000121388 001__ 121388 000121388 005__ 20230126110109.0 000121388 0247_ $$2doi$$a10.1016/j.scitotenv.2018.04.400 000121388 0248_ $$2sideral$$a132018 000121388 037__ $$aART-2018-132018 000121388 041__ $$aeng 000121388 100__ $$0(orcid)0000-0002-3974-2947$$aBeguería, Santiago 000121388 245__ $$aComputation of rainfall erosivity from daily precipitation amounts 000121388 260__ $$c2018 000121388 5060_ $$aAccess copy available to the general public$$fUnrestricted 000121388 5203_ $$aRainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases. 000121388 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/CGL2014-52135-C3-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/CGL2017-83866-C3-3-R 000121388 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000121388 590__ $$a5.589$$b2018 000121388 591__ $$aENVIRONMENTAL SCIENCES$$b27 / 250 = 0.108$$c2018$$dQ1$$eT1 000121388 592__ $$a1.536$$b2018 000121388 593__ $$aEnvironmental Chemistry$$c2018$$dQ1 000121388 593__ $$aWaste Management and Disposal$$c2018$$dQ1 000121388 593__ $$aPollution$$c2018$$dQ1 000121388 593__ $$aEnvironmental Engineering$$c2018$$dQ1 000121388 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000121388 700__ $$0(orcid)0000-0001-7663-1202$$aSerrano-Notivoli, Roberto$$uUniversidad de Zaragoza 000121388 700__ $$aTomas-Burguera, Miquel 000121388 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi. 000121388 773__ $$g637-638 (2018), 359-373$$pSci. total environ.$$tSCIENCE OF THE TOTAL ENVIRONMENT$$x0048-9697 000121388 8564_ $$s7160942$$uhttps://zaguan.unizar.es/record/121388/files/texto_completo.pdf$$yPostprint 000121388 8564_ $$s1333719$$uhttps://zaguan.unizar.es/record/121388/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000121388 909CO $$ooai:zaguan.unizar.es:121388$$particulos$$pdriver 000121388 951__ $$a2023-01-26-09:30:57 000121388 980__ $$aARTICLE