Effect of inlet modelling on surface drainage in coupled urban flood simulation

Jang, Jiun Huei; Chang, Tien Hao; Chen, Wei Bo

doi:10.1016/j.jhydrol.2018.05.010

Cited by 53 publications

(28 citation statements)

References 32 publications

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“…A review of flood models conducted by Jajarmizadeh (2014), for example, identified that different users of the models and overlapping characteristics within the model itself create complexity with their classification. For this review, therefore, hydrological models are classified simply as lumped, semi-distributed, or fully distributed (Cunderlik 2003;Jajarmizadeh 2014;Buddika and Coulibaly 2020). Lumped models are relatively simple as they represent catchment characteristics as average 'lumped' values.…”

Section: Flood Source Area Identificationmentioning

confidence: 99%

“…Although conceptual models can minimise the computational power needed and establish practical UFR type approaches, the simplicity and coarse representation of catchment parameters potentially raise questions regarding the accuracy of the results and the scale of applicability for interventions. They therefore face further debate regarding the required resolution of modelling for efficient FRM (Apel et al 2009;Jajarmizadeh 2014). In recent years, the use of computer graphic processing unit (GPU) parallelisation offers faster simulation times (García-Feal et al 2018;Kalyanapu et al 2011;Prakash et al, 2020) and hence have the potential to optimise simulations that use fully distributed models, or UFR approaches.…”

Section: Adoption Of Ufr Approaches In Practicementioning

confidence: 99%

“…Both hydrological and hydraulic models are now established as crucial tools for managing excess water. Hydrological modelling has been traditionally used to model the generation of flow from a catchment under rainfall drivers, and hydraulic models have been used to simulate the resulting flow through river channels and floodplains (Syme et al 2004;Krysanova and Bronstert 2009;Jajarmizadeh 2014;Teng et al 2017). Increasingly, these two categories of models now overlap in their capabilities and many modelling packages enable the representation of both the hydrology and hydraulics of a catchment.…”

Section: Introductionmentioning

confidence: 99%

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A review of modelling methodologies for flood source area (FSA) identification

et al. 2021

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Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises.

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Section: Flood Source Area Identificationmentioning

confidence: 99%

Section: Adoption Of Ufr Approaches In Practicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review of modelling methodologies for flood source area (FSA) identification

et al. 2021

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“…4 shows the process of linking pipes/areas in close vicinity to each other. The benchmarks to determine the zone of sub-catchment area drains to each link are the two nodes connected to the link (Jang et al, 2018;Xie et al, 2017). GIS database layers are used to determine catchment area properties that intersect with drainage system elements.…”

Section: Excel2gis Tool: Extracting Input Data From Gis Layersmentioning

confidence: 99%

Modelling data of an urban drainage design using a Geographic Information System (GIS) database

Abbas

Salloom

Ruddock

et al. 2019

Journal of Hydrology

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“…Current related studies for urban catchments mainly pertain to the scale effects from land use data, drainage data or rain gauges. Meanwhile, few studies examine the topographical data, probably due to the relatively flat terrain in urban areas and the complex interference from human factors such as the drainage by sewer systems [ 9 ]. In addition, in widely used sub-catchment-based urban NPS pollution models, such as the storm water management model (SWMM) and the hydrological simulation program—FORTRAN (HSPF).…”

Section: Introductionmentioning

confidence: 99%

Scaling Effects of Elevation Data on Urban Nonpoint Source Pollution Simulations

Dai

Chen

Zhang

et al. 2019

Entropy

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The scale effects of digital elevation models (DEM) on hydrology and nonpoint source (NPS) pollution simulations have been widely reported for natural watersheds but seldom studied for urban catchments. In this study, the scale effect of DEM data on the rainfall-runoff and NPS pollution was studied in a typical urban catchment in China. Models were constructed based on the DEM data of nine different resolutions. The conventional model performance indicators and the information entropy method were applied together to evaluate the scale effects. Based on the results, scaling effects and a resolution threshold of DEM data exist for urban NPS pollution simulations. Compared with natural watersheds, the urban NPS pollution simulations were primarily affected by the local terrain due to the overall flat terrain and dense sewer inlet distribution. The overland process simulation responded more sensitively than the catchment outlet, showing prolonged times of concentration for impervious areas with decreasing DEM resolution. The diverse spatial distributions and accumulation magnitudes of pollutants could lead to different simulation responses to scaling effects. This paper provides information about the specific characteristics of the scale effects of DEM data in a typical urban catchment, and these results can be extrapolated to other similar catchments as a reference for data collection.

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Effect of inlet modelling on surface drainage in coupled urban flood simulation

Cited by 53 publications

References 32 publications

A review of modelling methodologies for flood source area (FSA) identification

A review of modelling methodologies for flood source area (FSA) identification

Modelling data of an urban drainage design using a Geographic Information System (GIS) database

Scaling Effects of Elevation Data on Urban Nonpoint Source Pollution Simulations

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