A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

Journal Article · 10 October 2018 · Water Resources Research

DOI: https://doi.org/10.1029/2017WR022040 · OSTI ID:1498742

Yigzaw, Wondmagegn ^[1]; Li, Hong -Yi  ^[1]; Demissie, Yonas  ^[2]; Hejazi, Mohamad I.  ^[3]; Leung, L. Ruby  ^[4]; Voisin, Nathalie  ^[4]; Payn, Rob ^[5]

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1. Univ. of Houston, Houston, TX (United States); Montana State Univ., Bozeman, MT (United States)
2. Washington State Univ., Pullman, WA (United States)
3. Joint Global Change Research Institute, College Park, MD (United States)
4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
5. Montana State Univ., Bozeman, MT (United States)

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 dataset 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 minimizes the error of total storage and surface area estimation. We applied this algorithm 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. Furthermore, the new global reservoir storage-area-depth dataset 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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1498742
• Report Number(s): PNNL-SA-140190
• Journal Information: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 12 Vol. 54; ISSN 0043-1397
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English