Lake Water Level Fluctuations Research Articles

Paleoenvironmental reconstruction of Lake Van and Lake Erçek over the last millennium using varved sediments (Eastern Türkiye)

Detailed lithological and geochemical studies of the bottom sediments were conducted based on the original sampling of brackish adjacent lakes: Lake Van and Lake Erçek in Eastern Anatolia, Türkiye. Seasonal variations in the accumulation of weather-driven element content were determined using high-resolution Synchrotron Radiation X-Ray Fluorescence (SR-XRF) analysis of the annual layers in the solid prepare. The results were utilized to approximate the regional paleo-temperature of both lakes and the water level of Lake Van. Visual counting of the varve as well as annual oscillation of measured geochemical series was employed to create a time scale. The element content series were synchronized with recent changes in external climatic environments and lake water level fluctuations. Subsequently, the geochemical series were transformed into climate units, using calibration on synchronous meteorological data. Finally, the original quantitative reconstruction of the regional climate variations was performed over the last millennium period with annual time resolution; for Lake Erçek (840 years ago) and Lake Van (1300 years ago). It was revealed that the sediments of these adjacent lakes (Lake Van and Lake Erçek), located 30 km apart from each other, provided synchronous responses to regional environmental changes, which confirmed the reliability of the reconstructions.

Evaluating D-InSAR performance to detect small water level fluctuations in two small lakes in Sweden

Monitoring lake water level fluctuations is critical for managing water resources, predicting the impacts of climatic change, and preserving ecosystem services lakes provide. However, traditional gauging stations are insufficient to monitor all lakes worldwide due to the large number of existing lakes, the challenges of installation and maintenance, and the remote locations of some. Although satellite altimetry is an alternative for measuring water levels, it cannot monitor small lakes effectively. This study evaluates the potential of Differential Interferometric Synthetic Aperture Radar (D-InSAR) for tracking minor water level changes in small lakes, a method more typically used in wetland studies. We investigate two Swedish lakes using Sentinel-1A and Sentinel-1B data from 2019, generating six-day interferograms and filtering out those with in situ water level changes exceeding one phase cycle. Our results show that D-InSAR can detect small water level changes with Lin’s correlations up to 0.63 and 0.89 and RMSE values of approximately 9 and 4 mm, respectively. These results evidence the potential of future L-band SAR missions with larger wavelengths, such as the NASA-ISRO SAR (NISAR) of the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organisation (ISRO), to track water level changes in lakes and aid water tracking missions such as the SWOT (Surface Water and Ocean Topography).

Taxonomic and functional response of fish assemblages to flood-triggered water level fluctuations in a large floodplain lake along the Yangtze River

Taxonomic and functional response of fish assemblages to flood-triggered water level fluctuations in a large floodplain lake along the Yangtze River

Lake-level responses to climate change in an inland basin in the Japanese Islands during the last 16 kyr

During the last deglaciation and middle Holocene, rapid climatic changes occurred repeatedly. Previous research has primarily focused on investigating these periods in East Asia's coastal and inland regions of the Eurasian continent. In contrast, a limited number of studies have reported on climate change and its environmental impacts in island areas near the Pacific Ocean. This study aims to reconstruct paleosol drainage conditions and lake water level fluctuations in the Suwa Basin, an inland basin in central Japan, after the last deglaciation. We utilize the identification of paleosols and the geochemistry of lacustrine sediments to discuss climate change and its impacts on terrestrial environments in the East Asian coastal region. During the Last Glacial Maximum, the basin experienced a lowstand period; meandering fluvial systems led to the development of dry and well-drained paleosols. The lacustrine environment expanded between ca. 16 cal kyr BP and 5 cal kyr BP during a transgressive period. Short-term lake-level fluctuations were reconstructed by examining repeated paleosol development and changes in the bulk SiO2 content originating from biogenic silica in lacustrine sediments. Our data suggest that the lake's water level fell at ca. 12 cal kyr BP, 8 cal kyr BP, and 7 cal kyr BP. After 5 cal kyr BP, thin paleosols repeatedly formed during short exposure times due to the rapid sedimentation rate in the delta system, which corresponds to the highstand period. Short-term fluctuations between 16 kyr BP and 5 kyr BP, in the lake water level in the inland basin of the Japanese Islands was influenced by drying and cooling trends in the East Asian coastal region, as well as declines in sea surface temperatures in the western tropical Pacific Ocean. These fluctuations roughly synchronize with the Younger Dryas stadial and an 8.2 ka cooling event. Changes in precipitation, which responded to fluctuations in the East Asian summer monsoon intensity and tropical depressions, likely regulated the lake water level. These findings imply the predominant influence of paleoclimate fluctuations over thousands of years on the hydrological regime of lake systems across the island area of the East Asia.

