利用二维浅水方程模拟哈维飓风期间洪水的不确定性

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Donghui Xu, Gautam Bisht, Darren Engwirda, Dongyu Feng, Zeli Tan, Valeriy Y. Ivanov
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Thus, relatively coarser spatial resolutions are used for large-scale simulations of flooding, which introduce uncertainties in the results. It is unclear how the uncertainty associated with the model resolution compares to the uncertainties in precipitation data sets and assumptions regarding boundary conditions when channelized flows interact with other water bodies. In this study, we compare these three sources of uncertainties in 2D SWE simulations for the 2017 Houston flooding event. Our results show that precipitation uncertainty and mesh resolution have more significant impacts on the simulated streamflow and inundation dynamics than the choice of the downstream boundary condition at the watershed outlet. We point out the viability to confine the uncertainty of coarsening mesh resolution by using the variable resolution mesh (VRM) which refines critical topographic features with far fewer grid cells. Specifically, in simulations with VRM, the simulated inundation depths over the refined region are comparable to that use the finest uniform mesh. This study contributes to understanding the challenges and pathways for applying 2D SWE models to improve the realism of flood simulations over large scales.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"96 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Uncertainties in Simulating Flooding During Hurricane Harvey Using 2D Shallow Water Equations\",\"authors\":\"Donghui Xu, Gautam Bisht, Darren Engwirda, Dongyu Feng, Zeli Tan, Valeriy Y. Ivanov\",\"doi\":\"10.1029/2024wr038032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flooding is one of the most impactful weather-related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first-principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. 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position: relative;\\\" tabindex=\\\"0\\\"><mjx-math aria-hidden=\\\"true\\\" location=\\\"graphic/wrcr27642-math-0002.png\\\"><mjx-semantics><mjx-mrow data-semantic-children=\\\"2,6\\\" data-semantic-content=\\\"3\\\" data-semantic- data-semantic-role=\\\"subtraction\\\" data-semantic-speech=\\\"10 Superscript 0 Baseline minus 10 Superscript 1\\\" data-semantic-type=\\\"infixop\\\"><mjx-msup data-semantic-children=\\\"0,1\\\" data-semantic- data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"superscript\\\"><mjx-mn data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"2\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn><mjx-script style=\\\"vertical-align: 0.393em;\\\"><mjx-mn data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"2\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\" size=\\\"s\\\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup><mjx-mo data-semantic- data-semantic-operator=\\\"infixop,−\\\" data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"subtraction\\\" data-semantic-type=\\\"operator\\\" rspace=\\\"4\\\" space=\\\"4\\\"><mjx-c></mjx-c></mjx-mo><mjx-msup data-semantic-children=\\\"4,5\\\" data-semantic- data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"superscript\\\"><mjx-mn data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"6\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn><mjx-script style=\\\"vertical-align: 0.393em;\\\"><mjx-mn data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"6\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\" size=\\\"s\\\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\\\"inline\\\" unselectable=\\\"on\\\"><math altimg=\\\"urn:x-wiley:00431397:media:wrcr27642:wrcr27642-math-0002\\\" display=\\\"inline\\\" location=\\\"graphic/wrcr27642-math-0002.png\\\" xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><semantics><mrow data-semantic-=\\\"\\\" data-semantic-children=\\\"2,6\\\" data-semantic-content=\\\"3\\\" data-semantic-role=\\\"subtraction\\\" data-semantic-speech=\\\"10 Superscript 0 Baseline minus 10 Superscript 1\\\" data-semantic-type=\\\"infixop\\\"><msup data-semantic-=\\\"\\\" data-semantic-children=\\\"0,1\\\" data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"superscript\\\"><mn data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"2\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\">10</mn><mn data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"2\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\">0</mn></msup><mo data-semantic-=\\\"\\\" data-semantic-operator=\\\"infixop,−\\\" data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"subtraction\\\" data-semantic-type=\\\"operator\\\">−</mo><msup data-semantic-=\\\"\\\" data-semantic-children=\\\"4,5\\\" data-semantic-parent=\\\"7\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"superscript\\\"><mn data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"6\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\">10</mn><mn data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"6\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\">1</mn></msup></mrow>${10}^{0}-{10}^{1}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>) m) is needed in 2D SWE simulations to capture flood dynamics accurately, resulting in formidable computational challenges. 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引用次数: 0

摘要

洪水是影响最大的与天气有关的自然灾害之一。求解二维(2D)浅水方程(SWE)的数值模型代表了模拟所有类型的空间洪水的第一性原理方法,例如雨洪、河流和海岸洪水及其复合动力学。二维SWE模拟需要高空间分辨率(例如O$\mathcal{O}$(100−101${10}^{0}-{10}^{1}$)m)才能准确捕获洪水动态,这导致了巨大的计算挑战。因此,相对较粗的空间分辨率用于大规模洪水模拟,这在结果中引入了不确定性。目前尚不清楚与模式分辨率相关的不确定性与降水数据集的不确定性以及渠化水流与其他水体相互作用时关于边界条件的假设的不确定性相比如何。在本研究中,我们比较了2017年休斯顿洪水事件二维SWE模拟中的这三种不确定性来源。结果表明,降水不确定性和网格分辨率对模拟径流和淹没动态的影响比流域出口下游边界条件的选择更显著。我们指出了利用可变分辨率网格(VRM)来限制粗化网格分辨率的不确定性的可行性,该网格可以用更少的网格单元来细化关键地形特征。具体而言,在VRM模拟中,精细化区域的模拟淹没深度与使用最细均匀网格的模拟淹没深度相当。该研究有助于理解应用二维SWE模型提高大尺度洪水模拟真实感的挑战和途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertainties in Simulating Flooding During Hurricane Harvey Using 2D Shallow Water Equations
Flooding is one of the most impactful weather-related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first-principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. High spatial resolution (e.g., O$\mathcal{O}$ (100101${10}^{0}-{10}^{1}$) m) is needed in 2D SWE simulations to capture flood dynamics accurately, resulting in formidable computational challenges. Thus, relatively coarser spatial resolutions are used for large-scale simulations of flooding, which introduce uncertainties in the results. It is unclear how the uncertainty associated with the model resolution compares to the uncertainties in precipitation data sets and assumptions regarding boundary conditions when channelized flows interact with other water bodies. In this study, we compare these three sources of uncertainties in 2D SWE simulations for the 2017 Houston flooding event. Our results show that precipitation uncertainty and mesh resolution have more significant impacts on the simulated streamflow and inundation dynamics than the choice of the downstream boundary condition at the watershed outlet. We point out the viability to confine the uncertainty of coarsening mesh resolution by using the variable resolution mesh (VRM) which refines critical topographic features with far fewer grid cells. Specifically, in simulations with VRM, the simulated inundation depths over the refined region are comparable to that use the finest uniform mesh. This study contributes to understanding the challenges and pathways for applying 2D SWE models to improve the realism of flood simulations over large scales.
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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