Abstract
The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rates of rainfall at locations adjacent to those with no record of rain. Paraíba experiences localized periods of drought within rainy seasons and distinct precipitation patterns among the state’s mesoregions. The mean precipitation values observed at several irregularly spaced rain gauge stations from 1994 to 2014 showed remarkable variations among the mesoregions in Paraíba throughout the year. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba. The rainfall data indicate intense spatial and temporal variabilities that directly affect the water resources of the entire region. The results provide a detailed spatial analysis of sectors experiencing precipitation conditions ranging from a scarcity to an excess of rainfall. The present study should help drive future research into spatiotemporal rainfall patterns across all of NEB.
Highlights
The spatiotemporal behavior of rainfall is of great importance in the regional management of water resources and has a direct influence on human activities such as livestock management, agriculture, and commerce
A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba, which is located in the northeastern region of Brazil (NEB)
The other stations excluded from the adjustment of the variogram were used to perform spatiotemporal kriging
Summary
The spatiotemporal behavior of rainfall is of great importance in the regional management of water resources and has a direct influence on human activities such as livestock management, agriculture, and commerce. The interpolation of spatiotemporal observations presents benefits compared to purely spatial predictions. One of these benefits is that interpolation can be applied to georeferencing positions over space-time [1,2]. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rainfall rates at locations adjacent to those with no record of rain [3]. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time
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