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Hydrodynamics and Sediment Transport in Rivers, Lakes, Coasts and Estuaries
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Hydrodynamics and Sediment Transport in Rivers, Lakes, Coasts and Estuaries

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Erosion and Sediment Transport".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1272

Special Issue Editors

Prof. Dr. Zhi-jun Dai

E-Mail Website
Guest Editor
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Dongchuan Road 500, Shanghai 200241, China
Interests: sediment discharge; runoff changes; hydrological process; riverine, estuarine and ocean hydrology; dam regulation and human activities
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Wang

E-Mail Website
Guest Editor
State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: estuarine-coastal hydrodynamics and sediment transport under extreme hydrological scenarios (river floods, droughts and tropical cyclones) based on field observations and numerical simulations; the response mechanisms of flow/sediment dynamics, disaster risk, geomorphic stability in coastal urban areas that triggered by the changed river and marine forcings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rivers, lakes, coasts, and estuaries constitute Earth’s critical hydro-geomorphic systems, sustaining key ecosystem services and supporting socio-economic resilience. In the Anthropocene, amplified climate variability (e.g., extreme hydrological events, accelerated sea-level rise) and intensive anthropogenic perturbations (e.g., dam construction, coastal engineering, catchment land-use/cover change) are profoundly altering hydro-sedimentary dynamics across these systems. Such perturbations disrupt sediment budget, reshape morphological evolution trajectories, and impair habitats, posing severe challenges to the sustainable governance of these vulnerable systems.

To address these knowledge gaps and advance frontier research, this Special Issue aims to assemble cutting-edge, interdisciplinary studies that deepen fundamental understanding of hydro-sedimentary processes under changing environmental forces. This issue prioritizes research that integrates field observations, numerical modeling, and theoretical analysis to unravel the mechanisms governing sediment transport, morphological change, and eco-hydrodynamic feedbacks in rivers, lakes, coasts, and estuaries. We kindly invite researchers worldwide to submit original research articles, comprehensive reviews, and short communications in this field.

Topics of interest include, but are not limited to, the following:

  • Multi-scale hydro-sedimentary process dynamics and their response to climate–human perturbations;
  • Coupled effects of extreme events (e.g., floods, storm surges) on sediment budget and morphological evolution;
  • Eco-hydrodynamic interactions and ecological service change;
  • Innovative monitoring, observation, and modeling techniques for hydro-sedimentary systems;
  • Adaptive management and restoration strategies for perturbed hydro-geomorphic systems.

Prof. Dr. Zhi-jun Dai
Dr. Jie Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydrodynamics
  • sediment transport
  • geomorphic evolution
  • eco-hydrodynamic interaction
  • climate change
  • an-thropogenic perturbation
  • sustainable governance
  • fluvial-lacustrine-coastal-estuarine systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 12045 KB  
Article
A Comparative Data-Driven Framework for Total Sediment Load Prediction Using Multi-Algorithm ANN, Hydro-Meteorological Inputs, and Advanced Preprocessing Techniques
by Md. Jobayer Parvez Ratul, Fahdah Falah Ben Hasher, Zoe Kanetaki and Mohamed Zhran
Water 2026, 18(10), 1182; https://doi.org/10.3390/w18101182 - 14 May 2026
Viewed by 227
Abstract
In the domain of river engineering, estimating the total sediment load in rivers is a crucial challenge. For tens to hundreds of kilometers downstream, the additional sand and gravel in the sediment can raise the elevation of channel beds. For highly braided rivers [...] Read more.
In the domain of river engineering, estimating the total sediment load in rivers is a crucial challenge. For tens to hundreds of kilometers downstream, the additional sand and gravel in the sediment can raise the elevation of channel beds. For highly braided rivers like the Brahmaputra-Jamuna, the accurate prediction of the total sediment load depends on the complex relationships among different hydro-meteorological variables. As a result, manual selection of the lagged features from only antecedent sediment records can produce suboptimal predictions, which can be considered a significant research gap. In addition, the predictive accuracy can be further enhanced through the application of advanced decomposition techniques. To address these deficiencies, we implemented three sophisticated feature selection methodologies: SelectKBest, Mutual Information, and Random Forest utilizing the Boruta Algorithm as an alternative to manual feature selection. Furthermore, we investigated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), and the Hodrick–Prescott Filter (HPF) to improve data mining efficiency. Four distinct artificial neural network (ANN) training algorithms were considered: back propagation (BP), cascade correlation (CC), conjugate gradient (CG), and Levenberg–Marquardt (LM), as alternatives to the conventional BP-based training approach. The effectiveness of the variants of the ANN was assessed in comparison to a powerful ensemble learning model, specifically the decision tree (DT). Results indicate that the HPF-enhanced ANN-LM model exhibited the strongest performance metrics when compared to alternative techniques, with values of NRMSE = 0.004, MAE = 455.242 kg/s, NSE = 0.998, and KGE = 0.990. The outcomes from Sobol’s sensitivity analysis suggest that the sediment dynamics in this region can be better predicted through the inclusion of rainfall-based features. Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Rivers, Lakes, Coasts and Estuaries)
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24 pages, 3894 KB  
Article
Turbidity Prediction in a Large, Shallow Lake Using Machine Learning
by Nicholas von Stackelberg and Michael Barber
Water 2026, 18(9), 1026; https://doi.org/10.3390/w18091026 - 25 Apr 2026
Viewed by 809
Abstract
Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for [...] Read more.
Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Rivers, Lakes, Coasts and Estuaries)
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