Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions

Tahroudi, Mohammad Nazeri; Ramezani, Yousef; Michele, Carlo De; Mirabbasi, Rasoul

doi:10.2166/nh.2020.036

Cited by 36 publications

(18 citation statements)

References 35 publications

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“…Various Copula families have been introduced so far. Application of Archimedean ( Tahroudi et al., 2020 ; Xu et al., 2019 ) and other Copula families are common in hydrological problems ( Dehghani et al., 2019 ; Li et al., 2019 ; Zhang and Singh, 2007 ). Hence, this study used some of these families (i.e.…”

Section: Methodsmentioning

confidence: 99%

Copula based post-processing for improving the NMME precipitation forecasts

Yazdandoost

Zakipour

Izadi

2021

Heliyon

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Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

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Section: Methodsmentioning

confidence: 99%

Copula based post-processing for improving the NMME precipitation forecasts

Yazdandoost

Zakipour

Izadi

2021

Heliyon

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“…where X 11 À X 12 ð Þand X 21 À X 22 ð Þare independent and evenly distributed copies of X 1 , X 2 ð Þ (Ramezani et al, 2019;Tahroudi et al, 2020b).…”

Section: Kendall's Tau Correlationmentioning

confidence: 99%

Simulation of potential evapotranspiration values based on vine copula

Khashei-Siuki

Shahidi

Ramezani

et al. 2021

Meteorological Applications

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Vine copula had a great impact on the study and analysis of dependence structures in various sciences. In multivariate analyses with dimensions of more than two variables, it is associated with computational complexities that solve vine copulas and these problems. In this study, in order to provide an approach to simulate potential evapotranspiration based on meteorological parameters in Birjand meteorological station from different family copulas including Rvine, independent R-vine, Gaussian, independent Gaussian, C-vine, C-vine independent, D-vine and D-vine independent were used. In this regard, vine copula simulation and conditional density were used. In pair correlation analysis of the studied variables using Kendall's tau statistic, dependence structure confirmed the studied parameters. The results showed a minimum correlation of À0.32 and a maximum of 0.77. The results of Akaike's information criteria (AIC), Bayesian information criteria (BIC) and LogLike statistics in evaluating the performance of vine copula dependency structure introduced the C-vine copula as the superior copula for analysing the pair dependence of the studied variables. By introducing the superior dependency structure and internal copulas, the tree sequence of the pair of values under study was obtained. Pair of simulated values was performed using vine copula. Comparison of Kendall's tau values in both simulation and observation modes showed that Kendall's tau values were close to each other in both modes and were approximately similar. The simulation results of vine copula potential evapotranspiration values and precipitation, temperature and relative humidity values showed 92% efficiency. The efficiency of C-vine copula in dependence analysis and simulation of potential evapotranspiration (PET) values is very high, which shows the ability of vine family copulas in multivariate analysis.

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“…In the traditional methods of flood frequency analysis, one of the assumptions for analyzing flood frequency is that hydrologic series are independent and follow the same distribution (Maidment 1993). However, human activities and climate change have influenced all rivers around the world ( Jain & Lall 2000;Zhang et al 2008;Vogel et al 2011;Li et al 2013;Hu et al 2015;Machado et al 2015;Tahroudi et al 2019Tahroudi et al , 2020bWei et al 2020;Sharifi et al 2021) as well as non-stationary properties in hydrological phenomena (Jain & Lall 2000). These features affect the base assumptions of flood frequency analysis and the associated reliability (Khozeymehnezhad & Nazeri-Tahroudi 2020).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…The proposed approach was adopted to investigate the dynamics of hydrological and meteorological droughts in the Zarinehroud basin, Lake Urmia, Iran. Tahroudi et al (2020b) used the conditional behavior of two signatures to analyzing the signatures of rainfall deficiency and groundwater-level deficiency in the Naqadeh sub-basin, Lake Urmia Basin, Iran, based on copulas. They showed that the presented conditional density function was an alternative method to the conditional return period.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Flood routing via a copula-based approach

et al. 2021

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Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, two-dimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash–Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance.

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Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions

Cited by 36 publications

References 35 publications

Copula based post-processing for improving the NMME precipitation forecasts

Copula based post-processing for improving the NMME precipitation forecasts

Simulation of potential evapotranspiration values based on vine copula

Flood routing via a copula-based approach

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