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Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 14617

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Special Issue Editor

Dr. Fangxin Fang

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Guest Editor

Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK
Interests: geophysical models; numerical methods; data assimilation; model reduction; air pollution; flooding; coastal and ocean; hydraulics; machine learning

Special Issue Information

Dear Colleagues,

The rational management of a city and its infrastructure in response to increased pollution, climate change, natural and other disasters, for daily operation and emergency response, is becoming critical to enhance livability for citizens. Creating healthy, sustainable urban environments necessitates advanced numerical tools for optimal design and management processes.

As is well known, numerical models are often hindered by difficulties fully capturing the relevant physics, particularly at sub-grid scales; difficulties in capturing the variability of physical properties at the scale of the grid; and uncertainty in the measurements used to calibrate and initialize the models. These manifest as unacceptably long computational times and uncertainty in predictions.

This special issue aims at exploring advanced numerical techniques for real-time prediction and optimal management in coastal and hydraulic engineering, for example (but not limited to):

uncertainty quantification: modelling key inputs as probability distributions rather than single values, to predict the range of possible physical responses;
sensitivity analysis: identifying the model inputs upon which a particular prediction is most dependent;
adaptive meshes: an effective way to encompass different scales (e.g., local, urban, regional, in a unified modelling system to better capture the details of local flows and the interactions among the relevant processes at each scale;
error quantification: quantifying the impact of discretization errors on prediction quality;
data assimilation: incorporating information from experiments and observations to reduce uncertainties in predictions;
adaptive observations: identifying where best to acquire data to improve a particular prediction;
data-driven modelling based on machine learning: building up the relationship between inputs and outputs from data patterns;
reduced order modelling: developing approximate models that can run in seconds, for immediate useful predictions for rapid response in emergency situations (e.g., flooding, pollutant emissions and toxic releases in cities).

Dr. Fangxin Fang
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

numerical modelling
machine learning
data assimilation
uncertainty
adaptive meshes
model reduction

Published Papers (6 papers)

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Editorial

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3 pages, 176 KiB

Open AccessEditorial

Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering

by Fangxin Fang

Water 2021, 13(4), 509; https://0-doi-org.brum.beds.ac.uk/10.3390/w13040509 - 16 Feb 2021

Cited by 1 | Viewed by 1686

Abstract

This special issue aims at exploring advanced numerical techniques for real-time prediction and optimal management in coastal and hydraulic engineering [...] Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

Research

Jump to: Editorial

12 pages, 1829 KiB

Open AccessFeature PaperArticle

A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model

by Juan Du, Fei Zheng, He Zhang and Jiang Zhu

Water 2021, 13(2), 122; https://0-doi-org.brum.beds.ac.uk/10.3390/w13020122 - 07 Jan 2021

Cited by 2 | Viewed by 1729

Abstract

Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles. Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

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18 pages, 8011 KiB

Open AccessArticle

Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast

by Maria Fattorini and Carlo Brandini

Water 2020, 12(12), 3445; https://0-doi-org.brum.beds.ac.uk/10.3390/w12123445 - 08 Dec 2020

Cited by 3 | Viewed by 1766

Abstract

In this article, we discuss possible observing strategies for a simplified ocean model (Double Gyre (DG)), used as a preliminary tool to understand the observation needs for real analysis and forecasting systems. Observations are indeed fundamental to improve the quality of forecasts when data assimilation techniques are employed to obtain reliable analysis results. In addition, observation networks, particularly in situ observations, are expensive and require careful positioning of instruments. A possible strategy to locate observations is based on Singular Value Decomposition (SVD). SVD has many advantages when a variational assimilation method such as the 4D-Var is available, with its computation being dependent on the tangent linear and adjoint models. SVD is adopted as a method to identify areas where maximum error growth occurs and assimilating observations can give particular advantages. However, an SVD-based observation positioning strategy may not be optimal; thus, we introduce other criteria based on the correlation between points, as the information observed on neighboring locations can be redundant. These criteria are easily replicable in practical applications, as they require rather standard studies to obtain prior information. Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

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14 pages, 18850 KiB

Open AccessArticle

Modelling Study of Transport Time Scales for a Hyper-Tidal Estuary

by Guanghai Gao, Junqiang Xia, Roger A. Falconer and Yingying Wang

Water 2020, 12(9), 2434; https://0-doi-org.brum.beds.ac.uk/10.3390/w12092434 - 30 Aug 2020

