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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 5 | Copyright
Hydrol. Earth Syst. Sci., 14, 751-764, 2010
https://doi.org/10.5194/hess-14-751-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 May 2010

12 May 2010

Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

W. Korres, C. N. Koyama, P. Fiener, and K. Schneider W. Korres et al.
  • Department of Geography, University of Cologne, Cologne, Germany

Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes that are affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable test site that are located within the Rur catchment in Western Germany. Surface soil moisture (0–6 cm) was measured in an approx. 50×50 m grid during 14 and 17 measurement campaigns (May 2007 to November 2008) in both test sites. To analyse the spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to link the patterns to related factors and processes. For the grassland test site, the analysis resulted in one significant spatial structure (first EOF), which explained 57.5% of the spatial variability connected to soil properties and topography. The statistical weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable test site, the analysis resulted in two significant spatial structures, the first EOF, which explained 38.4% of the spatial variability, and showed a highly significant correlation to soil properties, namely soil texture and soil stone content. The second EOF, which explained 28.3% of the spatial variability, is linked to differences in land management. The soil moisture in the arable test site varied more strongly during dry and wet periods at locations with low porosity. The method applied is capable of identifying the dominant parameters controlling spatio-temporal patterns of surface soil moisture without being affected by single random processes, even in intensively managed agricultural areas.

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