scholarly journals Modelling of bivariate meteorological drought analysis in Lake Urmia Basin using Archimedean copula functions

2021 ◽  
Vol 28 (6) ◽  
Author(s):  
Farzad Khezri ◽  
Mohsen Irandoost ◽  
Navid Jalalkamali ◽  
Najme Yazdanpanah
Keyword(s):  
Meteorological Drought ◽  
Archimedean Copula ◽  
Lake Urmia ◽  
Copula Functions ◽  
Drought Analysis

scholarly journals Modelling High Dimensional Paddy Production Data using Copulas

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Nuranisyha Mohd Roslan ◽  
Wendy Ling Shinyie ◽  
Sim Siew Ling
Keyword(s):  
Time Series ◽  
Marginal Distribution ◽  
Reference Group ◽  
Estimation Method ◽  
Information Criterion ◽  
Copula Function ◽  
Archimedean Copula ◽  
Copula Functions ◽  
Estimation Errors ◽  
Elliptical Copula

As the climate change is likely to be adversely affecting the yield of paddy production, thence it has brought a limelight of the probable challenges on human particularly regional food security issues. This paper aims to fit multivariate time series of paddy production variables using copula functions and predicts the next year event based on the data of five countries in southeast Asia. In particular, the most appropriate marginal distribution for each univariate time series was first identified using maximum likelihood parameter estimation method. Next, we performed multivariate copula fitting using two types of copula families, namely, elliptical copula family and Archimedean copula family. Elliptical copula family studied are normal and t copula, while Archimedean copula family considered are Joe, Clayton and Gumbel copulas. The performance of marginal distribution and copula fitting was examined using Akaike information criterion (AIC) values. Finally, we used the best fitted copula model to forecast the succeeding event. In order to assess the performance of copula function, we computed the forecast means and estimation errors of copula function with a generalized autoregressive conditional heteroskedasticity model as reference group. Based on the smallest AIC, the majority of the data favoured the Gumbel copula, which belongs to Archimedean copula family as well as extreme value copula family. Likewise, applying the historical data to forecast the future trends may assist all relevant stakeholders, for instance government, NGO agencies, and professional practitioners in making informed decisions without compromising the environmental as well as economical sustainability in the region.

scholarly journals Comparison Between Bivariate and Trivariate Flood Frequency Analysis Using the Archimedean Copula Functions, A Case Study of the Karun River in Iran

2021 ◽  
Author(s):  
Mohamad Haytham Klaho ◽  
Hamid R. Safavi ◽  
Mohamad H. Golmohammadi ◽  
Maamoun Alkntar
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Goodness Of Fit ◽  
Flood Frequency ◽  
Archimedean Copula ◽  
Flood Frequency Analysis ◽  
Cumulative Probability ◽  
Copula Functions ◽  
Flood Peak ◽  
Multiple Variables

Abstract Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak, volume and duration poses more motivation for hydrologists to study. In this paper, the bivariate and trivariate flood frequency analysis and modeling using Archimedean copula functions is focused. For this purpose, the annual flood data over a 55-year historical period recorded at the Dez Dam hydrometric station were used. The results showed that based on goodness of fit criteria, the Frank function built upon the couple of the flood peak-volume and the couple of the flood peak-duration as well as the Clayton function built upon the flood volume-duration were identified to be the best copula families to be adopted. The trivariate analysis was conducted and the Clayton family was chosen as the best copula function. Thereafter, the common and conditional cumulative probability distribution functions were built and analyzed to determine the periodic "and", "or" and "conditional" bivariate and trivariate flood return periods. The results suggest that the bivariate conditional return period obtained for short-term periods is more reliable than the trivariate conditional return period. Additionally, the trivariate conditional return period calculated for long-term periods is more reliable than the bivariate conditional return period.

scholarly journals Multivariate groundwater drought analysis using copulas

2020 ◽  
Vol 51 (4) ◽  
pp. 666-685
Author(s):  
Bahram Saghafian ◽  
Hamid Sanginabadi
Keyword(s):  
Goodness Of Fit ◽  
Strong Dependence ◽  
Management Strategies ◽  
Meteorological Drought ◽  
Scientific Basis ◽  
Distribution Functions ◽  
Drought Analysis ◽  
Copula Models ◽  
Drought Characteristics ◽  
Groundwater Drought

Abstract Drought characteristics are among major inputs in the planning and management of water resources. Although numerous studies on probabilistic aspects of meteorological drought characteristics and their joint distribution functions have been reported, multivariate analysis of groundwater (GW) drought is rarely available. In this paper, while proposing a framework for statistical analysis of disturbed hydrological systems, copula-based multivariate GW drought analysis was performed in an over-drafted aquifer. For this purpose, a 1,000-year synthetic time series of naturalized GW level was produced. GW drought was monitored via the Standardized GW Index (SGI) index while the multivariate GW drought probability and return period were determined via copulas. Comparison between the copula and empirical GW drought probabilities using statistical goodness-of-fit tests proved sufficient accuracy of copula models in multivariate drought analysis. The results showed strong dependence among GW drought characteristics. Generally speaking, multivariate GW drought analysis incorporates major drought characteristics and provides concrete scientific basis for planning drought management strategies.

Modeling of joint rainfall rate and rain attenuation statistics using Archimedean Copula functions

2014 ◽  
Author(s):  
Charilaos I. Kourogiorgas ◽  
Spiros N. Livieratos ◽  
Athanasios D. Panagopoulos ◽  
George E. Chatzarakis
Keyword(s):  
Rain Attenuation ◽  
Rainfall Rate ◽  
Archimedean Copula ◽  
Copula Functions

Meteorological drought analysis case study: Central Anatolia

2011 ◽  
Vol 26 (1-3) ◽  
pp. 14-23 ◽  
Author(s):  
Ülker Güner Bacanli ◽  
Fatih Dikbaş ◽  
Türkay Baran
Keyword(s):  
Meteorological Drought ◽  
Central Anatolia ◽  
Drought Analysis
Sign in / Sign up

Export Citation Format

Share Document