Soil moisture: variable in space but redundant in time

Mirko Mälicke,Sibylle K. Hassler,Markus Weiler,Erwin Zehe,Theresa Blume

doi:10.5194/hess-24-2633-2020

Abstract

Abstract. Soil moisture at the catchment scale exhibits a huge spatial variability. This suggests that even a large amount of observation points would not be able to capture soil moisture variability. We present a measure to capture the spatial dissimilarity 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 used the mean shift clustering algorithm to identify and analyze clusters. We found that soil moisture observations from the 19.4 km2 Colpach catchment in Luxembourg 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 average hillslope length in the study area of about 500 m. 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, and 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 (differences in spatial patterns over time are within the error margins). Thus, they can be compressed (all cluster members can be substituted by one representative member) 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 in 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.

Highlights

Soil water is by far the smallest freshwater stock on earth, it plays a key role in the functioning of terrestrial ecosystems
Since these early studies published by Topp, spatially and temporally distributed time domain reflectometry (TDR) and frequency domain reflectometry (FDR) measurements have been widely used to characterize soil moisture dynamics at the transect (e.g., Blume et al, 2009), hillslope (e.g., Starr and Timlin, 2004; Brocca et al, 2007) and catchment scale (e.g., Western et al, 2004; Bronstert et al, 2012)
A common conclusion for the catchment scale is that soil moisture exhibits pronounced spatial variability and that distributed point sampling often does not yield representative data for the catchment

Summary

Introduction

Soil water is by far the smallest freshwater stock on earth, it plays a key role in the functioning of terrestrial ecosystems. Technologies and experimental strategies to observe soil water dynamics across scales have been at the core of the hydrological research agenda for more than 20 years (Topp et al, 1982, 1984). Since these early studies published by Topp, spatially and temporally distributed time domain reflectometry (TDR) and frequency domain reflectometry (FDR) measurements have been widely used to characterize soil moisture dynamics at the transect (e.g., Blume et al, 2009), hillslope (e.g., Starr and Timlin, 2004; Brocca et al, 2007) and catchment scale (e.g., Western et al, 2004; Bronstert et al, 2012). A common conclusion for the catchment scale is that soil moisture exhibits pronounced spatial variability and that distributed point sampling often does not yield representative data for the catchment (see, e.g., Zehe et al, 2010; Brocca et al, 2012, or numerous studies given in Sect. 2.2 of Vereecken et al, 2008)

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Discussion

Conclusion