RESUMO
The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.