The Spatiotemporal Weighted Efficient Drought Index—A new generalized procedure of regional drought indicator

• Published in: Ecohydrology Vol. 15; no. 7
• Main Authors: Ali, Farman, Riaz, Saba, Ali, Zulfiqar, Qamar, Sadia, Li, Bing‐Zhao, Khan, Muhammad Asif
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01-10-2022
• Subjects: Climate; Climate change; climate warming; Distribution functions; Drought; Drought index; Environmental monitoring; Environmental risk; Global warming; Hydrologic data; Indicators; Meteorological data; Mitigation; mixture probability models; Performance assessment; Precipitation; Probability theory; Procedures; Standardized Precipitation Index; Statistical analysis; Time series; Weather stations; Weighting
• ISSN: 1936-0584; 1936-0592
• DOI: 10.1002/eco.2454

Drought is a multifaceted climate phenomenon. Due to recent climate warming, the risk of drought has increased. Regional drought management requires accurate regional drought characterization to prepare early warning drought mitigation policies. Therefore, a more flexible and efficient procedure is necessary for characterizing drought at a regional level. This study provides a new spatiotemporal weighting scheme for integrating precipitation data from various meteorological stations located in a specific region. Consequently, this article presents a new generalized regional drought indicator—The Spatiotemporal Weighted Efficient Drought Index (STWEDI). The methodology of STWEDI consists of two stages: (1) The first stage combines the meteorological data of various stations located in a certain region under the proposed weighting scheme, and (2) the second stage standardizes the cumulative distribution function (CDF) of mixture probability models. In application, this research estimated the time series data of STWEDI of seven regions containing a varying number of meteorological stations. To assess the performance of STWEDI, we compared the time series data of STWEDI with the most commonly used Standardized Precipitation Index (SPI) using the Pearson correlation. Results show that STWEDI is strongly correlated with individual SPIs in all the clusters and scales. Moreover, we found that STWEDI is strongly associated with SPI at all time scales. These statistical measures show that STWEDI is the more effective drought indicator for regional drought monitoring.