Assessment of Spatio-Temporal Changes in Water Surface Extents and Lake Surface Temperatures Using Google Earth Engine for Lakes Region, Türkiye

Albarqouni, Mohammed M. Y.; Yagmur, Nur; Balçık, Filiz Bektaş; Şekertekin, Aliihsan

doi:10.3390/ijgi11070407

Cited by 26 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is despite of an overall trend of increasing precipitation (Figure 3A) over the observation period reported across the river basin (Matsuyama and Kezer, 2009;Propastin, 2012;Yu et al, 2021), in particular in the spring and summer seasons (Duan et al, 2020). Our results clearly suggest that local precipitation over the lake has minimal impact on turbidity variation, most likely relating to the small absolute amount of rainfall as well as high rates of evaporation loss from large water bodies (Dostay et al, 2012;, Frontiers in Environmental Science frontiersin.org 2018a; Yu et al, 2021;Albarqouni et al, 2022) arid and semi-arid climatic settings. At the catchment scale, precipitation is much higher in the headwater regions, and is particularly pronounced in the climatically diverse Ile-Balkhash catchment; catchment-scale rainfall and snow has an indirect impact on surface runoff and stream discharge upstream region (Petr, 1992;Göransson et al, 2013;Yu et al, 2021).…”

Section: Effect Of Climatic and Anthropogenic Variables On Turbidity ...mentioning

confidence: 60%

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Mishra,

Choudhary,

Fitzsimmons

2024

Front. Environ. Sci.

View full text Add to dashboard Cite

Lake Balkhash is Asia’s third-largest lake and an endorheic basin. The lake and its contributing tributaries provide essential water and ecosystem services to the surrounding population, particularly in the Kazakh region. With approximately 2.5 million people living in the areas such as Almaty oblast, Zhetisu oblast, several districts of Karagandy oblast, and Abay province, monitoring and maintaining the lake’s health and water quality is essential for the sustainable management of water resources. The hydrology of Lake Balkhash has been significantly impacted in recent decades by a warming climate, landuse landcover changes, and water-consuming economic activities, the latter of which are driven by population growth and expansion. Turbidity—the measurement of water clarity—serves as a major indicator of water health. Here, we analyze spatial and temporal variability in turbidity across Lake Balkhash by mapping the normalized difference turbidity index (NDTI) based on Landsat data for 1991–2022. We consider major exploratory variables such as precipitation, near-surface temperature, wind speed and direction, water level, and landuse landcover (LULC) within the catchment. We find an overall decrease in turbidity over interannual and seasonal timescales. We observe significant negative correlations between NDTI, near-surface temperature, and water level at both scales but no clear relationship between turbidity and precipitation or wind variables. Among the LULC variables, grassland and bareland near Lake Balkhash showed a positive correlation with NDTI but have spatially decreased over time. Conversely, shrubland and wetland exhibit a negative correlation with NDTI; however, this has spatially increased with time. Our results highlight the significant impact of rising temperatures, anthropogenically influenced water levels, and the LULC variables on turbidity. The turbidity dynamics, in turn, influence the circulation, oxidation, and overall health of Lake Balkhash’s water. Therefore, the study emphasizes that the warming climate and alterations in the lake’s hydrology have a considerable impact on water quality. This suggests that monitoring water health alone may not suffice to mitigate the impacts of climate change and human activities. However, a more comprehensive approach is needed to sustainably manage and conserve dryland water resources.

show abstract

Section: Effect Of Climatic and Anthropogenic Variables On Turbidity ...mentioning

confidence: 60%

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Mishra,

Choudhary,

Fitzsimmons

2024

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…As one of the most eminent satellite series, Landsat has been observing the Earth for 50 years since 1972, which provides mediumresolution images for scientific research (Wulder et al, 2022). The Landsat eight is one of the newest Landsat satellites, and its data has been widely used for surface water detection (Donchyts et al, 2016a;Pekel et al, 2016;Albarqouni et al, 2022). Herein, the Landsat eight satellite imagery was used to obtain lake occurrences for machine learning modeling via visual interpretation.…”

