Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs
<p>Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the am...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-07-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/2781/2023/essd-15-2781-2023.pdf |
| _version_ | 1797787137389625344 |
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| author | Y. Shen K. Nielsen M. Revel D. Liu D. Yamazaki D. Yamazaki |
| author_facet | Y. Shen K. Nielsen M. Revel D. Liu D. Yamazaki D. Yamazaki |
| author_sort | Y. Shen |
| collection | DOAJ |
| description | <p>Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental
significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that
the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream
catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring
reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 <span class="inline-formula">km<sup>3</sup></span> (73.2 % of reservoir water storage
capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and
accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing
areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both
full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six
categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic
activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support
hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive
reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and
evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These
products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial
resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions
within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a
valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current
satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as
water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through <a href="https://doi.org/10.5281/zenodo.7664489">https://doi.org/10.5281/zenodo.7664489</a>
(Shen et al., 2022c).</p> |
| first_indexed | 2024-03-13T01:18:40Z |
| format | Article |
| id | doaj.art-9ada874454544905a0d1c23f7199da21 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-03-13T01:18:40Z |
| publishDate | 2023-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-9ada874454544905a0d1c23f7199da212023-07-05T07:57:56ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-07-01152781280810.5194/essd-15-2781-2023Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirsY. Shen0K. Nielsen1M. Revel2D. Liu3D. Yamazaki4D. Yamazaki5Department of Civil Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, JapanDTU Space, National Space Institute, Technical University of Denmark, 2800, Kongens Lyngby, DenmarkGlobal Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, JapanState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, ChinaDepartment of Civil Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, JapanGlobal Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan<p>Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 <span class="inline-formula">km<sup>3</sup></span> (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through <a href="https://doi.org/10.5281/zenodo.7664489">https://doi.org/10.5281/zenodo.7664489</a> (Shen et al., 2022c).</p>https://essd.copernicus.org/articles/15/2781/2023/essd-15-2781-2023.pdf |
| spellingShingle | Y. Shen K. Nielsen M. Revel D. Liu D. Yamazaki D. Yamazaki Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Earth System Science Data |
| title | Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs |
| title_full | Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs |
| title_fullStr | Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs |
| title_full_unstemmed | Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs |
| title_short | Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs |
| title_sort | res cn reservoir dataset in china hydrometeorological time series and landscape attributes across 3254 chinese reservoirs |
| url | https://essd.copernicus.org/articles/15/2781/2023/essd-15-2781-2023.pdf |
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