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Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods

dc.contributor.authorPereira, Paulo
dc.contributor.authorOliva, Marc
dc.contributor.authorMisiune, Ieva
dc.date.accessioned2017-06-26T11:51:09Z
dc.date.available2017-06-26T11:51:09Z
dc.date.issued2016
dc.description.abstractThe objective of this paper is to examine the spatial distribution of several precipitation indexes in Sierra Nevada, Spain: mean annual number of wet days (R ≥ 1 mm), mean annual number of heavy rainy days (R ≥ 10 mm) and mean annual number of very heavy precipitation days (R ≥ 20 mm) and test the performance of several interpolation methods using these variables. In total, 17 univariate and multivariate methods were tested. A set of 36 metereological stations distributed in Sierra Nevada and neighbouring areas was analysed in this study. The original data did not followed the normal distribution; thus, a logarithm was applied to data meet normality purposes. Interpolator’s performance was assessed using the root mean square error generated from cross-validation. The results showed that the mean annual R ≥ 10 mm and R ≥ 20 mm have a higher variability than R ≥ 1 mm. While the elevation and longitude did not show a significant correlation with the studied indexes, the latitude (i.e. distance to the sea) showed a significant negative correlation. The regressions carried out confirmed that elevation was the covariate with higher capacity to explain the variability of the indexes. The incorporation of elevation and longitude slightly increased the explanation capacity of the models. The data of LogR ≥ 1 mm, LogR ≥ 10 mm and LogR ≥ 20 mm displayed a clustered pattern, especially the last two indexes that also showed a strong spatial dependency attributed to the effects of local topography, slope, aspect and valley orientation. The best fitted variogram model to LogR ≥ 1 mm was the linear one while for the LogR ≥ 10 mm and LogR ≥ 20 mm, the Gaussian was the most appropriate. The best interpolator for LogR ≥ 1 mm was the local polinomyal with the power of 1, whereas for LogR ≥ 10 mm and LogR ≥ 20 mm, regression kriging (ROK) using as auxiliary variables the elevation, latitude and longitude was the most accurate. ROK methods significantly improved the interpolations accuracy, especially in LogR ≥ 10 mm and LogR ≥ 20 mm. Nevertheless, the covariates, when used as auxiliary information in ordinary kriging, did not improve the precision of the interpolation.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPereira, P., Oliva, M. & Misiune, I. (2016). Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods. Theor Appl Climatol, 126, 683–698. https://doi.org/10.1007/s00704-015-1606-8pt_PT
dc.identifier.doi10.1007/s00704-015-1606-8pt_PT
dc.identifier.issn0177-798X
dc.identifier.urihttp://hdl.handle.net/10451/28177
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Verlagpt_PT
dc.subjectInterpolation methodspt_PT
dc.subjectSpatial interpolationpt_PT
dc.subjectPrecipitation indexespt_PT
dc.subjectSierra Nevadapt_PT
dc.subjectSpainpt_PT
dc.titleSpatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage698pt_PT
oaire.citation.issue3-4pt_PT
oaire.citation.startPage683pt_PT
oaire.citation.titleTheoretical and Applied Climatologypt_PT
oaire.citation.volume126pt_PT
person.familyNameOliva
person.givenNameMarc
person.identifier.ciencia-id5510-AC71-085A
person.identifier.orcid0000-0001-6521-6388
person.identifier.ridK-5423-2014
person.identifier.scopus-author-id35225198700
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd416bf65-77f6-488e-b275-5fbb257df6d4
relation.isAuthorOfPublication.latestForDiscoveryd416bf65-77f6-488e-b275-5fbb257df6d4

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