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dc.contributor.authorBeguería, Santiagoes_ES
dc.contributor.authorSerrano-Notivoli, Robertoes_ES
dc.contributor.authorTomás-Burguera, Miqueles_ES
dc.date.accessioned2018-05-29T06:37:20Z-
dc.date.available2018-05-29T06:37:20Z-
dc.date.issued2018-10-
dc.identifier.citationBeguería S, Serrano-Notivoli R, Tomás-Burguera M. Computation of rainfall erosivity from daily precipitation amounts. Science of the Total Environment 637-638: 359-373 (2018)es_ES
dc.identifier.issn0048-9697-
dc.identifier.urihttp://hdl.handle.net/10261/165189-
dc.description42 Pags.- 2 Tabls.- 16 Figs. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697es_ES
dc.description.abstractRainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.es_ES
dc.description.sponsorshipThis work has been supported by research projects CGL2014-52135-C3-1-R andCGL2017-83866-C3-3-R, financed by the Spanish Ministerio de Economía, Industria y Competitividad (MINECO) and EU-FEDER. The work of M. Tomas-Burguera was supported by apredoctoral grant under the FPU program 2013 of the Spanish Ministerio de Educación, Cultura y Deporte.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.isversionofPostprintes_ES
dc.rightsopenAccessen_EN
dc.subjectSoil erosiones_ES
dc.subjectRainfall erosivityRUSLEUSLERfactores_ES
dc.subjectRainfall erosivityes_ES
dc.subjectRUSLEes_ES
dc.subjectUSLEes_ES
dc.subjectR factores_ES
dc.subjectE130es_ES
dc.titleComputation of rainfall erosivity from daily precipitation amountses_ES
dc.typeartículoes_ES
dc.identifier.doihttp://dx.doi.org/10.1016/j.scitotenv.2018.04.400-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.scitotenv.2018.04.400es_ES
dc.identifier.e-issn1879-1026-
dc.embargo.terms2020-10-31es_ES
dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.contributor.funderMinisterio de Economía, Industria y Competitividad (España)es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003176es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100010198es_ES
dc.contributor.orcidBeguería, Santiago [0000-0002-3974-2947]es_ES
dc.contributor.orcidSerrano-Notivoli, Roberto [0000-0001-7663-1202]es_ES
dc.contributor.orcidTomás-Burguera, Miquel [0000-0002-3035-4171]es_ES
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