Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 364150
Title Describing the soil physical characteristics of soil samples with cubical splines
Author(s) Wesseling, J.G.; Ritsema, C.J.; Stolte, J.; Oostindie, K.; Dekker, K.
Source Transport in Porous Media 71 (2008)3. - ISSN 0169-3913 - p. 289 - 309.
DOI https://doi.org/10.1007/s11242-007-9126-3
Department(s) Land Degradation and Development
Soil Science Centre
Laboratory of Soil Science and Geology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2008
Keyword(s) unsaturated hydraulic conductivity - pedotransfer functions - water-retention - parameter-estimation - outflow experiments - field measurement - inverse problem - flow - infiltration - transport
Abstract The Mualem-Van Genuchten equations have become very popular in recent decades. Problems were encountered fitting the equations¿ parameters through sets of data measured in the laboratory: parameters were found which yielded results that were not monotonic increasing or decreasing. Due to the interaction between the soil moisture retention and the hydraulic conductivity relationship, some data sets yield a fit that seems not to be optimal. So the search for alternatives started. We ended with the cubical spline approximation of the soil physical characteristics. Software was developed to fit the spline-based curves to sets of measured data. Five different objective functions are tested and their results are compared for four different data sets. It is shown that the well-known least-square approximation does not always perform best. The distance between the measured points and the fitted curve, as can be evaluated numerically in a simple way, appears to yield good fits when applied as a criterion in the optimization procedure. Despite an increase in computational effort, this method is recommended over the least square method.
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