J. Geophys. Res.-Earth, 124, 1596–1624, https://doi.org/10.1029/2018JF004790, 2019. a
Biondi, D., Freni, G., Iacobellis, V., Mascaro, G., and Montanari, A.: Validation of hydrological models: Conceptual basis, methodological approaches and a proposal for a code of practice, Phys. Chem. Earth Pt. a/b/c, 42–44, 70–76, https://doi.org/10.1016/j.pce.2011.07.037, 2012. a
Blossier, B., Bryan, K. R., Daly, C. J., and Winter, C.: Shore and bar cross-shore migration, rotation, and breathing processes at an embayed beach, J. Geophys. Res.-Earth, 122, 1745–1770, https://doi.org/10.1002/2017JF004227, 2017. a, b
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions. 1. Model description and validation, J. Geophys. Res., 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999. a
Buscombe, D. and Goldstein, E. B.: A reproducible and reusable pipeline for segmentation of geoscientific imagery, Earth Space Sci., 9, e2022EA002332, https://doi.org/10.1029/2022EA002332, 2022. a
Buscombe, D., Wernette, P., Fitzpatrick, S., Favela, J., Goldstein, E. B., and Enwright, N. M.: A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments, Sci. Data, 10, 46, https://doi.org/10.1038/s41597-023-01929-2, 2023. a
Cagigal, L., Rueda, A., Anderson, D., Ruggiero, P., Merrifield, M. A., Montaño, J., Coco, G., and Méndez, F. J.: A multivariate, stochastic, climate-based wave emulator for shoreline change modelling, Ocean Model., 154, 101695, https://doi.org/10.1016/j.ocemod.2020.101695, 2020. a
Calkoen, F., Luijendijk, A., Rivero, C. R., Kras, E., and Baart, F.: Traditional vs. machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines, Remote Sens., 13, 934, https://doi.org/10.3390/rs13050934, 2021. a
Camus, P., Losada, I., Izaguirre, C., Espejo, A., Menéndez, M., and Pérez, J.: Statistical wave climate projections for coastal impact assessments, Earth's Future, 5, 918–933, https://doi.org/10.1002/2017EF000609, 2017. a
Castelle, B. and Harley, M.: Extreme events: Impact and recovery, in: Sandy Beach Morphodynamics, Elsevier, 533–556, https://doi.org/10.1016/B978-0-08-102927-5.00022-9, 2020. a
Castelle, B., Dodet, G., Masselink, G., and Scott, T.: A new climate index controlling winter wave activity along the Atlantic coast of Europe: The West Europe Pressure Anomaly, Geophys. Res. Lett., 44, 1384–1392, https://doi.org/10.1002/2016GL072379, 2017. a, b
Cooper, J. and McKenna, J.: Social justice in coastal erosion management: The temporal and spatial dimensions, Geoforum, 39, 294–306, https://doi.org/10.1016/j.geoforum.2007.06.007, 2008. a
Davidson, M. and Turner, I.: A behavioral template beach profile model for predicting seasonal to interannual shoreline evolution, J. Geophys. Res.-Earth, 114, F01020, https://doi.org/10.1029/2007JF000888, 2009. a
Duveiller, G., Fasbender, D., and Meroni, M.: Revisiting the concept of a symmetric index of agreement for continuous datasets, Sci. Rep., 6, 1–14, https://doi.org/10.1038/srep19401, 2016. a, b, c, d
Ellenson, A. N., Simmons, J. A., Wilson, G. W., Hesser, T. J., and Splinter, K. D.: Beach State Recognition Using Argus Imagery and Convolutional Neural Networks, Remote Sens., 12, 3953, https://doi.org/10.3390/rs12233953, 2020. a
Elsayed, S. M. and Oumeraci, H.: Effect of beach slope and grain-stabilization on coastal sediment transport: An attempt to overcome the erosion overestimation by XBeach, Coas. Eng., 121, 179–196, https://doi.org/10.1016/j.coastaleng.2016.12.009, 2017. a
Gers, F. A., Schmidhuber, J., and Cummins, F.: Learning to forget: Continual prediction with LSTM, Neural Comput., 12, 2451–2471, https://doi.org/10.1162/089976600300015015, 2000. a
Goldstein, E. B., Coco, G., and Plant, N. G.: A review of machine learning applications to coastal sediment transport and morphodynamics, Earth-Sci. Rev., 194, 97–108, https://doi.org/10.1016/j.earscirev.2019.04.022, 2019. a, b, c
