Department of Statistics, Quaid-i-Azam University, Islamabad, Punjab, Pakistan.
Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil, Saudi Arabia.
PeerJ. 2022 May 3;10:e13377. doi: 10.7717/peerj.13377. eCollection 2022.
The Standardized Precipitation Index (SPI) is a vital component of meteorological drought. Several researchers have been using SPI in their studies to develop new methodologies for drought assessment, monitoring, and forecasting. However, it is challenging for SPI to provide quick and comprehensive information about precipitation deficits and drought probability in a homogenous environment. This study proposes a Regional Intensive Continuous Drought Probability Monitoring System (RICDPMS) for obtaining quick and comprehensive information regarding the drought probability and the temporal evolution of the droughts at the regional level. The RICDPMS is based on Monte Carlo Feature Selection (MCFS), steady-state probabilities, and copulas functions. The MCFS is used for selecting more important stations for the analysis. The main purpose of employing MCFS in certain stations is to minimize the time and resources. The use of MCSF makes RICDPMS efficient for drought monitoring in the selected region. Further, the steady-state probabilities are used to calculate regional precipitation thresholds for selected drought intensities, and bivariate copulas are used for modeling complicated dependence structures as persisting between precipitation at varying time intervals. The RICDPMS is validated on the data collected from six meteorological locations (stations) of the northern area of Pakistan. It is observed that the RICDPMS can monitor the regional drought and provide a better quantitative way to analyze deficits with varying drought intensities in the region. Further, the RICDPMS may be used for drought monitoring and mitigation policies.
标准化降水指数 (SPI) 是气象干旱的重要组成部分。几位研究人员在他们的研究中使用 SPI 来开发新的干旱评估、监测和预测方法。然而，SPI 很难在同质环境中提供关于降水不足和干旱概率的快速和全面信息。本研究提出了一种区域密集连续干旱概率监测系统 (RICDPMS)，以获取有关区域水平干旱概率和干旱时间演变的快速和全面信息。RICDPMS 基于蒙特卡罗特征选择 (MCFS)、稳态概率和 Copulas 函数。MCFS 用于选择对分析更重要的站点。在某些站点中使用 MCFS 的主要目的是最小化时间和资源。使用 MCSF 使 RICDPMS 能够有效地监测所选区域的干旱情况。此外，稳态概率用于计算所选干旱强度的区域降水阈值，双变量 Copulas 用于建模不同时间间隔降水之间持续存在的复杂依赖结构。RICDPMS 在巴基斯坦北部六个气象地点（站点）收集的数据上进行了验证。结果表明，RICDPMS 可以监测区域干旱，并提供一种更好的定量方法来分析该区域不同干旱强度下的亏缺情况。此外，RICDPMS 可用于干旱监测和缓解政策。
