Development of DRIP - drought representation index for CMIP climate model performance, application to Southeast Brazil

Abstract

With the escalating impacts of drought events driven by climate change, reducing the uncertainty of drought projections becomes critical for enhancing risk management and adaptation strategies. This study aimed to develop an index for assessing the performance of CMIP6 Global Climate Models in simulating meteorological drought scenarios across regional hydrological systems, intended to provide more reliable information for management purposes. Named the ‘Drought Representation Index for CMIP Climate Model Performance’ (DRIP), this index evaluates CMIP models' performance to represent drought severity, duration, and return period. DRIP was used to select CMIP models and create an ensemble of the best-performing models (E-DRIP) to improve the reliability of drought projections. E-DRIP was then compared with a general ensemble of available CMIP6 models (E-CMIP). We applied this method in Southeast Brazil, a region known for its climate uncertainties and low predictability; specifically, it was implemented within the Paraíba do Sul River Basin, a nationally strategic watershed in a highly populated and industrialized area, which has recently faced unprecedented drought-related water crises. Results showed that DRIP effectively assessed the individual performance of CMIP models, which exhibited considerable variability, and identified the top-performing models for a multi-model ensemble. Additionally, the E-DRIP ensemble significantly reduced uncertainties in drought projections, achieving an average reduction of 63 % in the study area compared to E-CMIP. Furthermore, the proposed method enables evaluations across any standardized drought index scale, reference period, or threshold, and can be readily adapted to other hydrological systems.