Publication | Open Access
A New Global Storage‐Area‐Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models
85
Citations
32
References
2018
Year
EngineeringHydrologic EngineeringEarth System ScienceSurface Area EstimationEarth ScienceReservoir EngineeringEarth System ModelsLand SurfaceReservoir CharacterizationThermal StratificationReservoir Surface AreaResource EstimationGeographyReservoir SimulationHydrologyReservoir ModelingWater ResourcesSurface-water HydrologyLand Surface ModelingReservoir GeologyReservoir Management
Reservoir storage‑area‑depth relationships govern thermal stratification and the water, energy, and biogeochemical dynamics of reservoirs, yet most land‑surface and Earth‑system models ignore the gradual changes in surface area with depth, limiting accurate mass, energy, and biogeochemical balances. The authors develop a physically coherent, globally applicable parameterization of reservoir storage‑area‑depth relationships. They iteratively select the optimal regular geometric shape from five candidates to minimize storage and surface‑area estimation error, applying this algorithm to over 6,800 reservoirs in the Global Reservoir and Dam database. The resulting dataset achieves ≤5% relative error for 66% and ≤50% for 99% of reservoirs, matches remote‑sensing and ground‑truth depth profiles, and is essential for improving reservoir process modeling and downstream hydrological, ecological, and biogeochemical cycle simulations.
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.
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