2022
DOI: 10.1016/j.scitotenv.2021.151289
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Assessing the potential impact of glacial lake outburst floods on individual objects using a high-performance hydrodynamic model and open-source data

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Cited by 9 publications
(5 citation statements)
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References 62 publications
(63 reference statements)
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“…485 The results of this study showcase how object-based GLOF exposure and impact assessments can be carried out for multiple 490 glacial lakes across the data-scarce Himalayan region on a national scale, leveraging established techniques and methods. In doing so, this study relies on several key components, including remote sensing techniques for accurate glacial lake area delineation, Bayesian regression models for deriving lake water depth and peak discharge relationships (Veh & Walz, 2020), state-of-the-art flood modelling technology supported by parallelized high-performance computing (Zhao et al, 2022), and object-based GLOF exposure and impact evaluation using open-source data (Chen et al, 2022). Open data and images from 495 various sources are harnessed to generate input data for flood modelling and object-based exposure datasets, addressing the challenges posed by limited data availability and the inaccessibility of many glacial lakes in high-altitude regions.…”
Section: Sensitivity Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…485 The results of this study showcase how object-based GLOF exposure and impact assessments can be carried out for multiple 490 glacial lakes across the data-scarce Himalayan region on a national scale, leveraging established techniques and methods. In doing so, this study relies on several key components, including remote sensing techniques for accurate glacial lake area delineation, Bayesian regression models for deriving lake water depth and peak discharge relationships (Veh & Walz, 2020), state-of-the-art flood modelling technology supported by parallelized high-performance computing (Zhao et al, 2022), and object-based GLOF exposure and impact evaluation using open-source data (Chen et al, 2022). Open data and images from 495 various sources are harnessed to generate input data for flood modelling and object-based exposure datasets, addressing the challenges posed by limited data availability and the inaccessibility of many glacial lakes in high-altitude regions.…”
Section: Sensitivity Analysismentioning
confidence: 99%
“…Previous studies have typically relied on census data 85 at coarse spatial resolutions or aggregated land use data that encompass various objects like properties and infrastructure, to estimate the potential socio-economic impact of GLOFs. Benefiting from the emergence of new data technologies and the resulting enhancements in data quantity and quality, a spatially explicit assessment method has been developed to identify GLOF exposure at an object level and applied to the Tsho Rolpa Lake (Chen et al, 2022). Employing a similar strategy, essential socio-economic information is collected and processed from various sources, including OpenStreetMap, Google 90…”
mentioning
confidence: 99%
“…HiPIMS solves the above governing equations using a Godunov-type finite volume numerical scheme, making it suitable for simulating different types of shallow flow hydrodynamics, including the high-transient flash flooding processes induced by dam breaks or intense rainfall. HiPIMS is also implemented on multiple graphics processing units (GPUs) to achieve high-performance 220 computing and has been intensively tested for modelling catchment-scale overland flow and flooding processes as well as other types of flood hydrodynamics (Ming et al, 2022;Chen et al, 2022). HiPIMS is therefore suited for predicting the transient and complex flow hydrodynamics across different flow regimes in the debris flow triggering area, as required by this work.…”
Section: Hydrodynamic Modelingmentioning
confidence: 99%
“…There are also studies on the Learning from Floods (LFF) model to compare flood resilience in two environments [ 114 ]. In addition to traditional models, some scholars also predict flood risk based on high-performance [ 115 , 116 ] and large-scale flood models [ 52 ], in order to propose development strategies for urban flood resilience.…”
Section: Systematic Analysismentioning
confidence: 99%