Title
Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: Cluster Size Filter and Drought Indicator Threshold Optimization
Author
Diaz, Vitali (TU Delft Digital Technologies; IHE Delft Institute for Water Education) 
Corzo Perez, Gerald A. (IHE Delft Institute for Water Education)
Van Lanen, Henny A.J. (Wageningen University & Research)
Solomatine, D.P. (TU Delft Water Resources; Water Problems Institute of Russian Academy of Sciences; IHE Delft Institute for Water Education)
Date
2024
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.
Subject
Spatiotemporal drought analysis
Drought tracking
Drought dynamics
Drought characterization
Drought clustering
To reference this document use:
http://resolver.tudelft.nl/uuid:af8e2827-331c-464c-aa5f-bd6c7d1a2b96
DOI
https://doi.org/10.1002/9781119639268.ch11
Publisher
AGU/Wiley, Hoboken, NJ
Embargo date
2024-06-15
ISBN
9781119639312
Source
Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources
Series
Special Publications (78)
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
book chapter
Rights
© 2024 Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, D.P. Solomatine