Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of Deep Learning and its many variants into traditional and emerging hydrology-related workflows. We welcome abstracts related to novel theory development, new methodologies, or practical applications of Deep Learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:
(1) Development of novel deep learning models or modeling workflows.
(2) Probing, exploring and improving our understanding of the (internal) states/representations of deep learning models to improve models and/or gain system insights.
(3) Understanding the reliability of deep learning, e.g., under non-stationarity and climate change.
(4) Modeling human behavior and impacts on the hydrological cycle.
(5) Deep Learning approaches for extreme event analysis, detection, and mitigation.
(6) Natural Language Processing in support of models and/or modeling workflows.
(7) Applications of Large Language Models, Large Multimodal Models and Agentic frameworks in the context of hydrology.
(8) Deep Learning-based uncertainty estimation methods.
(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.
(10) Exploration of different training strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.
(11) Transfer learning and generalization of deep learning models across hydrological, climatic and geographical settings.