Application of Boruta algorithms as a robust methodology for performance evaluation of CMIP6 general circulation models for hydro-climatic studies

Abstract

Regional climate models are essential for climate change projections and hydrologic modelling studies, especially in watersheds that are overly sensitive to changes in climate. Accurate hydrologic model development is a daunting task in data-sparse regions where climate change’s impact on hydrologic and water quality processes is necessary for a well-informed policy decision on adaptation and hazard mitigation strategies. Novel approaches have been evolving that evaluated GCMs with the objective of improved parameterization to limit uncertainty and improve hydrologic model development. However, conclusions drawn should be purpose-driven based on intended usage. This study provides an overview of the state-of-the-art Boruta random forest as a robust methodology in the performance evaluation of GCMs models for hydroclimatic study. Highlights from the assessment indicate that (1) there is consistency in replicating the three observed climate variables of daily precipitation, maximum and minimum temperature respectively, (2) better temporal correlation (R2 = 0.95) in annual precipitation with a mean bias of 0.638mm/year, when compared to symmetrical uncertainty (SU) (R2 = 0.82), and all models ensembles (AME) (R2 = 0.88) with associated biases of 68.19mm/year and 10.57mm/year, respectively. Evaluation of the multi-year climate extreme indices, trends and magnitude reveal that there is a fair representation of basin-scale observed climate extreme events. However, the Boruta random forest approach exhibited a better statistical trend and magnitude of the extreme event in the basin. The findings of the study revealed enhanced GCM dataset evaluation and present a simple and efficient methodology to examine the limitations associated with the selected GCM ensemble for impact study in hydrology.