The Zambezi River Basin (ZRB) is a critical resource for Southern Africa, supporting hydropower production, livelihoods, food security and ecosystems. With increasing freshwater scarcity and climate change induced droughts and floods in the ZRB, water allocation is increasingly critical, especially to those already carrying the burdens of climate change without reaping the profits of economic development. Effective management of this transboundary water system requires balancing competing objectives such as economic efficiency, social equity, and environmental sustainability.
In light of the DAFNE project, funded by the EU to create a Decision-Analytic Framework (DAF), an Evolutionary Multi Objective Direct Policy Search (EMODPS) framework was applied to the ZRB. EMODPS models combine Direct Policy Search (DPS) with Multi-Objective Evolutionary Algorithms (MOEA) to process complex simulations and continuously optimize for sequential decisions. The ZRB EMODPS model was created to identify the Pareto-optimal release policies for the five hydropower reservoirs and eight irrigation districts in the river basin. In the modelling process, there was a lack of consideration for distributive justice. In the baseline configuration, the five reservoirs were aggregated into one hydropower objective and the eight irrigation districts in the system were aggregated into one irrigation objective. The environmental flow at the Zambezi Delta constituted the third objective for the initial optimization.
This research disaggregates the hydropower and irrigation objectives to analyse what the effects are on the optimal release policies, particularly for smaller irrigation districts and reservoirs. The research question is: How does the disaggregation of objectives influence the Pareto space for an EMODPS simulation-optimization model? Four levels of aggregation were optimized: the baseline configuration with three objectives, the irrigation case with 11 objectives (including an individual objective for each irrigation district), the hydropower case with eight objectives (including the five hydropower reservoirs as objectives) and the full case with 16 objectives in total where the three baseline objectives are complemented with one objective for each irrigation district and hydropower reservoir. The Pareto set of the four different problem framings is visualized and analysed to conduct a comparison between the levels of aggregation.
Higher levels of aggregation may limit the insights provided by the Pareto front and increase the risk of further burdening marginalized groups. The initial hypothesis was that smaller irrigation districts and hydropower reservoirs would benefit from being considered as individual objectives. However, this hypothesis was not confirmed. The baseline aggregation of three objectives yielded better results for the total hydropower and irrigation deficits, even for the smaller districts and reservoirs.
The results reveal that disaggregation provides a more nuanced understanding of trade-offs but increases computational demands and complexity. The increased number of variables and constraints decreased the efficiency of the Generational Borg algorithm, making the study less feasible. Many-objective optimizations with more than 10 objectives pushed computational limits, displayed unexpected convergence behaviour, and posed challenges in presenting and interpreting large amounts of data. More sophisticated algorithms may better handle the consequences and limitations of objective aggregation in EMODPS models. This research highlights the trade-offs between equity and efficiency in water resource management and provides insights into the possibilities of disaggregating objectives for more just and precise policy-making.