Numerical analysis of flood risk change due to obstruction

Lee, Jin Woo; Yoon, Kwang Seok; Cho, Yong-Sik

doi:10.1007/s12205-012-0003-4

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…Flood modeling in two dimensions is carried out by conservation of volume for sediment flow or water flow and numerical integration of motion equations. In FLO-2D, the equations of motion include the continuity equation and the momentum equation (Kim et al, 2012;Haile et al, 2018):…”

Section: Hydraulic Modeling -Flo-2dmentioning

confidence: 99%

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Demir

KESKİN

2020

Geofizika (Online)

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Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

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Section: Hydraulic Modeling -Flo-2dmentioning

confidence: 99%

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Demir

KESKİN

2020

Geofizika (Online)

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“…The characteristics of water resources include reproducibility, finiteness, uneven spatial-temporal distribution, and social versatility [1,2]. Also, water resources and their interdependent natural environments are fragile and vulnerable [3,4]. Flooding is a problem in many parts of the world where water is relatively abundant.…”

Section: Introductionmentioning

confidence: 99%

Research on the Allocation of Flood Drainage Rights of the Sunan Canal Based on a Bi-level Multi-Objective Programming Model

Zhang

Shen

Sun

et al. 2019

Water

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To reduce flood disasters and optimize of the comprehensive benefit of the water basin, the allocation of regional flood drainage rights is of great significance. Using the “top-down” allocation mode, we consider the influence of the social, economic, and ecological environments, flood drainage demand and efficiency, and other factors on the allocation of flood drainage rights. A bi-level multi-objective programming model from the perspective of fairness and efficiency is established for the allocation. The Sunan Canal is taken as a typical case study. The model is solved by the multi-objective optimal allocation method and the master–slave hierarchical interactive iteration algorithm. After three iterations of the initial solution, the allocation of flood drainage rights in six flood control regions finally reach an effective state. The results of the model were compared with results based on historical allocation principles, showing that the bi-level multi-objective programming model, based on the principles of fairness and efficiency, is more in line with the current social and economic development of the canal. In view of the institutional background of water resources management in China and the flood drainage pressure faced by various regions, the allocation of flood drainage rights should be comprehensively considered in combination with various factors, and the market mechanism should be utilized to optimize the allocation.

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“…Both sediment and LW transport can lead to bridge clogging with subsequent channel outbursts [5]. Concomitantly, bridges may be severely damaged and flood magnitude may be increased in the floodplain [5][6][7][8][9]. If sediment deposition in the river channel and subsequent riverbed aggradation or LW jam formation potentially occur in a specific site, obstruction of bridges due to these processes has to be considered in the In contrast, Mazzorana et al [59] presented an approach that also considers the recruitment process.…”

Section: Introductionmentioning

confidence: 99%

Modelling Spatiotemporal Dynamics of Large Wood Recruitment, Transport, and Deposition at the River Reach Scale during Extreme Floods

Zischg

Galatioto

Deplazes

et al. 2018

Water

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Large wood (LW) can lead to clogging at bridges and thus cause obstruction, followed by floodplain inundation. Moreover, colliding logs can cause severe damage to bridges, defense structures, and other infrastructure elements. The factors influencing spatiotemporal LW dynamics (LWD) during extreme floods vary remarkably across river basins and flood scenarios. However, there is a lack of methods to estimate the amount of LW in rivers during extreme floods. Modelling approaches allow for a reliable assessment of LW dynamics during extreme flood events by determining LW recruitment, transport, and deposition patterns. Here, we present a method for simulating LWD on a river reach scale implemented in R (LWDsimR). We extended a previously developed LW transport model with a tree recognition model on the basis of Light Detection and Ranging (LiDAR) data for LW recruitment simulation. In addition, we coupled the LWD simulation model with the hydrodynamic simulation model Basic Simulation Environment for Computation of Environmental Flow and Natural Hazard Simulation (BASEMENT-ETH) by adapting the existing LW transport model to be used on irregular meshes. The model has been applied in the Aare River basin (Switzerland) to quantify mobilized LW volumes and the associated flow paths in a probable maximum flood scenario.

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Numerical analysis of flood risk change due to obstruction

Cited by 15 publications

References 19 publications

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Research on the Allocation of Flood Drainage Rights of the Sunan Canal Based on a Bi-level Multi-Objective Programming Model

Modelling Spatiotemporal Dynamics of Large Wood Recruitment, Transport, and Deposition at the River Reach Scale during Extreme Floods

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