Water Table Fluctuations and Degradation Risk Assessment at the Waterlogged Site of La Draga (Lake Banyoles, Spain)

ABSTRACT Pile dwelling sites in Mediterranean lakes face increasing threats to their conservation and safeguarding from climate change and its associated impacts. The primary objective of this study is to investigate the influence of climate change and water table fluctuations on the preservation of archaeological layers, with a particular focus on organic layers and wooden remains. Based on a combination of lake water level fluctuation analysis, water table measurements, and stratigraphic data, we managed to gauge the effects of recent water table fluctuations on the preservation of the occupation levels at the Neolithic site of La Draga (Lake Banyoles). By simulating and analysing the relationship between water table levels and their effects on the stratigraphic sequence, we were able to identify areas that are most susceptible to the impact of water table fluctuations and therefore potential post-depositional processes. Moreover, statistical analysis of recent hydrological data has shown that there is a direct relationship between lake level and water table at the settlement. This would have been a factor affecting the preservation of both artefacts and deposits since the occupation of La Draga. Thus, the importance of considering hydrological factors when analysing and interpreting lakeside archaeological sites is highlighted.

Estimating and Assessing Monthly Water Level Changes of Reservoirs and Lakes in Jiangsu Province Using Sentinel-3 Radar Altimetry Data

Generating accurate monthly estimations of water level fluctuations in reservoirs and lakes is crucial for supporting effective water resource management and protection. The dual-satellite configuration of Sentinel-3 makes it possible to monitor water level changes with great coverage and short time intervals. However, the potential of Sentinel-3’s Synthetic Aperture Radar Altimetry (SRAL) data to enable operational monitoring of water levels across Jiangsu Province on a monthly basis has not yet been fully explored. This study demonstrated and validated the use of Sentinel-3’s SRAL to generate accurate monthly water level estimations needed to inform water management strategies. The monthly water levels of lakes and reservoirs from 2017 to 2021 were produced using Sentinel-3 level-2 land products. Results showed that, compared with in situ data across eight studied lakes, all lakes presented R (Pearson correlation coefficient) values greater than 0.5 and Root Mean Square Error (RMSE) values less than 1 m. Notably, water level estimates for Tai Lake, Gaoyou Lake, and Luoma Lake were particularly accurate, with R above 0.9 and RMSE below 0.5 m. Furthermore, the monthly water level estimates derived from the Sentinel-3 data showed consistent seasonal trends over the multi-year study period. The annual water level of all lakes did not change significantly, except for Shijiu Lake, of which the difference between the highest and lowest water level was up to about 5 m. Our findings confirmed the water level observation ability of Sentinel-3. The accuracy of water level monitoring could be influenced by internal water level differences, terrain features, as well as the area and shape of the lake. Larger lakes with more altimetry sampling points tended to yield higher accuracy estimates of water level fluctuations. These results demonstrate that the frequent, wide-area coverage offered by this satellite platform provides valuable hydrological information, especially across remote regions lacking in situ data. Sentinel-3 has immense potential to support improved water security in data-scarce regions.

