Trine Enemark

Trine Enemark serves as a Postdoc and Guest Researcher in the Department of Geosciences and Natural Resource Management at the University of Copenhagen, specializing in quantitative hydrogeology and groundwater systems analysis.

Her research centers on groundwater modeling methodologies, with emphasis on hydrostratigraphic uncertainty quantification, geological model strategies, and machine learning applications for drawdown prediction. She addresses critical water resource challenges including irrigation suitability, drinking water safety, and abstraction impacts across diverse geographical contexts from Denmark to India. Her work integrates field data with advanced computational techniques to improve predictive accuracy in complex aquifer systems.

Analysis of her 2022-2024 publications reveals a consistent trend toward hybrid modeling approaches combining traditional hydrogeological frameworks with artificial intelligence. Key thematic clusters include uncertainty propagation in 3D geological models, neural network implementation for abstraction forecasting, and comparative evaluation of modeling discretization techniques. This research trajectory demonstrates strong alignment with sustainable groundwater management priorities and computational hydrology advancements.

Her collaborative network includes prominent hydrogeologists at the University of Copenhagen such as K.H. Jensen and T.O. Sonnenborg, indicating active participation in major research initiatives. While specific grant details aren't provided, her publication output suggests involvement in projects addressing water security and modeling innovation.