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Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States

The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To addre...

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Detalles Bibliográficos
Autores principales: Yan, Hongxiang, Duan, Zhuoran, Wigmosta, Mark S., Sun, Ning, Gutmann, Ethan D., Kruyt, Bert, Arnold, Jeffrey R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695926/
https://www.ncbi.nlm.nih.gov/pubmed/38049456
http://dx.doi.org/10.1038/s41597-023-02680-4
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author Yan, Hongxiang
Duan, Zhuoran
Wigmosta, Mark S.
Sun, Ning
Gutmann, Ethan D.
Kruyt, Bert
Arnold, Jeffrey R.
author_facet Yan, Hongxiang
Duan, Zhuoran
Wigmosta, Mark S.
Sun, Ning
Gutmann, Ethan D.
Kruyt, Bert
Arnold, Jeffrey R.
author_sort Yan, Hongxiang
collection PubMed
description The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply.
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spelling pubmed-106959262023-12-06 Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States Yan, Hongxiang Duan, Zhuoran Wigmosta, Mark S. Sun, Ning Gutmann, Ethan D. Kruyt, Bert Arnold, Jeffrey R. Sci Data Data Descriptor The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply. Nature Publishing Group UK 2023-12-04 /pmc/articles/PMC10695926/ /pubmed/38049456 http://dx.doi.org/10.1038/s41597-023-02680-4 Text en © Battelle Memorial Institute, NCAR, MITRE Corp. 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Yan, Hongxiang
Duan, Zhuoran
Wigmosta, Mark S.
Sun, Ning
Gutmann, Ethan D.
Kruyt, Bert
Arnold, Jeffrey R.
Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title_full Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title_fullStr Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title_full_unstemmed Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title_short Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States
title_sort next-generation intensity-duration-frequency curves for diverse land across the continental united states
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695926/
https://www.ncbi.nlm.nih.gov/pubmed/38049456
http://dx.doi.org/10.1038/s41597-023-02680-4
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