Las sequías en la pampa húmeda: impacto en la productidad del maíz

Minetti, Juan Leonidas; Vargas, Walter M.; Vega, Brianda Guadalupe González; Costa, Maria Conceição da

doi:10.1590/s0102-77862007000200007

Cited by 25 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This humid period was also evident in Villa María Del Río Seco [8] and Rosario [23] stations, although with a lesser intensity. This behaviour was detected by indicators like the annual drought index by Minetti et al (2007). Dolores station [37] showed a trend towards increasing quantities of dry days since the 1950s.…”

Section: Resultsmentioning

confidence: 76%

Inter‐annual and inter‐decadal variability of dry days in Argentina

Rivera¹,

Penalba²,

Betolli³

2012

Intl Journal of Climatology

View full text Add to dashboard Cite

This work proposes to employ the number of dry days (days without precipitation) as a variable of study, and to analyse their spatial and temporal variability in Argentina. Climatological aspects of dry days, such as their annual mean values and its seasonal cycle, were discussed and compared with precipitation features in the country. Linear trends in the annual number of dry days (ANDD) were identified for the period 1960-2005. Most of the regions exhibited decreasing trends, but few stations showed significant ones. The most important trends were present in the Central-West region and over the Patagonian coast and their magnitudes indicated a decrease of two to six dry days per decade. These trends coincide with the observed increase of accumulated precipitation in part of the country during the second half of the 20th century. To identify long-term fluctuations in the ANDD, a low pass filter, a wavelet analysis and a cubic polynomial fit was applied to the longest time series of the selected locations. Through these methodologies, periodicities were identified in the inter-annual and inter-decadal ranges for the last 80 years. The most important significant cycles were observed in the range of 10-24 years, but also, depending on the region, significant inter-annual variations were found.

show abstract

Section: Resultsmentioning

confidence: 76%

Inter‐annual and inter‐decadal variability of dry days in Argentina

Rivera¹,

Penalba²,

Betolli³

2012

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…A quasi‐bidecadal oscillation was also found to modulate rainfall in semi‐arid subtropical and tropical Argentina (Vargas et al ., ). Dry spells and their extreme cases have also been analysed regionally (Minetti et al ., ; Vargas et al ., ) and for all the country (Rivera et al ., ). In general, results indicate a decreasing trend in the number of dry days in the country with the most important trends in the Patagonia coast and the central‐west region.…”

Section: Introductionmentioning

confidence: 99%

Observed and simulated changes in precipitation seasonality in Argentina

Doyle

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

Seasonality in Argentina is examined using precipitation observations from 56 weather stations north of 42°S and CRU gridded precipitation. The Walsh and Lawler Seasonality Index (SI) and Precipitation Concentration Index (PCI) are calculated to determine the different seasonality regimes. The main characteristic is an eastwest gradient, where precipitation tends to be more evenly distributed throughout the different months of the year in the east while rainfall in the west is concentrated in 3 months or less. High replicability values indicate that the wettest month generally occurs during a same period of months in central and northern Argentina. Several stations in eastern humid Argentina have significant positive changes in the mean of seasonality indices between 1961 and 1985 and 1986 and 2010, and increasing trend. In most cases this leads to a change in the regime from rather seasonal with a short dry season to a seasonal regime. Through a PAM Clustering technique a new regionalization of precipitation seasonality is obtained. The mean series in each cluster show a tendency towards more seasonal and highly seasonal regimes in all regions. The SI and PCI indices were calculated using the precipitation outputs of 18 global climate models (GCMs) from the Coupled Model Inter‐comparison Project Phase 5 (CMIP5) to examine the ability of these models to represent seasonality spatial distribution and changes in Argentina. To some extent the spatial distribution of seasonality is captured by models; however, there are considerable discrepancies in the location and extension of the eastwest gradient. The sign and location of trends and changes in the mean SI and PCI are adequately reproduced in only a few models. The low ability of GCMs to represent seasonality and its changes is a consequence of the difficulties GCMs have simulating precipitation characteristics in Argentina reported by different authors.

show abstract

“…Precipitaciones extremas asociadas con huracanes, ventiscas, inundaciones, sequías y deslizamientos tienen impactos socioeconómicos significativos para la sociedad (Futrell et al 2005, NRC 2010. Es muy común observar en la literatura referencias a los efectos de los eventos meteorológicos extremos sobre los diferentes cultivos (Scarpati et al 2008, Minetti et al 2007, Penalba et al 2007. Este interés se debe a que la variabilidad temporal y espacial del agua precipitada condiciona la productividad potencial de granos y pasturas (Brizuela et al 2015).…”

Section: Introductionunclassified

Metodología operativa para la obtención de datos históricos de precipitación a partir de la misión satelital Tropical Rainfall Measuring Mission. Validación de resultados con datos de pluviómetros

Gavilán

Pastore

Uranga

et al. 2019

revagro

View full text Add to dashboard Cite

La información de precipitación es crítica para la comprensión del equilibrio hidrológico a escala global. La lluvia, junto con otras variables ambientales tales como evapotranspiración, temperatura, humedad relativa, entre otras, representa un factor de interés para la producción agrícola. Debido a esto, surge la necesidad de llevar adelante estudios que posibiliten comprender mejor la variabilidad espacial y temporal de las mismas. En este trabajo se presenta una metodología que permite automatizar la descarga de series temporales de datos de precipitación de la Misión de Medición de la Lluvia Tropical (Tropical Rainfall Measuring Mission (TRMM)) desde la plataforma Google Earth Engine (GEE) y validar los datos obtenidos con una serie de datos históricos de una estación meteorológica. Con este fin se desarrolló un sistema bajo la plataforma GEE para la generación y descarga de datos TRMM. Como caso de estudio se fijó la cuenca del Arroyo Las Conchas de la Provincia de Entre Ríos, Argentina. Para la validación de los resultados, se generó un set de datos con la información de precipitaciones desde el 1 de enero del 2000 al 31 de diciembre del 2015, medida por pluviómetros, para el área de influencia de la estación meteorológica de la Estación Experimental Agropecuaria del INTA de Oro Verde, departamento de Paraná, provincia de Entre Ríos, Argentina. Los resultados obtenidos mediante el proceso de evaluación muestran que existe una estrecha relación entre ambas fuentes de información. La metodología propuesta permitirá generar sets de datos históricos de precipitación para estudiar el régimen hídrico en regiones de difícil acceso o en cuencas extensas y poco pobladas.

show abstract

Las sequías en la pampa húmeda: impacto en la productidad del maíz

Cited by 25 publications

References 12 publications

Inter‐annual and inter‐decadal variability of dry days in Argentina

Inter‐annual and inter‐decadal variability of dry days in Argentina

Observed and simulated changes in precipitation seasonality in Argentina

Metodología operativa para la obtención de datos históricos de precipitación a partir de la misión satelital Tropical Rainfall Measuring Mission. Validación de resultados con datos de pluviómetros

Contact Info

Product

Resources

About