A continuous simulation approach for the estimation of extreme flood inundation in coastal river reaches affected by meso- and macrotides

Sopelana, J.; Cea, Luís; Ruano, Sonia

doi:10.1007/s11069-018-3360-6

Cited by 30 publications

(40 citation statements)

References 34 publications

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“…Teng et al [20] developed and implemented a floodplain inundation model that uses highresolution DEM to derive floodplain storages and connectivity between them at different river stages. Moreover, with the support of interpolation techniques, Sopelana et al [21] proposed a continuous simulation method for the estimation of extreme inundation in coastal river reaches. It enhanced the effectiveness and practicability of space-filling method in inundation estimation.…”

Section: Space-filling Methodmentioning

confidence: 99%

Estimation Method of Inundation Extent Based on Mathematical Morphology

Lai

Safonov

2019

Mathematical Problems in Engineering

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Estimation of inundation extent is critical for the flood control system. It helps decision-maker determine the scope of damage to save residents' lives and property. In this paper, we analyzed the drawbacks of two kinds of methods of inundation estimation, hydrodynamic method and space-filling method, and divided inundation extent into water-body extent and water-invasion extent for the purpose of enhancing estimation accuracy. Based on Mathematical Morphology, an inundation estimation method suitable for flood mapping around natural and artificial lakes was put forward by integrating remote sensing (RS) data and digital elevation model (DEM) data. is method consists of three aspects, including the subdivision of water-body extent, the deduction of water-invasion extent which is viewed as the expansion of water-body extent, and the smoothing the boundaries of waterinvasion extent. With subdividing DEM data horizontally into cross sections to build equiangular subsets, the expansion operation was implemented by structuring elements which were established based on set operations and scalar flexibilities. us, water-invasion extent was easily described by the structuring elements under given water volume, so that inundation extent was conveniently estimated by spatial data. e experiment results show that the method has the advantages of high accuracy and expressiveness and low time consumption. It had been used to analyze the inundation caused by the water conservancy project named Hanjiang-to-Weihe River Water Transfer Project in China. e method served as decision support for building an efficient emergency response mechanism and helped implement flood disaster forecast and simulation.

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Section: Space-filling Methodmentioning

confidence: 99%

Estimation Method of Inundation Extent Based on Mathematical Morphology

Lai

Safonov

2019

Mathematical Problems in Engineering

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“…Because of the nature of the flood prediction problem and the assumptions involved in the physical models, they sometimes fail to make accurate predictions [10]. However, the ability of physical models to predict various hydrological events has improved through advanced simulations [11][12][13] and hybrid models [14]. Frameworks such as Hybridizing Bayesian and variational data, and a priori generation of computational grids have shown to improve the real-time estimation and forecasting [15,16].…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction and Uncertainty Estimation Using Deep Learning

Gude

Corns

Long

2020

Water

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Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems.

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“…There are also specific references for other modules, such as Numerical Schemes and Hydrological processes [18], Water Quality [19], and IberPLUS module [20]. Other publications investigate specific topics, such as the analysis of flooding scenarios in general [21], flood forecasting using coupled hydrological and hydraulic Models [22], extreme flood inundation in coastal river reaches [23], sensitivity of flood loss estimates for building representation and flow depth attribution methods in micro−scale flood modeling [24], real-time prediction of flood inundation [25], numerical distributed modeling of sedimentary and erosion processes [26], soil erosion predictions [27], bedload transport for mixed flows [28], sediment transport during the breakage of dams [29], drying and transport processes in distributed hydrological modeling [30], wood transport and sediment in rivers, flash floods, etc. [31][32][33][34][35][36], hydrodynamic movement in water bodies due to wind drag action [37], inundation modeling in coastal urban areas [38], local rainfall dynamics and uncertain boundary conditions [39,40], conservation of the cultural heritage due to flood risk [41], historical reconstruction [25], hazard mapping [42], research on the rivers confluence in order to analyze the geomorphic consequences of the backwater effect [43], assessment of the physical habitat suitability for fish [44], and many other works with a wide variety of themes.…”

Section: Introductionmentioning

confidence: 99%

Comparative Hydrodynamic Analysis by Using Two−Dimensional Models and Application to a New Bridge

Mateo-Lázaro

Castillo-Mateo

García‐Gil

et al. 2020

Water

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This document develops a methodology that evaluates the impact on the environment of the rivers produced by the creation of civil infrastructures. The methodology is based on the two-dimensional hydrodynamic calculation by using shallow water equations both in the conditions prior to the creation of the infrastructure, and in the new conditions after the infrastructure is created. Subsequently, several characteristics, such as water depth and velocity, among others, are compared between the initial and final conditions, and a two-dimensional zoning of the changes observed is obtained. The methodology herein presented is useful to verify the impact that the implantation of different infrastructures around the river currents could produce. In addition, it is also relevant for carrying out a study taking into account different infrastructure options related to river currents, as well as for selecting the most suitable one. By using the methodology presented, changes on the regime of the currents caused by the infrastructures can be deduced, including a qualitative and quantitative zoning of the changes, with a special emphasis on some characteristics, such as depth and velocity. The methodology is applied in a case study for the creation of a road bridge over the Jalon River in Spain.

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A continuous simulation approach for the estimation of extreme flood inundation in coastal river reaches affected by meso- and macrotides

Cited by 30 publications

References 34 publications

Estimation Method of Inundation Extent Based on Mathematical Morphology

Estimation Method of Inundation Extent Based on Mathematical Morphology

Flood Prediction and Uncertainty Estimation Using Deep Learning

Comparative Hydrodynamic Analysis by Using Two−Dimensional Models and Application to a New Bridge

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