Validating the data fusion-based drought index across Queensland, Australia, and investigating interdependencies with remote drivers

Drought monitoring and assessments are important tasks requiring comprehensive, validated indices. Exceptional circumstances (EC) data provided by the Queensland Government are used here as ground truth to validate the data fusion-based drought index (DFDI) and to recalibrate the regional drought thresholds for the purpose of increasing the predictive accuracy. To achieve this, Queensland is regionalized into relatively homogeneous regions following existing climate and land use classifications, followed by a recalibration of the index's drought thresholds over each subregion. As a benchmark, the rainfall percentile ranking (RPR) and standardized precipitation index (SPI), currently employed for drought monitoring by the Australian Bureau of Meteorology, have been compared with the EC maps and the DFDI, showing an underperformance in reliably detecting drought-affected areas by the precipitation-based indices. The results suggest that the DFDI's true-positive detection rate of water stress conditions closely resembles the EC data in 84.48, 74.23, and 76.31% of the cases across desert, grassland, and tropical/subtropical regions. In addition, correlations along with copulas have been applied to reveal teleconnections and interdependencies between DFDI and effective remote drivers across the whole state, as well as each subregion. Here, comparisons of the 6-month moving average of the DFDI and the equivalent average of the Southern Oscillation index (SOI) (2000-2016) showed that the SOI has the highest impact on drought conditions as SOI thresholds of <-4 associated with the whole state, while the climate subregions had thresholds of <-2.2, <-3.4, and <-4 related to desert, grassland, and tropical/subtropical regions, respectively.