Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature
Abstract Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing th...
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ftdoajarticles:oai:doaj.org/article:37afe59781f44c40ad9aa0fb3bb89c18 2024-09-15T18:23:37+00:00 Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature Rodrigo Martins Moreira Bruno César dos Santos Trent Biggs Fernando de Sales Stefan Sieber 2024-06-01T00:00:00Z https://doi.org/10.1038/s41598-024-63583-x https://doaj.org/article/37afe59781f44c40ad9aa0fb3bb89c18 EN eng Nature Portfolio https://doi.org/10.1038/s41598-024-63583-x https://doaj.org/toc/2045-2322 doi:10.1038/s41598-024-63583-x 2045-2322 https://doaj.org/article/37afe59781f44c40ad9aa0fb3bb89c18 Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024) Medicine R Science Q article 2024 ftdoajarticles https://doi.org/10.1038/s41598-024-63583-x 2024-08-05T17:49:08Z Abstract Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing the Google Earth Engine platform. Utilizing the silhouette method, we determine four distinct clusters representing zones of homogeneous precipitation patterns. Cluster 0 exhibits a significant median increase in precipitation of 3.20 mm year−1 over the period from 1981 to 2020. Cluster 1 shows a notable increase of 8.13 mm year−1, while Clusters 2 and 3 demonstrate reductions in precipitation of − 1.61 mm year−1 and − 3.87 mm year−1, respectively, all statistically significant. Notably, the region known as the arc of deforestation falls within Cluster 2, indicating a concerning trend of reduced precipitation. Additionally, our analysis reveals significant correlations between Sea Surface Temperature (SST) in various oceanic regions and precipitation patterns over the Brazilian Legal Amazon. Particularly noteworthy is the strong positive correlation with SST in the South Atlantic, while negative correlations are observed with SST in the South Pacific and North Atlantic. These findings provide valuable insights for enhancing climate adaptation strategies in the Brazilian Legal Amazon region. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Scientific Reports 14 1 |
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Medicine R Science Q Rodrigo Martins Moreira Bruno César dos Santos Trent Biggs Fernando de Sales Stefan Sieber Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
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Medicine R Science Q |
description |
Abstract Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing the Google Earth Engine platform. Utilizing the silhouette method, we determine four distinct clusters representing zones of homogeneous precipitation patterns. Cluster 0 exhibits a significant median increase in precipitation of 3.20 mm year−1 over the period from 1981 to 2020. Cluster 1 shows a notable increase of 8.13 mm year−1, while Clusters 2 and 3 demonstrate reductions in precipitation of − 1.61 mm year−1 and − 3.87 mm year−1, respectively, all statistically significant. Notably, the region known as the arc of deforestation falls within Cluster 2, indicating a concerning trend of reduced precipitation. Additionally, our analysis reveals significant correlations between Sea Surface Temperature (SST) in various oceanic regions and precipitation patterns over the Brazilian Legal Amazon. Particularly noteworthy is the strong positive correlation with SST in the South Atlantic, while negative correlations are observed with SST in the South Pacific and North Atlantic. These findings provide valuable insights for enhancing climate adaptation strategies in the Brazilian Legal Amazon region. |
format |
Article in Journal/Newspaper |
author |
Rodrigo Martins Moreira Bruno César dos Santos Trent Biggs Fernando de Sales Stefan Sieber |
author_facet |
Rodrigo Martins Moreira Bruno César dos Santos Trent Biggs Fernando de Sales Stefan Sieber |
author_sort |
Rodrigo Martins Moreira |
title |
Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
title_short |
Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
title_full |
Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
title_fullStr |
Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
title_full_unstemmed |
Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature |
title_sort |
identifying clusters of precipitation for the brazilian legal amazon based on magnitude of trends and its correlation with sea surface temperature |
publisher |
Nature Portfolio |
publishDate |
2024 |
url |
https://doi.org/10.1038/s41598-024-63583-x https://doaj.org/article/37afe59781f44c40ad9aa0fb3bb89c18 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024) |
op_relation |
https://doi.org/10.1038/s41598-024-63583-x https://doaj.org/toc/2045-2322 doi:10.1038/s41598-024-63583-x 2045-2322 https://doaj.org/article/37afe59781f44c40ad9aa0fb3bb89c18 |
op_doi |
https://doi.org/10.1038/s41598-024-63583-x |
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Scientific Reports |
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14 |
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1810463850478174208 |