The variogram sill and the sample variance

Barnes, Randal J.

doi:10.1007/bf02065813

Cited by 112 publications

(50 citation statements)

References 2 publications

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“…Although it is a well-known fact that the sill variance should not be forced to the sample variance (e.g. Barnes, 1991), a comparison between these measures can be used as a control. The comparison depends on boundary conditions that Barnes (1991) summarized in a rule-of-thumb: the data must be somewhat evenly distributed over an area with dimensions greater than three times the range of the variogram.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

“…Barnes, 1991), a comparison between these measures can be used as a control. The comparison depends on boundary conditions that Barnes (1991) summarized in a rule-of-thumb: the data must be somewhat evenly distributed over an area with dimensions greater than three times the range of the variogram. Since the rule-of-thumb is valid in our case, sill and sample variance of our data should approximately agree.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

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Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance

Zimmermann

Elsenbeer

2008

Journal of Hydrology

125

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Elektronisch veröffentlicht auf dem Publikationsserver der Universität Potsdam: http://opus.kobv. de/ubp/volltexte/2008/1640/ urn:nbn:de:kobv:517-opus-16402 [http://nbn-resolving.de/urn:nbn:de:kobv:517-opus16402] SummaryAs land-cover conversion continues to expand into ever more remote areas in the humid tropics, montane rainforests are increasingly threatened. In addition to the loss of biodiversity, land-use change potentially deteriorates regional water cycles, which may have undesirable effects for local populations such as decreased water supply during dry seasons, enhanced flooding in the rainy season, or deterioration of drinking water quality.Montane rainforests in the south Ecuadorian Andes are not only subject to man-made disturbances but also to naturally occurring landslides. I was interested in the impact of this ecosystem dynamics on a key parameter of the hydrologic cycle, the soil saturated hydraulic conductivity (synonym: permeability; Ks from here on), because it is a sensitive indicator for soil disturbances. Depending on the rainfall regime, the potential disturbance-induced decrease of Ks may become relevant for regional watersheds.My general objective was to quantify the effects of the regional natural and human disturbances on the saturated hydraulic conductivity and to describe the resulting spatialtemporal patterns. The main hypotheses were: 1) disturbances cause an apparent displacement of the less permeable soil layer towards the surface, either due to a loss of the permeable surface soil after land-sliding, or as a consequence of the surface soil compaction under cattle pastures; 2) 'recovery' from disturbance, either because of landslide re-vegetation or because of secondary succession after pasture abandonment, involves an apparent displacement of the less permeable layer back towards the original depth an 3) disturbances cause a simplification of the Ks spatial structure, i.e. the spatially dependent random variation diminishes; the subsequent recovery entails the re-establishment of the original structure.In my first study, I developed a synthesis of recent geostatistical research regarding its applicability to soil hydraulic data, including exploratory data analysis and variogram estimation techniques; I subsequently evaluated the results in terms of spatial prediction uncertainty. Concerning the exploratory data analysis, my main results were: 1) Gaussian uniand bivariate distributions of the log-transformed data; 2) the existence of significant local trends; 3) no need for robust estimation; 4) no anisotropic variation. I found partly considerable differences in covariance parameters resulting from different variogram estimation techniques, which, in the framework of spatial prediction, were mainly reflected in the spatial connectivity of the Ks-field. Ignoring the trend component and an arbitrary use of robust estimators, however, would have the most severe consequences in this respect.Regarding variogram modeling, I encouraged restricted maximum likelihood estimation becau...

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Section: Exploratory Data Analysismentioning

confidence: 99%

Section: Exploratory Data Analysismentioning

confidence: 99%

Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance

Zimmermann

Elsenbeer

2008

Journal of Hydrology

125

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show abstract

“…The range is the distance at which the semivariance reaches that stable value. As discussed by Barnes (1991), when the sample values are evenly distributed over an areal extent many times larger than the range of the variogram, then the sample variance is a reasonable first estimate for the variogram sill. When different conditions occur, the sample variance may, on the average, significantly underestimate the variogram sill.…”

Section: Geostatisticsmentioning

confidence: 99%

“…When different conditions occur, the sample variance may, on the average, significantly underestimate the variogram sill. However, comparing the sample variance and the sill may be a good criterion for testing the validity of adopting a given experimental variogram model because if sill and variance differ greatly the experimental model is suspect (Barnes 1991).…”

Section: Geostatisticsmentioning

confidence: 99%

Spatial Variability of Field Microtopography and Its Influence on Irrigation Performance

Bai¹,

Xu²,

Li³

et al. 2012

Problems, Perspectives and Challenges of Agricultural Water Management

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“…According to Barnes (1999), sill can be used to approximate the statistical variance of the observed data. The value of the sill for bulk density was 1.30E0+4 (kg m -3 ) 2 for both seasons indicating that, at distances greater than 32 m for 1999 and 12 m for 2000, the bulk density data pairs exhibit a semi-variance that oscillates with a value of about 1.30E+04 (kg m -3 ) 2 .…”

Section: Analysis Proceduresmentioning

confidence: 99%

Systematic Evaluation of Kriging and Inverse Distance Weighting Methods for Spatial Analysis of Soil Bulk Density.

Sajid¹,

Rudra²,

Parkin³

2013

Can. Biosyst. Eng.

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The variogram sill and the sample variance

Cited by 112 publications

References 2 publications

Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance

Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance

Spatial Variability of Field Microtopography and Its Influence on Irrigation Performance

Systematic Evaluation of Kriging and Inverse Distance Weighting Methods for Spatial Analysis of Soil Bulk Density.

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