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Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States
Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequ...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110431/ https://www.ncbi.nlm.nih.gov/pubmed/35577792 http://dx.doi.org/10.1038/s41597-022-01304-7 |
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author | Takhellambam, Bijoychandra S. Srivastava, Puneet Lamba, Jasmeet McGehee, Ryan P. Kumar, Hemendra Tian, Di |
author_facet | Takhellambam, Bijoychandra S. Srivastava, Puneet Lamba, Jasmeet McGehee, Ryan P. Kumar, Hemendra Tian, Di |
author_sort | Takhellambam, Bijoychandra S. |
collection | PubMed |
description | Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequate to quantify projected changes in hydrologic characteristics of a watershed. Therefore, it is critical to temporally disaggregate coarse-resolution precipitation data to finer resolutions for studies sensitive to precipitation characteristics. In this study, we generated novel 15-minute precipitation datasets from hourly precipitation datasets obtained from five NA-CORDEX downscaled climate models under RCP 8.5 scenario for the historical (1970–1999) and projected (2030–2059) years over the Southeast United States using a modified version of the stochastic method. The results showed conservation of mass of the precipitation inputs. Furthermore, the probability of zero precipitation, variance of precipitation, and maximum precipitation in the disaggregated data matched well with the observed precipitation characteristics. The generated 15-minute precipitation data can be used in all scientific studies that require precipitation data at that resolution. |
format | Online Article Text |
id | pubmed-9110431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91104312022-05-18 Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States Takhellambam, Bijoychandra S. Srivastava, Puneet Lamba, Jasmeet McGehee, Ryan P. Kumar, Hemendra Tian, Di Sci Data Data Descriptor Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequate to quantify projected changes in hydrologic characteristics of a watershed. Therefore, it is critical to temporally disaggregate coarse-resolution precipitation data to finer resolutions for studies sensitive to precipitation characteristics. In this study, we generated novel 15-minute precipitation datasets from hourly precipitation datasets obtained from five NA-CORDEX downscaled climate models under RCP 8.5 scenario for the historical (1970–1999) and projected (2030–2059) years over the Southeast United States using a modified version of the stochastic method. The results showed conservation of mass of the precipitation inputs. Furthermore, the probability of zero precipitation, variance of precipitation, and maximum precipitation in the disaggregated data matched well with the observed precipitation characteristics. The generated 15-minute precipitation data can be used in all scientific studies that require precipitation data at that resolution. Nature Publishing Group UK 2022-05-16 /pmc/articles/PMC9110431/ /pubmed/35577792 http://dx.doi.org/10.1038/s41597-022-01304-7 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Takhellambam, Bijoychandra S. Srivastava, Puneet Lamba, Jasmeet McGehee, Ryan P. Kumar, Hemendra Tian, Di Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title | Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title_full | Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title_fullStr | Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title_full_unstemmed | Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title_short | Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States |
title_sort | temporal disaggregation of hourly precipitation under changing climate over the southeast united states |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110431/ https://www.ncbi.nlm.nih.gov/pubmed/35577792 http://dx.doi.org/10.1038/s41597-022-01304-7 |
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