Modeling the volatility changes in Lake Urmia water level time series
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ORIGINAL PAPER
Modeling the volatility changes in Lake Urmia water level time series Farshad Fathian 1
&
Babak Vaheddoost 2
Received: 15 June 2020 / Accepted: 28 September 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The decline in Lake Urmia (LU) water level during the past two decades has been addressed by several studies. However, the conducted studies could not come across a practical solution by considering the sample mean in the lake water level time series. For this, the present study suggests a fresh look to the lake water level decline in LU by addressing the volatility changes instead. The Bayesian change-point detection method was used to define the major and critical change points during the study period from January 1966 to December 2016 on a daily scale. Results indicated that major changes occurred in early 2000, and the time series can be studied under the pre- and post-change point events. Afterward, several methods namely shift-track and mono- and multiple-trend line analyses were used to remove the trends associated with the lake water level time series. The de-trending approaches later were applied separately for the entire study period, before 2000 (i.e., 1966–1999) and afterward (i.e., 2000– 2016). Then, the de-trended time series were used, and a generalized autoregressive conditional heteroscedasticity (GARCH) model was fitted to the de-trended time series to predict the volatility changes in the data run. Results indicated to descending and ascending changes, respectively, in short- and long-term persistence after 2000. The GARCH(1,1) model was found to be satisfactory to interpret the pre- and post-turn point events, while the changes in short- and the long-term persistence were calculated as 0.53 to 0.75 and 0.46 to 0.24, respectively. In addition, by considering the lake water level anomaly and coefficient of variation in LU and two neighboring cases of Lake Sevan and Lake Van, it is concluded that the changes are exclusive to LU, and the rate of changes was accelerated after 2006.
1 Introduction Lake Urmia (LU) as the second-largest hypersaline lake in the world has been losing water level for the past two decades. Due to the importance of LU, as an international wetland, a protected zone, and a national park, the drastic changes in the lake water level urged United Nations to call for immediate action (Pengra 2012). In this respect, the future of the lake was conceptualized with major environmental failure, together with the collapse of the ecosystem, widespread health issues, and the industrial and/or the agricultural recession (Fathian et al. 2016b). By considering this, it is vital to define models
* Farshad Fathian [email protected] Babak Vaheddoost [email protected] 1
Department of Water Science & Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 77188-97111, Rafsanjan, Iran
2
Department of Civil Engineering, Bursa Technical University, Bursa, Turkey
in simulation/predicting the lake water level in LU, which is reported
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