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An inter-comparison of similarity-based methods for organisation and classification of groundwater hydrographs

Journal article
Authors Ezra Haaf
Roland Barthel
Published in Journal of Hydrology
Volume 559
Pages 222-237
ISSN 0022-1694
Publication year 2018
Published at Department of Earth Sciences
Pages 222-237
Language en
Links https://doi.org/10.1016/j.jhydrol.2...
https://www.sciencedirect.com/scien...
Keywords Groundwater time series Similarity Hydrograph classification Cluster analysis Groundwater systems PUB for groundwater
Subject categories Climate Research, Geology, Hydrology

Abstract

Classification and similarity based methods, which have recently received major attention in the field of surface water hydrology, namely through the PUB (prediction in ungauged basins) initiative, have not yet been applied to groundwater systems. However, it can be hypothesised, that the principle of ‘‘similar systems responding similarly to similar forcing” applies in subsurface hydrology as well. One fundamental prerequisite to test this hypothesis and eventually to apply the principle to make ‘‘predictions for ungauged groundwater systems” is efficient methods to quantify the similarity of groundwater system responses, i.e. groundwater hydrographs. In this study, a large, spatially extensive, as well as geologically and geomorphologically diverse dataset from Southern Germany and Western Austria was used, to test and compare a set of 32 grouping methods, which have previously only been used individually in localscale studies. The resulting groupings are compared to a heuristic visual classification, which serves as a baseline. A performance ranking of these classification methods is carried out and differences in homogeneity of grouping results were shown, whereby selected groups were related to hydrogeological indices and geological descriptors. This exploratory empirical study shows that the choice of grouping method has a large impact on the object distribution within groups, as well as on the homogeneity of patterns captured in groups. The study provides a comprehensive overview of a large number of grouping methods, which can guide researchers when attempting similarity-based groundwater hydrograph classification.

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