Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs
-
Published:2023-07-05
Issue:7
Volume:15
Page:2781-2808
-
ISSN:1866-3516
-
Container-title:Earth System Science Data
-
language:en
-
Short-container-title:Earth Syst. Sci. Data
Author:
Shen YoujiangORCID, Nielsen KarinaORCID, Revel MenakaORCID, Liu Dedi, Yamazaki Dai
Abstract
Abstract. 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 km3 (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 https://doi.org/10.5281/zenodo.7664489
(Shen et al., 2022c).
Funder
Japan Society for the Promotion of Science
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference98 articles.
1. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.:
TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018. 2. Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.:
The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 3. Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.:
The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. 4. Balmer, M. B. and Downing, J. A.:
Carbon dioxide concentrations in eutrophic lakes: undersaturation implies atmospheric uptake, Inland Waters, 1, 125–132, https://doi.org/10.5268/IW-1.2.366, 2011. 5. Barbarossa, V., Schmitt, R. J., Huijbregts, M. A., Zarfl, C., King, H., and Schipper, A. M.:
Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide, P. Natl. Acad. Sci. USA, 117, 3648–3655, 2020.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|