Publikationen

GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features
Ohmer, M.; Liesch, T.; Habbel, B.; Heudorfer, B.; Gomez, M.; Clos, P.; Nölscher, M.; Broda, S.
2026. Earth System Science Data, 18 (1), 77–95. doi:10.5194/essd-18-77-2026

Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity
Nolte, A.; Heudorfer, B.; Bender, S.; Hartmann, J.
2025. Journal of Hydroinformatics, 27 (10), 1632–1651. doi:10.2166/hydro.2025.095

Are Deep Learning Models in Hydrology Entity Aware?
Heudorfer, B.; Gupta, H. V.; Loritz, R.
2025. Geophysical Research Letters, 52 (6), e2024GL113036. doi:10.1029/2024GL113036

Disentangling coastal groundwater level dynamics in a global dataset
Nolte, A.; Haaf, E.; Heudorfer, B.; Bender, S.; Hartmann, J.
2024. Hydrology and Earth System Sciences, 28 (5), 1215–1249. doi:10.5194/hess-28-1215-2024

On the challenges of global entity-aware deep learning models for groundwater level prediction
Heudorfer, B.; Liesch, T.; Broda, S.
2024. Hydrology and Earth System Sciences, 28 (3), 525–543. doi:10.5194/hess-28-525-2024

On the challenges of global entity-aware deep learning models for groundwater level prediction
Heudorfer, B.; Liesch, T.; Broda, S.
2023. Hydrology and earth system sciences discussions. doi:10.5194/hess-2023-192