“…Random forests are robust against overfitting (i.e., explaining the training data instead of finding general patterns) even in case of small data sets (Breiman, ). Applications of random forests in hydrology include empirical simulation of monthly streamflow (Shortridge et al, ), downscaling of precipitation data (He et al, ), and evaluation of flood hazard risk (Wang et al, ). In this study, we used the random forest implementation in the R statistical software (package “randomForest”) to quantify the prediction strength, expressed as variable importance, of the following predictors for F yw , α , and β : mean catchment slope, median surface flow path length, catchment area, percentage of agricultural land, forest and urban areas in 2012, soil fractions of sand, silt, and clay, and the mean values of annual precipitation, annual PET, annual runoff coefficient (determined from modeled discharge), and daily baseflow index (determined from event separation of modeled discharge) between 2013 and 2015.…”