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

Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations.The 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 coverages.This dataset primarily covers countries north of 40°latitude with 0.25°spatial resolution.Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products.Precipitation 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 datasets.Surrogate parameter values were used in some cases,such as with wind parameters.Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations.These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations.Two 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),respectively.Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5%of ground-based parameterizations,effectively improving climate data availability.

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

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.

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

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.

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning(ML)is becoming an ever more important tool in hydrologic modeling.Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones.However,there is another advantage of ML which is its lower computational demand.This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale.Using traditional models like Rangeland Hydrology and Erosion Model(RHEM)requires too much computation time and resources.In this study,we designed an Artificial Neural Network that is able to recreate the RHEM outputs(annual average runoff,soil loss,and sediment yield and not the daily storm event-based values)with high accuracy(Nash-Sutcliffe Efficiency≈1.0)and a very low computational time(13 billion times faster on average using a GPU).We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them.We also,fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios(more than 32,000)so the Emulator remains comprehensive while it works specifically accurately for the real-world cases.We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies.Finally,the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.