Trend and abrupt change analysis in water quality of Urmia Lake in comparison with changes in lake water level

Purpose Urmia lake water has impressively decreased recently and seriously endangered the lives of the inhabitants. In this paper, the effects of various factors on the reduction of the lake water are investigated and appropriate scenarios are proposed for future improvement. Due to the significant impact of agricultural issues on this crisis, this paper has focused specifically on agriculture. So, this paper aims to forecast and improve the lake water level. Design/methodology/approach In this paper, a system dynamics (SD) model, which is capable to consider various parameters and variables affecting the lake water level within nonlinear and dynamic relations, is developed. Findings To show the effectiveness of SD model, real data are used to run the model and the results show that the actual behavior of the lake water is reproduced with high validation (around 98.28 per cent). Also, five different scenarios are proposed to increase lake water volume. The hybrid Scenario 5 (which combines three other scenarios including increasing irrigation efficiency in the agricultural sector, changing cultivation pattern of agricultural products and returning some dams’ water that are consumed in the agricultural sector into the lake) is chosen as the most effective scenario for increasing lake volume about 15 billion m3. Originality/value The main contributions of this paper are systemic view to the whole problem, paying attention to the agriculture subject as one of the most important issues, considering many critical variables (e.g. evaporation, salinity and precipitation) and providing improvement policies along with assessing the effects of them.

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Dynamic memory of Urmia Lake water-level fluctuations in hydroclimatic variables

Theoretical and Applied Climatology ◽

10.1007/s00704-019-02844-6 ◽

2019 ◽

Vol 138 (1-2) ◽

pp. 591-603 ◽

Cited By ~ 3

Author(s):

Farshad Fathian

Keyword(s):

Water Level ◽

Lake Water ◽

Urmia Lake ◽

Water Level Fluctuations ◽

Dynamic Memory ◽

Lake Water Level ◽

Hydroclimatic Variables

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ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-219-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 219-226

Author(s):

M. Boueshagh ◽

M. Hasanlou

Keyword(s):

Water Level ◽

Lake Water ◽

Kernel Method ◽

Urmia Lake ◽

Gaussian Kernel ◽

Support Vector ◽

Substantial Impact ◽

Lake Water Level ◽

Global Environmental Issue ◽

Rbf Kernel

Abstract. Lakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most momentous ecosystems in Iran, which is almost close-ended and has become a global environmental issue in recent years. One of the parameters affecting this lake water level is snowfall, which has a key role in the fluctuations of its water level and water resources management. Hence, the purpose of this paper is the Urmia Lake water level estimation during 2000–2006 using observed water level, snow cover, direct precipitation, and evaporation. For this purpose, Support Vector Regression (SVR), which is the most outstanding kernel method (with various kernel types), has been used. Furthermore, four scenarios are considered with different variables as inputs, and the output of all scenarios is the water level of the lake. The results of training and testing data indicate the substantial impact of snow on retrieving the water level of the Urmia Lake at the desired period, and due to the complexity of the data relationships, the Gaussian kernel generally had better results. On the other hand, Quadratic and Cubic kernels did not work well. The fourth scenario, with RBF kernel has the best results [Training: R2 = 97% and RMSE = 0.09 m, Testing: R2 = 96.97% and RMSE = 0.08 m].

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Urmia Lake water-level change detection and modeling

Modeling Earth Systems and Environment ◽

10.1007/s40808-016-0253-0 ◽

2016 ◽

Vol 2 (4) ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Farshad Fathian ◽

Reza Modarres ◽

Zohreh Dehghan

Keyword(s):

Change Detection ◽

Water Level ◽

Lake Water ◽

Urmia Lake ◽

Water Level Change ◽

Lake Water Level ◽

Level Change

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Investigating the effect of hydroclimatological variables on Urmia Lake water level using wavelet coherence measure

Journal of Water and Climate Change ◽

10.2166/wcc.2018.261 ◽

2018 ◽

Vol 10 (1) ◽

pp. 13-29 ◽

Cited By ~ 5

Author(s):

Vahid Nourani ◽

Mahsa Ghasemzade ◽

Ali Danande Mehr ◽

Elnaz Sharghi

Keyword(s):

Time Series ◽

Water Level ◽

Lake Water ◽

Geographical Location ◽

Urmia Lake ◽

Wavelet Coherence ◽

Water Level Fluctuations ◽

Lake Water Level ◽

North West ◽

North East

Abstract In this paper, wavelet transform coherence is implemented to examine the impacts of hydroclimatological variables on water level fluctuations in two large saline lakes in the Middle East with a similar geographical location, namely, Urmia Lake in north-west Iran, which has an extremely simple ecological pyramid where water level decrease produces a very sensitive ecosystem, and Van Lake in north-east Turkey. The present study investigates trends in higher order moments of hydrological time series. The aim of this paper is to investigate the complexity of Urmia Lake water level time series which could lead to decrease fluctuations of time series. To this end, the strength and relationships between five hydroclimatological variables, including rainfall, runoff, temperature, relative humidity, as well as evaporation and water level fluctuations in the lakes were determined and discussed in terms of high common power region, phase relationships, and local multi-scale correlations. The results showed that among the hydroclimatological variables, runoff has the most coherencies (0.9–1) with water level fluctuations in the lakes. Although both lakes are located in a similar climatic region, for the recent 15 years, adverse trend in water level fluctuations of Urmia Lake indicates a critical condition for this lake.

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