Articles | Volume 23, issue 3
Research article
 | Highlight paper
20 Mar 2019
Research article | Highlight paper |  | 20 Mar 2019

Geostatistical interpolation by quantile kriging

Henning Lebrenz and András Bárdossy

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Cited articles

Adhikary, P. P., Dash, C. J., Bej, R., and Chandrasekharan, H.: Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India, Environ. Monit. Assess., 176, 663–676,, 2011. a
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Basistha, A., Arya, D. S., and Goel, N. K.: Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region, Water Resour. Manag., 22, 1325–1346,, 2008. a
Bourennane, H., King, D., and Couturier, A.: Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities, Geoderma, 97, 255–271,, 2000. a
Short summary
Many variables, e.g., in hydrology, geology, and social sciences, are only observed at a few distinct measurement locations, and their actual distribution in the entire space remains unknown. We introduce the new geostatistical interpolation method of quantile kriging, providing an improved estimator and associated uncertainty. It can also host variables, which would not fulfill the implicit presumptions of the traditional geostatistical interpolation methods.