Building knowledge to optimise the management and protection of Danish water resources and the public’s drinking water supply as well as the impact of groundwater on Danish nature and the environment. Programme areas include mapping, establishing and managing monitoring programmes, understanding the water cycle and water quality.

The Water resources programme includes contributions from the following departments at GEUS:
  • Groundwater and Quaternary Geology Mapping
  • Geochemistry
  • Hydrology

Explore our research group and project dataverses and datasets below.

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

41 to 43 of 43 Results
Dec 7, 2021
Koch, Julian, 2021, "Catchment Dataset Denmark", https://doi.org/10.22008/FK2/YCQXTR, GEUS Dataverse, V1
This dataset can be used as input data for rainfall-runoff modelling for over 300 Danish catchments. Specifically, this dataset was used for a machine learning (LSTM) model application using the neuralhydrology codebase: https://neuralhydrology.github.io/ The data provided allows...
Aug 6, 2021
Bjerre, Elisa, 2021, "Metamodel transferability dataset", https://doi.org/10.22008/FK2/QS5PSL, GEUS Dataverse, V1
The dataset contains all input covariates and target variable for the random forest model presented in the manuscript "A Random Forest Metamodel for Predicting Drainage Fraction and Assessment of Model Transferability to New Spatial Domains" submitted to Water Resources Research
Jun 21, 2021
Stisen, Simon; Soltani, Mohsen; Mendiguren, Gorka; Langkilde, Henrik; Garcia, Monica; Koch, Julian, 2021, "Gridded European Evapotranspiration Climatologies", https://doi.org/10.22008/FK2/T6NBHH, GEUS Dataverse, V1
Spatial patterns in long-term evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models at river basin to continental scale. This kind of model evaluation is getting increased attention, ackn...
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.