An estimation of tropospheric corrections using GPS and synoptic data: Improving Urmia Lake water level time series from Jason-2 and SARAL/AltiKa satellite altimetry

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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Statistical analysis of rainfall and streamflow time series in the Lake Tana Basin, Ethiopia

Journal of Water and Climate Change ◽

10.2166/wcc.2018.008 ◽

2018 ◽

Vol 11 (1) ◽

pp. 258-273 ◽

Cited By ~ 2

Author(s):

Tibebe B. Tigabu ◽

Georg Hörmann ◽

Paul D. Wagner ◽

Nicola Fohrer

Keyword(s):

Time Series ◽

Water Level ◽

Lake Water ◽

Land Use Changes ◽

Annual Rainfall ◽

Lake Tana ◽

Agricultural Water Management ◽

Lake Water Level ◽

Rainfall Variation ◽

Sample Data

Abstract This research focuses on the statistical analyses of hydrometeorological time series in the basin of Lake Tana, the largest freshwater lake in Ethiopia. We used autocorrelation, cross-correlation, Mann–Kendall, and Tukey multiple mean comparison tests to understand the spatiotemporal variation of the hydrometeorological data in the period from 1960 to 2015. Our results show that mean annual streamflow and the lake water level are varying significantly from decade to decade, whereas the mean annual rainfall variation is not significant. The decadal mean of the lake outflow and the lake water level decreased between the 1990s and 2000s by 11.34 m3/s and 0.35 m, respectively. The autocorrelation for both rainfall and streamflow were significantly different from zero, indicating that the sample data are non-random. Changes in streamflow and lake water level are linked to land use changes. Improvements in agricultural water management could contribute to mitigate the decreasing trends.

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Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods

Water Resources Management ◽

10.1007/s11269-013-0420-2 ◽

2013 ◽

Vol 27 (13) ◽

pp. 4469-4492 ◽

Cited By ~ 38

Author(s):

Hossein Kakahaji ◽

Hamed Dehghan Banadaki ◽

Abbas Kakahaji ◽

Abdulamir Kakahaji

Keyword(s):

Water Level ◽

Lake Water ◽

Urmia Lake ◽

Water Level Fluctuations ◽

Linear Statistic ◽

Lake Water Level ◽

Intelligent Methods

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INVESTIGATING AND MODELLING EFFECTS OF CLIMATICALLY AND HYDROLOGICALLY INDICATORS ON THE URMIA LAKE COASTLINE CHANGES USING TIME SERIES ANALYSIS

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

10.5194/isprs-archives-xlii-4-w4-5-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 5-9

Author(s):

M. Ahmadijamal ◽

M. Hasanlou

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Water Level ◽

Polynomial Regression ◽

Urmia Lake ◽

Lake Basin ◽

Fourth Degree ◽

Periodic Behavior ◽

Lake Water Level ◽

Series Analysis

Study of hydrological parameters of lakes and examine the variation of water level to operate management on water resources are important. The purpose of this study is to investigate and model the Urmia Lake water level changes due to changes in climatically and hydrological indicators that affects in the process of level variation and area of this lake. For this purpose, Landsat satellite images, hydrological data, the daily precipitation, the daily surface evaporation and the daily discharge in total of the lake basin during the period of 2010-2016 have been used. Based on time-series analysis that is conducted on individual data independently with same procedure, to model variation of Urmia Lake level, we used polynomial regression technique and combined polynomial with periodic behavior. In the first scenario, we fit a multivariate linear polynomial to our datasets and determining RMSE, NRSME and R² value. We found that fourth degree polynomial can better fit to our datasets with lowest RMSE value about 9 cm. In the second scenario, we combine polynomial with periodic behavior for modeling. The second scenario has superiority comparing to the first one, by RMSE value about 3 cm.

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

Environmental Monitoring and Assessment ◽

10.1007/s10661-020-08577-8 ◽

2020 ◽

Vol 192 (10) ◽

Author(s):

Mohammad Taghi Sattari ◽

Rasoul Mirabbasi ◽

Salar Jarhan ◽

Fatemeh Shaker Sureh ◽

Sajjad Ahmad

Keyword(s):

Water Quality ◽

Water Level ◽

Lake Water ◽

Abrupt Change ◽

Urmia Lake ◽

Change Analysis ◽

Lake Water Level ◽

Abrupt Change Analysis

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A system dynamics approach for basin policy design: Urmia lake case study

Kybernetes ◽

10.1108/k-04-2019-0226 ◽

2019 ◽

Vol 49 (6) ◽

pp. 1691-1720

Author(s):

Alireza Lari ◽

Mir Saman Pishvaee ◽

Pouria Khodabakhsh

Keyword(s):

System Dynamics ◽

Water Level ◽

Lake Water ◽

Agricultural Sector ◽

Urmia Lake ◽

Water Volume ◽

Actual Behavior ◽

Content Type ◽

Lake Water Level ◽

Sd Model

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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Stochastic modeling of Lake Van water level time series with jumps and multiple trends

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2297-2013 ◽

2013 ◽

Vol 17 (6) ◽

pp. 2297-2303 ◽

Cited By ~ 13

Author(s):

H. Aksoy ◽

N. E. Unal ◽

E. Eris ◽

M. I. Yuce

Keyword(s):

Time Series ◽

Water Level ◽

Lake Water ◽

Linear Trend ◽

Lake Van ◽

Lake Water Level ◽

Trend Model ◽

Level Data ◽

Water Level Data ◽

Surrounding Areas

Abstract. In the 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey, has risen up about 2 m. Analysis of the hydrometeorological data shows that change in the water level is related to the water budget of the lake. In this study, stochastic models are proposed for simulating monthly water level data. Two models considering mono- and multiple-trend time series are developed. The models are derived after removal of trend and periodicity in the dataset. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. In the so-called mono-trend model, the time series is treated as a whole under the hypothesis that the lake water level has an increasing trend. In the second model (so-called multiple-trend), the time series is divided into a number of segments to each a linear trend can be fitted separately. Application on the lake water level data shows that four segments, each fitted with a trend line, are meaningful. Both the mono- and multiple-trend models are used for simulation of synthetic lake water level time series under the hypothesis that the observed mono- and multiple-trend structure of the lake water level persist during the simulation period. The multiple-trend model is found better for planning the future infrastructural projects in surrounding areas of the lake as it generates higher maxima for the simulated lake water level.

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