Missing data imputation for multisite rainfall networks: A comparison between geostatistical interpolation and pattern-based estimation on different terrain types

Fabio Oriani, Simon Stisen, Mehmet C. Demirel, Gregoire Mariethoz

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too limited data in space to feed complex predictive models. In this study we compare different automatic approaches for missing data imputation, including geostatistical interpolation and pattern-based estimation algorithms. We introduce two pattern-based approaches based on the analysis of historical data patterns: (i) an iterative version of K-nearest neighbor (IKNN) and (ii) a new algorithm called vector sampling (VS) that combines concepts of multiple-point statistics and resampling. Both algorithms can draw estimations from variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset. Tested on five 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 homogeneous rain events, geostatistical interpolation tends to minimize the average error, while in mountainous regions with nonstationary rainfall statistics, data mining can recover better the rainfall patterns. The VS algorithm, requiring minimal parameterization, turns out to be a convenient option for routine application on complex and poorly gauged terrains.

Original languageEnglish
Pages (from-to)2325-2341
Number of pages17
JournalJournal of Hydrometeorology
Volume21
Issue number10
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Hydrologic models
  • Hydrometeorology
  • Numerical analysis/modeling
  • Pattern detection
  • Statistical techniques

Programme Area

  • Programme Area 2: Water Resources

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