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.

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

“…Boruta's primary advantage over traditional threshold-based feature selection methods is its comprehensive evaluation of feature importance, mitigating the risk of overlooking potentially critical features due to subjectively determined thresholds. This method, grounded in statistics, provides a more objective and holistic mechanism for feature selection, making it particularly effective for tackling issues of feature redundancy and correlation in high-dimensional data [31]. In our research, the Boruta algorithm was utilized for selecting features in the soil salinity monitoring model.…”

Integrated framework for hydrologic modelling in data-sparse watersheds and climate change impact on projected green and blue water sustainability

“…Before predicting the future climate, it was necessary to modify the climate model's outputs because they contain biases. The delta and quantile mapping methods were used to downscale and correct the known biases in the precipitation and temperature data, respectively, based on a study conducted in the basin using CPC and PGF gridded data in line with study requirements (Lawal et al, 2023). The methods are non-parametric and corrected the predicted climate data based on point-wise empirical cumulative distribution functions.…”

“…The methodology of the optimization process of the input dataset is discussed in Lawal et al (2023). The proposed strategy is required to address potential shortcomings of the conventional modelling methodologies, such as their incapacity to analyze stochastic aspects, complicated variable input features, and interrelated climatic and hydrological properties that restrict the process' ability to address crucial temporal behaviour (Adamowski et al, 2012).…”

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

“…Several studies have evaluated the performance of CMIP6-GCMs in different regions of the globe, such as North America [1,6,7], Central America [7,8], Africa [9][10][11][12], Asia [13][14][15][16][17], Europe [18,19], and Oceania [20]. For South America (SA), such analyses are even more problematic and necessary, given that the continent has climate complexity resulting from its great latitudinal extension and topographic heterogeneity [21,22].…”