A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS)

Xia, Xilin; Liang, Qiuhua; Ming, Xiaodong

doi:10.1016/j.advwatres.2019.103392

Cited by 108 publications

(92 citation statements)

References 35 publications

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“…Hence, another key issue is that collaborative flood modeling can achieve what is critical for making any type of science useful: iterative interaction between researchers (modelers) and end-users (DeLorme et al, 2016;Dilling & Lemos, 2011). Finally, we note that fine-resolution urban flood modeling is rapidly advancing and now supports realistic, street-level flood visualizations (Bhola et al, 2019;Fewtrell et al, 2011;Kobayashi et al, 2015;Sanders, 2017;Xia et al, 2019).…”

Section: Introductionmentioning

confidence: 92%

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

et al. 2020

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Existing needs to manage flood risk in the United States are underserved by available flood hazard information. This contributes to an alarming escalation of flood impacts amounting to hundreds of billions of dollars per year and countless disrupted lives and affected communities. Making information about flood hazards useful for the range of decisions that dictate the consequences of flooding poses many challenges. Here, we describe collaborative flood modeling, whereby researchers and end‐users at two coastal sites co‐develop fine‐resolution flood hazard models and maps responsive to decision‐making needs. We find, first of all, that resident perception and awareness of flooding are enhanced more by fine‐resolution depth contour maps than Federal Emergency Management Agency (FEMA) flood hazard classification maps and that viewing fine‐resolution depth contour maps helps to minimize differences in flood perception across subgroups within the community, generating a shared understanding. We also find that collaborative flood modeling supports the engagement of a wide range of end‐users in contemplating the risks of flooding and provides strong evidence that the co‐produced knowledge can be readily adopted and applied for Flood Risk Management (FRM). Overall, collaborative flood modeling advances FRM by providing multiple points of entry for diverse groups of end‐users to contemplate the spatial extent, intensity, timing, chance, and consequences of flooding, thus enabling the web of decision‐making related to flooding to be better informed with the best available science. This transdisciplinary approach emphasizes vulnerability reduction and is complementary to FEMA Flood Insurance Rate Maps used for flood insurance administration.

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Section: Introductionmentioning

confidence: 92%

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

et al. 2020

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“…The generated bathymetries can supplement the existing data sets which are limited by the spatial-temporal coverage of satellite altimetry. Moreover, the spatially explicit model of reservoir bathymetry promotes various studies across different regions including such as hydrodynamic process modeling (Xia et al, 2019), sedimentation rate estimation (Minear & Kondolf, 2009), water quality modeling (Lindim et al, 2011), and aquatic ecosystem vegetation assessment (Zhang et al, 2015).…”

Section: 1029/2020wr027147mentioning

confidence: 99%

Remote Sensing‐Based Modeling of the Bathymetry and Water Storage for Channel‐Type Reservoirs Worldwide

Liu

Song

Wang

et al. 2020

Water Resources Research

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Estimations of reservoir bathymetry and storage are of great significance due to their substantial impacts on hydrological processes and water resource management. However, existing approaches for reservoir bathymetry construction often rely on field measurements, which restricts their application at regional and global scales. This study proposes a novel Approach for Determining the BAthymetry and water storage of channel-type Reservoirs, hereafter referred to as ADBAR, for which only open-access digital elevation model (DEM) and satellite images are required. The basic idea of ADBAR is to utilize the geomorphological similarity and topographical continuity of the reservoir inundation area with its lateral valleys and upstream/downstream regions to predict underwater bathymetry. Forty-eight reservoirs with different topographic and geometric characteristics were selected for method validation. The selected reservoirs were all impounded after the year 2000, so the modeled reservoir bathymetry can be validated by the "reference" reservoir storage calculated using the exposed topography in SRTM DEM and the mapped water extents from spectral images. The difference between the estimated and reference storages is about 13% on average. Furthermore, the modeled results in two selected basins with dense reservoir distributions, the Upper Yellow River Basin in China and the Tocantins River Basin in Brazil, are comparable with the documented effective storage capacities. The validations for both individual reservoirs and the two large basins demonstrate that ADBAR is a robust tool for estimating reservoir bathymetries and storage capacities and thus facilitates the modeling of reservoir impacts on water budgets at large and global scales.

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“…However, in the case of models using shallow water equations (SWEs), such as FLO-2D, proper flood simulation is not possible because drainage is not considered [40]. In addition, there is a research trend where the flooding accuracy is low when the flooding simulation is performed using low-resolution data and is not suitable for a large-scale flooding simulation [41,42].…”

Section: The Background and Review Of Model Selectionmentioning

confidence: 99%

Impact Assessment of Urban Flood on Traffic Disruption using Rainfall–Depth–Vehicle Speed Relationship

Choo

Kang

Kim

2020

Water

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The transportation network enables movement of people and goods and is the basis of economic activity. Recently, short-term locally heavy rains occur frequently in urban areas, causing serious obstacles to road flooding and increasing economic and social effects. Therefore, in advanced weather countries, many studies have been conducted on realistic and reliable impact forecasting by analyzing socioeconomic impacts, not just information transmission as weather forecasts. In this paper, we use the Spatial Runoff Assessment Tool (S-RAT) and Flood Inundation model (FLO-2D model) to calculate the flooding level in urban areas caused by rainfall and use the flooding rate. In addition, the rainfall–flood depth curve and the Flood–Vehicle Speed curve were presented during the analysis, and the traffic disruption map was prepared using this. The results of this study were compared with previous studies and verified by rainfall events in 2011. As a result of the verification, the result was similar to the actual flooding, and when the same rainfall occurred within the range of the target area, it was confirmed that there were sections that could not be passed and sections that could be passed smoothly. Therefore, the results suggested in this study will be helpful for the driver’s route selection by using the urban flood damage analysis and vehicle driving speed analysis.

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A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS)

Cited by 108 publications

References 35 publications

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

Remote Sensing‐Based Modeling of the Bathymetry and Water Storage for Channel‐Type Reservoirs Worldwide

Impact Assessment of Urban Flood on Traffic Disruption using Rainfall–Depth–Vehicle Speed Relationship

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