Uncertainty quantification in climate change impacts on hydrology using ANOVA

The climate model projections obtained from the Regional Climate Models (RCMs) and their forcing through the hydrological models are known to include multi-source uncertainties. Using the subsequent modelling data for their intended purposes, such as watershed studies, flood mitigation, and climate change adaptation policies while containing unsupervised uncertainties of such nature, will prove detrimentally misjudged and potentially do more harm than good. The uncertainty propagation takes place at each stage along the modelling process and depends on the choice of climate model projections, Representative Concentration Pathways (RCPs), bias correction methods (BCs),  hydrological models, and hydrological parameters among other contributors.

 

The aim of this paper is to quantify the overall uncertainty in climate change impacts using Analysis of Variance (ANOVA) to decompose or disaggregate it into components and assess relative contribution of each of the components. The approach is demonstrated through a case study in Little River Experimental Watershed (LREW watershed) under two different emission scenarios (RCP 4.5 and RCP 8.5), five sets of RCM and driving GCM combinations, two bias correction methods by utilizing the Soil & Water Assessment Tool (SWAT) hydrological model. The results will indicate breakdown of the total uncertainty (T) into the respective uncertainties caused by the climate models (C), emission scenario (R), bias correction method (B) and unlike most of the uncertainty decomposition studies, further uncertainty breakdown due to the interactions between various components are also presented. The findings from this study will be useful to the modelers involved with flood mitigation or policy management by enhancing their understanding about the nature of streamflow projections and effectively aid in better decisions concerned with adapting to a changing climate subjected to uncertainties.