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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29277
Title: Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador
Authors: Campozano Parra, Lenin Vladimir
Feyen, Jan Jozef Albert
Samaniego Alvarado, Esteban Patricio
Sanchez Merchan, Esteban Andrés
Tenelanda Patiño, Daniel Orlando
metadata.dc.ucuenca.correspondencia: Campozano, L.; Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de CuencaEcuador; email: lenin.campozano@ucuenca.edu.ec
Issue Date: 1-Jan-2016
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 2016
metadata.dc.source: Advances in Meteorology
metadata.dc.identifier.doi: 10.1155/2016/6526341
metadata.dc.type: Article
Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador's main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959351794&doi=10.1155%2f2016%2f6526341&partnerID=40&md5=fb5215406d580ad471e8f8013c9ce7df
ISSN: 16879309
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