Dynamic modelling of climate change effects on aquaculture sustainability using data science techniques: A review

Abstract

Climate change is reshaping aquaculture systems worldwide by altering water temperature, oxygen availability, hydrological cycles, and disease dynamics. These changes threaten the stability, productivity, and long-term sustainability of both freshwater and marine aquaculture. At the same time, advances in data science offer new opportunities to understand, anticipate, and manage climate-related risks through dynamic modelling and predictive decision support. This review synthesises current knowledge on climate change impacts on aquaculture and examines how data science techniques are being used to model these impacts over time. We review climate drivers relevant to aquaculture, discuss the strengths and limitations of different modelling approaches, and evaluate emerging applications that integrate climate data with farm-level observations. Particular attention is given to the challenges of uncertainty, data scarcity, and model transferability across regions and production systems. The review highlights the need for interpretable, scalable, and context-sensitive modelling frameworks that can support adaptive management under climate uncertainty. By linking climate science with data-driven modelling, this paper provides a foundation for more resilient and sustainable aquaculture systems.