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Globally the lake surface water temperature (LSWT) has shown an upward trend and exhibits significant spatial heterogeneity, but previous studies have indeed delved it under current period. Here, we investigated the characteristics of LSWT variation in major lakes of China over two centuries. First we used the AIR2WATER model to construct the data set of LSWT in Chinese major lakes based on the data from CMIP6. Considering the rapid urbanization and climate change, the year of 1900–2014 can be divided into two phases: a stable period (1900–1970‐Phase I, 0.01°C/10a) and a warming period (1971–2014‐ Phase II, 0.16°C/10a). For the future (2015–2100), under the low emission scenario model (SSP1‐RCP2.6, 0.12°C/10a); under the medium emission scenario model (SSP2‐RCP4.5, 0.18°C/10a); and under the high emission scenario model (SSP5‐RCP8.5, 0.38°C/10a). We have designed three spatial classes to analyze the characteristics of LSWT in Chinese major lakes. Particularly, when analyzing the spatial pattern based on China's famous population‐economic demarcation line (the Hu Huanyong Line), we found that the LSWT growth rate in lakes east of the Hu Huanyong Line is higher than that in lakes to the west in Phase II, as well as under the SSP1‐RCP2.6 and SSP2‐RCP4.5 scenario models. However, in Phase I and under the SSP5‐RCP8.5 scenarios, the LSWT growth rate in lakes east of the Hu Huanyong Line is lower than that in the west. This study helps improve our understanding of Chinese major lakes and their changing mechanisms under the warming climate.

Urbanization, agriculture, and climate change affect water quality and water hyacinth growth in lakes. This study examines the spatiotemporal variability of lake surface water temperature, turbidity, and chlorophyll-a (Chl- a ) and their association with water hyacinth biomass in Lake Tana. MODIS Land/ Lake surface water temperature (LSWT), Sentinel 2 MSI Imagery, and in-situ water quality data were used. Validation results revealed strong positive correlations between MODIS LSWT and on-site measured water temperature (R = 0.90), in-situ turbidity and normalized difference turbidity index (NDTI) (R = 0.92), and in-situ Chl- a and normalized difference chlorophyll index (NDCI) (R = 0.84). LSWT trends varied across the lake, with increasing trends in the northeastern, northwestern, and southwestern regions and decreasing trends in the western, southern, and central areas (2001–2022). The spatial average LSWT trend decreased significantly in pre-rainy (0.01 ℃/year), rainy (0.02 ℃/year), and post-rainy seasons (0.01℃/year) but increased non-significantly in the dry season (0.00 ℃/year) (2001–2022, P  < 0.05). Spatial average turbidity decreased significantly in all seasons, except in the pre-rainy season (2016–2022). Likewise, spatial average Chl- a decreased significantly in pre-rainy and rainy seasons, whereas it showed a non-significant increasing trend in the dry and post-rainy seasons (2016–2022). Water hyacinth biomass was positively correlated with LSWT (R = 0.18) but negatively with turbidity (R = -0.33) and Chl- a (R = -0.35). High spatiotemporal variability was observed in LSWT, turbidity, and Chl- a , along with overall decreasing trends. The findings suggest integrated management strategies to balance water hyacinth eradication and its role in water purification. The results will be vital in decision support systems and preparing strategic plans for sustainable water resource management, environmental protection, and pollution prevention.

This study aims to extract water surface area and lake surface water temperature (LSWT), and to present long-term spatio-temporal analysis of these variables together with meteorological parameters. Three lakes in Türkiye’s Lakes Region, namely, Lake Burdur, Egirdir, and Beysehir, were considered as test sites. The normalized difference water index (NDWI) was applied to Landsat 5 and 8 data from 2000 to 2021 to extract the water extent in the Google Earth Engine (GEE) cloud-based platform. In addition to the lake surface area, Landsat thermal images were used to examine the LSWT. The findings indicated that water pixels could be extracted rather accurately using NDWI, with an overall accuracy of 98%. Between 2000 and 2021, the water surface area value of Lake Burdur decreased by more than 22%, while Lake Egirdir has dropped by less than 4%, and Lake Beysehir has not changed noticeably. LSWT of Burdur and Egirdir Lakes increased by more than 2.13 °C and 0.32 °C, respectively, while it decreased about 1.5 °C for Beysehir Lake. The obtained results were evaluated with meteorological parameters and our findings indicated that human-induced activities were more dominant than climate effects over Lake Burdur, unlike the others.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash–Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT ( R  = 0.822, α  < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor.

In modeling species distributions and population dynamics, spatially‐interpolated climatic data are often used as proxies for real, on‐the‐ground measurements. For shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite‐derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high‐resolution global maps of air‐LSWT offsets, corresponding uncertainty measures and derived LSWT‐based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature‐based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab‐measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically‐meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.

Through numerical simulation using the three-dimensional Delft3D-Flow model, a unique phenomenon was found in a tropical, large, riverine reservoir in China on the Lancang-Mekong River, namely the Nuozhadu Reservoir. The surface water temperature rises significantly from the upper end of the reservoir to the dam, by about + 3.8 ℃ per 100 km, far exceeding the original longitudinal increase rate before construction of the reservoir. As a result, the water is always warmer than the air in front of the dam all the year round. Analysis illustrated that this phenomenon results from the strong solar radiation in the tropical region and the strong thermal stratification in the reservoir and the increase of surface water temperature is positively correlated with the hydraulic residence time. This phenomenon may have an important effect on the local environment; since there are many large, riverine reservoirs in tropical regions across the world, this study can serve as a reference for the management of the reservoirs with similar characteristics.

Accurate prediction of lake surface water temperature (LSWT) is essential for understanding the impacts of climate change on aquatic ecosystems and for guiding environmental management strategies. Predictions of LSWT for two prominent lakes in northern China, Qinghai Lake and Hulun Lake, under various future climate scenarios, were conducted in the present study. Utilizing historical hydrometeorological data and MODIS satellite observations (MOD11A2), we employed three advanced machine learning models—Random Forest (RF), XGBoost, and Multilayer Perceptron Neural Network (MLPNN)—to predict monthly average LSWT across three future climate scenarios (ssp119, ssp245, ssp585) from CMIP6 projections. Through the comparison of training and validation results of the three models across both lake regions, the RF model demonstrated the highest accuracy, with a mean MAE of 0.348 °C and an RMSE of 0.611 °C, making it the most optimal and suitable model for this purpose. With this model, the predicted LSWT for both lakes reveals a significant warming trend in the future, particularly under the high-emission scenario (ssp585). The rate of increase is most pronounced under ssp585, with Hulun Lake showing a rise of 0.55 °C per decade (R2 = 0.72) and Qinghai Lake 0.32 °C per decade (R2 = 0.85), surpassing trends observed under ssp119 and ssp245. These results underscore the vulnerability of lake ecosystems to future climate change and provide essential insights for proactive climate adaptation and environmental management.

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km2 of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, R2 > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 ∘C) and temporal (7–25 ∘C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade−1) than those in semi-humid and humid regions (0.19 °C decade−1) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions.