Spatially based reconstruction of daily precipitation instrumental data series

51 Pags.- 12 Figs.- 3 Tabls. The definitive version is available at: http://www.int-res.com/journals/cr/cr-home/

Detalhes bibliográficos
Autores: Serrano-Notivoli, Roberto, Luis, Martín de, Saz-Sánchez, Miguel Ángel, Beguería, Santiago
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/155261
Acesso em linha:http://hdl.handle.net/10261/155261
Access Level:acceso abierto
Palavra-chave:Daily precipitation
Spatial analysis
Quality control
Missing values
Grid
ReddPrec
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spelling Spatially based reconstruction of daily precipitation instrumental data seriesSerrano-Notivoli, RobertoLuis, Martín deSaz-Sánchez, Miguel ÁngelBeguería, SantiagoDaily precipitationSpatial analysisQuality controlMissing valuesGridReddPrec51 Pags.- 12 Figs.- 3 Tabls. The definitive version is available at: http://www.int-res.com/journals/cr/cr-home/This work presents a method for the reconstruction of fragmentary daily precipitation datasets. The method aims to preserve the local and temporal variability characteristic of high-frequency precipitation data, and does not use the time-structure of the data. Based on the precipitation values recorded at closest neighbours during a target day, 2 reference values (RVs) are computed: a binomial prediction (BP) expressing the probability of occurrence of a wet day; and a magnitude prediction (MP), referring to the amount of precipitation. Generalised linear models (GLMs) are used to compute the RVs using the precipitation data (occurrence and magnitude) of the 10 nearest neighbours as the dependent variable, and the geographic information of each station (latitude, longitude, and altitude) as the independent variables. The RVs are then used to (1) apply quality control to the data, flagging suspect records according to 5 predefined criteria; (2) obtain serially complete time series by imputing RVs to missing observations in the original dataset; and (3) create new time series at locations where there were no observations or gridded datasets with even spatial coverage over the study area. The routines used were compiled into an R-package called ‘reddPrec’ (reconstruction of daily data - Precipitation) available to any user. We applied these methods to the complete daily precipitation dataset of the island of Majorca in Spain, spanning the period from 1971-2014.This study was supported by research projects CGL2012-31668, CGL2015-69985-R, CGL2011-24185 and CGL2014-52135-C3-1-R, financed by the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER-ERDF funds. The researchers were supported by the Government of Aragón through the ‘Programme of research groups’ (groups H38, ‘Clima, Cambio Global y Sistemas Naturales’ and ‘E68, Geomorfología y Cambio Global’).Peer reviewedInter ResearchMinisterio de Economía y Competitividad (España)European CommissionGobierno de AragónConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201720172017info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/155261reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.3354/cr01476Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1552612026-05-22T06:33:51Z
dc.title.none.fl_str_mv Spatially based reconstruction of daily precipitation instrumental data series
title Spatially based reconstruction of daily precipitation instrumental data series
spellingShingle Spatially based reconstruction of daily precipitation instrumental data series
Serrano-Notivoli, Roberto
Daily precipitation
Spatial analysis
Quality control
Missing values
Grid
ReddPrec
title_short Spatially based reconstruction of daily precipitation instrumental data series
title_full Spatially based reconstruction of daily precipitation instrumental data series
title_fullStr Spatially based reconstruction of daily precipitation instrumental data series
title_full_unstemmed Spatially based reconstruction of daily precipitation instrumental data series
title_sort Spatially based reconstruction of daily precipitation instrumental data series
dc.creator.none.fl_str_mv Serrano-Notivoli, Roberto
Luis, Martín de
Saz-Sánchez, Miguel Ángel
Beguería, Santiago
author Serrano-Notivoli, Roberto
author_facet Serrano-Notivoli, Roberto
Luis, Martín de
Saz-Sánchez, Miguel Ángel
Beguería, Santiago
author_role author
author2 Luis, Martín de
Saz-Sánchez, Miguel Ángel
Beguería, Santiago
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
European Commission
Gobierno de Aragón
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Daily precipitation
Spatial analysis
Quality control
Missing values
Grid
ReddPrec
topic Daily precipitation
Spatial analysis
Quality control
Missing values
Grid
ReddPrec
description 51 Pags.- 12 Figs.- 3 Tabls. The definitive version is available at: http://www.int-res.com/journals/cr/cr-home/
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/155261
url http://hdl.handle.net/10261/155261
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.3354/cr01476
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Inter Research
publisher.none.fl_str_mv Inter Research
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419197284483072
score 15,812455

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