Dataset Open Access
Yigzaw, Wondmagegn; Li, HongYi; Demissie, Yonas; Hejazi, Mohamad I; Leung, Lai-yung Ruby; Voisin, Nathalie; Payn, Rob
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.
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