Gomez-de la Pena, E.: eduardogomezdelapena/DL_shoreline_prediction: Code release with paper publication (Version 1), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10088350, 2023. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, https://doi.org/10.1007/s10710-017-9314-z, 2016. a
Guedes, R. M. C., Bryan, K. R., Coco, G., and Holman, R. A.: The effects of tides on swash statistics on an intermediate beach, J. Geophys. Res.-Oceans, 116, C04008, https://doi.org/10.1029/2010JC006660, 2011. a
Gupta, M., Kodamana, H., and Sandeep, S.: Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks, IEEE Geosci. Remote Sens. Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2020.3032353, 2022. a
Harley, M. D., Turner, I. L., Short, A. D., and Ranasinghe, R.: Interannual variability and controls of the Sydney wave climate, Int. J. Climatol., 30, 1322–1335, https://doi.org/10.1002/joc.1962, 2010. a
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., and Sainath, T. N.: Deep neural networks for acoustic modelling in speech recognition: The shared views of four research groups, IEEE Sig. Process. Mag., 29, 82–97, https://doi.org/10.1109/MSP.2012.2205597, 2012a. a
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R.: Improving neural networks by preventing co-adaptation of feature detectors, arXiv [preprint], https://doi.org/10.48550/arXiv.1207.0580, 2012b. a
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
Ibaceta, R., Splinter, K. D., Harley, M. D., and Turner, I. L.: Improving multi-decadal coastal shoreline change predictions by including model parameter non-stationarity, Front. Mar. Sci., 9, 1012041, https://doi.org/10.3389/fmars.2022.1012041, 2022. a
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A.: Deep learning for time series classification: a review, Data Min. Knowl. Discov., 33, 917–963, https://doi.org/10.1007/s10618-019-00619-1, 2019. a
Jaramillo, C., Jara, M. S., González, M., and Medina, R.: A shoreline evolution model considering the temporal variability of the beach profile sediment volume (sediment gain/loss), Coast. Eng., 156, 103612, https://doi.org/10.1016/j.coastaleng.2019.103612, 2020. a
Jaramillo, C., González, M., Medina, R., and Turki, I.: An equilibrium-based shoreline rotation model, Coast. Eng., 163, 103789, https://doi.org/10.1016/j.coastaleng.2020.103789, 2021. a
Kobayashi, N., Buck, . M., Payo, . A., and Johnson, B. D.: Berm and Dune Erosion during a Storm, J. Waterway Port Coast. Ocean Eng., 135, 1–10, https://doi.org/0.1061/(ASCE)0733-950X(2009)135:1(1), 2009. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using long short-term memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep convolutional neural networks, Adv. Neural Inform. Process. Syst., 25, 1097–1105, https://doi.org/10.1145/3065386, 2012. a, b
Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P.: Exploring strategies for training deep neural networks, J. Mach. Learn. Res., 10, 1–40, 2009. a
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L.: Handwritten digit recognition with a back-propagation network, Adv. Neural Inform. Process. Syst., 2, 396–404, 1989. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015. a, b
Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., Kumar Sahu, R., Greve, P., Slater, L., and Dadson, S. J.: Hydrological concept formation inside long short-term memory (LSTM) networks, Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, 2022. a
Lim, C., Kim, T.-K., and Lee, J.-L.: Evolution model of shoreline position on sandy, wave-dominated beaches, Geomorphology, 415, 108409, https://doi.org/10.1016/j.geomorph.2022.108409, 2022. a
Lu, W.-S., Tseng, C.-H., Hsiao, S.-C., Chiang, W.-S., and Hu, K.-C.: Future Projection for Wave Climate around Taiwan Using Weather-Type Statistical Downscaling Method, J. Mar. Sci. Eng., 10, 1823, https://doi.org/10.3390/jmse10121823, 2022. a
Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., and Aarninkhof, S.: The state of the world's beaches, Sci. Rep., 8, 1–11, https://doi.org/10.1038/s41598-018-24630-6, 2018. a
