Several drought indices have been developed based on various processes (e.g., precipitation, soil moisture, vegetation health) that respond differently to modes of climate variability, shadowing their relatability to teleconnections, which in turn, limits drought forecasting. In this study, we advanced the multivariate analysis of droughts by using long-term Terrestrial Water Storage estimates, soil moisture and precipitation data along with normalized difference vegetation index. To this end, we employed a Vine copula approach using Archimedean and Elliptical copula families to generate two novel multivariate drought indices called Combined Standardized Drought Index (CSDI), based on agricultural, meteorological, hydrological and ecological univariate indices (i.e., the Eco-meteo-hydrologic index and the Agro-meteo-hydrologic index) for 33 major river basins across the globe between 1982 and 2015. To overcome the challenges associated with vine copula building blocks, we exhausted the possible choices of vine trees and selected the superior model based on a variety of performance metrics. CSDIs showed an integrated representation of univariate drought indices and revealed a more comprehensive and improved picture of intensity, duration and frequency of droughts. Our composite analysis showed that El Niño and La Niña have a significant impact on the regional drought occurrences across the globe, with highest impacts observed for fall. Results also showed that CSDIs can extract more conclusive anomalies in response to ENSO signals than univariate indices, as they better represent the ecosystem response to teleconnections.

已经根据各种过程（例如，降水、土壤水分、植被健康）开发了若干干旱指数，这些过程对气候变率模式的反应不同，掩盖了它们与遥相关的相关性，这反过来又限制了干旱预报。在这项研究中，我们通过使用长期陆地蓄水量估计值、土壤水分和降水数据以及归一化差异植被指数来推进干旱的多变量分析。为此，我们采用了 Vine copula 方法，使用阿基米德和椭圆 copula 家族来生成两个新的多元干旱指数，称为联合标准化干旱指数 (CSDI)，基于农业、气象、水文和生态单变量指数（即，1982 年至 2015 年间全球 33 个主要流域的生态气象水文指数和农业气象水文指数）。基于各种性能指标的高级模型。CSDI 显示了单变量干旱指数的综合表现，并揭示了干旱强度、持续时间和频率的更全面和改进的画面。我们的综合分析表明，厄尔尼诺和拉尼娜对全球区域性干旱的发生有显着影响，秋季影响最大。结果还表明，与单变量指数相比，CSDI 可以提取出更多决定性的异常以响应 ENSO 信号，