Bivariate Simulation of Potential Evapotranspiration Using Copula-GARCH Model

Mohammad Nazeri-Tahroudi, Yousef Ramezani (), Carlo Michele () and Rasoul Mirabbasi ()
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Mohammad Nazeri-Tahroudi: University of Birjand
Yousef Ramezani: University of Birjand
Carlo Michele: Politecnico Di Milano
Rasoul Mirabbasi: Shahrekord University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 3, No 14, 1007-1024

Abstract: Abstract Developing statistical period and simulating the required values in case of data shortage increases certainty and reliability of simulations and statistical analyses, which is very important in studies on hydrology and water resources. Therefore, in this study, for simulating values of potential evapotranspiration at Birjand Station located in eastern Iran, contemporaneous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroskedasticity (GARCH), and Copula-GARCH models were used in statistical period of 1984–2019. The potential evapotranspiration and relative humidity time series were simulated using these three models. CARMA model has acceptable accuracy for simulating potential evapotranspiration values due to the effect of the second parameter on simulations. Nash–Sutcliffe efficiency (NSE) coefficient of CARMA model for simulating potential evapotranspiration values was estimated as 0.85. NSE coefficient of CARMA-GARCH model was obtained as 0.87 through extracting residuals of CARMA model and simulating variance of data using GARCH model. Comparing the CARMA and CARMA-GARCH models with each other, it was concluded that a combination of two linear and non-linear time series models increases simulation accuracy to some extent. Using Clayton copula (the selected copula from the studied copulas), the mentioned values were simulated by Copula-GARCH model. The results showed that among the three models used, Copula-GARCH model reduced root mean square error of bivariate simulation compared to CARMA and CARMA-GARCH models by 15 and 13%, respectively. The results also showed that the proposed model simulates the average, first, and third quarters and range of changes in the data by 5 and 95% better than the two CARMA and CARMA-GARCH models.

Keywords: ARCH models; CARMA model; Conditional heteroskedasticity; Clayton; Simulation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03065-9

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