Multivariate drought risk analysis based on copula functions: a case study

Seyedabadi, Mohammadreza; Kavianpour, Mohammad Reza; Moazami, Saber

doi:10.2166/ws.2020.153

Cited by 11 publications

(2 citation statements)

References 23 publications

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“…The Kendall's tau (τ ), measures the rank correlation providing insights into the strength and direction of dependence between variables. Several examples of modeling extreme cases of drought variables can be found at [24,31] and references therein. However, in these studies, variables such as drought severity and drought duration have been used as the drought variables [16,17,32].…”

Section: Introductionmentioning

confidence: 99%

Modeling and indexing the drought severity by augmenting SPI metric system to incorporate multi-modal ground temperature data

Karunarathne,

De Silva,

Perera

2023

Preprint

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Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on the weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in the primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods ofelevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI index.

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Section: Introductionmentioning

confidence: 99%

Modeling and indexing the drought severity by augmenting SPI metric system to incorporate multi-modal ground temperature data

Karunarathne,

De Silva,

Perera

2023

Preprint

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“…Each characteristic variable of drought has a certain correlation. Only by selecting multiple drought variables for simultaneous analysis can drought events be better described [15][16][17][18]. There are many methods for the identification of drought characteristic variables, among which the most widely used method is the theory of running distance.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Drought Characteristics in Northern Shaanxi Based on Copula Function

Wang

Rong

et al. 2021

Water

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Precipitation is low and drought occurs frequently in Northern Shaanxi. To study the characteristics and occurrence and development of drought events in Northern Shaanxi is beneficial to the prevention and control of drought disasters. Based on the monthly rainfall data of 10 meteorological stations in Northern Shaanxi from 1960 to 2019, the characteristic variables of drought events at each meteorological station in Northern Shaanxi were extracted by using run theory and copula function. The joint probability distribution and recurrence period were obtained by combining the duration and intensity of drought, and the relationship between drought characteristics and crop drought affected area was studied. The results show that (1) from 1960 to 2019, drought events mainly occurred in Northern Shaanxi with long duration and low severity, short duration and high severity, or short duration and low severity, among which the frequency of drought events that occurred in Yuyang and Baota districts was higher. The frequency of light drought and extreme drought was more in the south and less in the north, while the frequency of moderate drought and severe drought was more in the north and less in the south. (2) The optimal edge distribution of drought intensity and drought duration in Northern Shaanxi is generalized Pareto distribution, and the optimal fitting function is Frank copula function. The greater the duration and intensity of drought, the greater the cumulative probability and return period. (3) The actual recurrence interval and the theoretical recurrence interval of drought events in Northern Shaanxi were close, and the error was only 0.1–0.3a. The results of the joint return period can accurately reflect the actual situation, and this study can provide effective guidance for the prevention and management of agricultural dryland in Northern Shaanxi.

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