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Published July 28, 2018 | Version v1.0.0
Dataset Open

Global Reservoir Geometry Database

Creators

  • 1. Montana State University, Bozeman, MT, USA
  • 2. Washington State University - Tri Cities, Richland, WA, USA
  • 3. Joint Global Change Research Institute, College Park, MD, USA
  • 4. Pacific Northwest National Laboratory, RIchland, WA, USA
  • 5. Pacific Northwest National Laboratory, Richland, WA, USA

Description

This is a global-scale reservoir storage-area-depth dataset including 6,824 major reservoirs. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimizes the error of total storage and surface area estimation. This algorithm has been applied to 6,800 reservoirs included in the Global Reservoir and Dam database (GRanD). The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all GRanD reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies, and ground-truth measurements for 34 reservoirs in the United States and China.

Notes

This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program (Grant No. 59534). We thank Dengfeng Liu for his technical assistance in collecting and processing the validation data. PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. Derived using mathematical approximation and data from GRanD (Lehner et al., 2011). Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., and Nilsson, C. (2011). High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management. Frontiers in Ecology and the Environment, 9(9), 494-502.

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Additional details

References

  • Yigzaw, Wondmagegn, HongYi Li, Yonas Demissie, Mohamad Hejazi, Ruby Leung, Nathalie Voisin, Rob Payn, in revision. A New Global Storage-Area-Depth Dataset for Modeling Reservoirs in Land Surface and Earth System Models. Water Resources Research