Integrated Drought Index based on Vine Copula Modelling,International Journal of Climatology

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Integrated Drought Index based on Vine Copula Modelling
International Journal of Climatology ( IF 3.5 ) Pub Date : 2022-08-29 , DOI: 10.1002/joc.7840
P. Kanthavel ₁ , C.K. Saxena ₁ , R.K. Singh ₁

Affiliation

As a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D-vine copula was applied to construct a four-dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1- and 4-month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c-means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought.

中文翻译：

基于 Vine Copula 模型的综合干旱指数

作为一种自然灾害，干旱是一种复杂的多变量现象，不仅取决于一个水文气象变量，而且考虑到特定类型的干旱可能无法有效地进行干旱管理。考虑到这一点，许多多变量干旱指数是基于线性假设或传统的 copula 假设单变量干旱指数之间存在对称关系而开发的。在这项研究中，D-vine copula 被应用于构建一个四维指数，命名为综合干旱指数 (IDI)，通过结合四个单变量干旱指数（标准化降水指数 [SPI]、勘察干旱指数 [RDI]、标准化土壤水分指数 [SSI] 和标准化河流流量干旱指数 [SDI]），以更好地反映许多水文气象变量（降水、蒸散、土壤水分和河流流量）和不同类型的干旱（气象、农业和水文）同时发生。Vine copula 被用来解决干旱指数之间的非线性和不对称关系，因为它在高维建模的层次结构的每个步骤中自由选择 copula(s) 的灵活性。IDI 是针对印度中部地区塔普蒂盆地上游的 1 个月和 4 个月时间尺度构建的。IDI 的性能通过依赖性测量（皮尔逊相关系数、互信息）进行了测试，并根据重力恢复和气候实验 (GRACE) 任务得出的陆地水储存异常数据进行了评估。干旱的空间分析是通过带有 IDI 的模糊 c 均值 (FCM) 聚类算法进行的。基于vine copula的IDI有效地解决了与干旱发生相关的不同变量之间的非线性和不对称关系，与针对不同类型干旱的单一干旱指数相比，减少了不确定性。分析揭示了上游和下游流域的干旱风险在空间上存在差异。总的来说，藤蔓连接解决了与干旱等自然灾害相关的变量之间存在的非线性和不对称关系。

更新日期：2022-08-29

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