Luijendijk, A., de Schipper, M., and Ranasinghe, R.: Morphodynamic acceleration techniques for multi-timescale predictions of complex sandy interventions, J. Mar. Sci. Eng., 7, 78, https://doi.org/10.3390/jmse7030078, 2019. a
McCall, R. T., De Vries, J. V. T., Plant, N., Van Dongeren, A., Roelvink, J., Thompson, D., and Reniers, A.: Two-dimensional time dependent hurricane overwash and erosion modelling at Santa Rosa Island, Coast. Eng., 57, 668–683, https://doi.org/10.1016/j.coastaleng.2010.02.006, 2010. a
McCarroll, R. J., Masselink, G., Valiente, N. G., Scott, T., Wiggins, M., Kirby, J.-A., and Davidson, M.: A rules-based shoreface translation and sediment budgeting tool for estimating coastal change: ShoreTrans, Mar. Geol., 435, 106466, https://doi.org/10.1016/j.margeo.2021.106466, 2021. a
Mentaschi, L., Vousdoukas, M. I., Pekel, J.-F., Voukouvalas, E., and Feyen, L.: Global long-term observations of coastal erosion and accretion, Sci. Rep., 8, 1–11, https://doi.org/10.1038/s41598-018-30904-w, 2018. a
Miller, J. K. and Dean, R. G.: A simple new shoreline change model, Coast. Eng., 51, 531–556, https://doi.org/10.1016/j.coastaleng.2004.05.006, 2004. a
Murray, A. B.: Which Models Are Good (Enough), and When?, in: Treatise on Geomorphology, vol. 2, Elsevier Inc., 50–58, https://doi.org/10.1016/B978-0-12-374739-6.00027-0, 2013. a
Pape, L., Ruessink, B. G., Wiering, M. A., and Turner, I. L.: Recurrent neural network modelling of nearshore sandbar behavior, Neural Networks, 20, 509–518, https://doi.org/10.1016/j.neunet.2007.04.007, 2007. a
Pape, L., Kuriyama, Y., and Ruessink, B.: Models and scales for cross-shore sandbar migration, J. Geophys. Res.-Earth, 115, F03043, https://doi.org/10.1029/2009JF001644, 2010. a, b, c
Pérez, J., Méndez, F. J., Menéndez, M., and Losada, I. J.: ESTELA: a method for evaluating the source and travel time of the wave energy reaching a local area, Ocean Dynam., 64, 1181–1191, https://doi.org/10.1007/s10236-014-0740-7, 2014. a
Rampal, N., Shand, T., Wooler, A., and Rautenbach, C.: Interpretable Deep Learning Applied to Rip Current Detection and Localization, Remote Sens., 14, 6048, https://doi.org/10.3390/rs14236048, 2022. a
Ranasinghe, R.: Assessing climate change impacts on open sandy coasts: A review, Earth-Sci. Rev., 160, 320–332, https://doi.org/10.1016/j.earscirev.2016.07.011, 2016. a
Ranasinghe, R.: On the need for a new generation of coastal change models for the 21st century, Sci. Rep., 10, 2010, https://doi.org/10.1038/s41598-020-58376-x, 2020. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and Carvalhais, N.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a, b, c, d
Robinet, A., Castelle, B., Idier, D., Le Cozannet, G., Déqué, M., and Charles, E.: Statistical modelling of interannual shoreline change driven by North Atlantic climate variability spanning 2000–2014 in the Bay of Biscay, Geo-Mar. Lett., 36, 479–490, https://doi.org/10.1007/s00367-016-0460-8, 2016. a
Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., and Lescinski, J.: Modelling storm impacts on beaches, dunes and barrier islands, Coast. Eng., 56, 1133–1152, https://doi.org/10.1016/j.coastaleng.2009.08.006, 2009. a
Serafin, K. A. and Ruggiero, P.: Simulating extreme total water levels using a time-dependent, extreme value approach, J. Geophys. Res.-Oceans, 119, 6305–6329, https://doi.org/10.1002/2014JC010093, 2014. a
Sherwood, C. R., Van Dongeren, A., Doyle, J., Hegermiller, C. A., Hsu, T.-J., Kalra, T. S., Olabarrieta, M., Penko, A. M., Rafati, Y., and Roelvink, D.: modelling the morphodynamics of coastal responses to extreme events: what shape are we in?, Annu. Rev. Mar. Sci., 14, 457–492, https://doi.org/10.1146/annurev-marine-032221-090215, 2022. a
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-C.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: Advances in Neural Information Processing Systems, vol. 28, edited by: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R., Curran Associates, Inc., ISBN 9781510825024, 2015. a