Transformer Based Water Level Prediction in Poyang Lake, China

Water level is an important indicator of lake hydrology characteristics, and its fluctuation significantly affects lake ecosystems. In recent years, deep learning models have shown their superiority in the long-time range prediction of hydrology processes, while the application of deep learning models with the attention mechanism for lake water level prediction is very rare. In this paper, taking Poyang Lake as a case study, the transformer neural network model is applied to examine the model performance in lake water level prediction, to explore the effects of the Yangtze River on lake water level fluctuations, and to analyze the influence of hyper-parameters (window size and model layers) and lead time on the model accuracy. The result indicated that the transformer model performs well in simulating the lake water level variations and can reflect the temporal water level variation characteristics in Poyang Lake. In the testing stage, the RMSE values were recorded in the range of 0.26–0.70 m, and the NSE values are higher than 0.94. Moreover, the Yangtze River inflow has a great influence on the lake water level fluctuation of Poyang Lake, especially in flood and receding periods. The contribution rate of the Yangtze River in RMSE and NSE is higher than 80% and 270%, respectively. Additionally, hyper-parameters, such as window size and model layers, significantly influence the transformer model simulation accuracy. In this study, a window size of 90 d and a model layer of 6 are the most suitable hyper-parameters for water level prediction in Poyang Lake. Additionally, lead time may affect the model accuracy in lake water level prediction. With the lead time varied from one to seven days, the model accuracy was high and RMSE values were in the range of 0.46–0.73 m, while the RMSE value increased to 1.37 m and 1.82 m with the lead time of 15 and 30 days, respectively. The transformer neural network model constructed in this paper was the first to be applied to lake water forecasting and showed high efficiency in Poyang Lake. However, few studies have tried to use transformer model coupling with the attention mechanism for forecasting hydrological processes. It is suggested that the model can be used for long sequence time-series forecasting in hydrological processes in other lakes to test its performance, providing further scientific evidence for the control of lake floods and management of lake resources.

Comparative assessment of advanced machine learning techniques for simulation of lake water level fluctuations based on different dimensionality reduction methods

Comparative assessment of advanced machine learning techniques for simulation of lake water level fluctuations based on different dimensionality reduction methods

Dry–wet seasonal variations of microbially mediated carbon metabolism in soils of a floodplain lake

AbstractSeasonal water level fluctuations in floodplain lakes are known to alter carbon transport, yet it is unclear how such changes affect microbially mediated carbon metabolism. Poyang Lake is a typical floodplain lake linked to the Yangtze River and is significantly affected by water level fluctuations. In this study, we examined the differences of soil organic matter (SOM) properties, bacterial communities and carbon metabolic functions between dry (March) and wet (August) seasons in lake centre, delta and floodplain from Poyang Lake. Generally, the SOM properties (quantity and quality) and microbially mediated carbon metabolism in the three types of sampling areas have undergone greater changes from dry to wet season than bacterial community. Structural equation modelling and variation partition analysis indicated that SOM properties contributed more in shaping the carbon metabolism of microorganisms than bacterial community structures, especially in lake centre. Dry–wet seasonal changes of microbially mediated carbon metabolism in lake centre were mainly affected by amide C=O1650 and aliphatic C‐H2850, 2924, while the delta and floodplain were mainly affected by aromatic C‐O1234. Furthermore, we also found that bacterial community composition could better explain the effects of seasonal hydrology on carbon metabolism, rather than the diversity and assembly processes. We highlight that studies on tightly coupled changes in SOM and microbially mediated carbon metabolism can enhance the understanding of soil carbon dynamics across the floodplain lake.

Responses of lakeshore herbaceous plant guilds to altered water level fluctuations in Yangtze floodplain lakes, China

River–lake disconnection has led to alterations in natural water level fluctuations (WLFs) in lakes along the middle and lower reaches of the Yangtze River to various extents. To predict the effects of altered WLFs on lakeshore vegetation, lakeshore herbaceous plant guilds (LPGs) in six Yangtze floodplain lakes were classified in this study. The lakeshore herbaceous plant distribution pattern, main influencing factors, and responses of different LPGs to altered WLFs were analyzed. A total of 130 plant species were identified from the six lakes. With the increase in the amplitude of WLFs, the total species number in the six lakes showed a hump–shaped trend, whereas the lakeshore herbaceous plant coverage showed a positive correlation. The Qili Lake with the largest WLFs had the lowest species number and highest plant coverage. The species numbers, average biomass, and coverage of lakeshore herbaceous plants in all the six lakes were obviously higher in spring than in autumn. The lakeshore herbaceous plants in the six lakes were classified into eight guilds. Small differences in pH, organic matter, total nitrogen, and total phosphorus were detected in the substrate among the eight LPGs, but obvious differences in soil moisture content and duration and depth of submergence were observed. The canonical correspondence analysis showed that the main environmental factors affecting the distribution of different LPGs in the six lakes in spring and autumn were the amplitude of WLFs and duration of submergence. The regression analyses showed that the biomass of perennial mesophyte with erect stem guild and perennial hygrophyte with well–developed rhizome guild responded sensitively to alterations in the amplitude of WLFs and duration of submergence, and the guilds could be considered as indicator groups at a regional scale. Morphological parameters such as plant height, stem diameter, leaf thickness, and specific leaf area of the eight LPGs showed no significant linear relationships with the increase in WLFs. The use of these parameters in the evaluations of alterations in WLFs needs to be further analyzed.

Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were {X}_{t-1},; {X}_{t-2}, ;{X}_{t-3}, ;{X}_{t-4}, ; {X}_{t-12}) the ANFIS-WOA model attained root mean square error (RMSE approx 0.08 m), mean absolute error (MAE approx 0.06 m), and coefficient of determination (R2approx 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Long-Term Water Level Fluctuations in Terminal Lakes of Central Asia

Long-Term Water Level Fluctuations in Terminal Lakes of Central Asia

Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (ν-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.

Detection of lake shoreline active zones and water volume changes using digital lake bottom model and water level fluctuations

The article presents an assessment of water level fluctuations in eight lakes located in the Bory Tucholskie National Park (Poland). Mann-Kendall and Sen tests were applied to analyze the extent and direction of lake water level changes. For the purpose of detection of shoreline active zones, digital lake bottom models were developed based on bathymetric measurements. They were integrated with catchment digital terrain model, developed based on data from Airborne Laser Scanning. In analysed period, water levels in the majority of lakes showed a decreasing tendency, as a result of climate change. An increasing tendency was only observed for two lakes caused by the activity of beavers (castor fiber). The study permitted the identification of active shoreline zones with long-term exposure to drying and flooding. These zones are of the highest importance from the point of view of the functioning of water and water-dependent ecosystems, particularly protected species.

Review of modern trends and historical stages of development of lake research in Mongolia

There is the need to divide Mongolia's lake research into trends of development stages and to provide a detailed analysis of modern lake research. The aim of this study is to identify the development stages of lake research in Mongolia and to analyse lake research development. This study provides a comparative analysis of the development trends of lake research carried out in the country. The historical development of lake research in Mongolia was divided into 4 stages in chronological order, highlighting the current research direction, scope, and contribution to geography. Since 1990, researchers from many countries have been able to conduct extensive research in Mongolia, and especially since the 2000s, lake research intensified owing to global warming and environmental change. Studies, in particular, focus on paleogeography, paleoclimate changes, lake water regimes and water level fluctuation. Mongolian lake-based studies have tended to identify environmental evolution from the Pleistocene to the Holocene. The investigated areas were primarily Khuvsgul Lake and Darkhad Basin, Lakes Valley and Gobi Lakes, the Great Lake Depression, Mongolian Altai and Khangai Mountain lakes. Since 1990, scientists from Russia, Mongolia, Germany, China, Japan, USA and South Korea have been increasingly focusing on research into lakes in Mongolia. Paleogeographic and paleoclimatic reconstruction, based on the lakes of Central Asia, is being conducted in an innovative manner. Dividing Mongolia's lake research into development stages and identifying research trends will help determine which areas of research can be developed in the future.

Morphometry of Juodis and Lydekinis lakes

Bathymetric surveys of Juodis and Lydekinis Lakes (located in outskirts of Vilnius city, Lithuania) were carried out on April 28 and May 1, 2020, from a boat with attached Lowrance HDI SKIMMER XDCR transducer (200 kHz), Simrad GO5 chartplotter and Lowrance Point-1 GPS/Glonass receiver. Average distance between profiles was ~10 m and depth measurement accuracy up to 2–5 cm. Accuracy of water surface elevation measurement using GNSS receiver Topcon Hiper SR up to 1–2 cm. Later, detailed (0.5x0.5 m) digital lake bottom models (DEM) were developed, bathymetric plans of the lakes were created, the main morphometric features were calculated, the boundaries and area of the lake catchments were defined, and the physical-geographical characteristics of the lakes were described based on field observations and various data. Juodis and Lydekinis are small shallow lakes of glaciokarst origin, located in Tapeliai glacial tunnel valley. Juodis Lake is considerably elongated while shape of Lydekinis Lake is close to the circle. According to survey data, the surface area of Juodis Lake is 9.2 ha, water surface elevation 135.8 m a.s.l., maximum depth 3.7 m, mean depth 1.2 m, volume 114.9 thousands m3, catchment area 344 ha. The surface area of Lydekinis Lake is 1.7 ha, water surface elevation 138.2 m a.s.l., maximum depth 4.9 m, mean depth 2.2 m, volume 36.3 thousands m3, catchment area 71 ha. During the observation period (from June of 2018 to April of 2022), the annual amplitude of water level fluctuations in Juodis Lake reached up to 0.16 m. Water level fluctuations in Lydekinis lake were very large due to the reconstruction of the dam and the installation of new culverts. Currently, the water level in Lydekinis Lake is significantly raised, most of the lake shore and the surrounding wetland are flooded. Keywords: Juodis, Lydekinis, glaciokarst, lake, bathymetric chart, morphometry, Lithuania

