| Journal: | Boletim de ciencias geodesicas |
| Database: | PERIÓDICA |
| System number: | 000456635 |
| ISSN: | 1413-4853 |
| Authors: | Pisoni, Alana1 Pazini, Juliano de Bastos2 Seidel, Enio Junior3 |
| Institutions: | 1Universidade Federal de Santa Maria, Programa de Pos-Graduacao en Ciencias Ambientais, Santa Maria, Rio Grande do Sul. Brasil 2Universidade de Sao Paulo, Escola Superior de Agricultura "Luiz de Queiroz", Piracicaba, Sao Paulo. Brasil 3Universidade Federal de Santa Maria, Departamento de Estatistica, Santa Maria, Rio Grande do Sul. Brasil |
| Year: | 2023 |
| Volumen: | 29 |
| Number: | 2 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English abstract | The mapping of rainfall is fundamental in the hydrological modeling process. In this sense, the importance of knowing the geographic and seasonal dynamics of average estimates of rainfall and associated uncertainties is evident. Thus, the present study aimed to predict the spatial and seasonal distribution of rainfall, with the estimation of related uncertainties, in the state of Rio Grande do Sul (RS). Average rainfall varies over the months of the year. In January, February, June, July, August, and September it rains more north and northeast. In March, April, May, October, November, and December it rains more northwest and north. In general, it rains a lot in October and little rain in August. From a geographical point of view, it is possible to highlight that greater volumes of rain occur in the northern part of the state of RS. The uncertainties associated with rainfall estimates show divergent temporal dynamics, with the greatest uncertainties tending to occur in January, February, September, and October and that the smallest uncertainties are observed in June, July, and August |
| Disciplines: | Geociencias |
| Keyword: | Ciencias de la atmósfera, Lluvia, Geoestadística, Krigeaje, Dinámica espacial, Brasil |
| Keyword: | Atmospheric sciences, Rainfall, Geostatistics, Kriging, Spatial dynamics, Brazil |
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