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Título : | Improving stochastic modelling of daily rainfall using the ENSO index: Model development and application in Chile |
Autor: | Urdiales, Diego Hernán Meza, Francisco Gironás, Jorge A. Gilabert, Horacio B. |
Correspondencia: | Urdiales, Diego Hernán, dhurdiales@uc.cl |
Palabras clave : | Chile Daily Precipitation Enso Index Generalized Lineal Model Mixed Exponential Distribution Stochastic Simulation |
Área de conocimiento FRASCATI amplio: | 2. Ingeniería y Tecnología |
Área de conocimiento FRASCATI detallado: | 2.7.1 Ingeniería Ambiental y Geológica |
Área de conocimiento FRASCATI específico: | 2.7 Ingeniería del Medio Ambiente |
Área de conocimiento UNESCO amplio: | 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas |
ÁArea de conocimiento UNESCO detallado: | 0521 - Ciencias Ambientales |
Área de conocimiento UNESCO específico: | 052 - Medio Ambiente |
Fecha de publicación : | 2018 |
Volumen: | volumen 10 |
Fuente: | Water |
metadata.dc.identifier.doi: | |
Tipo: | ARTÍCULO |
Abstract: | Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values. © 2018 by the authors. |
Resumen : | Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values. © 2018 by the authors. |
URI : | http://dspace.ucuenca.edu.ec/handle/123456789/33284 https://www.scopus.com/record/display.uri?eid=2-s2.0-85041459217&doi=10.3390%2fw10020145&origin=inward&txGid=6f2d674c56fe554c112b1c2ebf9210db |
URI Fuente: | https://www.mdpi.com/journal/water |
ISSN : | 2073-4441 |
Aparece en las colecciones: | Artículos
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