A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale.
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| Title: | A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale. |
|---|---|
| Authors: | Schubert, Jochen E.1,2 (AUTHOR), Luke, Adam2 (AUTHOR), AghaKouchak, Amir1,3 (AUTHOR), Sanders, Brett F.1,2,4 (AUTHOR) bsanders@uci.edu |
| Source: | Water Resources Research. Oct2022, Vol. 58 Issue 10, p1-26. 26p. |
| Subjects: | Flood warning systems, Flood forecasting, United States. Federal Emergency Management Agency, Floods, Storm surges, Watermarks, Hurricane Harvey, 2017, Weather forecasting, Precipitation forecasting |
| Abstract: | Urban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs. Plain Language Summary: Major cities across the U.S. and globally are experiencing severe flooding that impacts large populations of people, disrupts economies and livelihoods, and causes extensive damage. While short‐term weather forecasts are now able to predict the extreme precipitation, storm surge, and/or streamflow which create conditions conducive to urban flooding, forecasting of local flood inundation on a street‐by‐street or house‐by‐house basis is not generally available. Here, we present a new modeling system capable of making street‐level forecasts of flood inundation with lead times of hours to several days. We report the level of accuracy in terms of hydrologic skill and the ability to predict distress and damage within the built environment. We also show that the modeling system runs sufficiently fast to support timely decision‐making. This study reports information that cities across the U.S. and elsewhere can use to develop forecast systems useful for damage avoidance and public safety. Key Points: A mechanistic framework is presented for flood inundation forecasting at 3 m resolution and metropolitan scaleHurricane Harvey forecast shows sub‐meter accuracy for high water marks and executes 26 times faster than real‐timeFramework demonstrates capacity to forecast human impacts including distress and damage [ABSTRACT FROM AUTHOR] |
| Copyright of Water Resources Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 159863239 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Schubert%2C+Jochen+E%2E%22">Schubert, Jochen E.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luke%2C+Adam%22">Luke, Adam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22AghaKouchak%2C+Amir%22">AghaKouchak, Amir</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sanders%2C+Brett+F%2E%22">Sanders, Brett F.</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<i> bsanders@uci.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Resources+Research%22">Water Resources Research</searchLink>. Oct2022, Vol. 58 Issue 10, p1-26. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Flood+warning+systems%22">Flood warning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+forecasting%22">Flood forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%2E+Federal+Emergency+Management+Agency%22">United States. Federal Emergency Management Agency</searchLink><br /><searchLink fieldCode="DE" term="%22Floods%22">Floods</searchLink><br /><searchLink fieldCode="DE" term="%22Storm+surges%22">Storm surges</searchLink><br /><searchLink fieldCode="DE" term="%22Watermarks%22">Watermarks</searchLink><br /><searchLink fieldCode="DE" term="%22Hurricane+Harvey%2C+2017%22">Hurricane Harvey, 2017</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitation+forecasting%22">Precipitation forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Urban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs. Plain Language Summary: Major cities across the U.S. and globally are experiencing severe flooding that impacts large populations of people, disrupts economies and livelihoods, and causes extensive damage. While short‐term weather forecasts are now able to predict the extreme precipitation, storm surge, and/or streamflow which create conditions conducive to urban flooding, forecasting of local flood inundation on a street‐by‐street or house‐by‐house basis is not generally available. Here, we present a new modeling system capable of making street‐level forecasts of flood inundation with lead times of hours to several days. We report the level of accuracy in terms of hydrologic skill and the ability to predict distress and damage within the built environment. We also show that the modeling system runs sufficiently fast to support timely decision‐making. This study reports information that cities across the U.S. and elsewhere can use to develop forecast systems useful for damage avoidance and public safety. Key Points: A mechanistic framework is presented for flood inundation forecasting at 3 m resolution and metropolitan scaleHurricane Harvey forecast shows sub‐meter accuracy for high water marks and executes 26 times faster than real‐timeFramework demonstrates capacity to forecast human impacts including distress and damage [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Water Resources Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2021WR031279 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Flood warning systems Type: general – SubjectFull: Flood forecasting Type: general – SubjectFull: United States. Federal Emergency Management Agency Type: general – SubjectFull: Floods Type: general – SubjectFull: Storm surges Type: general – SubjectFull: Watermarks Type: general – SubjectFull: Hurricane Harvey, 2017 Type: general – SubjectFull: Weather forecasting Type: general – SubjectFull: Precipitation forecasting Type: general Titles: – TitleFull: A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Schubert, Jochen E. – PersonEntity: Name: NameFull: Luke, Adam – PersonEntity: Name: NameFull: AghaKouchak, Amir – PersonEntity: Name: NameFull: Sanders, Brett F. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00431397 Numbering: – Type: volume Value: 58 – Type: issue Value: 10 Titles: – TitleFull: Water Resources Research Type: main |
| ResultId | 1 |
