Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1633-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-1633-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
20 Mar 2019
Research article | Highlight paper |
| 20 Mar 2019
| 20 Mar 2019
Geostatistical interpolation by quantile kriging
Henning Lebrenz
CORRESPONDING AUTHOR
University of Applied Sciences and Arts – Northwestern
Switzerland, Institute of Civil Engineering, Muttenz, Switzerland
University of Stuttgart, Institute for Modelling Hydraulic and
Environmental Systems, Stuttgart, Germany
András Bárdossy
University of Stuttgart, Institute for Modelling Hydraulic and
Environmental Systems, Stuttgart, Germany
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- Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation E. Varouchakis et al.
- A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm M. Assiri & S. Qureshi
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- A Method for Digital Terrain Reconstruction Using Longitudinal Control Lines and Sparse Measured Cross Sections Y. Pan et al.
- Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh’s Urban centers M. Waliullah et al.
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- Advanced Leak Detection and Quantification of Methane Emissions Using sUAS D. Hollenbeck et al.
- Temporal variability of precipitation and humidity in Mandi, Himachal Pradesh, India using GIS modelling: a multi decadal study A. Sharma et al.
- Technical Note: Space–time statistical quality control of extreme precipitation observations A. El Hachem et al.
Saved (final revised paper)
Latest update: 24 May 2026
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.
Many variables, e.g., in hydrology, geology, and social sciences, are only observed at a few...
