121388 20230126110109.0 doi 10.1016/j.scitotenv.2018.04.400 sideral 132018 ART-2018-132018 eng Beguería, Santiago (orcid)0000-0002-3974-2947 Computation of rainfall erosivity from daily precipitation amounts 2018 Rainfall 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. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MINECO/CGL2014-52135-C3-1-R info:eu-repo/grantAgreement/ES/MINECO/CGL2017-83866-C3-3-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 5.589 2018 ENVIRONMENTAL SCIENCES 27 / 250 = 0.108 2018 Q1 T1 1.536 2018 Environmental Chemistry 2018 Q1 Waste Management and Disposal 2018 Q1 Pollution 2018 Q1 Environmental Engineering 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Serrano-Notivoli, Roberto Universidad de Zaragoza (orcid)0000-0001-7663-1202 Tomas-Burguera, Miquel 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. 637-638 (2018), 359-373 Sci. total environ. SCIENCE OF THE TOTAL ENVIRONMENT 0048-9697 7160942 http://zaguan.unizar.es/record/121388/files/texto_completo.pdf Postprint 1333719 http://zaguan.unizar.es/record/121388/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:121388 articulos driver 2023-01-26-09:30:57 ARTICLE