The field of data-driven and hybrid groundwater modelling continues to gain significant momentum within the hydrological community, reflecting growing interest in machine learning, artificial intelligence, and approaches that integrate data-driven techniques with physical process understanding. These methods are increasingly essential for addressing complex challenges in groundwater quantity and quality forecasting, uncertainty quantification, and sustainable management under changing climatic and anthropogenic pressures. Data-driven approaches — including time-series models, statistical methods, machine and deep learning techniques, and emulators — are transforming how we study, manage and forecast groundwater systems. By learning directly from observations, remote sensing and other data sources, these methods can complement, accelerate or in some cases substitute detailed process-based models. Recent advances in physics-informed methods, spatio-temporal deep learning architectures, probabilistic machine learning and foundation-model approaches are rapidly expanding possibilities for groundwater science. We welcome novel methodological developments and practical applications addressing real-world groundwater management problems. Submissions may address (but are not limited to):
• Advanced data-driven techniques for predicting groundwater quantity and quality in space and/or time (ML/DL, statistical and time-series models).
• Hybrid approaches combining ML with physically based models, including Physics-Informed Neural Networks and surrogate modelling.
• ML-based emulation of numerical models to support efficient modelling and data assimilation.
• Uncertainty quantification and sensitivity analysis using probabilistic ML, quantile regression and deep ensembles.
• Explainable and interpretable ML to improve hydrogeological understanding.
• Methods for big and heterogeneous datasets, data fusion (satellite, models, in-situ), and solutions for data scarcity, non-stationarity and irregular time steps.
• Transferability and regionalization to ungauged sites, and foundation-model approaches for temporal and spatial extrapolation.
We especially encourage submissions that link methods to management-relevant outcomes, climate-change impact assessments, adaptation strategies, and practical case studies. Join us to share research at the intersection of data science and groundwater hydrology and to advance this dynamic field through knowledge exchange and discussion.