Interdependence between temperature and precipitation: modeling using copula method toward climate protection

Hussain, Bushra; Qureshi, Naeem; Buriro, Riaz Ali; Qureshi, Sania; Pirzado, Ali Akbar; Saleh, Tawfik A.

doi:10.1007/s40808-021-01256-8

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…Understanding the shared behavior between local climatic conditions such as the number of dry days and the total amount of precipitation is considered important as an early stage to form a probabilistic-based model in the future. The copula model is a very effective way to understand the shared behavior of climate indicators (Dixit & Jayakumar, 2022;Hussain et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

NURDIATI¹,

NAJIB²,

THALIB³

2022

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El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can affect the increase in rainfall intensity and the number of dry days, also known as dry spells that can cause drought and increase the potential for forest fires. This study examines the effect of ENSO and IOD conditions on the joint distribution of the number of dry days and total precipitation in a fire-prone area in southern Sumatra, Indonesia. The joint distribution is constructed using rotated copulas from several families, including Gaussian, student's t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8. Fire-prone areas are defined using k-mean clustering, while the copula parameters are estimated using the inference of function for margins (IFM) method. Based on the peak of joint probability density functions (PDFs), ENSO and IOD conditions had a significant effect in the dry season but had no significant effect in the rainy season. The peak of joint PDFs is getting to the dry-dry conditions when the ENSO and IOD indexes increase in the dry season. However, based on coincidence probability, ENSO conditions still influence the joint distribution between the number of dry days and total precipitation during the rainy season but not with IOD conditions. The lower the ENSO index, the higher the probability of wet conditions co-occurring in the number of dry days and total precipitation. Meanwhile, ENSO and IOD conditions significantly affect the coincidence probability between the number of dry days and total precipitation. Moderate-Strong El Niño has the most considerable coincidence probability of 68.5%, followed by Positive IOD with 62.6%. The two conditions had similar effects on the joint distribution of the number of dry days and total precipitation. Moreover, the association between the number of dry days and the total precipitation was stronger in the dry season than in the rainy season.

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Section: Discussionmentioning

confidence: 99%

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

NURDIATI¹,

NAJIB²,

THALIB³

2022

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“…According to Sklar's theorem, a multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula that describes the dependence between the variables (e.g., Sklar, 1959;Schweizer, 1991). The dependence between precipitation and temperature using copulas was investigated by Cong and Brady (2012) and more recently in the studies by Mesbahzadeh et al (2019), Wazneh et al (2020) and Hussain et al (2021). Studies were conducted in different climatic zones; hence, the conclusions about the goodness of fit of copula types differ between studies.…”

Section: Bivariate Methodsmentioning

confidence: 99%

“…(2020) and Hussain et al . (2021). Studies were conducted in different climatic zones; hence, the conclusions about the goodness of fit of copula types differ between studies.…”

Section: Methodsmentioning

confidence: 99%

Bivariate bias correction of the regional climate model ensemble over the Adriatic region

Jurković

Güttler

Pasarić

2022

Intl Journal of Climatology

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The main goal of this analysis is to provide an assessment of the fine grid regional climate model (RCM) ensemble over the broader Adriatic region, which is the domain with orographically complex terrain and a developed coastline. The results of this study are a beneficial complement to earlier work on using modified bivariate bias correction methods. This analysis was based on an ensemble of 12 combinations of three RCMs and four global climate models (GCMs) from 1971 to 2004, divided into calibration and validation parts. A comparison of one univariate and three versions of bivariate bias correction methods of summer and winter monthly temperature and precipitation was performed. To examine the effect of various methods, we used different marginal distributions and distributions of dependence (copula). Bivariate bias correction was conducted with parametric and empirical marginal distributions. In the quantile mapping (univariate) and the parametric bivariate method, gamma (precipitation) and normal (temperature) probability distributions were used. We documented the bias and the influence of bias correction methods on precipitation and temperature, including their climate change signals in historical data. The considered methods maintained the spatial distribution of trends from the native ensemble. If the relationship between variables was not distinct and the correlation coefficient was low, bivariate bias correction methods tended to weaken the correlation coefficient from the model. Two experiments on how the bias correction methods influence statistical measures of the considered variables and their relationships showed that the bivariate method with empirical distributions (eeG) performed slightly better than other methods. Although the eeG method performed better than the other methods, none of the considered methods was able to fully preserve the correlation coefficient between variables detected from observations. Considering two copulas (Gaussian and Student's t), we conclude that the effect of different copula types on method performance is small.

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“…However, to explore the interdependency between two variables, the multivariate probability distribution needs consideration. The Copula method, a multivariate distribution function, is recognized as the best approach for measuring the possible interdependence of two variables compared to the traditional methods (Cong & Brady, 2012;Hussain et al, 2022;Pandey et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

Prodhan,

Islam

2024

Preprint

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Climate change presents a significant challenge to global ecosystems and human well-being, primarily through alterations in temperature and precipitation patterns. Understanding the interdependence between these crucial climatic parameters is crucial for assessing the impacts of climate change, particularly on extreme events like floods and droughts. This study employed copula functions to model the joint distribution of temperature and precipitation, surpassing their individual marginal distributions. The findings revealed strong correlations, as indicated by Kendall's tau coefficients and Spearman's rank correlation coefficients, between precipitation and mean temperature (τ = 0.524, ρ = 0.7), precipitation and maximum temperature (τ = 0.306, ρ = 0.456), and precipitation and minimum temperature (τ = 0.645, ρ = 0.795) at the 1% level of significance. Precipitation and minimum temperature both showed a marginal distribution of generalized Pareto, whereas mean temperature and maximum temperature showed marginal distributions of generalized extreme value and Weibull, respectively. Despite differing marginal distributions, copula modeling enables the establishment of a joint distribution. Among the copula functions tested, the Clayton copula emerges as the most suitable, exhibiting minimal AIC, BIC, RMSE, and maximal log-likelihood (LL) for all temperature (mean, maximum, and minimum) types. Overall, this study emphasizes the utility of copula-based approaches in analyzing the complex interdependence of climatic variables and their implications for climate change assessment and extreme event analysis.

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Interdependence between temperature and precipitation: modeling using copula method toward climate protection

Cited by 12 publications

References 24 publications

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

Bivariate bias correction of the regional climate model ensemble over the Adriatic region

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

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