Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.creatorMedeiros, Elias Silva de-
dc.creatorLima, Renato Ribeiro de-
dc.creatorOlinda, Ricardo Alves de-
dc.creatorDantas, Leydson G.-
dc.creatorSantos, Carlos Antonio Costa dos-
dc.identifier.citationMEDEIROS, E. S. de et al. Space-time kriging of precipitation: modeling the large-scale variation with model GAMLSS. Water, [S.l], v. 11, n. 11, 2019.pt_BR
dc.description.abstractKnowing the dynamics of spatial–temporal precipitation distribution is of vital significance for the management of water resources, in highlight, in the northeast region of Brazil (NEB). Several models of large-scale precipitation variability are based on the normal distribution, not taking into consideration the excess of null observations that are prevalent in the daily or even monthly precipitation information of the region under study. This research proposes a novel way of modeling the trend component by using an inflated gamma distribution of zeros. The residuals of this regression are generally space–time dependent and have been modeled by a space–time covariance function. The findings show that the new techniques have provided reliable and precise precipitation estimates, exceeding the techniques used previously. The modeling provided estimates of precipitation in nonsampled locations and unobserved periods, thus serving as a tool to assist the government in improving water management, anticipating society’s needs and preventing water crises.pt_BR
dc.publisherMultidisciplinary Digital Publishing Institutept_BR
dc.rightsAttribution 4.0 International*
dc.rightsacesso abertopt_BR
dc.subjectWater resourcespt_BR
dc.titleSpace-time kriging of precipitation: modeling the large-scale variation with model GAMLSSpt_BR
Appears in Collections:DES - Artigos publicados em periódicos
DEX - Artigos publicados em periódicos

This item is licensed under a Creative Commons License Creative Commons