H21C-1392
Geostatistical and Fractal Analysis of Soil Moisture Patterns in a Mesoscale Catchment Using Plot to Catchment Scale Datasets

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Wolfgang Korres1, Tim G. Reichenau1, Peter Fiener2, Christian Koyama3, Heye R Bogena4, Thomas Cornelissen5, Roland Baatz6, Michael Herbst7, Bernd Diekkrüger8, Harry Vereecken9 and Karl Schneider1, (1)University of Cologne, Cologne, Germany, (2)University of Augsburg, Augsburg, Germany, (3)Tohoku University, Center for Northeast Asian Studies, Sendai, Japan, (4)Forschungszentrum Jülich GmbH, Jülich 52428, Germany, (5)University of Bonn, Bonn, Germany, (6)Agrosphere Institute (IBG-3), Forschungszentrum Jülich, Jülich, Germany, (7)Research Centre Juelich GmbH, Juelich, Germany, (8)University of Bonn, Geography, Bonn, Germany, (9)Forschungszentrum Julich GmbH, Julich, Germany
Abstract:
Soil moisture and its spatio-temporal pattern is a key variable in hydrology, meteorology and agriculture. The aim of the current study is to analyze spatio-temporal soil moisture patterns of 11 datasets from the Rur catchment (Western Germany) with a total area of 2364 km2, consisting of a low mountain range (forest and grassland) and a loess plain dominated by arable land. Data was acquired across a variety of land use types, on different spatial scales (plot to mesoscale catchment) and with different methods (field measurements, remote sensing, and modelling).

In a geostatistical analysis we found three main groups of sill and range values of the theoretical variogram with similar characteristics in the autocorrelation structure: (i) modelled and measured datasets from a forest sub-catchment (influenced by soil properties and topography), (ii) remotely sensed datasets from the cropped part of the total catchment (influenced by the land-use structure of the cropped area), and (iii) modelled datasets from the cropped part of the Rur catchment (influenced by large scale variability of soil properties).

The fractal analysis revealed that all analyzed soil moisture patterns show a multi-fractal behavior (varying fractal dimensions, that are only self-similar over certain ranges of scales), with at least one scale break and generally high fractal dimensions (high spatial variability). Corresponding scale breaks were found in various datasets and the factors explaining these scale breaks are consistent with the findings of the geostatistical analysis.

The joined analysis of the different datasets showed that small differences in soil moisture dynamics, especially at maximum porosity and wilting point in 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 can cause a decrease or an increase of spatial variability in the soil moisture pattern.

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