Publication:
A global probabilistic dataset for monitoring meteorological droughts

Simple item page
dc.contributor.authorJerez Rodríguez, Sonia
dc.contributor.authorTurco, Marco
dc.contributor.authorDonat, Markus G.
dc.contributor.authorToreti, Andrea
dc.contributor.authorVicente-Serrano, Sergio M.
dc.contributor.authorDoblas-Reyes, Francisco J.
dc.contributor.departmentFísica
dc.date.accessioned2026-02-16T12:38:08Z
dc.date.available2026-02-16T12:38:08Z
dc.date.copyright© 2020 American Meteorological Society
dc.date.issued2020-10-09
dc.description.abstractAccurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. A number of drought datasets are already available. They cover the last three decades and provide data in near–real time (using different sources), but they are all “deterministic” (i.e., single realization), and input and output data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the standardized precipitation index. Then, by applying an ensemble approach, mimicking weather/climate prediction studies, we develop Drought Probabilistic (DROP), a new global land gridded dataset, in which an ensemble of observation-based datasets is used to obtain the best near-real-time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end users. The highquality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging
dc.formatapplication/pdf
dc.format.extent17
dc.identifier.citationBulletin of the American Meteorological Society, Volume 101, Issue 10(2020)
dc.identifier.doihttps://doi.org/10.1175/BAMS-D-19-0192.1
dc.identifier.eissn0003-0007
dc.identifier.eissn1520-0477
dc.identifier.urihttp://hdl.handle.net/10201/205761
dc.languageeng
dc.publisherAmerican Meteorological Society
dc.relationM.T. has received funding from the European Union’s Horizon 2020 Research And Innovation Programme under the Marie Skłodowska-Curie Grant Agreement 740073 (CLIM4CROP project) and from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is cofinanced with the European Regional Development Fund (ERDF/FEDER). S.J. was supported by the Spanish Ministry of Science, Innovation and Universities through the project EASE (RTI2018-100870-A-I00), the Fundación Séneca—Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18) and by the Plan Propio de Investigación of the University of Murcia (Grant UMU-2017-10604). M.G.D. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities Ramón y Cajal Grant Reference RYC-2017-22964. The authors thank the data providers listed in Table 1 for providing access to these datasets. Special thanks to Dr. Meng Zhao to provide the GRACE data and Dr. Hong Xuan Do for providing R scripts to read and process the GSIM data.
dc.relation.publisherversionhttps://journals.ametsoc.org/view/journals/bams/101/10/bamsD190192.xml
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.odsObjetivo 13: Cambio climático
dc.titleA global probabilistic dataset for monitoring meteorological droughts
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication55999738-2809-4ca7-86ba-a59168f75404
relation.isAuthorOfPublicationf39cf43c-f4a5-47b1-a28a-b8172f52893d
relation.isAuthorOfPublication.latestForDiscovery55999738-2809-4ca7-86ba-a59168f75404
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Turco_BAMS_2020.pdf
Size:
4.25 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.37 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos