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• W2985715496 abstract "Knowing 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." @default.
• W2985715496 created "2019-11-22" @default.
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• W2985715496 date "2019-11-12" @default.
• W2985715496 modified "2023-10-14" @default.
• W2985715496 title "Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS" @default.
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• W2985715496 doi "https://doi.org/10.3390/w11112368" @default.
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