Comprehensive framework for long-term reservoir management under deep uncertainty

Reservoir systems play a crucial role in providing essential services such as water supply, flood protection, and energy generation. However, reservoir management is highly complex due to (i) multiple conflicting management goals, (ii) long-term changes in water availability and demand over the long life span of these systems, and (iii) deep uncertainty. While some of these challenges have been addressed in previous studies, there is a lack of a comprehensive framework that can maximize the co-benefits of addressing these challenges in an integrated manner. Such an optimization framework has been developed in this study. By incorporating deep uncertainty, the causal relationships between decisions, system performance, and robustness can be explored. By adapting both operation policy and infrastructure upgrade decisions to long-term changes, infrastructure investments can be reduced without compromising system performance. By explicitly accounting for multiple conflicting objectives, the framework also provides a platform for negotiation in the decision-making process. • A new comprehensive framework for reservoir management has been developed. • Trade-offs among management goals are obtained without predefined preferences. • Operation and infrastructure upgrades are co-optimized to adapt to future changes. • Robustness is optimized for performance consistency under deep uncertainty. • Application to a Northern Australia reservoir system demonstrates benefits of framework.

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Publication Details

Journal
Environmental Modelling & Software
Published
2025-10-10
DOI
https://doi.org/10.1016/j.envsoft.2025.106740
Citations
1
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
1.15
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article

Comprehensive framework for long-term reservoir management under deep uncertainty

Holger R. Maier, Quan J. Wang, Jiajia Huang, Wenyan Wu et al.
1 citations
Environmental Modelling & Software
Water resources management and optimization
1.15
article

Comprehensive framework for long-term reservoir management under deep uncertainty

Holger R. Maier, Quan J. Wang, Jiajia Huang, Wenyan Wu, Yuan Cao, Justin Hughes
article en
1 citations

Abstract

Reservoir systems play a crucial role in providing essential services such as water supply, flood protection, and energy generation. However, reservoir management is highly complex due to (i) multiple conflicting management goals, (ii) long-term changes in water availability and demand over the long life span of these systems, and (iii) deep uncertainty. While some of these challenges have been addressed in previous studies, there is a lack of a comprehensive framework that can maximize the co-benefits of addressing these challenges in an integrated manner. Such an optimization framework has been developed in this study. By incorporating deep uncertainty, the causal relationships between decisions, system performance, and robustness can be explored. By adapting both operation policy and infrastructure upgrade decisions to long-term changes, infrastructure investments can be reduced without compromising system performance. By explicitly accounting for multiple conflicting objectives, the framework also provides a platform for negotiation in the decision-making process. • A new comprehensive framework for reservoir management has been developed. • Trade-offs among management goals are obtained without predefined preferences. • Operation and infrastructure upgrades are co-optimized to adapt to future changes. • Robustness is optimized for performance consistency under deep uncertainty. • Application to a Northern Australia reservoir system demonstrates benefits of framework.

Environmental Modelling & SoftwareVol. 195
Commonwealth Scientific and Industrial Research Organisation (AU), The University of Melbourne (AU), The University of Adelaide (AU)
Openalex Percentile: Top 18%
Water resources management and optimization
1.15
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Comprehensive framework for long-term reservoir management under deep uncertainty — Holger R. Maier, Quan J. Wang, et al. · Environmental Modelling & Software (2025) | TGRS Research Map | TGRS