Bivariate Risk Analysis of Droughts Using a Nonparametric Multivariate Standardized Drought Index and Copulas

Vazifehkhah, Saeed; Tosunoğlu, Fatih; Kahya, Ercan

doi:10.1061/(asce)he.1943-5584.0001775

Cited by 25 publications

(4 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different type of distributions was considered for the present analysis, and the best one was selected. The probability distribution functions considered in this study are Normal, Log-normal, Weibull, Exponential, Gamma, Generalized 5) (Shiau 2006) (Vazifehkhah, et al 2019) (Genest andFavre 2007, Tootoonchi, et al 2021):…”

Section: Marginal Distributionmentioning

confidence: 99%

Copula-based multivariate analysis of hydro-meteorological drought

Shaw

Chithra

2023

Theor Appl Climatol

View full text Add to dashboard Cite

Droughts have a wide range of adverse impacts on the environment, society, and economy ranging from regional to national levels. Multivariate analysis of drought characteristics (i.e., drought duration, severity, peak, etc.) is necessary to get a picture of the actual drought situation in a region as these characteristics are interrelated. However, such studies are limited. Therefore, the current study developed a framework to investigate the meteorological and hydrological droughts based on a multivariate analysis of drought characteristics in the Pennar river basin of India which is located in a semi-arid region where agricultural activity is dominant. Long-term observations of precipitation, temperature, and streamflow for the period of 1980-2015 were used to calculate Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), at a 3-month timescale. Based on these indices, three drought characteristics (i.e., drought duration, severity, and peak) were abstracted using the Run Theory. The best-fit marginal distribution was determined for each of the drought variables and those best-fit marginal distributions were used to get the bivariate joint probability distribution, i.e., the copula function. The Copula function was used to figure out the joint probability distribution of multiple variables. Based on the best-fit copula function, joint multivariate distributions have been established and the joint probabilities and the joint return period have been estimated. Results revealed that, out of the nine copula functions, for the overall region of the river basin, for meteorological drought, Frank Copula is found to be the best-fit copula function for the joint risk of drought duration and severity, whereas Survival Clayton copula is found to be the best-fit copula for the combination of both drought duration and peak as well as drought severity and peak. The probability of occurrence of a drought event with specified characteristics of duration, severity, and peak was obtained for the study area based on Copula-based joint probability analysis. The joint return period of a drought event with characteristics greater than the 25 th percentile of the same for the study area is varying from 1 to 2 years whereas it varies from 1 year to 45 years for the 50 th percentile. This analysis will provide important information required for water management and planning in a region to mitigate droughts.

show abstract

Section: Marginal Distributionmentioning

confidence: 99%

Copula-based multivariate analysis of hydro-meteorological drought

Shaw

Chithra

2023

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…The association among these drought characteristics is graphically represented using Severity‐Duration‐Frequency (S‐D‐F) curves in a multivariate frequency analysis framework. Copulas have become a popular tool for multivariate drought frequency analysis recently (Kwon and Lall, 2016; Zhao et al ., 2017; Amirataee et al ., 2018; Fang et al ., 2019; Vazifehkhah et al ., 2019). Copula allows joint distribution modelling of severity and duration irrespective of their marginal distributions to estimate the corresponding return period (Shiau, 2006).…”

Section: Introductionmentioning

confidence: 99%

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

Samantaray

Ramadas

Panda

2020

Intl Journal of Climatology

View full text Add to dashboard Cite

Drought patterns are better understood by analysing the joint dependence among long-term drought characteristics: severity, duration, and frequency. Changes in these drought characteristics are also likely due to potential climate change, for instance, increased drought severity and duration, increased frequency of drought events, and increased spatial extent of drought impacts. In this study, we performed a regional-scale analysis of potential changes in meteorological drought characteristics in a tropical region in India under a warming future climate scenario. The meteorological drought characteristics were computed using the Standardized Precipitation Index. The Coordinated Regional Downscaling Experiment South Asia Regional Climate Model future precipitation simulation under the RCP 4.5 scenario was bias-corrected and utilized for future drought frequency analysis. Regionalization of meteorological drought characteristics was performed using the simple k-means clustering technique to identify the homogeneous regions for baseline and future periods. Bivariate copula was utilized to model the dependence among the drought characteristics in order to develop the S-D-F curves. The changes in regionalization and regional S-D-F curves under future potential climate change were then investigated. The results of the study suggest an increase in drought severity by 2-3% for long-term droughts (duration more than 5 months) in the future as indicated by upward shift and changes in the slope of the regional S-D-F curves. There is a projected increase in the occurrence of long-term droughts in the future when compared to short-term (duration of 1 to 3 months) droughts. We also observed a considerable spatial shift of drought hotspots, the severe drought-prone regions lie in the eastern part of the study region in the future. Future projections of regional S-D-F curves and Spatiotemporal drought characteristics obtained from the proposed framework may be utilized for devising drought mitigation strategies.

show abstract

“…For drought, many studies have modeled and analyzed drought characteristics and indices, such as SPI, SPEI, and PDSI [10][11][12][13][14]. Since droughts are characterized by more than one variable, the copula model has gained popularity in risk assessment of drought [15].…”

Section: Introductionmentioning

confidence: 99%

Modified Maximum Pseudo Likelihood Method of Copula Parameter Estimation for Skewed Hydrometeorological Data

Joo

Shin

Heo

2020

Water

View full text Add to dashboard Cite

For multivariate frequency analysis of hydrometeorological data, the copula model is commonly used to construct joint probability distribution due to its flexibility and simplicity. The Maximum Pseudo-Likelihood (MPL) method is one of the most widely used methods for fitting a copula model. The MPL method was derived from the Weibull plotting position formula assuming a uniform distribution. Because extreme hydrometeorological data are often positively skewed, capacity of the MPL method may not be fully utilized. This study proposes the modified MPL (MMPL) method to improve the MPL method by taking into consideration the skewness of the data. In the MMPL method, the Weibull plotting position formula in the original MPL method is replaced with the formulas which can consider the skewness of the data. The Monte-Carlo simulation has been performed under various conditions in order to assess the performance of the proposed method with the Gumbel copula model. The proposed MMPL method provides more precise parameter estimates than does the MPL method for positively skewed hydrometeorological data based on the simulation results. The MMPL method would be a better alternative for fitting the copula model to the skewed data sets. Additionally, applications of the MMPL methods were performed on the two weather stations (Seosan and Yeongwol) in South Korea.

show abstract

Bivariate Risk Analysis of Droughts Using a Nonparametric Multivariate Standardized Drought Index and Copulas

Cited by 25 publications

References 75 publications

Copula-based multivariate analysis of hydro-meteorological drought

Copula-based multivariate analysis of hydro-meteorological drought

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

Modified Maximum Pseudo Likelihood Method of Copula Parameter Estimation for Skewed Hydrometeorological Data

Contact Info

Product

Resources

About