Cited by 6 | Viewed by 2542

Abstract

This paper presents a study of two transport timescales (TTS), i.e., the residence time and exposure time, of a hyper-tidal estuary using a widely used numerical model. The numerical model was calibrated against field measured data for various tidal conditions. The model simulated current speeds and directions generally agreed well with the field data. The model was then further developed and applied to study the two transport timescales, namely the exposure time and residence time for the hyper-tidal Severn Estuary. The numerical model predictions showed that the inflow from the River Severn under high flow conditions reduced the residence and exposure times by 1.5 to 3.5% for different tidal ranges and tracer release times. For spring tide conditions, releasing a tracer at high water reduced the residence time and exposure time by 49.0% and 11.9%, respectively, compared to releasing the tracer at low water. For neap tide conditions, releasing at high water reduced the residence time and exposure time by 31.6% and 8.0%, respectively, compared to releasing the tracer at low water level. The return coefficient was found to be vary between 0.75 and 0.88 for the different tidal conditions, which indicates that the returning water effects for different tidal ranges and release times are all relatively high. For all flow and tide conditions, the exposure times were significantly greater than the residence times, which demonstrated that there was a high possibility for water and/or pollutants to re-enter the Severn Estuary after leaving it on an ebb tide. The fractions of water and/or pollutants re-entering the estuary for spring and neap tide conditions were found to be very high, giving 0.75–0.81 for neap tides, and 0.79–0.88 for spring tides. For both the spring and neap tides, the residence and exposure times were lower for high water level release. Spring tide conditions gave significantly lower residence and exposure times. The spatial distribution of exposure and residence times showed that the flow from the River Severn only had a local effect on the upstream part of the estuary, for both the residence and exposure time. Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

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27 pages, 3127 KiB

Open AccessEditor’s ChoiceArticle

Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning

by Minxue He, Liheng Zhong, Prabhjot Sandhu and Yu Zhou

Water 2020, 12(8), 2088; https://0-doi-org.brum.beds.ac.uk/10.3390/w12082088 - 23 Jul 2020

Cited by 9 | Viewed by 2713

Abstract

Salinity management is a subject of particular interest in estuarine environments because of the underlying biological significance of salinity and its variations in time and space. The foremost step in such management practices is understanding the spatial and temporal variations of salinity and the principal drivers of these variations. This has traditionally been achieved with the assistance of empirical or process-based models, but these can be computationally expensive for complex environmental systems. Model emulation based on data-driven methods offers a viable alternative to traditional modeling in terms of computational efficiency and improving accuracy by recognizing patterns and processes that are overlooked or underrepresented (or overrepresented) by traditional models. This paper presents a case study of emulating a process-based boundary salinity generator via deep learning for the Sacramento–San Joaquin Delta (Delta), an estuarine environment with significant economic, ecological, and social value on the Pacific coast of northern California, United States. Specifically, the study proposes a range of neural network models: (a) multilayer perceptron, (b) long short-term memory network, and (c) convolutional neural network-based models in estimating the downstream boundary salinity of the Delta on a daily time-step. These neural network models are trained and validated using half of the dataset from water year 1991 to 2002. They are then evaluated for performance in the remaining record period from water year 2003 to 2014 against the process-based boundary salinity generation model across different ranges of salinity in different types of water years. The results indicate that deep learning neural networks provide competitive or superior results compared with the process-based model, particularly when the output of the latter are incorporated as an input to the former. The improvements are generally more noticeable during extreme (i.e., wet, dry, and critical) years rather than in near-normal (i.e., above-normal and below-normal) years and during low and medium ranges of salinity rather than high range salinity. Overall, this study indicates that deep learning approaches have the potential to supplement the current practices in estimating salinity at the downstream boundary and other locations across the Delta, and thus guide real-time operations and long-term planning activities in the Delta. Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

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24 pages, 19473 KiB

Open AccessArticle

Evaluation and Application of Newly Designed Finite Volume Coastal Model FESOM-C, Effect of Variable Resolution in the Southeastern North Sea

by Ivan Kuznetsov, Alexey Androsov, Vera Fofonova, Sergey Danilov, Natalja Rakowsky, Sven Harig and Karen Helen Wiltshire

Water 2020, 12(5), 1412; https://0-doi-org.brum.beds.ac.uk/10.3390/w12051412 - 15 May 2020

Cited by 11 | Viewed by 3467

Abstract

A newly developed coastal model, FESOM-C, based on three-dimensional unstructured meshes and finite volume, is applied to simulate the dynamics of the southeastern North Sea. Variable horizontal resolution enables coarse meshes in the open sea with refined meshes in shallow areas including the Wadden Sea and estuaries to resolve important small-scale processes such as wetting and drying, sub-mesoscale eddies, and the dynamics of steep coastal fronts. Model results for a simulation of the period from January 2010 to December 2014 agree reasonably well with data from numerous regional autonomous observation stations with high temporal and spatial resolutions, as well as with data from FerryBoxes and glider expeditions. Analyzing numerical solution convergence on meshes of different horizontal resolutions allows us to identify areas where high mesh resolution (wetting and drying zones and shallow areas) and low mesh resolution (open boundary, open sea, and deep regions) are optimal for numerical simulations. Full article

(This article belongs to the Special Issue Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering)

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