09 Jan 2023
09 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

Youjiang Shen1, Karina Nielsen2, Menaka Revel3, Dedi Liu4, and Dai Yamazaki1,3 Youjiang Shen et al.
  • 1Department of Civil Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, Japan
  • 2DTU Space, National Space Institute, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
  • 3Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
  • 4State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China

Abstract. Dams and reservoirs are human-made infrastructures that have attracted increasing attentions 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 water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of 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 % 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 full upstream 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 catchment, topography, climate, soil and geology, land cover and use, and anthropogenic activity). 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 data set on water level (data available for 20 % of 3,254 reservoirs), water 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 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 (Shen et al., 2022a).

Youjiang Shen et al.

Status: open (until 06 Mar 2023)

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Youjiang Shen et al.

Data sets

A dataset for reservoir-catchment characteristics for 3254 Chinese reservoirs, i.e., Res-CN Shen, Youjiang; Nielsen, Karina; Revel, Menaka; Liu, Dedi; Yamazaki, Dai

Youjiang Shen et al.


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Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3,254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3,254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.