Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework

Aug 4, 2026

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The influence of forcing mechanisms on the variability of suspended sediments in an estuary is synoptically quantified for both prevailing (“normal”) conditions and extreme events. The study analyzes the macrotidal Gironde Estuary, France, assessing how tides, river discharge, and winds shape suspended-sediment variability through a machine learning framework. High-frequency field measurements, hourly numerical modeling, and semi-daily satellite remote sensing are integrated to spatially estimate relative contributions, with tides found dominant.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Keywords

Forcing mechanisms and study drivers

Relative contributions and uncertainty ranges

Spatial consistency across data sources

Limitations of satellite observations and role of modeling

Validation against Singular Spectral Analysis

Continental Shelf Research 287 (2025) 105429 Contents lists available at ScienceDirect Continental Shelf Research journal [homepage: www.elsevier.com/locate/csr](homepage: www.elsevier.com/locate/csr) | Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework
Juliana Tavora a,f,* , Roy El Hourany b, Elisa Helena Fernandes c, Isabel Jal´on-Rojas d,
Aldo Sottolichiod, Mhd Suhyb Salamaa, Daphne van der Wala,e
a Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE, Enschede, the Netherlands b Univ. Littoral Cˆote D’Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire D’Oc´eanologie et de G´eosciences, F 62930, Wimereux, France c Laborat´orio de Oceanografia Costeira e Estuarina, Instituto de Oceanografia, Universidade Federal Do Rio Grande (FURG), Rio Grande, Brazil d Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, F-33600, Pessac, France
e Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, PO Box 140, 4400 AC, Yerseke, the Netherlands fIfremer, DYNECO-DHYSED, CS10070, 29280 Plouzane, France | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords: turbidity1
Surface sediment concentration2 tide3
River discharge4
wind5
Extreme events6
Self organizing maps7
Machine learning8 | | The influence of forcing mechanisms on the variability of suspended sediments in an estuary is, for the first time, synoptically quantified over prevailing (’normal’) conditions and extreme events. This study investigates the complex and non-linear influence of tides, river discharge, and winds on the variability of suspended sediments in the macrotidal Gironde Estuary, France. Employing a machine learning-based framework, we integrated highfrequency field data, hourly numerical modeling outputs, and semi-daily satellite remote sensing to spatially quantify the relative contributions of forcing mechanisms. Our results reveal that tides are the primary driver of sediment variability (42.3–58.9%), followed by river discharge (21.2–34.7%) and wind (8.7–16.9%). Uncertainties range between 7% and 13.6%. In addition, the spatial variability of their contributions is consistent across numerical modeling and satellite remote sensing data, with differences not exceeding 10%. However, satellite data is limited by cloud cover and may miss extreme events. In contrast, hourly numerical modeling indicates tides are the dominant forcing mechanism under extreme events significantly affecting suspended sediment variability in the estuary. This study verifies the effectiveness of our machine learning approach against traditional Singular Spectral Analysis using field data. We demonstrate that machine learning techniques can effectively synthesize spatial distribution patterns of hydrodynamic and sedimentological variability, includingthe influence of winds. Our findings highlight not only the potential of satellite observations to analyze prevailing conditions despite data gaps but also that with hourly numerical modeling, the impact of forcings can be synoptically quantified under prevailing (’normal’) conditions and extreme events. | 1. Introduction Estuaries, as dynamic transitional zones between riverine and marine environments, have an important role in buffering excess continental drainage and regulating the transport of nutrients (Wetz and Paerl, 2008), sediments, and other components like pathogens and pollutants (Robins et al., 2016) to the coastal sea. Much of the ability of estuaries to regulate transport between land and sea is also defined by the dynamic interplay of forcing mechanisms such as tides, river discharge, and winds (Meade, 1972; Dalrymple et al., 2012). These forcing mechanisms are critical in shaping the variability of sediment dynamics by influencing sediment transport, deposition, and

FAQs

What forcing mechanisms are quantified in the study?

The study quantifies how tides, river discharge, and winds contribute to the variability of estuarine surface suspended sediments.

What machine learning framework is used and what data are integrated?

A machine learning framework integrates high-frequency field data, hourly numerical modeling outputs, and semi-daily satellite remote sensing to map relative contributions.

Which mechanism is the dominant driver and how do uncertainties compare?

Tides are the primary driver (42.3–58.9%), followed by river discharge (21.2–34.7%) and wind (8.7–16.9%), with uncertainties ranging from 7% to 13.6%.

Aug 4, 2026

Upload by Miles