Home > Publications database > Spatio-temporal soil moisture patterns - A meta-analysis using plot to catchment scale data > print

001     173377
005     20200702121720.0
024 7 _ |a 10.1016/j.jhydrol.2014.11.042
|2 doi
024 7 _ |a 0022-1694
|2 ISSN
024 7 _ |a 1879-2707
|2 ISSN
024 7 _ |a 2128/8152
|2 Handle
024 7 _ |a WOS:000348255900027
|2 WOS
037 _ _ |a FZJ-2014-06787
082 _ _ |a 690
100 1 _ |a Korres, W.
|0 P:(DE-HGF)0
|b 0
|e Corresponding Author
245 _ _ |a Spatio-temporal soil moisture patterns - A meta-analysis using plot to catchment scale data
260 _ _ |a Amsterdam [u.a.]
|c 2015
|b Elsevier
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 173377
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
520 _ _ |a 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.
536 _ _ |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255)
|0 G:(DE-HGF)POF3-255
|c POF3-255
|x 0
|f POF III
536 _ _ |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255)
|0 G:(DE-HGF)POF3-255
|c POF3-255
|x 1
|f POF III
588 _ _ |a Dataset connected to CrossRef, juser.fz-juelich.de
700 1 _ |a Reichenau, T. G.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Fiener, P.
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Koyama, C. N.
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Bogena, Heye
|0 P:(DE-Juel1)129440
|b 4
|u fzj
700 1 _ |a Cornelissen, T.
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Baatz, R.
|0 P:(DE-Juel1)144513
|b 6
|u fzj
700 1 _ |a Herbst, M.
|0 P:(DE-Juel1)129469
|b 7
|u fzj
700 1 _ |a Diekkrüger, B.
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Vereecken, H.
|0 P:(DE-Juel1)129549
|b 9
|u fzj
700 1 _ |a Schneider, K.
|0 P:(DE-HGF)0
|b 10
773 _ _ |a 10.1016/j.jhydrol.2014.11.042
|g Vol. 520, p. 326 - 341
|0 PERI:(DE-600)1473173-3
|p 326 - 341
|t Journal of hydrology
|v 520
|y 2015
|x 0022-1694
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/173377/files/FZJ-2014-06787.pdf
856 4 _ |u https://juser.fz-juelich.de/record/173377/files/FZJ-2014-06787.jpg?subformat=icon-144
|x icon-144
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/173377/files/FZJ-2014-06787.jpg?subformat=icon-180
|x icon-180
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/173377/files/FZJ-2014-06787.jpg?subformat=icon-640
|x icon-640
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:173377
|p openaire
|p open_access
|p driver
|p VDB:Earth_Environment
|p VDB
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)129440
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)144513
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)129469
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)129549
913 0 _ |a DE-HGF
|b Erde und Umwelt
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF2-240
|0 G:(DE-HGF)POF2-246
|2 G:(DE-HGF)POF2-200
|v Modelling and Monitoring Terrestrial Systems: Methods and Technologies
|x 0
913 0 _ |a DE-HGF
|b Marine, Küsten- und Polare Systeme
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|2 G:(DE-HGF)POF3-200
|v Terrestrial Systems: From Observation to Prediction
|x 1
913 1 _ |a DE-HGF
|b Marine, Küsten- und Polare Systeme
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|2 G:(DE-HGF)POF3-200
|v Terrestrial Systems: From Observation to Prediction
|x 0
914 1 _ |y 2015
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 3.0
|0 LIC:(DE-HGF)CCBYNCND3
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a FullTexts
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21