EGU2020-5249
https://doi.org/10.5194/egusphere-egu2020-5249
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types

Fabio Oriani1, Simon Stisen2, Mehmet C. Demirel3, and Gregoire Mariethoz1
Fabio Oriani et al.
  • 1Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, Switzerland
  • 2Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen, Denmark
  • 3Department of Civil Engineering, Istanbul Technical University, Instanbul, Turkey

In the era of big data, missing data imputation remains a delicate topic for both the analysis of natural processes and to provide input data for physical models. We propose here a comparative study for missing data imputation on daily rainfall, a variable that can exhibit a complex structure composed of a dry/wet pattern and anisotropic sharp variations.

The seven algorithms considered can be grouped in two families: geostatistical interpolation techniques based on inverse-distance weighting and Kriging, widely used in gap-filling [1], and data-driven techniques based on the analysis of historical data patterns. This latter family of algorithms has been already applied to rainfall generation [2, 3], but it is not originally suitable to historical datasets presenting many data gaps. This happens because they usually operate in a rigid framework where, when a rainfall value is estimated for a station, the others are considered as predictor variables and require to be informed. To overcome this limitation, we propose here i) an adaptation of k-nearest neighbor (KNN) and ii) a new algorithm called Vector Sampling (VS), that combines concepts of multiple-point statistics and resampling. These data-driven algorithms can draw estimations from largely and variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset.

Tested on different case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially uniform rain events, geostatistical interpolation tends to minimize the error, while, in mountainous regions with non-stationary rainfall statistics, data mining can recover better the complex rainfall patterns. The VS algorithm, being faster than KNN and requiring minimal parametrization, turns out to be a convenient option for routine application if a representative historical dataset is available. VS is open-source and freely available at .

 

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How to cite: Oriani, F., Stisen, S., Demirel, M. C., and Mariethoz, G.: Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5249, https://doi.org/10.5194/egusphere-egu2020-5249, 2020

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  • AC1: References, Fabio Oriani, 30 Apr 2020

    For some reasons the references could not be displayed in the abstract. Here are they:

    [1] Di Piazza, A., F. Lo Conti, L. V. Noto, F. Viola, and G. La Loggia, 2011: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation, 13 (3), 396–408, .

    [2] Oriani, F., J. Straubhaar, P. Renard, and G. Mariethoz, 2014: Simulation of rainfall time series from different climatic regions using the direct sampling technique. Hydrology and Earth System Sciences, 18 (8), 3015–3031, .

    [3] Apipattanavis, S., G. Podesta, B. Rajagopalan, and R. W. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resources Research, 43 (11), W11 401, .