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Öğe Interaction of groundwater with Lake Urmia in Iran(Wiley, 2018) Vaheddoost, Babak; Aksoy, HafzullahBeing a large hyper-saline water body, Lake Urmia in north-western Iran deals with a gradual decline in its water level. Most of the studies on Lake Urmia have neglected the groundwater issue. In this study, as a direct approach, the interaction between the groundwater level and the lake water level is investigated both in time and space by analysing the groundwater data compiled from observation wells surrounding the lake. Baseflow separation is considered as an indirect approach to understand the groundwater contribution to the river network flowing into the lake. It is determined that about 70% of run-off in the rivers draining into the lake is born in the form of baseflow. An interaction between the lake and the groundwater storage is clearly seen from the analysis to conclude that groundwater has a potential to recharge the lake. Thus, the shrinkage of water level in Lake Urmia could be expected to accelerate with the drastic use of groundwater, which will be a disaster with no return.Öğe Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition(Springer, 2019) Vaheddoost, Babak; Aksoy, HafzullahFrequency domain analysis using an additive decomposition method is proposed to reconstruct the missing hydrometeorological data of selected sites in Lake Urmia basin in Iran. Precipitation, evaporation, streamflow and groundwater time series are used for this aim. Trends, within- and multi-year cycles, and randomness are taken into account to reconstruct each of the time series for which models are developed, calibrated and validated separately. Statistical similarity between the observed and reconstructed time series is checked. Statistical characteristics including the average, standard deviation, skewness, and the first-order autocorrelation coefficient are well preserved at the reconstructed time series. A conceptual water budget model is also established to check for the consistency between the reconstructed and the observed datasets. The water budget model is taken as a quantitative way to confirm that the frequency domain analysis using the additive decomposition is an effective method for the reconstruction of the missing hydrometeorological data based on the case study performed for the Lake Urmia basin in Iran.Öğe Regressive-stochastic models for predicting water level in Lake Urmia(Taylor and Francis Ltd., 2021) Vaheddoost, Babak; Aksoy, HafzullahThis study develops a set of models to investigate the water budget of Lake Urmia in Iran, a permanent hypersaline lake that has suffered a declining water level since the late 1990s. The models are of the regressive-stochastic type, a combination of multilinear regression and autoregressive integrated moving average stochastic models. The multilinear regression models were used to construct the core of the relationship of lake water level to streamflow, precipitation, evaporation and groundwater depth. Afterward, stochastic models were used to generate data for each independent variable to estimate the oscillation in the lake water depth. Several criteria were used to compare the performance of the models in the aggregated and disaggregated cases with which the pre- and post-encroachment periods are considered, respectively. The regressive-stochastic models are found to be competitive with the existing models developed so far for Lake Urmia water level.