Characterization and return period analysis of meteorological drought under the humid subtropical climate of Manipur, northeast India

Vanita Pandey, Pankaj Kumar Pandey, H.P. Lalrammawii
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Abstract

Monitoring drought characteristics is crucial for understanding drought behaviour and developing effective mitigation plans. In this study, we analyze the characteristics of meteorological droughts in the eastern Himalayan region by utilizing both the Standardized Precipitation Index (SPI) and Copula functions. In this study, we utilized monthly rainfall data spanning 35 years to estimate three critical characteristics of droughts: duration (D), severity (S), and Intensity (I). To determine the best fit marginal distribution of each univariate drought characteristic, we employed five commonly used probability distribution functions (PDFs). We conducted Kolmogorov-Smirnov (K–S) and Anderson-Darling (A-D) tests.

The bivariate modelling for the joint D-S, S–I, and I-D datasets involves fitting Archimedean families such as Frank, Clayton, Gumbel, and Joe copulas. To perform the trivariate modelling, two meta elliptical copulas, including Normal and Frank, and two Archimedean families, namely Clayton and Gumbel, are fitted using the test statistics BIC (Bayesian Information Criterion) and AIC (Akaike Information Criterion). The cross-validation process using Maximum Likelihood Estimation (MLE) is employed to identify the most appropriate Copula model based on its goodness of fit. This step is crucial for selecting the best model to accurately describe the joint behaviour of drought characteristics. Once the best-fit Copula model is determined, it is utilized to estimate the return period of various drought characteristics, thereby facilitating the investigation of their joint return period. Furthermore, the distribution of S, D, and I classes is categorized into different return periods (T) to facilitate drought management planning.

The findings revealed moderate drought conditions were recorded for SPI 1 and SPI 3 with a 2–5 years return period. For SPI 1, this drought class remains seasonal even for higher return periods. Further, the drought class transitions from seasonal to quarter for SPI 3 and a return period of 10–50 years. Regarding SPI 6 and SPI 12, the drought class is seasonal for a return period of 2 years, but it later progresses into the quarter to long-term drought class.

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印度东北部曼尼普尔湿润亚热带气候下气象干旱特征及回归期分析
监测干旱特征对于了解干旱行为和制定有效的缓解计划至关重要。本文利用标准化降水指数(SPI)和Copula函数分析了喜马拉雅东部地区的气象干旱特征。在这项研究中,我们利用35年的月降雨量数据来估计干旱的三个关键特征:持续时间(D)、严重程度(S)和强度(I)。为了确定每个单变量干旱特征的最佳拟合边际分布,我们使用了五种常用的概率分布函数(pdf)。我们进行了Kolmogorov-Smirnov (K-S)和Anderson-Darling (A-D)检验。联合D-S、S-I和I-D数据集的二元模型包括拟合阿基米德家族,如Frank、Clayton、Gumbel和Joe copulas。采用贝叶斯信息准则(BIC)和赤池信息准则(AIC)的检验统计量拟合两个元椭圆copulas (Normal)和Frank (Frank),以及两个阿基米德科(Clayton)和Gumbel (Gumbel),实现了三元模型的拟合。利用最大似然估计(MLE)进行交叉验证,根据拟合优度确定最合适的Copula模型。这一步对于选择最佳模型来准确描述干旱特征的共同行为至关重要。确定最佳拟合Copula模型后,利用该模型估计各种干旱特征的回归期,从而便于对其联合回归期的研究。此外,将S、D、I类的分布划分为不同的回归期(T),以方便干旱管理规划。结果表明,SPI 1和SPI 3为中等干旱条件,重现期为2 ~ 5年。对于SPI 1,即使在较高的回归期,这种干旱类别仍然是季节性的。此外,SPI 3的干旱类别从季节性转变为季度性,回归期为10-50年。在SPI 6和SPI 12中,干旱级别为季节性,回归周期为2年,之后发展为季度级,为长期干旱级别。
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