School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, PR China; Earth System and Global Change Lab, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, PR China; Department of Mathematics, City University of Science and Information Technology, Peshawar, Pakistan.
Department of Computer Science, SZABIST Islamabad Campus, Pakistan.
J Environ Manage. 2022 Jun 15;312:114951. doi: 10.1016/j.jenvman.2022.114951. Epub 2022 Mar 29.
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.
干旱灾害是全球变暖与气候变化的主要后果之一。与其他自然灾害不同，干旱具有复杂的气候特征。因此，准确监测干旱是一项具有挑战性的任务。本文提出了一种在区域层面评估干旱分类的框架。所提出的框架提供了一种新的干旱监测指标，称为多尺度季节性合并区域标准化降水蒸散指数（MSARSPEI）。MSARSPEI 是标准化降水蒸散（SPEI）（Vicente-Serrano 等人，2010）和区域改进加权标准化干旱指数（RIWSDI）（Jiang 等人，2020）的合并。在所提出的框架中，配置特征选择的 Boruta 算法以组合位于特定区域的各个气象站的蒸发月时间序列数据。此外，该框架建议对 K-Component Gaussian（K-CG）混合分布函数的累积分布函数（CDF）进行标准化，以获得 MSARSPEI 数据。该框架的应用基于巴基斯坦的七个不同地区。为了进行比较分析，本文使用 Pearson 相关性比较了 MSARSPE 和 SPEI 的性能。与该研究相关的结果表明，所提出的区域干旱指数在各种时间尺度上与竞争指标具有很强的相关性。此外，该研究还评估了 MSARSPEI 下各种干旱分类的空间范围。总之，本研究得出结论，选择 MSARSPEI 是合理有效的，更适合在全球变暖情景下进行区域干旱评估。
