Modeling the volatility changes in Lake Urmia water level time series

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dc.authorid0000-0002-4767-6660en_US
dc.contributor.authorFathian, Farshad
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2021-03-20T20:09:19Z
dc.date.available2021-03-20T20:09:19Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.1007/s00704-020-03417-8en_US
dc.identifier.endpage72en_US
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue1-2en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage61en_US
dc.identifier.urihttp://doi.org/10.1007/s00704-020-03417-8
dc.identifier.urihttps://hdl.handle.net/20.500.12885/361
dc.identifier.volume143en_US
dc.identifier.wosWOS:000574809900002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorVaheddoost, Babak
dc.language.isoenen_US
dc.publisherSpringer Wienen_US
dc.relation.ispartofTheoretical And Applied Climatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleModeling the volatility changes in Lake Urmia water level time seriesen_US
dc.typeArticleen_US

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