Long-term evolution of an inner bar at the mouth of a microtidal river

Baldoni, Agnese; Perugini, Eleonora; Soldini, Luciano; Calantoni, Joseph; Brocchini, Maurizio

doi:10.1016/j.ecss.2021.107573

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Moving downriver, the width increases, reaching almost 40m at the mouth. In terms of bed elevation, although this globally tends to decrease between QR1 and the mouth, a small bed perturbation is visible just downriver of QR3 (Figure 1e), which gave rise to a river mouth bar in the years following the experimental campaign (Baldoni et al, 2021).…”

Section: Field Experimentsmentioning

confidence: 99%

A storm driven turbidity maximum in a microtidal estuary

Postacchini¹,

Manning²,

Calantoni³

et al. 2023

Estuarine, Coastal and Shelf Science

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Section: Field Experimentsmentioning

confidence: 99%

A storm driven turbidity maximum in a microtidal estuary

Postacchini¹,

Manning²,

Calantoni³

et al. 2023

Estuarine, Coastal and Shelf Science

Self Cite

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“…Many of these repositories have associated publications documenting their development, like the Station-Design-Toolbox, a helpful planning tool for installing coastal imaging observation stations [64], the CoastSnap-Toolbox, a unique citizen science effort to use imagery from smart phones to monitor shoreline change [19], and the Video-Currents toolbox, an algorithm to extract longshore current magnitudes based off of the techniques described in [55]. Additional toolboxes include algorithms to digitize wave runup from timestacks of video imagery (runupTool-Toolbox), propagate uncertainty through photogrammetry equations (Photogrammetry Propagated Uncertainty), extract the wet/dry shoreline from imagery (Shoreline-Mapping Toolbox) [65], and to digitize emergent sandbars [66]. Development on the CIRN repositories is active, and CIRN expects to see additions of new satellite-based depth inversion algorithms in the coming year (e.g., [67]).…”

Section: Cirn Code Repositorymentioning

confidence: 99%

The Coastal Imaging Research Network (CIRN)

Palmsten

Brodie

2022

Remote Sensing

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The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging.

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“…As shown by Brocchini et al (2017), the MR showed a cyclic behaviour with accumulation of sediment during low flow periods (summer) and expulsion of sediment to sea during high flow periods (winter). However, due to the reduced precipitation occurring over the last years, such cycle was altered, leading to the formation of an inner mouth bar frequently emerging (Baldoni et al, 2021) in the final stretch of the river.…”

Section: Field Sitementioning

confidence: 99%

“…Based on in situ samplings (Report -Marche Region, 2020), the upper stretch of the MR was characterized with 100% cohesive sediments, then, the presence of silt and clay decreased seaward until reaching 100% of sand in the sea area. Moreover, in the last stretch of the river, in correspondence of the inner bar location (Baldoni et al, 2021), a small percentage of gravel was added (2-3%). The sand and the gravel were characterized using a D50 of 0.18 mm and 6 mm, respectively, while for the cohesive sediments we set the critical shear stress for erosion equal to 0.3 N/m 2 , the critical shear stress for deposition equal to 0.4 N/m 2 and the erosion parameter equal to 10 -4 kg/m 2 /s (see Table 3) .…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…The hydrodynamics of the model was already calibrated in Baldoni et al (2021), where the capabilities of the model to well describe the evolution of the riverbed under different forcing were also shown. Here, we verified that the parameters used in Baldoni et al (2021) were appropriate also for the description of the plume evolution and the suspended sediment transport. We tuned some parameters, shown in Table 3, and compared the results with some available measures, relative to an experiment carried out in January 2014 (Brocchini et al, 2017).…”

Section: Calibration Of the Model Morphological Parametersmentioning

confidence: 99%

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A comprehensive study of the river plume in a microtidal setting

Baldoni¹,

Perugini²,

Penna³

et al. 2021

Preprint

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On the basis of observations and modelling of the plume generated by the Misa River (AN, Italy), we performed a comprehensive study, which integrated different sources of information (field data, numerical simulations, etc.), of the generation and transport mechanisms of river plumes flowing into microtidal environments. First, we analysed images simultaneously acquired by both two shore-based stations and satellite to determine plume fronts and extensions. Then, we correlated such information with the estuarine forcing to recognize the plume generation and transport mechanisms. Being reallife events influenced by a combination of factors, we run numerical simulations to separately study each force and its influence on the plume evolution. We also performed simulations of two real-life cases, to compare the modelled and observed results. We identified the river discharge and the wind as the main generation and transport mechanisms, respectively. Moreover, waves could stir, suspend, and drag plume sediments, even if results showed that a river discharge associated with a return period smaller than 1 year produced a plume denser than 5-year return period waves. The transport

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Long-term evolution of an inner bar at the mouth of a microtidal river

Cited by 7 publications

References 21 publications

A storm driven turbidity maximum in a microtidal estuary

A storm driven turbidity maximum in a microtidal estuary

The Coastal Imaging Research Network (CIRN)

A comprehensive study of the river plume in a microtidal setting

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