Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon

Hjelmstad, Annika; Shrestha, Ashish; Garcia, Margaret; Mascaro, Giuseppe

doi:10.1080/02626667.2021.1980216

Cited by 15 publications

(6 citation statements)

References 67 publications

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“…Event-based rainfall data were particularly accessible during flood seasons, with temporal resolutions varying from 6 hours to 1 hour, depending on rainfall intensity. Hjelmstad et al (2021) assessed the effectiveness of three radar-derived quantitative precipitation estimates (QPEs), namely Stage IV, Multi-Radar Multi-Sensor (MRMS), and gauge-corrected MRMS (GCMRM), along with the Storm Water Management Model (SWMM) hydrologic-hydraulic model for modeling pluvial flooding incidents during the North American monsoon in Phoenix. Their study focused on a specific urban catchment spanning 2.38 km 2 , and for four distinct storm events, they conducted simulations using both the original QPEs and an ensemble of 100 QPEs that accounted for radar uncertainty by incorporating a statistical error model.…”

Section: The Global Study Reviewmentioning

confidence: 99%

A Compilation of Benchmark Pluvial Flood Datasets

Ghalandari

2023

Preprint

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urban pluvial flooding is a dangerous natural hazard that has become more common in recent years, making it a global threat to metropolitan areas. Surface water floods and flash floods are two common types of pluvial flooding that the second one is extremely hazardous and damaging due to both the power of the water and the debris that is frequently swept up in the flow. Buildings and their contents are damaged by pluvial floods, which also disrupts stormwater drainage, transportation, and electrical supply. Future flood risks are anticipated to rise because of urbanization, climate change, and global warming. Many essential features of urban flood hazards are still poorly understood due to the complex nature of the relevant processes and the paucity of long-term field observations. Therefore, in this paper we have collected different sources of data which are used for flood forecasting. Satellite data, gauge measurements, and citizen observations are the most important source of precipitation and flood data. conducting a global case study review also showed that Digital elevation model (DEM) and rainfall are the most crucial variables when it comes to floods, and they are nearly universally employed in all models. The discussion suggests that soil moisture conditions is likely to be the predominant mechanism causing the observed flood trends

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Section: The Global Study Reviewmentioning

confidence: 99%

A Compilation of Benchmark Pluvial Flood Datasets

Ghalandari

2023

Preprint

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“…Event-based rainfall data were particularly accessible Ghalandari A Compilation of Benchmark Pluvial Flood Datasets during flood seasons, with temporal resolutions varying from 6 hours to 1 hour, depending on rainfall intensity. Hjelmstad et al (2021) assessed the effectiveness of three radar-derived quantitative precipitation estimates (QPEs), namely Stage IV, Multi-Radar Multi-Sensor (MRMS), and gauge-corrected MRMS (GCMRM), along with the Storm Water Management Model (SWMM) hydrologic-hydraulic model for modeling pluvial flooding incidents during the North American monsoon in Phoenix. Their study focused on a specific urban catchment spanning 2.38 km 2 , and for four distinct storm events, they conducted simulations using both the original QPEs and an ensemble of 100 QPEs that accounted for radar uncertainty by incorporating a statistical error model.…”

Section: The Global Study Reviewmentioning

confidence: 99%

Compilation of Benchmark Pluvial Flood Datasets

Ghalandari

2023

EJDEVELOP

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Urban pluvial flooding is a dangerous natural hazard that has become more common in recent years, making it a global threat to metropolitan areas. Surface water floods and flash floods are two common types of pluvial flooding. The second one is extremely hazardous and damaging due to both the power of the water and the debris that is frequently swept up in the flow. Buildings and their contents are damaged by pluvial floods, which also disrupt stormwater drainage, transportation, and electrical supply. Future flood risks are anticipated to rise because of urbanization, climate change, and global warming. Many essential features of urban flood hazards are still poorly understood due to the complex nature of the relevant processes and the paucity of long-term field observations. Therefore, in this paper, we have collected different sources of data that are used for flood forecasting. Satellite data, gauge measurements, and citizen observations are the most important sources of precipitation and flood data. Conducting a global case study review also showed that the Digital elevation model (DEM) and rainfall are the most crucial variables when it comes to floods, and they are nearly universally employed in all models. The discussion suggests that soil moisture conditions are likely to be the predominant mechanism causing the observed flood trends.

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“…Mapping flash flood hazards in arid regions from CubeSats can lead to an improved understanding of hydrological processes as well as support for water resources decisionmaking. For instance, the performance of two-dimensional floodplain models can be evaluated through comparisons to the flood-affected areas determined from CubeSat data for different storm events [52][53][54][55], as shown here. Compared with traditional validation approaches that rely on in situ measurements, the flooding extent offered by CubeSat remote sensing can build confidence in the spatial model performance and corroborate other types of observations obtained from affected citizens, news media reports or postflood surveys [26,[56][57][58].…”

Section: Potential Applications For Post-flood Analyses In Arid Regionsmentioning

confidence: 99%

Mapping Flash Flood Hazards in Arid Regions Using CubeSats

Wang

Vivoni²

2022

Remote Sensing

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Flash flooding affects a growing number of people and causes billions of dollars in losses each year with the impact often falling disproportionally on underdeveloped regions. To inform post-flood mitigation efforts, it is crucial to determine flash flooding extents, especially for extreme events. Unfortunately, flood hazard mapping has often been limited by a lack of observations with both high spatial and temporal resolution. The CubeSat constellation operated by Planet Labs can fill this key gap in Earth observations by providing 3 m near-daily multispectral imagery at the global scale. In this study, we demonstrate the imaging capabilities of CubeSats for mapping flash flood hazards in arid regions. We selected a severe storm on 13–14 August 2021 that swept through the town of Gila Bend, Arizona, causing severe flood damages, two deaths, and the Declaration of a State of Emergency. We found the spatial extent of flooding can be mapped from CubeSat imagery through comparisons of the near-infrared surface reflectance prior to and after the flash flood event (ΔNIR). The unprecedented spatiotemporal resolution of CubeSat imagery allowed the detection of ponded (ΔNIR ≤ −0.05) and flood-affected (ΔNIR ≥ +0.02) areas that compared remarkably well with the 100-year flood event extent obtained by an independent hydraulic modeling study. Our findings demonstrate that CubeSat imagery provides valuable spatial details on flood hazards and can support post-flood activities such as damage assessments and emergency relief.

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Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon

Cited by 15 publications

References 67 publications

A Compilation of Benchmark Pluvial Flood Datasets

A Compilation of Benchmark Pluvial Flood Datasets

Compilation of Benchmark Pluvial Flood Datasets

Mapping Flash Flood Hazards in Arid Regions Using CubeSats

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