Dynamics of plankton biocenoses under water level fluctuations in steppe lakes

Barun-Torey and Zun-Torey lakes are located in the arid steppe zone of the Central (Inner) Asia, Eastern Siberia, Russia. The Torey lakes are characterizes unstable hydrological regime. The water level variation is explained by the periodic filling and drying of the lakes due to cyclical climatic changes in humidity and temperature. We conducted our studies various water level phases of the climatic cycle: from high water level (1999, 2003) to drying out and the initial filling phase (2007, 2011, 2014, 2016, 2018, and 2020). The aim of this paper is to present long-term research results on changes in the plankton biocenoses of the Torey lakes during a climate cycle, the drying and initial filling of basins. Succession of plankton dominant species is in the direction of: diatoms+green algae and rotifers+crustaceans → green algae and crustaceans → green algae+cyanobacteria and crustaceans → no planktonic algae and invertebrates → cyanobacteria+diatoms+green algae and rotifers+cladocerans+copepods.

A Future Study of an Environment Driving Force (EDR): The Impacts of Urmia Lake Water-Level Fluctuations on Human Settlements

Lake Urmia, one of the world’s largest salt lakes, is rapidly losing water and drying up. This environmental hazard has raised concerns about the consequences and impact on the surrounding communities. In this paper, we use a futuristic view (horizon of 10 years based on medium-term planning) to identify the main environmental drivers in the surrounding settlements of the Urmia Lake basin. A qualitative method, based on cross-impact analysis, was used as a means of future research. We also used a Delphi-based expert panel method to collect data and extract the environmental impacts of Urmia Lake. After the three rounds of the Delphi process, the expert panel reached a high level of agreement (100%) on the top 17 environmental consequences. Then, these consequences were classified by driving force and dependency using the MICMAC method. The results show that reducing pasture area, soil and water salinity, groundwater decline and depletion, and destruction of surrounding agricultural lands play a significant role in environmental change in Urmia Lake. Overall, any small change in these variables may lead to fundamental changes in the entire system.

Depth distribution of three submerged macrophytes under water level fluctuations in a large plateau lake

Submerged plants are crucial for maintaining a clear water state in lakes, and their distribution area is usually determined by the lowest distribution boundary (LDB). However, LDBs of submerged macrophytes often exhibit species differences and vary with seasonal water level fluctuations. In this study, we carried out a three-year field investigation to compare differences in the LDBs of three submerged macrophyte species (i.e., Vallisneria natans, Ceratophyllum demersum, and Potamogeton maackianus) and explore their distinct determinants. We assumed that LDB differences would exist among the three species and that water level (WL) would be a key environmental driver of LDB dynamics, with an association between the LDBs of submerged macrophytes and water level fluctuations. Indeed, our results showed significant seasonal differences between C. demersum and V. natans in the period of September - May, with mean annual values in the order (low to high) V. natans, C. demersum, and P. maackianus. Furthermore, water level was a major driving force of the LDB dynamics of the three species. Changes in the LDBs of the three species in response to water level fluctuations could be divided into three periods: i.e., recovery growth period (April to June), stress tolerance period (July to September), and recession period (October to following March). This study not only clarifies the impact of water level on submerged macrophyte growth and distribution, but also has larger practical implications for the ecological restoration and management of large deep lakes.

Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region

Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions.