En-GARD Downscaled Climate Data over the Colorado River Basin

Input data (Precipitation/Temperature, 500 mb zonal and meridional wind speeds) were quantile mapped to the corresponding ERA-Interim data and the closest 200 analog days, or days in which the input data matched the large-scale surface and upper atmospheric features, were selected independently for each day to be downscaled and used to train a multivariate linear regression to predict the Livneh data from those analog days.

For precipitation, occurrence is modeled separately from magnitude by using a logistic regression with the same analog days to predict the probability of precipitation. To preserve realistic spatiotemporal variability, the residual term from the regression model is saved, and this residual is used to condition a stochastic sampling of the probability distribution for the prediction. Each output variable from En-GARD was quantile mapped to the Livneh meteorological data on a monthly basis to be used as input for a hydrological model that was calibrated using the Livneh meteorological data.

More description of the En-GARD methodology can be found in Gutmann et al. (2022).