A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)
<p>With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is a lack of a comprehensive global large-sample dataset for com...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-10-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/4463/2023/essd-15-4463-2023.pdf |
| _version_ | 1827798824757755904 |
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| author | X. Chen L. Jiang Y. Luo J. Liu J. Liu |
| author_facet | X. Chen L. Jiang Y. Luo J. Liu J. Liu |
| author_sort | X. Chen |
| collection | DOAJ |
| description | <p>With the booming big data techniques, large-sample
hydrological analysis on streamflow regime is becoming feasible, which could
derive robust conclusions on hydrological processes from a big-picture
perspective. However, there is a lack of a comprehensive global large-sample
dataset for components of the streamflow regime yet. This paper presents a
new time series dataset on global streamflow indices calculated from daily
streamflow records after data quality control. The dataset contains 79
indices over seven major components of streamflow regime (i.e., magnitude,
frequency, duration, changing rate, timing, variability, and recession) of
41 263 river reaches globally on yearly and multiyear scales. Streamflow
indices values until 2022 are covered in the dataset. Time span of the time
series dataset is from 1806 to 2022 with an average length of 36 years.
Compared to existing global datasets, this global dataset covers more
stations and more indices, especially those characterizing the frequency,
duration, changing rate, and recession of streamflow regime. With the
dataset, research on streamflow regime will become easier without spending
time handling raw streamflow records. This comprehensive dataset will be a
valuable resource to the hydrology community to facilitate a wide range of
studies, such as studies of hydrological behaviour of a catchment,
streamflow regime prediction in data-scarce regions, as well as variations
in streamflow regime from a global perspective. The dataset can be accessed
at <a href="https://doi.org/10.57760/sciencedb.07227">https://doi.org/10.57760/sciencedb.07227</a> (Chen et
al., 2023a).</p> |
| first_indexed | 2024-03-11T19:41:35Z |
| format | Article |
| id | doaj.art-846f211ba82c49daa997ed310b74f0a1 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-03-11T19:41:35Z |
| publishDate | 2023-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-846f211ba82c49daa997ed310b74f0a12023-10-06T08:15:36ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-10-01154463447910.5194/essd-15-4463-2023A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)X. Chen0L. Jiang1Y. Luo2J. Liu3J. Liu4School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, ChinaSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, and College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, ChinaSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, ChinaHenan Provincial Key Lab of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China<p>With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is a lack of a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 41 263 river reaches globally on yearly and multiyear scales. Streamflow indices values until 2022 are covered in the dataset. Time span of the time series dataset is from 1806 to 2022 with an average length of 36 years. Compared to existing global datasets, this global dataset covers more stations and more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective. The dataset can be accessed at <a href="https://doi.org/10.57760/sciencedb.07227">https://doi.org/10.57760/sciencedb.07227</a> (Chen et al., 2023a).</p>https://essd.copernicus.org/articles/15/4463/2023/essd-15-4463-2023.pdf |
| spellingShingle | X. Chen L. Jiang Y. Luo J. Liu J. Liu A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) Earth System Science Data |
| title | A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) |
| title_full | A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) |
| title_fullStr | A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) |
| title_full_unstemmed | A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) |
| title_short | A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) |
| title_sort | global streamflow indices time series dataset for large sample hydrological analyses on streamflow regime until 2022 |
| url | https://essd.copernicus.org/articles/15/4463/2023/essd-15-4463-2023.pdf |
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