Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Gomes, Mayara Maria de Arruda; Verçosa, Lívia Fragoso de Melo; Cirilo, José Almir

doi:10.1007/s11069-021-04817-3

Cited by 14 publications

(6 citation statements)

References 101 publications

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“…The second part of this study presented a subjective way to determine Manning's n roughness values while exploring how the changes in those values may affect the flood extent. Usually, river bed roughness is measured from a small area; the measured value is regarded as the average roughness and is assumed as spatially homogeneous, while a uniform roughness coefficient is determined per river channel and floodplain category [41,66,67,69,[88][89][90]. Few studies in flood inundation modeling, such as Dimitriadis et al [40], Sharma and Regonda [91], and Papaioannou et al, [36,42], have examined the usage of variable roughness coefficient values, but the channel roughness values were assumed spatially homogeneous.…”

Section: Discussionmentioning

confidence: 99%

“…This is probably related to the fact that the topography of the river bed and riverine area is complex because usually, the water in extreme flash flood events is affected by heavy sediment transport processes. The determination of roughness coefficient for an entire stream reach and large areas still remains a demanding process [66,69,[88][89][90], and that is the gap that this study attempted to fill [66,69,[88][89][90]. Especially at ungauged catchments, the implementation of flood inundation modeling and mapping is still a difficult but very important task [30,41,68].…”

Section: Discussionmentioning

confidence: 99%

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Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

Papaioannou

Markogianni

Λουκάς

et al. 2022

Water

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This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

Papaioannou

Markogianni

Λουκάς

et al. 2022

Water

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“…To better predict the ooding scenario, hydraulic model based on physical methods is used extensively for simulating the urban ood (Chang et al 2021). The most commonly used software, such as InfoWorks ICM (Innovyze 2019), LISFLOOD-FP (Bates and De Roo 2000) and HEC-RAS (de Arruda Gomes et al 2021), are based on the shallow water equations (SWEs) and its simpli ed form (Guidolin et al, 2016). However, solving SWEs at high spatial resolution is very complex and requires signi cant computational costs (Zhao et al 2020; Buttinger-Kreuzhuber et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring

Zheng,

Jing,

Lin

et al. 2023

Preprint

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With the impact of global climate change and urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction on flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapidly nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with the real-time monitoring data and high accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in deduction method for water level are 0.101 and 0.144, 0.124 and 0.162 respectively, while the flooding depth deduction is more stable compared to conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

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“…For extreme storms, maximum discharge was closely linked to the spatial structure of rainfall. de Arruda Gomes et al (2021) analyzed the Capibaribe River basin's hydrological response to extreme rainfalls and its impact on the city of Recife in Brazil using three lumped and distributed hydrologic and hydraulic models. A reservoir was also considered to investigate its effect on watershed hydrology.…”

Section: Introductionmentioning

confidence: 99%

An innovative approach of GSSHA model in flood analysis of large watersheds based on accuracy of DEM, size of grids, and stream density

et al. 2022

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Distributed modeling approach may have much better performance and accuracy compared with lumped-parameter hydrologic models. The main goals of this research are: investigating the possibility of combining distributed hydrological models with an one-dimensional hydraulic model and simulating waterways in large watersheds with limited hydrological and hydraulic data. Then performing sensitivity analysis on different parameters in order to identify the parameters containing the major influences on results. In the current research, an innovative approach in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, the cross-sections of all 414 waterways in the 3450 km2 Karvandar watershed, used for flow routing calculations, are uniquely extracted. Then, the effect of three essential factors are evaluated. These factors are accuracy of the digital topographic model, cell size of grid network, and density of streams, on the results of GSSHA model simulations. This watershed is located in southeastern Iran, has a dry climate with limited available hydrological data. Results showed that peak discharges obtained from the GSSHA model, developed based on a DEM with a spatial resolution of 12.5 m, are slightly (< 4%) lower than the corresponding values in the GSSHA model with a 30 m DEM resolution. This fact confirms that the use of the topographic model with a lower spatial resolution has no substantial effects on the accuracy of simulation. Also, the peak discharges increased significantly (44% to 57%) by increasing the density of waterways in the GSSHA model. Furthermore, results showed that peak discharge obtained from three models with grid cell sizes of 100, 150, and 200 m (base model), are close together. Comparing with two models of coarser grids (250 and 300 m), significant differences observed, which indicated that the grids larger than 200 m could induce substantial errors in results.

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Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Cited by 14 publications

References 101 publications

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring

An innovative approach of GSSHA model in flood analysis of large watersheds based on accuracy of DEM, size of grids, and stream density

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