Dam Reoperation to Mitigate Changing Climate Extremes in the Omo River Basin, Ethiopia
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 11
Abstract
Climate change is projected to increase the intensity and frequency of extremes in river basins around the world. Water infrastructure such as reservoirs are often used to buffer against these extremes, enabling a more reliable water supply for human uses like irrigation. Yet this can have negative impacts on the system’s ecological flows. In designing water infrastructure for human adaptation to climate change, it is important to consider whether the infrastructure is mitigating or exacerbating climate change impacts on ecological systems. Prior work has found that dams mitigate long-duration extremes but exacerbate short-duration extremes. In this study, we investigate whether reservoir operations can be designed to also yield beneficial climate adaptation outcomes for short-duration high and low flow extremes while still improving average socioeconomic and ecological objectives compared to uncontrolled conditions. We explore this research question in the Omo River Basin in Ethiopia, where controversy surrounding the socioecological impacts of recent and ongoing dam construction makes this a pressing issue to understand. Using multiobjective optimization of reservoir control rules, we are able to find several policies that can in fact mitigate the impact of climate change on annual maxima and annual 7-day minima. While we do see tradeoffs across reservoir operating policies that best preserve the distribution of these two statistics, we also find compromise policies that mitigate both of these extremes compared to uncontrolled conditions. This shows promise for the role dams can play in climate adaptation to short-duration flow extremes if their operations are designed with multiple objectives in mind.
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Data Availability Statement
Some code generated or used during the study are available in a repository online in accordance with funder data retention policies: https://github.com/sjordan29/DamReoperationOmo.
Acknowledgments
This work was supported by funding from the University of Virginia School of Engineering and Applied Science. The authors acknowledge Research Computing at the University of Virginia for providing computational resources and technical support that have contributed to the results reported within this publication (https://rc.virginia.edu). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding entities. We also acknowledge and thank Jared Oyler for his public debiasing and downscaling code, which made much of this work possible (https://github.com/scrim-network/red_river), and Liang-Jun Zhu, whose Github allowed us to update SWAT source code and create a Linux-compatible executable so we could run this model on Rivanna, the University of Virginia’s High Performance Computing system (https://github.com/WatershedModels/SWAT). We acknowledge ETH for the Omo River flow simulations used to calibrate our SWAT model, and DAFNE for funding that effort under the H2020 framework program of the European Union, grant number 690268.
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Journal of Water Resources Planning and Management
Volume 150 • Issue 11 • November 2024
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© 2024 American Society of Civil Engineers.
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Received: Oct 31, 2023
Accepted: Jun 21, 2024
Published online: Sep 14, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 14, 2025
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