Analysis of surface temperature variation of lakes in China using MODIS land surface temperature data

Abstract

China has a great wealth of lake resources over a great spatial extent and these lakes are highly sensitive to climate changes through their heat and water budgets. However, little is known about the changes in lake surface water temperature (LSWT) across China under the climate warming conditions over the past few decades. In this study, MODIS land surface temperature (LST) data were used to examine the spatial and temporal (diurnal, intra-annual, and inter-annual) variations in LSWT of China’s lakes during 2001–2016. Our results indicated that 169 large lakes included in the study exhibited an overall increasing trend in LSWT, with an average rate of 0.26 °C/decade. The increasing rate of nighttime LSWT is 0.31 °C/decade, faster than that of daytime temperature (0.21 °C/decade). Overall, 121 (71.6%) lakes showed an increase in daytime temperature with a mean rate of 0.38 °C/decade, while the rest 48 (28.4%) lakes decreased in temperature with a mean rate of − 0.21 °C/decade. We also quantitatively analyzed the relationship of the lake surface temperature and diurnal temperature differences (DTDs) with geographical location, topography, and lake morphometry by utilizing multivariate regression analysis. Our analysis suggested that the geographical location (latitude and longitude) and topography (altitude) were primary driving factors in explaining the national lake water temperature variation (P < 0.001), which were also mediated by morphometric factors such as lake surface area and volume. Moreover, the diurnal lake temperature variations were significantly correlated with altitude, latitude, and lake surface area (R2 = 0.426, P < 0.001). Correlation analyses of LSWT trend and air temperature trend for each lake indicated that LSWT was positively correlated with air temperature in both daytime and nighttime for most lakes.

Warming lake surface water temperatures in Lake Qiandaohu, China: Spatiotemporal variations, influencing factors and implications for the thermal structure

“…Many potential drivers for warming LSWTs have been explored including the AT (Zhang G. et al, 2014;Piccolroaz et al, 2018), RF (Wan et al, 2018), WL (Zhang G. et al, 2014), water clarity (Rose et al, 2016), lake-specific morphology (water depth, water area, water storage) (Becker and Daw, 2005;Xie et al, 2022) and land use and land cover (Tan et al, 2020). Among these factors, the AT is widely considered a main driving factor for LSWT warming (Fink et al, 2014).…”

“…Many lakes and reservoirs worldwide have warmed over the past several decades, with varied warming rates (Schneider and Hook, 2010;O'Reilly Catherine et al, 2015;Xie et al, 2022). From 1986 to 2021, the LSWTs of Lake Qiandaohu displayed a significant increasing trend, with a warming rate of 0.07 °C/year.…”

“…Satellite-inferred LSWT data have the ability to retrospectively examine spatiotemporal variations in LSWT with fine spatial and temporal resolutions. Thus, LSWTs have been remotely estimated from different satellite data using various LSWT retrieval algorithms (Becker and Daw, 2005;Schneider et al, 2009;Yang K. et al, 2019;Liu et al, 2019;Najwa et al, 2019;Xie et al, 2022). Among the various satellite data available, Landsat-series data have advantages in long-term data availability and fine spatial resolutions.…”

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

“…Yamak and Comert [27] used Landsat images for the extraction of the Land Surface Temperature (LST) map, and low LST values were observed in vegetated areas, forest areas, and wetlands. In the study of Xie et al [28], MODIS LST data were used to examine the spatial and temporal variations in lake surface water temperature (LSWT) of China's lakes between 2001 and 2016. Their findings indicated that the 169 large lakes included in the study had an overall increasing trend in LSWT, with an average rate of 0.26 • C/decade.…”

Lake Surface Temperature Dataset in the North Slave Region Retrieved from Landsat Satellite Series – 1984 to 2021

“…The largest LST variability between lakes however for summer was twice that of winter (16°C). This is expected as temperatures on lakes during this season are influenced by several factors including lake size, elevation, depth, latitude, longitude and volume(O'Reilly et al, 2015;Xie et al, 2022) in addition to air temperature. Seasonal LST spatial distribution provides an insight into the climate patterns of the NSR region.…”