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

TitleA New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models
Publication TypeJournal Article
Year of Publication2019
JournalWater Resources Research
Volume54
Number12
Pages10,372-10,386
Date Published02/2019
Abstract / Summary

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.

URLhttp://dx.doi.org/10.1029/2017wr022040
DOI10.1029/2017wr022040
Funding Program: 
Journal: Water Resources Research
Year of Publication: 2019
Volume: 54
Number: 12
Pages: 10,372-10,386
Date Published: 02/2019

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.

DOI: 10.1029/2017wr022040
Citation:
Yigzaw, W, H Li, Y Demissie, M Hejazi, L Leung, N Voisin, and R Payn.  2019.  "A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models."  Water Resources Research 54(12): 10372-10386.  https://doi.org/10.1029/2017wr022040.