Silva, A., Klein, A., Fetter-Filho, A., Hein, C. J., Méndez, F., Broggio, M., and Dalinghaus, C.: Climate-induced variability in South Atlantic wave direction over the past three millennia, Sci. Rep., 10, 18553, https://doi.org/10.1038/s41598-020-75265-5, 2020. a
Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I.: A comprehensive review of deep learning applications in hydrology and water resources, Water Sci. Technol., 82, 2635–2670, https://doi.org/10.2166/wst.2020.369, 2020. a
Splinter, K. D., Turner, I. L., Davidson, M. A., Barnard, P., Castelle, B., and Oltman-Shay, J.: A generalized equilibrium model for predicting daily to interannual shoreline response, J. Geophys. Res.-Earth, 119, 1936–1958, https://doi.org/10.1002/2014JF003106, 2014. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a, b
Toimil, A., Losada, I. J., Camus, P., and Díaz-Simal, P.: Managing coastal erosion under climate change at the regional scale, Coast. Eng., 128, 106–122, https://doi.org/10.1016/j.coastaleng.2017.08.004, 2017. a
Van, S. P., Le, H. M., Thanh, D. V., Dang, T. D., Loc, H. H., and Anh, D. T.: Deep learning convolutional neural network in rainfall–runoff modelling, J. Hydroinform., 22, 541–561, https://doi.org/10.2166/hydro.2020.095, 2020. a
Vitousek, S., Barnard, P. L., Limber, P., Erikson, L., and Cole, B.: A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change, J. Geophys. Res.-Earth, 122, 782–806, https://doi.org/10.1002/2016JF004065, 2017. a, b, c
Vitousek, S., Cagigal, L., Montaño, J., Rueda, A., Mendez, F., Coco, G., and Barnard, P. L.: The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions, J. Geophys. Res.-Earth, 126, e2019JF005506, https://doi.org/10.1029/2019JF005506, 2021. a
Vos, K., Harley, M. D., Turner, I. L., and Splinter, K. D.: Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation, Nat. Geosci., 16, 140–146, https://doi.org/10.1038/s41561-022-01117-8, 2023. a, b
Wang, N., Chen, Q., and Chen, Z.: Reconstruction of nearshore wave fields based on physics-informed neural networks, Coast. Eng., 176, 104167, https://doi.org/10.1016/j.coastaleng.2022.104167, 2022. a
Wright, L. D. and Short, A. D.: Morphodynamic variability of surf zones and beaches: a synthesis, Mar. Geol., 56, 93–118, https://doi.org/10.1016/0025-3227(84)90008-2, 1984. a
Xu, T., Coco, G., and Neale, M.: A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning, Water Res., 177, 115788, https://doi.org/10.1016/j.watres.2020.115788, 2020. a
Yan, B., Zhang, Q.-H., and Wai, O. W.: Prediction of sand ripple geometry under waves using an artificial neural network, Comput. Geosci., 34, 1655–1664, https://doi.org/10.1016/j.cageo.2008.03.002, 2008. a
Yates, M. L., Guza, R. T., and O'reilly, W. C.: Equilibrium shoreline response: Observations and modelling, J. Geophys. Res, 114, 9014, https://doi.org/10.1029/2009JC005359, 2009. a, b
Yoon, H.-D., Cox, D. T., and Kim, M.: Prediction of time-dependent sediment suspension in the surf zone using artificial neural network, Coast. Eng., 71, 78–86, https://doi.org/10.1016/j.coastaleng.2012.08.005, 2013. a
Zhang, K., Geng, X., and Yan, X.-H.: Prediction of 3-D Ocean Temperature by Multilayer Convolutional LSTM, IEEE Geosci. Remote Sens. Lett., 17, 1303–1307, https://doi.org/10.1109/LGRS.2019.2947170, 2020. a
Short summary
Predicting how shorelines change over time is a major challenge in coastal research. We here have turned to deep learning (DL), a data-driven modelling approach, to predict the movement of shorelines using observations from a camera system in New Zealand. The DL models here implemented succeeded in capturing the variability and distribution of the observed shoreline data. Overall, these findings indicate that DL has the potential to enhance the accuracy of current shoreline change predictions.
Predicting how shorelines change over time is a major challenge in coastal research. We here...
