Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics

Cluster Size Filter and Drought Indicator Threshold Optimization

Book Chapter (2024)
Author(s)

V. Diaz Mercado (TU Delft - Digital Technologies, IHE Delft Institute for Water Education)

G. A. Corzo Perez (IHE Delft Institute for Water Education)

Henny A.J. Van Lanen (Wageningen University & Research)

Dimitri Solomatine (TU Delft - Water Resources, IHE Delft Institute for Water Education, Water Problems Institute of Russian Academy of Sciences)

Research Group
Digital Technologies
Copyright
© 2024 Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, D.P. Solomatine
DOI related publication
https://doi.org/10.1002/9781119639268.ch11
More Info
expand_more
Publication Year
2024
Language
English
Copyright
© 2024 Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, D.P. Solomatine
Research Group
Digital Technologies
Pages (from-to)
319-342
ISBN (print)
9781119639312
ISBN (electronic)
9781119639268
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

In its three-dimensional (3-D) characterization, drought is 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 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.

Files

THREEDIMENSIONAL_CLUSTERING_IN... (pdf)
(pdf | 0 Mb)
- Embargo expired in 15-06-2024
License info not available