Using elevation to aid the geostatistical mapping of rainfall erosivity

Goovaerts, Pierre

doi:10.1016/s0341-8162(98)00116-7

Cited by 215 publications

(136 citation statements)

References 11 publications

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“…The drift and residuals can also be fitted separately and then summed afterwards. This was originally suggested by Odeh et al (1994Odeh et al ( , 1995, who named it ''Regression-kriging'' (further referred to as RK), whereas Goovaerts (1999b) uses the term ''Kriging after detrending''. UK, KED and RK are, in fact, equivalent methods and should, under the same assumptions, yield the same predictions (for more details, see Hengl et al, 2003b).…”

Section: The Spatial Prediction Technique: Regression-krigingmentioning

confidence: 99%

“…Bourennane et al (1996Bourennane et al ( , 2000 showed that prediction of horizon thickness is more accurate with the use of a slope map as external drift. In several other studies (Odeh et al, 1994(Odeh et al, , 1995Goovaerts, 1999b;Bishop and McBratney, 2001), combination of kriging and correlation with auxiliary data outperformed ordinary kriging, co-kriging and plain regression. Although the hybrid interpolation techniques are becoming increasingly popular, there is still a need for a generic methodology that combines theory of generalized linear models (GLM) with universal kriging.…”

Section: Introductionmentioning

confidence: 95%

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A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

902

558

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A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets. D

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Section: The Spatial Prediction Technique: Regression-krigingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

902

558

View full text Add to dashboard Cite

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“…Examples include combining rainfall data with a digital elevation model (DEM) as a covariate (Goovaerts 2000), combining rainfall data with satellite imagery or radar data (Grimes et al 1999;Cassiraga et al 1997), combining temperature with a DEM (Hudson and Wackernagel 1994), and combining temperature and land use for application in a crop growth model (Monestiez et al 2001). In soil science and hydrology KED is applied in mapping soil horizons using a DEM (Bourennane et al 1996), soil variables (Hengl et al 2004), erosion mapping (Goovaerts 1999), or water table depths (Desbarats et al 2002). Applications in air quality mapping are found in ozone mapping using a DEM (Pauly and Drueke 1996) or using dispersion model output for mapping ozone concentration around Paris (Bertino and Wackernagel 2002) or for urban air quality measurements (Genikhovich et al 2002).…”

Section: Introductionmentioning

confidence: 99%

External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

et al. 2007

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In the mid nineteen eighties the Dutch NO x air quality monitoring network was reduced from 73 to 32 rural and city background stations, leading to higher spatial uncertainties. In this study, several other sources of information are being used to help reduce uncertainties in parameter estimation and spatial mapping. For parameter estimation, we used Bayesian inference. For mapping, we used kriging with external drift (KED) including secondary information from a dispersion model. The methods were applied to atmospheric NO x concentrations on rural and urban scales. We compared Bayesian estimation with restricted maximum likelihood estimation and KED with universal kriging. As a reference we also included ordinary least squares (OLS). Comparison of several parameter estimation and spatial interpolation methods was done by cross-validation. Bayesian analysis resulted in an error reduction of 10 to 20% as compared to restricted maximum likelihood, whereas KED resulted in an error reduction of 50% as compared to universal kriging. Where observations were sparse, the predictions were substantially improved by inclusion of the dispersion model output and by using available prior information. No major improvement was observed as compared to OLS, the cause presumably being that much good information is contained in the dispersion model output, so that no additional spatial residual random field is required to explain the data. In all, we conclude that reduction in the monitoring network could be compensated by modern geostatistical methods, and that a traditional simple statistical model is of an almost equal quality.

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“…Previous studies in similar environments have shown the adequacy of regression-b ased approaches, particularly when high-elev ation station data are available to validate data [16,67,69 ].…”

Section: Input Interpola Tionmentioning

confidence: 99%

Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations

Tobin

Schaefli

Nicótina

et al. 2013

Advances in Water Resources

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Using elevation to aid the geostatistical mapping of rainfall erosivity

Cited by 215 publications

References 11 publications

A generic framework for spatial prediction of soil variables based on regression-kriging

A generic framework for spatial prediction of soil variables based on regression-kriging

External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations

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