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

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 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 newmore » 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.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [4];  [5]
  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)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1498742
Alternate Identifier(s):
OSTI ID: 1547788
Report Number(s):
PNNL-SA-140190
Journal ID: ISSN 0043-1397
Grant/Contract Number:  
AC05-76RL01830; AC05‐76RL01830; 59534
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 54; Journal Issue: 12; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
13 HYDRO ENERGY; 58 GEOSCIENCES; global; reservoir geometry; storage‐area‐depth profile; Earth system models

Citation Formats

Yigzaw, Wondmagegn, Li, Hong -Yi, Demissie, Yonas, Hejazi, Mohamad I., Leung, L. Ruby, Voisin, Nathalie, and Payn, Rob. A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models. United States: N. p., 2018. Web. doi:10.1029/2017WR022040.
Yigzaw, Wondmagegn, Li, Hong -Yi, Demissie, Yonas, Hejazi, Mohamad I., Leung, L. Ruby, Voisin, Nathalie, & Payn, Rob. A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models. United States. doi:10.1029/2017WR022040.
Yigzaw, Wondmagegn, Li, Hong -Yi, Demissie, Yonas, Hejazi, Mohamad I., Leung, L. Ruby, Voisin, Nathalie, and Payn, Rob. Thu . "A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models". United States. doi:10.1029/2017WR022040. https://www.osti.gov/servlets/purl/1498742.
@article{osti_1498742,
title = {A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models},
author = {Yigzaw, Wondmagegn and Li, Hong -Yi and Demissie, Yonas and Hejazi, Mohamad I. and Leung, L. Ruby and Voisin, Nathalie and Payn, Rob},
abstractNote = {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.},
doi = {10.1029/2017WR022040},
journal = {Water Resources Research},
issn = {0043-1397},
number = 12,
volume = 54,
place = {United States},
year = {2018},
month = {10}
}

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Cited by: 5 works
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Figures / Tables:

Figure 1 Figure 1: Flowchart presentation of steps followed in selecting shape and calculating storage-area-depth relationship (RS, remote sensing).

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