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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- Comprehensive evaluation of precipitation datasets over Iran P. Saemian et al. 10.1016/j.jhydrol.2021.127054
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- A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm M. Assiri & S. Qureshi 10.3390/rs14246389
- Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years D. Fosco et al. 10.1016/j.scitotenv.2024.173981
- Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022 A. Causse et al. 10.3390/atmos14121726
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- Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh’s Urban centers M. Waliullah et al. 10.1186/s12889-024-20920-z
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- Temporal variability of precipitation and humidity in Mandi, Himachal Pradesh, India using GIS modelling: a multi decadal study A. Sharma et al. 10.2166/ws.2024.217
- Technical Note: Space–time statistical quality control of extreme precipitation observations A. El Hachem et al. 10.5194/hess-26-6137-2022
Latest update: 14 May 2025
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...
