Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis
Abstract Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change‐induced uncertainties. This study focuses on creating a state‐wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysi...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.13033 |
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| author | D. Bazzett Lucas Marxen R. Wang |
| author_facet | D. Bazzett Lucas Marxen R. Wang |
| author_sort | D. Bazzett |
| collection | DOAJ |
| description | Abstract Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change‐induced uncertainties. This study focuses on creating a state‐wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysis and the assessment of climate change, using New Jersey as a case study. The Height Above Nearest Drainage (HAND) framework was employed for large‐scale flood mapping. The model was validated against high water marks (HWMs) collected after Hurricane Irene. Based on the National Water Model (NWM), synthetic rating curves in HAND were calibrated by tuning Manning's roughness, aligning the predicted and observed flood depths. The roughness values were generalized across the state from the validated water basins to the ungauged ones, using a multivariate regression with the hydrologic and geographic information. To map the future climate‐change‐induced flooding, a correlation between NOAA historical precipitation totals and NWM flow data from 2010 to 2020 was established to link precipitation and runoff. This study also invented a novel method for correcting catchment discontinuities, inherent in the HAND model, based on a computer vision scheme, the Sobel filter. The modeling results show that average and worst‐case storm events have the potential to increase 10%–50% in the state, where mountain areas and major river banks would be exposed to this impact more significantly. |
| format | Article |
| id | doaj-art-4c1007567a864e61a556b42e6f30cc6a |
| institution | Kabale University |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-4c1007567a864e61a556b42e6f30cc6a2025-08-20T03:44:06ZengWileyJournal of Flood Risk Management1753-318X2025-03-01181n/an/a10.1111/jfr3.13033Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysisD. Bazzett0Lucas Marxen1R. Wang2Department of Civil and Environmental Engineering Rutgers, The State University of New Jersey New Brunswick New Jersey USASEBS/NJAES Office of Research Analytics, Rutgers The State University of New Jersey New Brunswick New Jersey USADepartment of Civil and Environmental Engineering Rutgers, The State University of New Jersey New Brunswick New Jersey USAAbstract Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change‐induced uncertainties. This study focuses on creating a state‐wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysis and the assessment of climate change, using New Jersey as a case study. The Height Above Nearest Drainage (HAND) framework was employed for large‐scale flood mapping. The model was validated against high water marks (HWMs) collected after Hurricane Irene. Based on the National Water Model (NWM), synthetic rating curves in HAND were calibrated by tuning Manning's roughness, aligning the predicted and observed flood depths. The roughness values were generalized across the state from the validated water basins to the ungauged ones, using a multivariate regression with the hydrologic and geographic information. To map the future climate‐change‐induced flooding, a correlation between NOAA historical precipitation totals and NWM flow data from 2010 to 2020 was established to link precipitation and runoff. This study also invented a novel method for correcting catchment discontinuities, inherent in the HAND model, based on a computer vision scheme, the Sobel filter. The modeling results show that average and worst‐case storm events have the potential to increase 10%–50% in the state, where mountain areas and major river banks would be exposed to this impact more significantly.https://doi.org/10.1111/jfr3.13033climate changemodelingrainfall‐runoffrisk mapping |
| spellingShingle | D. Bazzett Lucas Marxen R. Wang Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis Journal of Flood Risk Management climate change modeling rainfall‐runoff risk mapping |
| title | Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis |
| title_full | Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis |
| title_fullStr | Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis |
| title_full_unstemmed | Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis |
| title_short | Advancing regional flood mapping in a changing climate: A HAND‐based approach for New Jersey with innovations in catchment analysis |
| title_sort | advancing regional flood mapping in a changing climate a hand based approach for new jersey with innovations in catchment analysis |
| topic | climate change modeling rainfall‐runoff risk mapping |
| url | https://doi.org/10.1111/jfr3.13033 |
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