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Estimating Spatial Patterns in Water Content, Matric Suction, and Hydraulic Conductivity
Author(s) -
Mulla D. J.
Publication year - 1988
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1988.03615995005200060005x
Subject(s) - geostatistics , water content , soil science , hydraulic conductivity , soil texture , spatial variability , transect , suction , soil water , environmental science , hydrology (agriculture) , variogram , pedotransfer function , kriging , geotechnical engineering , geology , mathematics , geography , statistics , meteorology , oceanography
A relatively rapid method for surveying and estimating field‐scale variability of water content, hydraulic conductivity, and matric suction using geostatistics is described. Two 660‐m long transects located 8 km west of Steptoe, WA were intensively sampled at 5‐m spacings for surface temperature. Soil samples were also collected to a depth of 12 cm at spacings of 20 m, and these were analyzed for sand, clay, and soil water content. The spatial variability in each property was described using spherical semivariograms. Kriging was used to estimate spatial patterns in clay and sand content along each transect at 5‐m spacings. Spherical cross‐semivariograms of surface temperature and water content were used along with cokriging techniques to estimate water content at 5‐m spacings. The geostatistical estimates of spatial patterns in water content, clay content, and sand content were used for texture‐based estimates of matric suction and hydraulic conductivity at 5‐m spacings along both transects. A comparison of texture‐based and laboratory‐based estimates of matric suction showed that although the two estimators gave similar spatial patterns and means, they had significantly different standard deviations. If it is important for a researcher to make quantitative predictions of matric suction, laboratory‐based estimates would be better than the texture‐based estimates. On the other hand, if what is important is estimating spatial patterns in matric suction, the texture‐based estimator would be acceptable.