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Three-dimensional clustering in the characterization of spatiotemporal drought dynamics: cluster size filter and drought indicator threshold optimization
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  • Vitali Diaz,
  • Gerald Augusto Corzo Perez,
  • Henny Van Lanen,
  • Dimitri Solomatine
Vitali Diaz
IHE Delft Institute for Water Education, IHE Delft Institute for Water Education

Corresponding Author:[email protected]

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Gerald Augusto Corzo Perez
UNESCO-IHE Institute for Water Education, UNESCO-IHE Institute for Water Education
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Henny Van Lanen
Wageningen University, Wageningen University
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Dimitri Solomatine
IHE Delft Institute for Water Education, IHE Delft Institute for Water Education
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In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.
21 Feb 2024Published in Advanced Hydroinformatics on pages 319-342. 10.1002/9781119639268.ch11