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Discussion papers
https://doi.org/10.5194/hess-2019-574
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-574
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 Nov 2019

Submitted as: research article | 08 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Soil moisture: variable in space but redundant in time

Mirko Mälicke1, Sibylle K. Hassler1, Theresa Blume2, Markus Weiler3, and Erwin Zehe1 Mirko Mälicke et al.
  • 1Institute for Water and River Basin Management, Karlsruhe Institute of Technology (KIT), Germany
  • 2GFZ German Research Centre for Geosciences, Section Hydrology, Germany
  • 3Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Germany

Abstract. Soil moisture at the headwater scale exhibits a huge spatial variability and single or even distributed TDR measurements yield non-representative data (Zehe et al., 2010, p. 874 l. 11–14). This suggests that even a huge amount of observation points would not be able to capture soil moisture variability. Here we ask whether spatial variability is the dead-end to spatially distributed point sampling – or whether point networks yield representative data on dynamic changes nevertheless?

We present a measure to capture the spatial dissimilarity, or dispersion, and its change over time. Statistical dispersion among observation points is related to their distance to describe spatial patterns. We analyzed the temporal evolution and emergence of these patterns and use Mean shift clustering algorithm to identify and analyze clusters. We found that soil moisture observations from the Colpach catchment in Luxembourg to cluster in two fundamentally different states. On the one hand, we found rainfall-driven data clusters, usually characterized by strong relationships between dispersion and distance. Their spatial extent roughly matches the hillslope scale. On the other hand, we found clusters covering the vegetation period. In drying and then dry soil conditions there is no particular spatial dependence in soil moisture patterns, but the values are highly similar beyond hillslope scale.

By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty (when are patterns different outside of uncertainty margins?). We were able to prove that the spatial information contained in soil moisture observations is highly redundant and can be compressed to only a fragment of the original data volume without significant information loss.

Our most interesting finding is that even a few soil moisture time series bear a considerable amount of information about dynamic changes of soil moisture. We argue that distributed soil moisture sampling reflects an organized catchment state, where soil moisture variability is not random. Thus, only a small amount of observation points is necessary to capture soil moisture dynamics.

Mirko Mälicke et al.
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Companion Code for: Soil moisture: variable in space but redundant in time. (10.5194/hess-2019-574) M. Mälicke https://doi.org/10.5281/zenodo.3532728

Mirko Mälicke et al.
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Short summary
We could show that distributed soil moisture time series bear a considerable amount of information about dynamic changes of soil moisture. We developed a new method to describe spatial patterns and analyze their persistency. By combining uncertainty propagation with information theory we were able to calculate the information content of spatial similarity with respect to measurement uncertainty. This does help to understand when and why the soil is drying in an organized manner.
We could show that distributed soil moisture time series bear a considerable amount of...
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