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Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, us...

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Autores principales: Xu, Huan, Wang, Ying, Fu, Xiaoran, Wang, Dong, Luan, Qinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002264/
https://www.ncbi.nlm.nih.gov/pubmed/36901653
http://dx.doi.org/10.3390/ijerph20054640
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author Xu, Huan
Wang, Ying
Fu, Xiaoran
Wang, Dong
Luan, Qinghua
author_facet Xu, Huan
Wang, Ying
Fu, Xiaoran
Wang, Dong
Luan, Qinghua
author_sort Xu, Huan
collection PubMed
description The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data.
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spelling pubmed-100022642023-03-11 Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China Xu, Huan Wang, Ying Fu, Xiaoran Wang, Dong Luan, Qinghua Int J Environ Res Public Health Article The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data. MDPI 2023-03-06 /pmc/articles/PMC10002264/ /pubmed/36901653 http://dx.doi.org/10.3390/ijerph20054640 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Huan
Wang, Ying
Fu, Xiaoran
Wang, Dong
Luan, Qinghua
Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title_full Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title_fullStr Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title_full_unstemmed Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title_short Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China
title_sort urban flood modeling and risk assessment with limited observation data: the beijing future science city of china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002264/
https://www.ncbi.nlm.nih.gov/pubmed/36901653
http://dx.doi.org/10.3390/ijerph20054640
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