Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1111/gwat.13261
Licence creative commons licence
Title (Primary) Defining hydrogeological site similarity with hierarchical agglomerative clustering
Author Kawa, N.; Cucchi, K.; Rubin, Y.; Attinger, S.; Heße, F.
Journal Groundwater
Year 2022
Department CHS
Volume 61
Issue 4
Page From 563
Page To 573
Language englisch
Topic T5 Future Landscapes
Keywords data-driven methods; informative priors; hierarchical cluster-ing; data assimilation; open source

Hydrogeological information about an aquifer is difficult and costly to obtain, yet essential for the efficient management of groundwater resources. Transferring information from sampled sites to a specific site of interest can provide information when site-specific data is lacking. Central to this approach is the notion of site similarity, which is necessary for determining relevant sites to include in the data transfer process. In this paper, we present a data-driven method for defining site similarity. We apply this method to selecting groups of similar sites from which to derive prior distributions for the Bayesian estimation of hydraulic conductivity measurements at sites of interest. We conclude that there is now a unique opportunity to combine hydrogeological expertise with data-driven methods to improve the predictive ability of stochastic hydrogeological models.

Persistent UFZ Identifier
Kawa, N., Cucchi, K., Rubin, Y., Attinger, S., Heße, F. (2022):
Defining hydrogeological site similarity with hierarchical agglomerative clustering
Groundwater 61 (4), 563 - 573