Development of an assessment framework for the proposed Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI) for regional drought classifications in global warming context. (15th June 2022)

• Record Type: Journal Article
• Authors: Khan, Muhammad Asif; Riaz, Saba; Jiang, He; Qamar, Sadia; Ali, Zulfiqar; Islamil, Muhammad; Nazeer, Amna; Faisal, Muhammad; Satti, Samina; Zhang, Xiang
• Is Part Of: Journal of environmental management. Volume 312(2022)
• Publication Date: 2022-06-15
• Subjects: Drought monitoring -- Global warming -- Boruta algorithm -- K-component Gaussian (K-CG) mixture distribution -- Standardized precipitation evapotranspiration index (SPEI); Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.114951
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Drought hazard is one of the main consequences of global warming and climate change. Unlike other natural disasters, drought has complex climatic features. Therefore, accurate drought monitoring is a challenging task. This paper proposes a framework for assessing drought classifications at the regional level. The proposed framework provides a new drought monitoring indicator called Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI). MSARSPEI is an amalgam of the Standardized Precipitation Evapotranspiration (SPEI) (Vicente-Serrano et al., 2010) and Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al., 2020). In the proposed framework, the Boruta algorithm of feature selection is configured to ensemble monthly time series data of evaporation in various meteorological stations located in specific regions. Further, the framework suggests the standardization of the Cumulative Distribution Function (CDF) of K-Component Gaussian (K-CG) mixture distribution function for obtaining MSARSPEI data. The application of the proposed framework is based on seven different regions of Pakistan. For comparative analysis, this paper compared the performance of MSARSPE with SPEI using Pearson correlation. Outcomes associated with this research show that the proposed regional drought index has a strong correlation with the competing indicator in various time scales. In addition, the study assessed the spatial extent of various drought classifications under MSARSPEI. In summation, this research concludes that the choice of the MSARSPEI is rationally valid and more appropriate for the regional assessment of drought under the global warming scenario. Highlights: Accurate estimation of meteorological variables is the most challenging task. The framework of MSARSPEI attempts its best to estimate drought by considering the effect of temperature. In the MSARSPEI mechanism, we have subjectively used the Boruta algorithm and K-CG mixture distribution. In future research, other machine learning algorithms and probabilistic models could be investigated.