DOI:
https://doi.org/10.14483/23448350.22448Published:
08/25/2025Issue:
Vol. 52 No. 1 (2025): January-April 2025Section:
Research ArticlesDownscaling for Rainfall Prediction in the Aburrá River Valley and its Supplying Watersheds in Antioquia (Colombia) Using Non-Homogeneous Hidden Markov Models
Reducción de escala para la predicción de lluvia en el Valle del Río Aburrá y sus cuencas abastecedoras en Antioquia (Colombia) mediante modelos ocultos de Markov no homogéneos
Keywords:
escenarios de cambio climático, downscaling, modelos ocultos de Markov no homogéneos, modelos de circulación global (es).Keywords:
climate change scenarios, downscaling, non-homogenous hidden Markov models, global circulation models (en).Downloads
Abstract (en)
One of the main issues in projecting climate change scenarios is the coarse scale of global circulation model products.This work employs the statistical downscaling methodology to obtain station-level precipitation series for three climate change scenarios in the region of the Aburrá River and its supplying watersheds, by means of non-homogenous hidden Markov models, using atmospheric variables such as wind, relative humidity, and atmospheric pressure as precipitation predictors. Two approaches are proposed: an annual model and a quarterly one. Each model is trained using precipitation stations to obtain the best number of hidden states that adequately represent the climatology of the area via the BIC criterion. Based on the climatology represented in the states, much better results were obtained with the quarterly model, whose calibrations were used for downscaling the three climate change scenarios. Slight differences were found in the monthly averages, as well as station-scale differences between the probability distributions of daily rainfall for the analyzed scenarios, indicating local alterations in precipitation due to climate change.
Abstract (es)
Uno de los principales problemas al proyectar escenarios de cambio climático es la escala gruesa de los productos de los modelos de circulación global. En este artículo se emplea la metodología de downscaling estadístico para obtener series de precipitación a escala de estación en tres escenarios de cambio climático en la región del Río Aburrá y sus cuencas abastecedoras, a través de modelos ocultos de Markov no homogéneos, utilizando variables atmosféricas como el viento, la humedad relativa y la presión atmosférica como predictores de precipitación. Se proponen dos enfoques: un modelo anual y uno trimestral. Cada modelo es entrenado mediante estaciones de precipitación para obtener el mejor número de estados ocultos que representen adecuadamente la climatología de la zona a través del criterio BIC. Con base en la climatología representada en los estados, se obtuvieron resultados mucho mejores con el modelo trimestral, cuyas calibraciones se utilizaron para el downscaling de los tres escenarios de cambio climático. Se encontraron ligeras diferencias en los promedios mensuales, así como diferencias a escala de estación entre las distribuciones de probabilidad de lluvia diaria para los escenarios analizados, indicando alteraciones locales en la precipitación por efectos del cambio climático.
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