Development of adaptive standardized precipitation index and its application in the Tibet Plateau region

Drought is one of the most complex natural hazards. Therefore, precise drought monitoring and forecasting are the biggest tasks for hydrologists and environmentalists. Under grid data structure, this paper provides a new drought index—the adaptive standardized precipitation index (ASPI), for the evolution of drought, inferring its spatio-temporal patterns and detecting trends. The methodology of the proposed index is based mainly on dynamic time warping clustering algorithm and dynamic principal components. Historical simulated precipitation data from the Australian community climate and earth-system simulator model of coupled model intercomparison project 6 of 727 grid points scattered around the Tibet Plateau has been considered. Results show that as the time scale increases, the severe and extreme drought trends have increased significantly. Further, the significant decreasing magnitude in ASPI reveals the persistence of future drought in the Tibet Plateau region. From a data mining point of view, the outcomes associated with this research recommend the endorsement of ASPI for effective and precise drought monitoring under grid data structure.