Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/113373
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dc.contributor.authorde Barros de Sousa, Lizandra-
dc.contributor.authorde Assunção Montenegro, Abelardo Antônio-
dc.contributor.authorda Silva, Marcos Vinícius-
dc.contributor.authorAlmeida, Thayná Alice Brito-
dc.contributor.authorde Carvalho, Ailton Alves-
dc.contributor.authorda Silva, Thieres George Freire-
dc.contributor.authorLima, João L. M .P. de-
dc.date.accessioned2024-02-19T10:17:43Z-
dc.date.available2024-02-19T10:17:43Z-
dc.date.issued2023-
dc.identifier.issn2072-4292pt
dc.identifier.urihttps://hdl.handle.net/10316/113373-
dc.description.abstractPrecipitation estimation is a challenging task, especially in regions where its spatial distribution is irregular and highly variable. This study evaluated the spatial distribution of annual rainfall in a semiarid Brazilian basin under different regimes and its impact on land use and land cover dynamics. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) records and observed data from 40 weather stations over a time series of 55 years were used, in addition to the Standardized Precipitation Index. Spatiotemporal analysis was carried out based on geostatistics. Remote sensing images were also interpreted for different rainfall regimes using the Normalized Difference Vegetation Index and Modified Normalized Difference Water Index. The Gaussian semivariogram model best represented the rainfall spatial structure, showing strong spatial dependence. Results indicated that rainfall amount in the basin significantly increases with elevation, as expected. There is high variation in the dynamics of water storage that can threaten water security in the region. Our findings point out that the application of geostatistics for mapping both the annual precipitation and the Standardized Precipitation Index provides a powerful framework to support hydrological analysis, as well as land use and land cover management in semiarid regions.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationThe Coordination for the Improvement of Higher Education Personnel (CAPES), the Foundation for the Support of Science and Technology of the State of Pernambuco (FACEPE— APQ-0300-5.03/17 and IBPG-0855-5.03/20), the National Council for Scientific and Technological Development (CNPq—308890/2018-3; 140281/2022-3), and the CAPES-PrInt/UFRPE financed and provided scholarships.pt
dc.relationUIDB/04292/2020pt
dc.relationUIDP/04292/2020pt
dc.relationLA/P/0069/2020pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectspatial dependencept
dc.subjecttemporal variabilitypt
dc.subjectsequential Gaussian simulationpt
dc.subjectkrigingpt
dc.subjectcokrigingpt
dc.subjectCHIRPSpt
dc.titleSpatiotemporal Analysis of Rainfall and Droughts in a Semiarid Basin of Brazil: Land Use and Land Cover Dynamicspt
dc.typearticle-
degois.publication.firstPage2550pt
degois.publication.issue10pt
degois.publication.titleRemote Sensingpt
dc.peerreviewedyespt
dc.identifier.doi10.3390/rs15102550pt
degois.publication.volume15pt
dc.date.embargo2023-01-01*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextCom Texto completo-
crisitem.author.researchunitMARE - Marine and Environmental Sciences Centre-
crisitem.author.orcid0000-0002-0135-2249-
Appears in Collections:FCTUC Eng.Civil - Artigos em Revistas Internacionais
I&D MARE - Artigos em Revistas Internacionais
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This item is licensed under a Creative Commons License Creative Commons