Geology topics

Giovanni Coco

Publications and source records attributed to Giovanni Coco.

10 recordsLinked to original sources

USGS2026

Unravelling equilibrium shoreline response to waves and sea-level rise: Numerical modelling of laboratory experiments

Knowledge gaps in the physics of shoreline response to the combined action of waves and sea-level rise (SLR) make long-term shoreline projections uncertain. The lack of sufficiently long-term shoreline data partly hinders a better understanding of shoreline change driven by SLR. Thereby, existing formulations related to the equilibrium approach, which states that a beach profile shape equilibrates with its local wave and sea-level conditions, have serious limitations. Recent physical modelling studies provided data of beach profile evolution under changing sea levels in controlled laboratory settings. Here, we bring together and analyze laboratory data from three different physical modelling studies to better understand and predict shoreline response to SLR in the context of equilibrium concept. The data indicate a clear relationship among the analyzed variables, highlighting the importance of accounting for interactions between SLR and incident wave power. To further understand these interactions, and given the limited range of conditions tested in the laboratory, we implement a quasi-2D shoreline change model to generate additional synthetic data. We first calibrate and validate the model to emulate the existing laboratory experiments. The model reproduces multiple laboratory experiments that cover a range of settings, providing confidence in its accuracy. Further work will include the generation of synthetic data obtained by forcing the model with new combinations of SLR and wave conditions to better capture the dependency of shoreline recession on SLR and wave conditions.

Conference Paper↗

USGS2025

Benchmarking shoreline prediction models over multi-decadal timescales

Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.

New South Wales↗

USGS2025

Are equilibrium shoreline models just convolutions?

Yes. Equilibrium shoreline models, which simulate wave-driven cross-shore erosion and accretion, are mathematically equivalent to a discrete convolution (i.e., a weighted, moving average) of a time series of wave-forcing conditions with a parameterized memory-decay kernel function. The direct equivalence between equilibrium shoreline models and convolutions reveals key theoretical aspects of equilibrium behavior. Convolutions (representing quasi-low-pass filter operations) provide an intuitive theoretical description of shoreline erosion and accretion behavior in response to waves: that is, shoreline position often mirrors the weighted moving average of wave time series. Model-convolution equivalence also provides a conceptual basis to interpret, evaluate, and construct data-driven Machine-Learning/Deep-Learning (ML/DL) models that use convolutions to extract features from data and then apply them for prediction (e.g., Convolutional Neural Networks (CNNs)). Finally, our findings provide a methodological pathway (based on Fourier transforms) for future understanding of wave-driven shoreline change, which can be used to interpret the coherence between the frequency spectrum of the processes of waves and shoreline change and construct more computationally efficient and effective shoreline-modeling approaches.

JGR Earth Surface↗

USGS2025

Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach

Robust and reliable models are needed to understand how coastlines will evolve over the coming decades, driven by both natural variability and climate change. This study evaluated how accurately five popular ‘reduced-complexity’ models replicate multi-decadal shoreline change at Narrabeen-Collaroy Beach, a sandy embayment in Sydney, Australia. Measured shoreline positions derived from approximately monthly field surveys were used for 20-year calibration and 20-year validation periods. The models performed similarly on average but with large variability between transects. The set-up of several models was modified to compensate for their sensitivity to imperfect input wave data, and further site-specific improvements were identified. Capturing interannual to decadal-scale variability in cross-shore and longshore dynamics at this site was challenging for all five models. Models appeared to aggregate key processes at this timescale into parameter values rather than representing them directly. This suggests time-varying parameters or changes to model structure may be necessary for decadal-scale simulations.

Coastal Engineering↗

USGS2021

The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions

Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.

JGR Earth Surface↗

USGS2020

Blind testing of shoreline evolution models

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Scientific Reports↗

USGS2019

A review of machine learning applications to coastal sediment transport and morphodynamics

A range of computer science methods under the heading of machine learning (ML) enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review some common ML methods and their application to studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’ ML methods contribute, such as ‘what’ science problems ML tools have been used to address, ‘what’ was learned when using ML, and ‘why’ authors used ML methods. We find a variety of research questions have been addressed, ranging from small-scale predictions of sediment transport to larger-scale sand bar morphodynamics and coastal overwash on a developed island. We find various reasons justify the use of ML, including maximize predictability, emulation of model components, smooth and continuous nonlinear regression through data, and explicit inclusion of uncertainty. Overall the expanding use of ML has allowed for an expanding set of questions to be addressed. After reviewing the studies we outline a set of ‘best practices’ for coastal researchers using machine learning methods. Finally we suggest possible areas for future research, including the use of novel machine learning techniques and exploring ‘open data’ that is becoming increasingly available.

