Aquatemp: Freshwater Temperature Forecasts for Industrial Planning
Developing a machine learning demonstrator that translates high resolution climate projections from the Destination Earth Climate Digital Twin into actionable freshwater temperature forecasts for industrial planning.
In a nutshell
- Climate change is driving more frequent and intense heatwaves across Europe, pushing surface water temperatures to levels that increasingly disrupt industries relying on freshwater for cooling. Thermal and nuclear power plants, data centres and chemical facilities face growing risks: reduced efficiency, curtailments and regulatory constraints on thermal discharge. No current service translates high‑resolution climate projections into actionable, site‑specific freshwater temperature forecasts for industrial planning.
- The Aquatemp demonstrator fills this gap by combining the Climate Change Adaptation Digital Twin’s projections with machine learning to generate storyline‑based freshwater temperature forecasts. Users can explore how temperatures may evolve under future heatwave scenarios and assess implications for operations, compliance and long‑term infrastructure planning.
- The demonstrator is integrated into the DestinE Platform and designed for scalable deployment across European river basins.
Technical Overview
Challenge
A critical, yet often underappreciated, factor in thermal electricity generation planning is the dependency on surface water for cooling purposes. Rising surface water temperatures pose a direct threat to industries that depend on freshwater for cooling, particularly thermal and nuclear power plants, data centres, and the chemical and manufacturing sectors. When temperatures exceed regulatory or operational thresholds, facilities face efficiency losses, curtailments or temporary shutdowns.
By 2030, over half of global data‑centre hubs are expected to experience high water stress. For energy plants, inadequate planning can trigger supply shortages, price volatility and reliance on carbon‑intensive backup units. This adds climatic risk to capacity planning and requires robust meteorological and hydrological modelling.
DestinE Solution
The Aquatemp demonstrator combines the advanced climate modelling capabilities of the Destination Earth Climate Digital Twin with state-of-the-art machine learning to deliver storyline-based freshwater temperature forecasts at high spatial and temporal resolution in industrial contexts. It enables users to examine how temperatures may evolve under future heatwave or drought conditions and to evaluate operational and infrastructure implications.
Machine learning models trained on historical and site‑specific data translate Climate DT projections into accurate freshwater temperature forecasts, with a focus on extreme events. This co‑designed approach supports industrial users in anticipating thermal risks, stress‑testing adaptation strategies and planning long‑term resilience.
By integrating high‑fidelity climate simulations with ML models trained on local hydrological and thermal characteristics, the demonstrator delivers storyline-based impact assessments, such as repeated heatwaves or drought sequences, helping operators and policymakers assess vulnerabilities and make informed investment and permitting decisions.
Impact
The machine learning demonstrator enables a shift from static, conservative infrastructure dimensioning to dynamic, scenario-based resilience planning on the long term. By combining the Climate DT projections with machine learning, the model reflects local conditions more accurately than standard empirical approaches, offering a more nuanced understanding of how thermal stress develops in vulnerable water bodies. Rather than supporting real-time operations, the demonstrator is designed to enable strategic planning and impact assessments for sectors that depend heavily on freshwater for cooling, such as thermal power plants and data centers.
As such, under certain storyline conditions, the number of days with elevated thermal stress could increase by more than 30 % compared to historical averages. Quantifying these shifts allows stakeholders to assess the robustness of current infrastructure, evaluate alternative cooling technologies and inform long-term investment and policy decisions.
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