A New Global Storage‐Area‐Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models. Issue 12 (20th December 2018)

• Record Type: Journal Article
• Authors: Yigzaw, Wondmagegn; Li, Hong‐Yi; Demissie, Yonas; Hejazi, Mohamad I.; Leung, L. Ruby; Voisin, Nathalie; Payn, Rob
• Is Part Of: Water resources research. Volume 54:Issue 12(2018)
• Page Start: 10, 372
• Page End: 10, 386
• Publication Date: 2018-12-20
• Subjects: global -- reservoir geometry -- storage‐area‐depth profile -- Earth system models; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022040
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Reservoir storage‐area‐depth relationships are the most important factors controlling thermal stratification in reservoirs and, more broadly, the water, energy, and biogeochemical dynamics in the reservoirs and subsequently their impacts on downstream rivers. However, most land surface or Earth system models do not account for the gradual changes of reservoir surface area and storage with the changing depth, inhibiting a consistent and accurate representation of mass, energy, and biogeochemical balances in reservoirs. Here we present a physically coherent parameterization of reservoir storage‐area‐depth data set at the global scale. For each reservoir, the storage‐area‐depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimize the error of total storage and surface area estimation. We applied this algorithm to over 6, 800 reservoirs included in the Global Reservoir and Dam database. The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all Global Reservoir and Dam 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. The new global reservoir storage‐area‐depth data set is critical for advancing future modeling and understanding of reservoir processes and subsequent effects on the terrestrial hydrological, ecological, and biogeochemical cycles at the regional and global scales. Key Points: This study developed the first global reservoir storage‐area‐depth database for physically based reservoir modeling at regional or global scales Derived using mathematical approximation and data from GRanD, the relative storage error for most of the 6, 824 global reservoirs is less than 25% The new database has been successfully validated against remote sensing and ground‐truth observations from 74 reservoirs