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Atlas of precipitation extremes for South America and Africa based on depth-duration-frequency relationships in a stochastic weather generator dataset

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International Soil and Water Conservation Research 2023年第4期11卷 726-742页

作者： Andrew Fullhart David C.Goodrich menberu b.meles Paulo Tarso S.Oliveira Cristiano das Neves Almeida JoséC.de Araújo Shea burns Southwest Watershed Research Center USDA-ARS 2000 E.Allen Rd.TucsonAZ85719United States Sustainable Agricultural Water Systems Unit USDA-ARS 239 Hopkins RoadDavisCA95616United States Faculty of Engineering Architecture and Urbanism and GeographyUniversidade Federal de Mato Grosso do SulCampo GrandeMS79070-900Brazil Water Resources and Environmental Engineering Laboratory Universidade Federal da ParaíbaJoao PessoaPB58051-900Brazil Department of Agricultural Engineering Campus do Pici Fortaleza Universidade Federal do CearáFortaleza60020-181Brazil

Information about extreme rainfall is lacking in regions of South America and *** study attempts to fill this scientific gap by use of a gridded parameterization for the stochastic weather generator,CLIGEN,to map depth-duration-frequency(DDF)*** of 500-year point-scale precipitation time series generated at each grid point allowed maps of return period precipitation to be produced for a se-lection of sixteen durations ranging from 10-min to 1-year and for nine return periods from 2 to 500 *** generalized extreme value(GEV)probability distribution was fitted for all durations,and given GEV quantiles,an interpolation method was applied to produce maps at 0.1° resolution that better resolve small-scale spatial climate *** addition to uncertainties related to GEV fitting,this study quantifies prediction intervals based on ground *** validation was important for identifying biases in CLIGEN,although uncertainties were not always satisfactorily defined due to sampling design and other *** daily/multi-day durations,100 stations with daily observations and ≥50-year records were selected for validation against the 0.1° CLIGEN map series,resulting in a median and average absolute error of 13%and 16%,*** sub-daily durations,prediction errors were larger *** analogy using available *** established the degree of bias in CUGEN for sub-daily durations,and three records in brazil with high temporal resolutions were used to confirm that applied bias adjustments resulted in error ranges similar to the daily/multi-day *** atlas is freely available for study of extreme precipitation.

关键词： Africa Depth-duration-frequency Extreme precipitation South America

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Towards global coverage of gridded parameterization for CLImate GENerator(CLIGEN)

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big Earth Data 2024年第1期8卷 142-165页

作者： Andrew T.Fullhart Guillermo E.Ponce-Campos menberu b.meles Ryan P.McGehee Haiyan Wei Gerardo Armendariz Shea burns 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 biosystems Engineering Iowa State UniversityAmesIAUSA

Stochastic weather generators create time series that reproduce key weather dynamics present in long-term *** dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator(CLIGEN)that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations,thereby obtaining near-global availability of combined *** dataset primarily covers countries north of 40°latitude with 0.25°spatial *** CLIGEN parameters were estimated based on 20-year records from four popular global climate *** parameters were statistically downscaled to estimate point-scale values,while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution *** parameter values were used in some cases,such as with wind ***-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN *** parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 *** sensitive parameters,monthly average storm accumulation and maximum 30-minute intensity,were shown have RMSE values of 1.48 mm and 4.67 mm hr^(−1),*** precipitation and the annual number of days with precipitation occurrence were both within 5%of ground-based parameterizations,effectively improving climate data availability.

关键词： Agriculture climate hydrology soil erosion stochastic weather generator

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

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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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