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

<p>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.</p><p>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.</p><p>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 .</p><p> </p><p>REFERENCES:</p><p></p><p><span>org/</span></p><p><span>org/</span></p>