Section: Lake Inventory Datasetmentioning

confidence: 99%

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Wang

Guo²,

Chen³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

Global warming is inducing dramatic changes in fluvial geomorphology and reshaping the hydrological connections between rivers and lakes. The water level and area of the Salt Lake have increased rapidly since the outburst of the Zonag Lake in the Hoh Xil region of the Qinghai–Tibet Plateau in 2011, threatening the downstream infrastructure. However, fewer studies have focused on its spatiotemporal variation and overflow risk over long time series. Here, we used three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) to extract the area of the Salt Lake for a long time series, analyzed its spatiotemporal variation from 1973 to 2021, and finally assessed the overflow risk. The Kappa coefficient (KAPPA) and the overall accuracy (OA) were used to evaluate the performance of the models. The results showed that Random Forest performs superior in lake extraction (KAPPA = 0.98, overall accuracy = 0.99), followed by Classification and Regression Trees and Support Vector Machine. normalized difference water index is the relatively important feature variable in both RF and CART. Before the outburst event, the area change of the Salt Lake was consistent with the variation in precipitation; after that, it showed a remarkable area increase (circa 350%) in all orientations, and the main direction was the southeast. Without the construction of the emergency drainage channel, the simulation result indicated that the earliest and latest times of the Salt Lake overflow event are predicted to occur in 2020 and 2031, respectively. The results of this paper not only demonstrate that RF is more suitable for water extraction and help understand the water system reorganization event.

show abstract

“…In addition, since wetlands are used as a food supply by birds and terrestrial animals, many bird species live in these areas [1]. Climate change and human activities may dramatically affect seasonal and annual variations in surface waters, which may also highly affect the resilience of the ecosystem and the long-term economic and social development of the lake and its surroundings [2,3]. With the decrease in water in wetlands, most aquatic plants stay out of the water, and fishing becomes impossible [1].…”

Section: Introductionmentioning

confidence: 99%

“…Monthly, seasonal, and annual observations can be made in extracting the water surface area. As far as is known, monthly analyses have only been considered by [4][5][6][7][8][9], seasonality analyses have been evaluated by [7,[10][11][12][13][14][15][16][17][18], and annual analyses have been assessed by [2,3,11,[19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Karakus

2023

Sustainability

View full text Add to dashboard Cite

Lakes and reservoirs, comprising surface water bodies that vary significantly seasonally, play an essential role in the global water cycle due to their ability to hold, store, and clean water. They are crucial to our planet’s ecology and climate systems. This study analyzed Harmonized Sentinel-2 images using the Google Earth Engine (GEE) cloud platform to examine the short-term changes in the surface water bodies of Çivril Lake from March 2018 to March 2023 with meteorological data and lake surface water temperature (LSWT). This study used the Sentinel-2 Level-2A archive, a cloud filter, the NDVI (normalized difference vegetation index), NDWI (normalized difference water index), MNDWI (modified NDWI), and SWI (Sentinel water index) methods on lake surfaces utilizing the GEE platform and the random forests (RFs) method to calculate the water surface areas. The information on the water surfaces collected between March 2018 and March 2023 was used to track the trend of changes in the lake’s area. The seasonal (spring, summer, autumn, and winter) yearly and monthly changes in water areas were identified. Precipitation, evaporation, and temperature are gathered meteorological parameters that impact the observed variation in surface water bodies for the same area. The correlations between the lake area reduction and the chosen meteorological parameters revealed a strong positive or negative significant association. Meteorological parameters and human activities selected during different seasons, months, and years have directly affected the shrinkage of the lake area.

show abstract

Assessment of Spatio-Temporal Changes in Water Surface Extents and Lake Surface Temperatures Using Google Earth Engine for Lakes Region, Türkiye

Cited by 26 publications

References 51 publications

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Contact Info

Product

Resources

About