Geostatistical modelling of uncertainty in soil science

Goovaerts, Pierre

doi:10.1016/s0016-7061(01)00067-2

Cited by 491 publications

(319 citation statements)

References 26 publications

Supporting

Mentioning

285

Contrasting

Unclassified

Order By: Relevance

“…Simulation algorithms instead of kriging methods were selected to achieve the goal, although the application of kriging methods essentially met the need. The reason lies in the advantages of simulation over kriging, the most notable was that a set of realizations generated by simulation allow one to model the spatial uncertainty and study the propagation of uncertainty by considering many locations simultaneously rather than one at a time (Goovaerts 2001).…”

Section: Discussionmentioning

confidence: 99%

“…The methodology proposed by Deutsch (1997) and Goovaerts (2001) was used to assess the local uncertainty by examining the performance of the reproduction of conditional cumulative distribution function (ccdf). At any validation location u i , a series of symmetric p-probability intervals (PI) bounded by the (1 − p)/2 and (1 + p)/2 quantiles can be calculated from the knowledge of the ccdf F(u i ; z|(n)).…”

Section: Quantitative Assessment Criteriamentioning

confidence: 99%

See 1 more Smart Citation

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Yao

Yang

Shao

2012

Environ Monit Assess

View full text Add to dashboard Cite

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC e 4-8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Quantitative Assessment Criteriamentioning

confidence: 99%

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Yao

Yang

Shao

2012

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

“…The data for soil sand content is a 10 km by 10 km raster data set constructed from soil profiles via spatial interpolation (Oberthür et al, 1999;Shi et al, 2004Shi et al, , 2006. Although a certain proportion of the immense spatial variation in soil properties may be lost after spatial interpolation (Goovaerts, 2001;van Bodegom et al, 2002), the gridded soil data are still the most detailed of the five model inputs. In descending order of data abundance, the other four factors are GY, OM, W ptn and VI.…”

Section: Pdfs Of the Model Input Variablesmentioning

confidence: 99%

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang

Huang

et al. 2014

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5-89.3 % of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44-7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3 % of the total emissions, ranging from 18.3-28.0 % for early, late and middle rice ecosystems. The 95 % confidence interval of the estimation was 4.5-8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

show abstract

“…However, its effectiveness relies on the accuracy of the interpolation method used to define the spatial variability of soil properties (Goovaerts 1998;1999;2001). The variogram is a mathematical description of the relationship (or structure) between the variance of pairs of observations (or data points) and the distance separating these observations (h) (Olea 1991).…”

Section: Geostatistical Optimization Of the Spatial Interpolation Methodsmentioning

confidence: 99%

GIS-based regional assessment of seismic site effects considering the spatial uncertainty of site-specific geotechnical characteristics in coastal and inland urban areas

Sun

Kim

2017

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Earthquake-induced hazards are profoundly affected by site effects related to the amplification of ground motions, which are strongly influenced by site-specific geologic conditions such as soil thickness, bedrock depth and soil stiffness. Seismic disasters are often more severe in coastal or riverside locations than over stiff soils or rocks due to differences in local site effects. In this study, a recently developed geographic information system-based framework was applied in coastal and inland urban areas in Korea, and its applicability for regional assessments was evaluated using appropriate geostatistical zonation of site-specific seismic site effects. The proposed framework was composed of four functional components: multivariable statistical clustering, geostatistical optimization, geotechnical analysis, and local visualization. The framework was applied in the Seoul and Busan areas of Korea for consideration of site effects in inland and coastal urban areas. Such zones of thick soil, or with a deep depth to bedrock, are susceptible to ground motion amplification due to site effects during earthquakes. The earthquake losses associated with possible building damage can be estimated based on spatial zoning maps considering geological and topographical characteristics and by a comparison of the spatial correlations of seismic site classes between inland and coastal areas of Korea.

show abstract

Geostatistical modelling of uncertainty in soil science

Cited by 491 publications

References 26 publications

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

GIS-based regional assessment of seismic site effects considering the spatial uncertainty of site-specific geotechnical characteristics in coastal and inland urban areas

Contact Info

Product

Resources

About