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 precipit...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.展开更多
Information about extreme rainfall is lacking in regions of South America and Africa.This study attempts to fill this scientific gap by use of a gridded parameterization for the stochastic weather generator,CLIGEN,to ...Information about extreme rainfall is lacking in regions of South America and Africa.This 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)relationships.Analysis 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 years.The 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 gradients.In addition to uncertainties related to GEV fitting,this study quantifies prediction intervals based on ground validation.This validation was important for identifying biases in CLIGEN,although uncertainties were not always satisfactorily defined due to sampling design and other factors.For 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%,respectively.For sub-daily durations,prediction errors were larger overall.An analogy using available U.S.data 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 cases.This atlas is freely available for study of extreme precipitation.展开更多
文摘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.
文摘Information about extreme rainfall is lacking in regions of South America and Africa.This 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)relationships.Analysis 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 years.The 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 gradients.In addition to uncertainties related to GEV fitting,this study quantifies prediction intervals based on ground validation.This validation was important for identifying biases in CLIGEN,although uncertainties were not always satisfactorily defined due to sampling design and other factors.For 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%,respectively.For sub-daily durations,prediction errors were larger overall.An analogy using available U.S.data 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 cases.This atlas is freely available for study of extreme precipitation.
