Regionalization of climate over the Argentine Pampas

Abstract

The aim of this study was to classify and characterize the climate of the Pampas, a vast region in the centre of Argentina. Due to its territorial extension, a climatic zoning of the region was performed, based on the topography and the most relevant climatological parameters. Climate data from 33 stations of the National Meteorological Service were analysed considering the period 1960–2010. A cluster analysis from the hierarchical method of Ward with an interval squared Euclidean distance as a measure of dissimilarity was applied. The obtained clusters responded to the north–south temperature gradient of the Pampas, influenced by geographical features such as the Tandilia and Ventania hills in Buenos Aires Province and the Pampean hills in Córdoba. Precipitation had a northeast–southwest gradient, and the influence of the arid southwest diagonal of the Pampas was appreciated by determining a semi‐arid environment. The proximity to the sea marked the average humidity values, whereas the winds of strong intensity were registered in the south and west of the region. On the other hand, the standard precipitation index was calculated to identify wet and dry cycles in each cluster. It allowed to characterize these events considering frequency, periodicity, duration and intensity, resulting in the definition of eight climatic subregions.

Climate regionalization and trends based on daily temperature and precipitation extremes in the south of the Pampas (Argentina)

“…The climate regionalization of the south of Pampas allows us to identify this as an area highly vulnerable to short-term climate variability, and its condition is exacerbated in a north-south direction. In this context, it is remarkable to consider that the semi-arid regions have a rainfall gradient influenced by their limiting condition between humid and arid environments (Aliaga et al, 2017).…”

“…It fits a function to a specified number of points or all points within a specified radius to determine the output value for each location. Kriging aims at producing a better linear unbiased estimate for an unknown location (Aliaga et al, 2017). It is linear because the projected values are weighted linear combinations of the available data, and unbiased because the mean of the error is 0; it aims to minimize the variance of the errors (Menafoglio et al, 2013).…”

Urban Microclimate: Thermo-Hygrometric variations of Bahia Blanca, Argentina

“…Esta señal se relaciona con la estacionalidad de los vientos . La ciudad se caracteriza por presentar una alternancia de masas de aire provenientes del sur caracterizadas por ser húmedas y frías con presencia de precipitaciones y otras provenientes del note con vientos secos e intensos (Aliaga et al, 2017). Esta señal no se encontró en la temperatura del aire.…”

Spatial and temporal dynamics of the mesozooplankton (Crustacea: Cladocera, Copepoda and Decapoda) in a Pampean shallow hyposaline lake during drought conditions

“…The Pampa Plain is the most highly developed agricultural region in Argentina, which contributes even more to the eutrophication process of the area (Quirós et al, 2002). The occurrence of long periods of droughts and floods characterizes the region, and affects water availability, the productivity of agricultural systems, and other human activities (Aliaga et al, 2017). Plankton is an essential indicator of the structure and function of lake ecosystems and their ecological status (Jeppesen et al, 2011).…”