Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston

Zhang, Wei; Villarini, Gabriele; Vecchi, Gabriel A.; Smith, James A.

doi:10.1038/s41586-018-0676-z

Cited by 474 publications

(299 citation statements)

References 40 publications

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“…Human activities, such as land‐cover changes and urbanization, can intensify the impact of coastal flooding on infrastructure (Bilskie et al, ). W. Zhang et al () found that urbanization exacerbated not only the flood response but also the hurricane total rainfall. These studies should make us alert for the intensified flooding on the highly urbanized and heavily populated coastal areas.…”

Section: Introductionmentioning

confidence: 99%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

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This study develops an effective and robust method to mine bitemporal and dual-polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network-based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics-aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel-1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land-cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km 2 are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost-sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane-induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications. Plain Language SummaryCoastal flooding caused by hurricanes has a huge impact on the safety and properties of people in coastal areas. Coastal flooding mapping using synthetic aperture radar (SAR) data is a low-cost and wide-coverage means; moreover, SAR has day-and-night, all-weather observation abilities. High-accuracy and robust coastal flooding mapping from SAR imagery information will help the management to formulate more targeted responses and the researchers to better understand coastal flooding mechanisms and develop more precise forecasting models. Based on deep convolutional neural networks, a specially tailored SAR coastal flooding mapping network method is developed to mine bitemporal and dual-polarization SAR imagery information for coastal flooding mapping. The performance of the proposed SAR coastal flooding mapping network is demonstrated by mapping the coastal flooding near Houston, Texas, USA, after 2017 Hurricane Harvey. The study verifies that the mapping accuracy and robustness of the proposed method are better than those of the classic and widely applied deep convolutional neural network method and also shows the mapping products contribute to a better understanding of the geospatial and temporal characteristics of coastal flooding.

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Section: Introductionmentioning

confidence: 99%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

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“…Contrasting urban impacts on two back‐to‐back storm episodes with distinct storm evolution properties highlight the necessity of flow regime analysis in better understanding the effects of land use/land cover changes on severe weather systems. The pronounced impact of urban‐induced rainfall anomalies on flood response further highlights the critical role of urbanization in shaping regional flood hydrology through land‐atmosphere interactions (similarly see Zhang et al, , for the impact of Houston on extreme rainfall and flooding associated with Hurricane Harvey 2017). Even though our results are based on Phoenix metropolitan region, they can be further extended to the existing worldwide urban centers close to complex terrain, for instance, Tucson, Los Angeles, the urban corridor of northeastern United States, Beijing‐Tianjin‐Hebei metropolitan region, China.…”

Section: Discussionmentioning

confidence: 91%

“…Observational and modeling studies have examined the impacts of urban heat island, urban canopy, and urban aerosols on anomalous rainfall both over and around cities (e.g., Bornstein & Lin, ; Chen et al, ; Dixon & Mote, ; Jin et al, ; Miao et al, ; Niyogi et al, ; Shepherd, ; Yang et al, ; Zhang et al, ). Our understandings of hydrometeorological impacts due to urbanization, however, are far from complete, especially for severe storm events under strong synoptic forcings, including extratropical systems (with large‐scale moisture transport; Debbage & Shepherd, ), monsoon systems (Paul et al, ; Singh et al, ), landfalling tropical cyclones (Zhang et al, ), and supercell storms (Reames & Stensrud, ). Extreme rainfall associated with these storm events and the resultant flooding has plagued city dwellers and sustainable urban development during recent decades.…”

Section: Introductionmentioning

confidence: 99%

Urban Impacts on Extreme Monsoon Rainfall and Flooding in Complex Terrain

Yang

Smith

Niyogi

2019

Geophysical Research Letters

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Hydrometeorological impacts due to urbanization for cities close to complex terrain are poorly understood due to the complexities of terrain‐related circulation and urban perturbations of atmospheric flow. In this study, we examine urban impacts on extreme monsoon rainfall and the resultant flooding over central Arizona based on high‐resolution atmospheric and hydrological model simulations. Strong positive rainfall anomalies at the urban‐rural interface downwind of the city are mainly related to dynamic effects (increased surface roughness) on convective outflow boundaries. Urban‐related thermodynamic disturbances slightly increase rain rates over the downtown core of Phoenix. Contrasting rainfall anomalies for two consecutive storm episodes highlight the importance of flow regime analysis in understanding urban impacts on extreme rainfall in complex terrain. Urban‐induced rainfall anomalies result in amplification of flood peak magnitudes by as much as a factor of 2 for Phoenix watersheds. Our results highlight the urban impacts on regional flood hydrology through land‐atmosphere interactions.

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“…Therefore, we present two case studies where we show the timing of SWOT passes with respect to flood hydrographs and display relevant SWOT swaths over the study areas, illustrating how spatial coverage may be during real flood events. The two case studies are the 2008 flood of Eastern Iowa, USA (Chen et al, ; Smith et al, ), and the 2017 flood of the Houston metropolitan area, USA, caused by Hurricane Harvey (Risser & Wehner, ; Zhang et al, ).…”

Section: Resultsmentioning

confidence: 99%

Will the Surface Water and Ocean Topography (SWOT) Satellite Mission Observe Floods?

Frasson

Schumann

Kettner

et al. 2019

Geophysical Research Letters

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The Surface Water and Ocean Topography (SWOT) mission will measure water surface elevations and inundation extents of rivers of the world but with limited temporal sampling. By comparing flood location and duration of 4,664 past flood events recorded by the Dartmouth Flood Observatory to SWOT's orbit ephemeris, we estimate that SWOT would have seen 55% of these, with higher probabilities associated with more extreme events and with those that displaced more than 10,000 people. However, SWOT measurements will exhibit uneven temporal sampling and may require a combination of data obtained at different times to accurately characterize large events. This is illustrated using recent flooding in the United States, in eastern Iowa and in Houston and surrounding areas from Hurricane Harvey. SWOT data have significant potential to improve flood forecasting models by offering data needed to enhance flow routing modeling, provided that users can overcome the potential hurdles associated with its temporal and spatial sampling characteristics.

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Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston

Cited by 474 publications

References 40 publications

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Urban Impacts on Extreme Monsoon Rainfall and Flooding in Complex Terrain

Will the Surface Water and Ocean Topography (SWOT) Satellite Mission Observe Floods?

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