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A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets
Runoff modelling is a crucial element in hydrologic sciences. However, a global runoff database is not currently available at a resolution higher than 0.1°. We use the recently developed Global Curve Number dataset (GCN250) to develop a dynamic runoff application (2015 – present) and that can be acc...
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/PMC9669002/ https://www.ncbi.nlm.nih.gov/pubmed/36385044 http://dx.doi.org/10.1038/s41597-022-01834-0 |
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author | Sujud, Lara H. Jaafar, Hadi H. |
author_facet | Sujud, Lara H. Jaafar, Hadi H. |
author_sort | Sujud, Lara H. |
collection | PubMed |
description | Runoff modelling is a crucial element in hydrologic sciences. However, a global runoff database is not currently available at a resolution higher than 0.1°. We use the recently developed Global Curve Number dataset (GCN250) to develop a dynamic runoff application (2015 – present) and that can be accessed via a Google Earth Engine application. We also provide a global mean monthly runoff dataset for April 2015-2021 in GeoTIFF format at a 250-meter resolution. We utilize soil moisture and GPM rainfall to dynamically retrieve the appropriate curve number and generate the corresponding runoff anywhere on Earth. Mean annual global runoff ratio results for 2021 were comparable to the runoff ratio from the Global Land Data Assimilation System (0.079 vs. 0.077, respectively). Mean annual global runoff from GCN and GLDAS were within 11% each other for 2020–2021 (0.18 vs. 0.16 mm/day, respectively). The GCN250 runoff application and the dataset are useful for many water applications such hydrologic design, land management, water resources management, and flood risk assessment. |
format | Online Article Text |
id | pubmed-9669002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96690022022-11-18 A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets Sujud, Lara H. Jaafar, Hadi H. Sci Data Data Descriptor Runoff modelling is a crucial element in hydrologic sciences. However, a global runoff database is not currently available at a resolution higher than 0.1°. We use the recently developed Global Curve Number dataset (GCN250) to develop a dynamic runoff application (2015 – present) and that can be accessed via a Google Earth Engine application. We also provide a global mean monthly runoff dataset for April 2015-2021 in GeoTIFF format at a 250-meter resolution. We utilize soil moisture and GPM rainfall to dynamically retrieve the appropriate curve number and generate the corresponding runoff anywhere on Earth. Mean annual global runoff ratio results for 2021 were comparable to the runoff ratio from the Global Land Data Assimilation System (0.079 vs. 0.077, respectively). Mean annual global runoff from GCN and GLDAS were within 11% each other for 2020–2021 (0.18 vs. 0.16 mm/day, respectively). The GCN250 runoff application and the dataset are useful for many water applications such hydrologic design, land management, water resources management, and flood risk assessment. Nature Publishing Group UK 2022-11-16 /pmc/articles/PMC9669002/ /pubmed/36385044 http://dx.doi.org/10.1038/s41597-022-01834-0 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 Sujud, Lara H. Jaafar, Hadi H. A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title | A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title_full | A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title_fullStr | A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title_full_unstemmed | A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title_short | A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets |
title_sort | global dynamic runoff application and dataset based on the assimilation of gpm, smap, and gcn250 curve number datasets |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669002/ https://www.ncbi.nlm.nih.gov/pubmed/36385044 http://dx.doi.org/10.1038/s41597-022-01834-0 |
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