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- de Souza, Itamara Parente, et al.
(author)
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Seasonal precipitation variability modes over South America associated to El Niño-Southern Oscillation (ENSO) and non-ENSO components during the 1951–2016 period
- 2021
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In: International Journal of Climatology. - : Wiley. - 0899-8418 .- 1097-0088. ; 41:8, s. 4321-4338
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Journal article (peer-reviewed)abstract
- Anomalous seasonal patterns of precipitation variability over South America (SA) associated with El Niño-Southern Oscillation (ENSO) and non-ENSO (residual) conditions were assessed during 1951–2016. Patterns were obtained from empirical orthogonal functions analysis of total and residual precipitation seasonal anomalies. In austral spring and summer, precipitation variability is dominated by a dipolar anomaly mode with a centre extending from northwestern to northeastern SA and another in central-eastern Brazil and part of southeastern SA (SESA) during spring, and a centre in northwestern SA and another extending from northeastern SA to central and eastern Brazil and central SESA, during summer. These modes are associated with ENSO to a greater extent during spring than summer. In summer, there is a strong association of the dipolar precipitation pattern with sea surface temperature (SST) anomalies on the east coast of Brazil, which indicates local influence. In austral fall, SST anomalies in the tropical South Atlantic relate to precipitation anomalies in northeast SA, and those in the tropical north Atlantic (TNA) to precipitation anomalies in northwestern SA, through the intertropical convergence zone anomalous position modulated by SST anomalies. In this same condition, the ENSO acts only to intensify or weaken the dominant precipitation pattern, depending on its phase, mainly over SESA. In contrast, the second variability mode in fall is characterized by positive SST anomalies in the Indian Ocean and equatorial and southern Atlantic Ocean and negative in the TNA. The importance of ENSO and the Indian Ocean in the characterization of the SST dipole in the tropical Atlantic explains the main changes in precipitation patterns over northeastern Brazil not been discussed in previous studies.
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| 2. |
- Rudke, Anderson Paulo, et al.
(author)
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Land cover data of Upper Parana River Basin, South America, at high spatial resolution
- 2019
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In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 83
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Journal article (peer-reviewed)abstract
- This study presents a new land cover map for the Upper Paraná River Basin (UPRB-2015), with high spatial resolution (30 m), and a high number of calibration and validation sites. To the new map, 50 Landsat-8 scenes were classified with the Support Vector Machine (SVM) algorithm and their level of agreement was assessed using overall accuracy and Kappa coefficient. The generated map was compared by area and by pixel with six global products (MODIS, GlobCover, Globeland30, FROM-GLC, CCI-LC and, GLCNMO). The results of the new classification showed an overall accuracy ranging from 67% to 100%, depending on the sub-basin (80.0% for the entire UPRB). Kappa coefficient was observed ranging from 0.50 to 1.00 (average of 0.73 in the whole basin). Anthropic areas cover more than 70% of the entire UPRB in the new product, with Croplands covering 46.0%. The new mapped areas of croplands are consistent with local socio-economic statistics but don't agree with global products, especially FROM-GLC (14,9%), MODIS (33.8%), GlobCover (71.2%), and CCI (67.8%). In addition, all global products show generalized spatial disagreement, with some sub-basins showing areas of cropland varying by an order of magnitude, compared to UPRB-2015. In the case of Grassland, covering 25.6% of the UPRB, it was observed a strong underestimation by all global products. Even for the Globeland30 and MODIS, which show some significant fraction of pasture areas, there is a high level of disagreement in the spatial distribution. In terms of general agreement, the seven compared mappings (including the new map) agree in only 6.6% of the study area, predominantly areas of forest and agriculture. Finally, the new classification proposed in this study provides better inputs for regional studies, especially for those involving hydrological modeling as well as offers a more refined LU/LC data set for atmospheric numerical models.
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