Print Email Facebook Twitter Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics 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 analysisDrought trackingDrought dynamicsDrought characterizationDrought 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 Files PDF THREEDIMENSIONAL_CLUSTERI ... MPORAL.pdf 2.64 MB Close viewer /islandora/object/uuid:af8e2827-331c-464c-aa5f-bd6c7d1a2b96/datastream/OBJ/view