On the sensitivity of tidal network characterization to power law estimation
M. Jiménez
Environmental Hydraulics Institute "IH Cantabria", Universidad de Cantabria, C/ Isabel Torres no. 5, 39011, Santander, Spain
S. Castanedo
Environmental Hydraulics Institute "IH Cantabria", Universidad de Cantabria, C/ Isabel Torres no. 5, 39011, Santander, Spain
Z. Zhou
Environmental Hydraulics Institute "IH Cantabria", Universidad de Cantabria, C/ Isabel Torres no. 5, 39011, Santander, Spain
Environmental Hydraulics Institute "IH Cantabria", Universidad de Cantabria, C/ Isabel Torres no. 5, 39011, Santander, Spain
R. Medina
Environmental Hydraulics Institute "IH Cantabria", Universidad de Cantabria, C/ Isabel Torres no. 5, 39011, Santander, Spain
I. Rodriguez-Iturbe
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
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Earth Surf. Dynam., 11, 1145–1160, https://doi.org/10.5194/esurf-11-1145-2023, https://doi.org/10.5194/esurf-11-1145-2023, 2023
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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.
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
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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
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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.
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
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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
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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
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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.
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
M. Konar, Z. Hussein, N. Hanasaki, D. L. Mauzerall, and I. Rodriguez-Iturbe
Hydrol. Earth Syst. Sci., 17, 3219–3234, https://doi.org/10.5194/hess-17-3219-2013, https://doi.org/10.5194/hess-17-3219-2013, 2013
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