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Water resources optimization conventionally maximizes system-wide efficiency, treating distributional equity as secondary. Here we present a framework that directly incorporates equity into optimization using Atkinson’s inequality measure. This approach makes distributional value judgements transparent through a single, interpretable inequality aversion parameter that spans principles from utilitarian efficiency to Rawlsian justice,

We are pleased to share a new publication in Water Resources Research developed in collaboration with colleagues from Duke University and the University of California Davis. The paper explores a fundamental question in water resources management: what information should guide operational decisions? While advances in monitoring, remote sensing, forecasting, and

The eiLab contributed to a systematic review assessing how machine learning has been used in drought research over the past two decades. Published in Water Resources Research, the review analyses 544 scientific papers on machine learning for drought science, focusing not only on which methods are used, but also on

We are pleased to share a new publication developed in collaboration with colleagues from the University of Cambridge, the Environment Agency, and several UK partners. This perspective explores the potential of artificial intelligence (AI) to support water quality management and regulation, addressing increasingly complex challenges driven by climate change, pollution,

Sea-level rise is increasing coastal flood risk, with storm surges playing a critical yet highly uncertain role. While physics-based hydrodynamic models remain the reference for simulating these processes, their computational cost limits their use for large ensembles and long term scenario analysis. In the study published in Earth’s Future, the

The eiLab carried out a novel study to identify how drought vulnerability is shifting across Europe and to develop more effective diagnostic tools for climate risk management. Drought is a complex hazard, yet conventional indices often fail to capture its actual socio-economic and ecological consequences. Since different regions respond differently

The eiLab contributed to a study developing an explainable machine learning model to estimate intense Mediterranean cyclone activity from large-scale climate variables. In the study, published in Journal of Geophysical Research: Machine Learning and Computation, the authors developed a convolutional neural network to estimate monthly Mediterranean cyclone activity, measured through

We are pleased to share a new publication resulting from a collaboration between our lab and colleagues from Cornell University and University of California Davis. In this study, we develop a novel framework to support adaptive reservoir operations under climate change, addressing a key challenge in water resources management: when

Prof. Andrea Castelletti, head of the eiLab, has been interviewed into L’Eco di Bergamo to recap main learning from the AMBER project (Adaptive Management of Barriers in European Rivers), founded in 2016 by the European Union’s Horizon 2020 research and innovation programme with the aim to deliver innovate solutions to