Sensitivity analysis of the tidal representation in Singapore Regional Waters in a data assimilation environment

Kurniawan, Alamsyah; Ooi, Seng Keat; Hummel, Stef; Gerritsen, H.

doi:10.1007/s10236-011-0415-6

Cited by 48 publications

(26 citation statements)

References 18 publications

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“…First to investigate the semi-diurnal to diurnal co-tide phases interaction at the Singapore Strait, stronger impact to Singapore Strait's tidal condition than Andaman Sea. This is agreed by Kurniawan et al [22], which showed Java Sea stronger influence toward the tidal condition at Singapore Strait when compared to Andaman Sea using the tidal amplitude component study. …”

Section: Hydrodynamic Simulation: Model Validationsupporting

confidence: 88%

“…However, the improvement in accuracy was usually restricted to only small simulated area (showed in [25,32]) or was not obvious in tidal current and water level simulations (showed in [19]). In comparison, by refining the local mesh resolution according to the local geometry can effectively improve the water levels and tidal currents simulation as proven in various studies (refer to studies by [9,21]), which it is also suggested to be a crucial element in the sensitivity analysis of SCW tidal simulations conducted by Kurniawan et al [22] and Sun et al [27].…”

Section: Introduction and Motivation Statementmentioning

confidence: 99%

“…Thus an in-depth hydrodynamic understanding of the surrounding sea and coastal area of Singapore Island is crucial in seeking solution to the rising environmental issues. In the research of Singapore coastal waters (SCW) hydrodynamic numerical modelling, there are a numbers of difficulty that were constantly suggested in various studies [3,19,22,25,33], especially for the modelling surrounded Strait of Singapore. They include:…”

Section: Introduction and Motivation Statementmentioning

confidence: 99%

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Conceptual hydrodynamic-thermal mapping modelling for coral reefs at south Singapore sea

2016

Applied Ocean Research

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Coral reefs are important ecosystems that not only provide shelter and breeding ground for many marine species, but can also control of carbon dioxide level in ocean and act as coastal protection mechanism. Reduction of coral reefs at Singapore coastal waters (SCW) region remains as an important study to identify the environmental impact from its busy industrial activities especially at the surrounding of Jurong Island in the south. This kind of study at SCW was often being related to issues such as turbidity, sedimentation, pollutant transport (from industry activities) effects in literatures, but seldom investigated from the thermal change aspect. In this paper, a computational model was constructed using the Delft3D hydrodynamic module to produce wave simulations on sea regions surrounding Singapore Island. The complicated semi-diurnal and diurnal tidal wave events experienced by SCW were simulated for two weeks duration and compared to the Admiralty measured data. To simulate the thermal mapping at the south Singapore coastal waters (SSCW) region, we first adapted a conversion of industrial to thermal discharge; then from the discharge affected area a thermal map was further computed to compare with the measured coral map. The outcomes show that the proposed novel thermal modelling approach has quite precisely simulated the coral map at SSCW, with the condition that the near-field thermal sources are considered (with the coverage area in the limit of 20km x 20km).Keywords: Coral reefs; hydrodynamics; Singapore coastal waters (SCW); thermal map 2 1 Introduction and Motivation StatementSingapore is regarded as one of the busiest island countries connecting the world by its shipping and haulage activities. In recent years various industrial activities have boomingly growing at Singapore and causing various environmental impacts to its surrounding waters. Thus an in-depth hydrodynamic understanding of the surrounding sea and coastal area of Singapore Island is crucial in seeking solution to the rising environmental issues. In the research of Singapore coastal waters (SCW) hydrodynamic numerical modelling, there are a numbers of difficulty that were constantly suggested in various studies [3,19,22,25,33], especially for the modelling surrounded Strait of Singapore. They include:(1) the effects diurnal to semi-diurnal tides that occur in between Singapore, Malay Peninsula and Indonesian Sumatra, (2) the huge amounts of small islands exist around Singapore (that causes difficulty in numerical meshes assignation), and (3) the effect from seasonal flow, such as Monsoon season. These difficulties couple with the grids resolution restrictions in various parts of SCW post a great challenge in the search of a numerically robust approach to reproduce its tidal currents.During the development of hydrodynamic models for SCW, published studies using various software packages (i.e. Delft3D, MIKE, POM and FVCOM) have been intensely conducted since last two decades. Among them, most studies were done using either two-di...

