Citas bibligráficas
Cabrera, J., (2023). Modelamiento de sequías agrícolas en la región sur del Perú mediante el uso de funciones cópula [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/6158
Cabrera, J., Modelamiento de sequías agrícolas en la región sur del Perú mediante el uso de funciones cópula []. PE: Universidad Nacional Agraria La Molina; 2023. https://hdl.handle.net/20.500.12996/6158
@phdthesis{renati/1114462,
title = "Modelamiento de sequías agrícolas en la región sur del Perú mediante el uso de funciones cópula",
author = "Cabrera Cabrera, Juan Walter",
publisher = "Universidad Nacional Agraria La Molina",
year = "2023"
}
The present study aims to model the occurrence of agricultural droughts in the upper basin of the Locumba and Caplina rivers using multivariate probability distribution functions, known as copula functions. As a first step, the influence exerted by ENSO, air temperature and sea surface temperature in the different ENSO zones on the occurrence of agricultural droughts was evaluated. The analysis showed that the existing correlations with the ENSO indices are low and very low, which is why their use was ruled out; On the other hand, the Sea Surface Temperature in zone 1+2 (SST1+2) and the air temperature (T) showed medium correlation, so they were selected for the construction of the model. With the defined variables, the families of copulas that best fit the data series were evaluated, using the Cramer Von Mises statistic, and subsequently the parameters of the copulas with the best fit were estimated based on the maximum pseudo likelihood method. , finding that the family of copulas that best represents the relationship between the SPI3 and EMI indices is the t-Student copula, while the copulas that best represent the relationship between SPI3 and Air Temperature and the relationship between SPI3 and SST1+2 is the Frank type. Subsequently, a three-dimensional copula was constructed considering the variables that presented the greatest correlation with the SPI3 drought index, thus defining the desired model. Finally, we proceeded to estimate the probability of joint occurrence under conditions of extreme drought, finding that these occur more frequently under conditions of average air temperatures between 10°C to 12|°C, and sea surface temperatures in zone 1+ 2 between 24°C to 26°C.
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