Spatial Variability of Field‐Measured Infiltration Rate

Vieira, Sidney Rosa; Nielsen, D. R.; Biggar, J. W.

doi:10.2136/sssaj1981.03615995004500060007x

Cited by 337 publications

(158 citation statements)

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“…According to Martinho (2000), owing to the soil physical changes implied to the soil surface layer in the no tillage system, it is necessary to study the spatial variabil-ity in order to adequately characterize the environment. The spatial variability can be analyzed using geostatistics that involves a sequence of procedures for the construction of contour maps (Campbell, 1978;Burgess & Webster, 1980;Vieira et al, 1981;Vauclin et al, 1983;Vauchaud et al, 1985;Wendroth et al, 1997;Vieira, 2000;Grego & Vieira, 2005).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical analysis for soil moisture content under the no tillage cropping system

Grego

Vieira

Antonio

et al. 2006

Sci. agric. (Piracicaba, Braz.)

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Experiments in agriculture usually consider the topsoil properties to be uniform in space and, for this reason, often make inadequate use of the results. The objective of this study was to assess the variability for soil moisture content using geostatistical techniques. The experiment was carried out on a Rhodic Ferralsol (typic Haplorthox) in Campinas, SP, Brazil, in an area of 3.42 ha cultivated under the no tillage system, and the sampling was made in a grid of 102 points spaced 10 m × 20 m. Access tubes were inserted down to one meter at each evaluation point in order to measure soil moisture contents (cm 3 cm -3 ) at depths of 30, 60 and 90 cm with a neutron moisture gauge. Samplings were made between the months of August and September of 2003 and in January 2004. The soil moisture content for each sampling date was analyzed using classical statistics in order to appropriately describe the central tendency and dispersion on the data and then using geostatistics to describe the spatial variability. The comparison between the spatial variability for different samplings was made examining scaled semivariograms. Water content was mapped using interpolated values with punctual kriging. The semivariograms showed that, at the 60 cm depth, soil water content had moderate spatial dependence with ranges between 90 and 110 m. However, no spatial dependence was found for 30 and 90 cm depths in 2003. Sampling density was insufficient for an adequate characterization of the spatial variability of soil moisture contents at the 30 and 90 cm depths.

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Section: Introductionmentioning

confidence: 99%

Geostatistical analysis for soil moisture content under the no tillage cropping system

Grego

Vieira

Antonio

et al. 2006

Sci. agric. (Piracicaba, Braz.)

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“…The concept of soil as an inhomogeneous media was established already at the turn of the century [Vieira et al, 1981]. Despite this, only a limited number of studies focusing specifically on the spatially variable nature of soil were published until approximately 2• decades ago.…”

Section: Introductionmentioning

confidence: 99%

A new two‐step stochastic modeling approach: Application to water transport in a spatially variable unsaturated soil

Loll¹,

Møldrup²

1998

Water Resources Research

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Abstract. A new two-step stochastic modeling approach based on stochastic parameter inputs to a deterministic model system is presented.Step I combines a Stratified sampling scheme with a deterministic model to establish a deterministic response surface (DRS).Step II combines a Monte Carlo sampling scheme with the DRS to establish the stochastic model response. The new two-step approach is demonstrated on a one-dimensional unsaturated water flow problem at field scale with a dynamic surface flux and two spatially variable and interdependent parameters: The Campbell [1974] soil water retention parameter (b) and the saturated hydraulic conductivity (Ks). The new two-step stochastic modeling approach provides a highly time efficient way to analyze consequences of uncertainties in stochastic parameter input at field scale. The new two-step approach is competitive in analyzing problems with time consuming deterministic model runs where the stochastic problem can be adequately described by up to two spatially variable parameters. IntroductionThe concept of soil as an inhomogeneous media was established already at the turn of the century [Vieira et al., 1981]. Despite this, only a limited number of studies focusing specifically on the spatially variable nature of soil were published until approximately 2• decades ago. Since then, however, research within this field has increased dramatically, and the results find applications within, for example, water resource management, pollution control, and agriculture. In recent years, there seems to be an increased bias toward development and validation of predictive modeling tools accounting for the effect of spatial variability.An accurate description of both water and solute transport in the unsaturated zone relies upon good descriptions of the soil-water retention properties and unsaturated hydraulic conductivity of the soil. In recognition of this many studies have dealt with the characterization of natural variability in parameters describing soil hydraulic properties [e.g., Nielsen et al., [1994a] point out that the equations arising from application of the stochastic theory are very complicated to solve (even numerically) because of intercoupling, nonlinearity, and high dimensionality. Furthermore, the estimation of the necessary statistics from measured field data, namely, correlation lengths and covariance functions between parameters, often requires larger data sets than the ones typically available at this point [Jensen and Mantoglou, 1992;Mantoglou, 1992]. Together with the fact that the stochastic theory is very mathematically complex, these points of critique have limited the practical use of the theory to a very small number of studies.Comparatively, there have been a much higher number of applied studies based on deterministic modeling with statistical sampling. Most often, these studies are based on a numerical or analytical solution to the governing partial differential equation of unsaturated water flow subject to appropriate initial and boundary conditions....

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“…By comparison, Wagenet (1981) and Vieira et al (1981) found a zone of influence for individual infiltration measures of 5.3 m and 50 m, respectively, on agricultural soils. Since cover (live and litter) explained only 18-36% of the variance associated with measured infiltration rates, the complex nature of rangeland soils and trampling patterns of cattle (on the grazed pasture) must account for the differences.…”

Section: Resultsmentioning

confidence: 98%

“…Most spatial variability studies of infiltration rates have been conducted on agricultural soils (Sisson & Wierenga, 1981;Vieira et al, 1981;Wagenet, 1981). A recent study (Achouri & Gifford, 1984) looked at spatial and temporal variability of infiltration rates on a seeded rangeland site in Utah.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of infiltration rates on a semiarid seeded rangeland

Merzougui

Gifford

1987

Hydrological Sciences Journal

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Field studies determined the spatial variability patterns of field-measured infiltration rates on a semiarid seeded rangeland near Eureka, Utah, USA. Two kinds of instruments, namely a double-ring infiltrometer and a modular type rainfall simulator, were used in both a moderately grazed pasture and an ungrazed exclosure (protected for over 20 years) to measure infiltration rates at 10 and 30 minutes. There were 104 grid points per instrument on a 24 m x 24 m grid (2 m spacing) at each site, with a total of 416 infiltrometer plots. Dependency of specific infiltration rate measurements on nearby measurements, as determined from autocorrelograms and semivariograms, was nonexistent. Percent cover (vegetation plus litter) explained from 18 to 36% of the variance associated with measured infiltration rates.

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Spatial Variability of Field‐Measured Infiltration Rate

Cited by 337 publications

References 0 publications

Geostatistical analysis for soil moisture content under the no tillage cropping system

Geostatistical analysis for soil moisture content under the no tillage cropping system

A new two‐step stochastic modeling approach: Application to water transport in a spatially variable unsaturated soil

Spatial variability of infiltration rates on a semiarid seeded rangeland

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