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Section: Hydrodynamic Simulation: Model Validationsupporting

confidence: 88%

Section: Introduction and Motivation Statementmentioning

confidence: 99%

Section: Introduction and Motivation Statementmentioning

confidence: 99%

See 1 more Smart Citation

Conceptual hydrodynamic-thermal mapping modelling for coral reefs at south Singapore sea

2016

Applied Ocean Research

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“…It is a multi-dimensional hydrodynamic and transport simulation program which calculates non-steady flow and transport phenomena that result from tidal and meteorological forcing on a rectilinear or a curvilinear grid. Recently a similar study has been performed in Singapore Regional Waters (SRW) and detailed sensitivity analysis done to enhance the accuracy of tidal predictions using OpenDA in combination with long track satellite altimeter data [10]. It has clearly been indicated in this study that sub regions of SRW are very sensitive to forcing boundaries, depth and bottom friction and it is understood that uncertainties in physical parameters could be the source of error in such kind of numerical modelling studies.…”

mentioning

confidence: 84%

“…2). Curvilinear boundary fitted grid allows to overcome the uncertainties of model results compared to rectilinear grids [10]. In this study varying grid resolution was considered with decreasing element sizes (i.e.…”

mentioning

confidence: 99%

Enhanced Representation of Java Sea Tidal Propagation through Sensitivity Analysis

Hasan¹,

A²

2017

JWRHE

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Abstract-Java Sea is a very complex tidal region due to its associated intrinsic geometrical characteristics which include coastline geometry, small islands and sharply varying bottom topography. Several numerical modelling studies have been conducted to evaluate the tidal characteristics of Java Sea. The present modelling study is an attempt to enhance the representation of tidal propagation of Java Sea by performing sensitivity analysis using Delft3D. Several simulations were done with Manning (0.015, 0.018 0.02, 0.03, 0.032 and 0.035) and Chezy (50, 55, 60, 65 and 70) bottom roughness coefficients in order to enhance the model performance. In general Java Sea is shallow in nature however based on sensitivity analysis with different bottom roughness coefficients, bottom roughness coefficients map was created and applied in combination with varying spatial wind to understand its effect on tidal propagation. The model performance has been verified by comparing with observed amplitudes and phases of M2, S2, K1, O1 tidal constituents at several locations on southern and northern coast of Java Sea. The root mean square error (RMSE) between observed and modelled amplitudes were 0.05, 0.04, 0.06, 0.04 calculated for M2, S2, K1 and O1 tidal constituents respectively at northern coast. Similarly, the maximum RMS error between observed and modelled phase of 34.07 was calculated for S2 tidal constituent. Most importantly, model results clearly indicated bottom roughness coefficients of 0.02, 0.03, 0.032 and 0.035 largely influencing the semi-diurnal tidal harmonic constituents compared to diurnal tidal constituents. However, the spatial varying wind had minimal effect on amplitude and phase. The present study clearly indicates that the assessment of tidal propagation in such a shallow water environment could be enhanced through detailed sensitivity analysis.

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Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Kumar

Leonardi

2022

Preprint

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As climate-driven risks for the world's coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models.Our results highlight the potential of deep learning and statistical models for coastal applications.

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Sensitivity analysis of the tidal representation in Singapore Regional Waters in a data assimilation environment

Cited by 48 publications

References 18 publications

Conceptual hydrodynamic-thermal mapping modelling for coral reefs at south Singapore sea

Conceptual hydrodynamic-thermal mapping modelling for coral reefs at south Singapore sea

Enhanced Representation of Java Sea Tidal Propagation through Sensitivity Analysis

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

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