Earth-Science Reviews↗

USGS2018

Field observations of alongshore runup variability under dissipative conditions in presence of a shoreline sandwave

Video measurements of runup were collected at low tide along several profiles covering an alongshore distance of 500 m. The morphology displayed a complex shape with a shoreline sandwave in the lower beach face of about 250 m long mirrored in the inner sandbar. Wave conditions were stationary and moderate (offshore height of 2 m and peak period of nearly 13 s) but yet dissipative. Runup energy was dominated by infragravity frequencies. Alongshore variations in runup (by a factor up to 3) observed both in the incident and infragravity bands were much higher than reported previously (e.g., Guedes et al., 2012, https://doi.org/10.1016/j.csr.2012.08.022 ; Ruggiero et al., 2004, https://doi.org/10.1029/2003JC002160 ) while the alongshore variations in other environmental parameters (e.g., foreshore beach slope) appear to be much lower. Our data suggest that the beach morphology in the inner surf zone plays a crucial role by inducing rapid and significant modification in the incident wave pattern and the alongshore coherence length scales were consistent with the typical alongshore length scale of the morphology.

Journal of Geophysical Research C: Oceans↗

USGS2017

Is “morphodynamic equilibrium” an oxymoron?

Morphodynamic equilibrium is a widely adopted yet elusive concept in the field of geomorphology of coasts, rivers and estuaries. Based on the Exner equation, an expression of mass conservation of sediment, we distinguish three types of equilibrium defined as static and dynamic, of which two different types exist. Other expressions such as statistical and quasi-equilibrium which do not strictly satisfy the Exner conditions are also acknowledged for their practical use. The choice of a temporal scale is imperative to analyse the type of equilibrium. We discuss the difference between morphodynamic equilibrium in the “real world” (nature) and the “virtual world” (model). Modelling studies rely on simplifications of the real world and lead to understanding of process interactions. A variety of factors affect the use of virtual-world predictions in the real world (e.g., variability in environmental drivers and variability in the setting) so that the concept of morphodynamic equilibrium should be mathematically unequivocal in the virtual world and interpreted over the appropriate spatial and temporal scale in the real world. We draw examples from estuarine settings which are subject to various governing factors which broadly include hydrodynamics, sedimentology and landscape setting. Following the traditional “tide-wave-river” ternary diagram, we summarize studies to date that explore the “virtual world”, discuss the type of equilibrium reached and how it relates to the real world.

Earth-Science Reviews↗

USGS2007

Sorted bed forms as self-organized patterns: 2. complex forcing scenarios

We employ a numerical model to study the development of sorted bed forms under a variety of hydrodynamic and sedimentary conditions. Results indicate that increased variability in wave height decreases the growth rate of the features and can potentially give rise to complicated, a priori unpredictable, behavior. This happens because the system responds to a change in wave characteristics by attempting to self-organize into a patterned seabed of different geometry and spacing. The new wavelength might not have enough time to emerge before a new change in wave characteristics occurs, leading to less regular seabed configurations. The new seabed configuration is also highly dependent on the preexisting morphology, which further limits the possibility of predicting future behavior. For the same reasons, variability in the mean current magnitude and direction slows down the growth of features and causes patterns to develop that differ from classical sorted bed forms. Spatial variability in grain size distribution and different types of net sediment aggradation/degradation can also result in the development of sorted bed forms characterized by a less regular shape. Numerical simulations qualitatively agree with observed geometry (spacing and height) of sorted bed forms. Also in agreement with observations is that at shallower depths, sorted bed forms are more likely to be affected by changes in the forcing conditions, which might also explain why, in shallow waters, sorted bed forms are described as ephemeral features. Finally, simulations indicate that the different sorted bed form shapes and patterns observed in the field might not necessarily be related to diverse physical mechanisms. Instead, variations in sorted bed form characteristics may result from variations in local hydrodynamic and/or sedimentary conditions.

Journal of Geophysical Research F: Earth Surface↗