2017
DOI: 10.1016/j.atmosres.2017.07.016
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Multiple imputation of rainfall missing data in the Iberian Mediterranean context

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Cited by 67 publications
(76 citation statements)
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“…These missing data were estimated by the nonlinear principal component analysis (NLPCA) method proposed by Scholz et al [66]. This method was applied to estimate rainfall missing data by Miró et al [67], who evaluated 10 methodologies to estimate missing data in daily and monthly rainfall data, and reported NLPCA as the best-performing method. Furthermore, this methodology was used by Canchala et al [68], who estimated missing data of monthly rainfall in southwestern Colombia using artificial neural networks (ANN).…”
Section: Filling Missing Datamentioning
confidence: 99%
See 1 more Smart Citation
“…These missing data were estimated by the nonlinear principal component analysis (NLPCA) method proposed by Scholz et al [66]. This method was applied to estimate rainfall missing data by Miró et al [67], who evaluated 10 methodologies to estimate missing data in daily and monthly rainfall data, and reported NLPCA as the best-performing method. Furthermore, this methodology was used by Canchala et al [68], who estimated missing data of monthly rainfall in southwestern Colombia using artificial neural networks (ANN).…”
Section: Filling Missing Datamentioning
confidence: 99%
“…The PCA is a well-established linear statistical method frequently employed to analyze hydroclimatological data, such as streamflows and rainfall [5,67,70,71]. Its objective is to reduce the size of the time series to some main orthogonal principal components (PCs) that explain most of the variability of the original variables [72] with minimum loss of information [73].…”
Section: Principal Component Analysismentioning
confidence: 99%
“…This spatial density of stations has been able to be used thanks to a nonlinear gap filling method for multiple observed rainfall series tested and compared in Miró et al . () for the study region. This method now allows the use a total of 890 series in the region (CHJ–CHS) to study on a spatial fine scale the rainfall trends between 1955 and 2016.…”
Section: Introductionmentioning
confidence: 99%
“…La figura 18 (superior) muestra las tendencias que presentan las precipitaciones anuales en el territorio valenciano, en el período 1955-2016, obtenidas a partir de más de 800 observatorios en las Cuencas Hidrográ-ficas del Júcar y Segura, y siguiendo los métodos de relleno de lagunas y homogeneización indicados en Miró et al (2017) y Domonkos (2015. Se aprecia un predominio de tendencias negativas ya en curso, particularmente en la cuenca alta y media del Júcar y la media-baja del Turia, así como en el Vinalopó y litoral central de Alicante.…”
Section: Evolución Futura De Las Precipitaciones a Efectos De Planifunclassified