Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
SILVA, Cleo Clayton Santos
 |
| Orientador(a): |
MENEZES, Rômulo Simões Cezar |
| Banca de defesa: |
MENEZES, Rômulo Simões Cezar,
STOSIC, Tatijana,
FIGUEIRÊDO, Pedro Hugo de |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biometria e Estatística Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7328
|
Resumo: |
Rainfall is one of the phases of the hydrological cycle responsible for the return of the condensed water from the atmosphere to the Earth's surface. It is a natural process of utmost importance for the functioning of ecosystems, because it regulates water availability for various uses and environmental services. Several studies have shown that climate change has significantly altered precipitation patterns in many parts of the globe. The present work aims to make an assessment of the rainfall time series in Pernambuco, Brazil, using monthly precipitation data for the period from 1950 to 2012, collected from the Meteorological Laboratory of Pernambuco State (LAMEP), division of the Technological Institute of Pernambuco (ITEP). Data were analyzed using the Sample Entropy method (SampEn) developed to quantify the complexity of nonlinear time series, and interpolated by Inverse Distance Weighting (IDW) method, providing an estimate of the complexity of the rainfall in the state of Pernambuco. The results shows that the higher values of entropy (greater complexity) concentrate in the Agreste and Atlantic Forest Zone, indicating that the rainfall dynamic is more regular in these regions than in other parts of the state. For all regions of Pernambuco, entropy values of monthly rainfall series are classified from moderate to low, indicating the feasibility of modeling of precipitation dynamics on monthly scale. |
