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@ARTICLE{Korres:173377,
      author       = {Korres, W. and Reichenau, T. G. and Fiener, P. and Koyama,
                      C. N. and Bogena, Heye and Cornelissen, T. and Baatz, R. and
                      Herbst, M. and Diekkrüger, B. and Vereecken, H. and
                      Schneider, K.},
      title        = {{S}patio-temporal soil moisture patterns - {A}
                      meta-analysis using plot to catchment scale data},
      journal      = {Journal of hydrology},
      volume       = {520},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2014-06787},
      pages        = {326 - 341},
      year         = {2015},
      abstract     = {Soil moisture is a key variable in hydrology, meteorology
                      and agriculture. It is influenced by many factors, such as
                      topography, soil properties, vegetation type, management,
                      and meteorological conditions. The role of these factors in
                      controlling the spatial patterns and temporal dynamics is
                      often not well known. The aim of the current study is to
                      analyze spatio-temporal soil moisture patterns acquired
                      across a variety of land use types, on different spatial
                      scales (plot to meso-scale catchment) and with different
                      methods (point measurements, remote sensing, and modeling).
                      We apply a uniform set of tools to determine method specific
                      effects, as well as site and scale specific controlling
                      factors. Spatial patterns of soil moisture and their
                      temporal development were analyzed using nine different
                      datasets from the Rur catchment in Western Germany. For all
                      datasets we found negative linear relationships between the
                      coefficient of variation and the mean soil moisture,
                      indicating lower spatial variability at higher mean soil
                      moisture. For a forest sub-catchment compared to cropped
                      areas, the offset of this relationship was larger, with
                      generally larger variability at similar mean soil moisture
                      values. Using a geostatistical analysis of the soil moisture
                      patterns we identified three groups of datasets with similar
                      values for sill and range of the theoretical variogram: (i)
                      modeled and measured datasets from the forest sub-catchment
                      (patterns mainly influenced by soil properties and
                      topography), (ii) remotely sensed datasets from the cropped
                      part of the Rur catchment (patterns mainly influenced by the
                      land-use structure of the cropped area), and (iii) modeled
                      datasets from the cropped part of the Rur catchment
                      (patterns mainly influenced by large scale variability of
                      soil properties). A fractal analysis revealed that all
                      analyzed soil moisture patterns showed a multifractal
                      behavior, with at least one scale break and generally high
                      fractal dimensions. Corresponding scale breaks were found
                      between different datasets. The factors causing these scale
                      breaks are consistent with the findings of the
                      geostatistical analysis. Furthermore, the joined analysis of
                      the different datasets showed that small differences in soil
                      moisture dynamics, especially at the upper and lower bounds
                      of soil moisture (at maximum porosity and wilting point of
                      the soils) can have a large influence on the soil moisture
                      patterns and their autocorrelation structure. Depending on
                      the prevalent type of land use and the time of year,
                      vegetation causes a decrease or an increase of spatial
                      variability in the soil moisture pattern.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / 255 - Terrestrial Systems: From Observation to
                      Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000348255900027},
      doi          = {10.1016/j.jhydrol.2014.11.042},
      url          = {https://juser.fz-juelich.de/record/173377},
}