Abstract
This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling-Gupta efficiency (KGE) value (after adding the new water management) is ≥0.5 and ≥0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is >0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44% of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human-earth system perspective.
Original language | English |
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Pages (from-to) | 5449-5472 |
Number of pages | 24 |
Journal | Geoscientific Model Development |
Volume | 16 |
Issue number | 18 |
DOIs | |
State | Published - Sep 22 2023 |
Externally published | Yes |
Funding
This research has been supported by the U.S. Department of Energy, Office of Science, through the MultiSector Dynamics, Earth and Environmental System Modeling Program. Guta Wakbulcho Abeshu and Hong-Yi Li also acknowledge the support from the U.S. National Science Foundation (EAR; grant no. 1804560) and the Research Computing Data Core at the University of Houston for assistance with the computations carried out in this work. The Pacific Northwest National Laboratory (PNNL) is operated for the U.S. Department of Energy by Battelle Memorial Institute (grant no. DE-AC05-76RL0183). The source code and input data for Xanthos used in this study can be freely downloaded at https://github.com/JGCRI/xanthos (last access: 1 August 2022, 10.5281/zenodo.5177210 , Braun et al., 2021). This research has been supported by the U.S. Department of Energy via the Pacific Northwest National Laboratory (grant no. 551981) and the National Science Foundation (EAR; grant no. 1804560).
Funders | Funder number |
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National Science Foundation | |
U.S. Department of Energy | |
Division of Earth Sciences | 1804560 |
Battelle | DE-AC05-76RL0183 |
Office of Science | |
Pacific Northwest National Laboratory | 551981 |