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Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN)

Big Earth Data 2023年第2期7卷 349-374页

作者： Andrew Fullhart Guillermo E.Ponce-Campos Menberu B.Meles Ryan P.McGehee Gerardo Armendariz Paulo Tarso S.Oliveira Cristiano Das Neves Almeida José C.de Araújo werner nel David C.Goodrich Southwest Watershed Research Center USDA-ARS TucsonAZUSA School of Natural Resources and the Environment University of ArizonaTucsonAZUSA Sustainable Agricultural Water Systems Unit USDA-ARS DavisCAUSA Agricultural and Biological Engineering Purdue UniversityWest LafayetteINUSA Faculty of Engineering Architecture and Urbanism and GeographyFederal University of Mato Grosso do Sul(UFMS)Campo GrandeMSBrazil Water Resources and Environmental Engineering Laboratory Federal University of ParaíbaJoao PessoaPBBrazil Federal University of Fortaleza Department of Agricultural EngineeringFortalezaBrazil Department of Geography and Environmental Science University of Fort HareAliceSouth Africa

CLIGEN is a stochastic weather generator that creates statistically representative timeseries of daily and sub-daily point-scale weather variables from observed monthly statistics and other parameters. CLIGEN precipitation timeseries are used as climate input for various risk-assessment modelling applications as an alternative to observe long-term, high temporal resolution records. Here, we queried gridded global climate datasets (TerraClimate, ERA5, GPM-IMERG, and GLDAS) to estimate various 20-year climate statistics and obtain complete CLIGEN input parameter sets with coverage of the African and South American continents at 0.25 arc degree resolution. The estimation of CLIGEN precipitation parameters was informed by a ground-based dataset of >10,000 locations worldwide. The ground observations provided target values to fit regression models that downscale CLIGEN precipitation input parameters. Aside from precipitation parameters, CLIGEN’s parameters for temperature, solar radiation, etc. were in most cases directly calculated according to the original global datasets. Cross-validation for estimated precipitation parameters quantified errors that resulted from applying the estimation approach in a predictive fashion. Based on all training data, the RMSE was 2.23 mm for the estimated monthly average single-event accumulation and 4.70 mm/hr for monthly maximum 30-min intensity. This dataset facilitates exploration of hydrological and soil erosional hypotheses across Africa and South America.

关键词： Climate CLIGEN Africa South America

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