Strengthened tropical cyclones and higher flood risk under compound effect of climate change and urbanization across China's Greater Bay Area

Deng, Zifeng; Wang, Zhaoli; Wu, Xushu; Lai, Chengguang; Zeng, Zhaoyang

doi:10.1016/j.uclim.2022.101224

Cited by 33 publications

(12 citation statements)

References 63 publications

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“…and the Time of Concentration (TC) [17]. Watershed Models (WMS) are effective hydrologic models that comprehensively simulate hydrologic processes.…”

Section: Understanding Urban Flooding: Causes Models and Predictive T...mentioning

confidence: 99%

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Hawamdeh,

Tarawneh,

Sharrab

et al. 2024

Preprint

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Natural disasters can be devastating to the environment and natural resources. Flood inundation mapping and hydraulic modeling are essential to forecast critical flood information, including flood depth and water surface height. In this research, several factors that influence floods were studied. These factors include the intensity of the rainstorm, the depth of precipitation, soil types, geologic settings, and topographic features. Furthermore, the research carried out hydraulic modeling of storm flows for 50- and 100-Year return periods and estimated that the water depth in Wadi Al Wala could reach 15m at 50 years of storm and 25m at 100 return years of storms. A DNN model is developed with good accuracy to predict flood flow based on historical records from 1980 to 2018 meteorological data. The goal of this research is to improve flood prediction, and risk assessment with the use of DNN integrated with hydrological and hydraulic models.

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“…and the Time of Concentration (TC) [17]. Watershed Models (WMS) are effective hydrologic models that comprehensively simulate hydrologic processes.…”

Section: Understanding Urban Flooding: Causes Models and Predictive T...mentioning

confidence: 99%

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Hawamdeh,

Tarawneh,

Sharrab

et al. 2024

Preprint

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“…Zhang et al, 2018). Numerical modeling also indicated that urban development leads to higher flood risk related to TC in the SC area (Deng et al, 2022).…”

mentioning

confidence: 93%

“…Moreover, numerical simulations of TCs Harvey and Lekima over Southeast US and East China demonstrate that urbanization can substantially increase precipitation in urban areas, which can be attributed to the strongly enhanced local moisture flux convergence over the city (Ao et al., 2022; W. Zhang et al., 2018). Numerical modeling also indicated that urban development leads to higher flood risk related to TC in the SC area (Deng et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

Hu,

Tam,

Loi

et al. 2023

JGR Atmospheres

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How urbanization affects tropical cyclone (TC) rainfall is inferred from station observations over the Great Bay Area (GBA, located on the South China coast) and numerical model experiments. Observations from 41 TCs indicate that surface wind is noticeably weaker in urban compared to rural stations during TC passages, while the urban heat island effect is considerably suppressed. Extreme (99th percentile of) hourly rainfall for these TC events during 2008–2017 is more intense over urban compared to rural stations. For eight selected TC cases, dynamical downscaling was carried out using the convection‐resolving Weather Research and Forecasting model, each with three parallel experiments: “Nourban” in which the urban area was replaced by cropland; “AH0” (“AH300”) in which the diurnal maximum anthropogenic heat (AH) was set to 0 (300 W/m2) in city locations. Both AH0 and AH300 show a significant increase in urban hourly rainfall intensity and probability in all ranges (most obvious for heavy rainfall >40 mm/hr), over the GBA mega‐city. Further diagnosis indicates increased moisture flux convergence in urban areas over a 2‐day period during the landfall, contributes to increased rainfall, likely induced by the surface roughness. The influence of AH, however, is found to be insignificant. The increase in accumulated rainfall due to urbanization is proportional to the storm residence time over the city, implying greater rainfall exacerbation for slower or larger TCs. Overall, urbanization intensifies extreme TC rainfall over the coastal GBA mega‐city mainly due to surface frictional convergence, with stronger intensification for storms residing longer over the city.

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“…Floods still remained the most prevalent and severe disaster worldwide and occupied the dominated composition in a total of 432 disaster events in 2021. Due to rapid urbanization and concentrated population and assets (Deng, Wang, Wu, & Lai, 2022;Dong et al, 2022), urban areas became more susceptive and vulnerable to flood events (P. . It was predicted that climate change and heavy rainfall were more frequent and intense with high reliability (Rifat & Liu, 2022;Steinhausen et al, 2021), and significantly increased urban flood risk, especially in developing countries (Zhu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Unbalanced relationship between flood risk perception and flood preparedness from the perspective of response intention and socio-economic factors: a case study of Nanjing, China

Li,

Wang

2024

Preprint

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Abstract. Although risk perception and flood preparedness were crucial in flood management, perceived flood risk was not always translated into flood preparedness. It was essential to investigate the potential association between risk perception and flood preparedness. This study focused on Nanjing as the research region, designed questionnaire survey and explored the influence relationship between risk perception and flood preparedness. Participants showed the medium perception of food risk and higher flood preparedness. Higher risk perception was observed in individuals with regular exercising, the elderly, low education level and long living time. Higher flood preparedness occurred among groups of females, the elderly and high education level. Individuals relied more on threat appraisal to perceive risk, which failed to trigger high enough coping appraisal. Inadequate risk perception led to a strenuous transform into flood preparedness with unbalanced relationship. Groups with distinct socio-economic characteristics exhibited varying preferences to achieve risk perception and flood preparedness. Females relied more on flood knowledge to perceive flood risk. Path analysis suggested that threat appraisal transformed into flood preparedness under the effect of response intention and social-economic features. Groups with high education level or bad health were more likely to perceive risk and engage in preventive behavior. These findings could provide critical insights into intervention strategies for enhancing public flood preparedness in flood management.

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Strengthened tropical cyclones and higher flood risk under compound effect of climate change and urbanization across China's Greater Bay Area

Cited by 33 publications

References 63 publications

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

Unbalanced relationship between flood risk perception and flood preparedness from the perspective of response intention and socio-economic factors: a case study of Nanjing, China

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