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.

过去的几十年中，乌尔米亚湖（LU）水位的下降已经得到解决。但是，进行的研究无法通过考虑湖泊水位时间序列中的样本均值来找到切实可行的解决方案。为此，本研究建议通过解决挥发性变化来重新观察LU中湖泊水位的下降。在从1966年1月到2016年12月的研究期间，每天使用贝叶斯变化点检测方法来定义主要和关键变化点。结果表明，主要变化发生在2000年初，可以在变化前和变化后的事件下研究时间序列。之后，使用了几种方法，即位移轨迹法和单趋势线和多趋势线分析，以消除与湖泊水位时间序列相关的趋势。后来的趋势研究方法在整个研究期间分别应用于2000年之前（即1966-1999年）和之后（即2000-2016年）。然后，使用趋势下降的时间序列，并将通用的自回归条件异方差（GARCH）模型拟合到趋势下降的时间序列，以预测数据运行中的波动性变化。结果表明，在2000年之后的短期和长期持久性中，下降和上升分别发生了变化。发现GARCH（1,1）模型对于解释转折点前后的事件是令人满意的，而在短期和长期持续性的计算公式为0.53至0.75和0.46至0.24，分别。此外，通过考虑湖泊水位异常和LU的变化系数以及Sevan湖和Van Van Lake的两个相邻案例，可以得出结论，这些变化是LU所独有的，并且变化速度在2006年之后有所加快。