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.

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.

Lake Surface Water Temperature (LSWT) influences critical bio-geological processes in lake ecosystems, and there is growing evidence of rising LSWT over recent decades worldwide and future shifts in thermal patterns are expected to be a major consequence of global warming. At a regional scale, assessing recent trends and anticipating impacts requires data from a number of lakes, but long term in situ monitoring programs are scarce, particularly in mountain areas. In this work, we propose the combined use of satellite-derived temperature with in situ data for a five-year period (2017-2022) from 5 small (<0.5km2) high altitude (1880-2680 masl) Pyrenean lakes. The comparison of in situ and satellite-derived data in a common period (2017-2022) during the summer season showed a notably high (r = 0.94, p < 0.01) correlation coefficient, indicative of a robust relationship between the two data sources. The root mean square errors ranged from 1.8 °C to 3.9 °C, while the mean absolute errors ranged from 1.6 °C to 3.6 °C. We applied the obtained in situ-satellite eq. (2017-2022) to Landsat 5, 7 and 8/9 data since 1985 to reconstruct the summer surface temperature of the five studied lakes with in situ data and to four additional lakes with no in situ monitoring data. Reconstructed LSWT for the 1985-2022 showed an upward trend in all lakes. Moreover, paleolimnological reconstructions based on sediment cores studies demonstrate large changes in the last decades in organic carbon accumulation, sediment fluxes and bioproductivity in the Pyrenean lakes. Our research represents the first comprehensive investigation conducted on high mountain lakes in the Pyrenees that compares field monitoring data with satellite-derived temperature records. The results demonstrate the reliability of satellite-derived LSWT for surface temperatures in small lakes, and provide a tool to improve the LSWT in lakes with no monitoring surveys.

Global warming has been monitored for many years. The increase in air temperature and changes in the distribution and frequency of high temperatures are recorded continually. Lakes are one of the important water resources for aquatic ecosystems and water supply, which are significantly affected by global warming. The increase in lake water temperature increases the evaporation from the free lake surface, lowering the lake level, and changes the water quality. In the last few decades, analysis of changes in lake water temperature has been increasing. In situ measurements of water temperature in Vrana Lake on Cres island (Croatia), the largest freshwater lake on the Mediterranean islands, were analysed over 43 years. The results showed that the mean annual lake surface water temperature (LSWT) increased by 0.47 °C decade-1 (p < 0.0001). The increase in the mean annual lake summer surface water temperature (July-September) was 0.44 °C decade-1 (p < 0.0001), and the maximum annual LSWT was 0.56 °C decade-1 (p < 0.0001). All these amounts are in accordance with the published data on the increase in water temperature in the investigated European lakes. The number of days with LSWT higher than 25 °C increased by almost 9 d decade-1. An increase in the minimum LSWT (0.17 °C decade-1) corresponding to isothermal conditions was also determined but was not statistically significant. The minimum mean monthly LSWT increased by 0.36 °C decade-1 (p < 0.0001). Because the increase in water temperature can negatively affect the lake\'s ecosystem, and become a threat to safe water supply; LSWT, thermal stratification and evaporation should be continuously monitored. The impacts of climate warming on the lake stratification and aquatic ecosystems need to be further investigated.

Lake surface water temperature (LSWT) is an important factor in lake ecological environments. It has been observed that LSWT have followed an upward trend in the last half century, which has had serious impacts on regional biodiversity and climate. It is important to understand the main reason for this phenomenon in order to have a basis for controlling and improving the regional ecological environment. In this study, the contribution rates of near surface air temperature (NSAT), surface pressure (SP), surface solar radiation (SSR), total cloud cover (TCC), wind speed (WS) and Secchi depth (SD) to LSWT of 11 naturally formed lakes in the Yunnan-Guizhou Plateau are quantified. The characteristics of and relationships between the various factors and LSWT in lakes of different types and attributes are revealed. The results show that: (1) from 2001 to 2018, most lakes were warming; the change rate of LSWT-day was higher than that of LSWT-night. The mean comprehensive warming rate (MCWR) of LSWT-day was 0.42 °C/decade, and the mean comprehensive change rate (MCCR) was 0.31 °C/decade; the MCWR of LSWT-night was 0.19 °C/decade, and the MCCR was 0.01 °C/decade. NSAT and SSR were most strongly correlated with LSWT-day/night. There were no large seasonal differences in the correlation between NSAT and LSWT-day, while seasonal differences in the correlations between NSAT with LSWT-night and SSR with LSWT-day/night were observed. (2) NSAT and SSR were the most important factors affecting LSWT-day/night changes, with contribution rates of 30.24% and 44.34%, respectively. LSWT-day was more affected by SP and SSR in small, shallow, and low-storage lakes. For larger lakes, LSWT-day was more affected by WS, while LSWT-night was greatly affected by TCC. Urban and semi-urban lakes were more affected by SSR and NSAT; for natural lakes, the decreasing SD affected the increases in LSWT, which indirectly reflects the impact of human activities. LSWT-day/night responded differently to different morphological characteristics of the lakes and different intensities of human activity.

Global warming and rapid urbanization in China have caused a series of ecological problems. One consequence has involved the degradation of lake water environments. Lake surface water temperatures (LSWTs) significantly shape water ecological environments and are highly correlated with the watershed ecosystem features and biodiversity levels. Analysing and predicting spatiotemporal changes in LSWT and exploring the corresponding impacts on water quality is essential for controlling and improving the ecological water environment of watersheds. In this study, Dianchi Lake was examined through an analysis of 54 water quality indicators from 10 water quality monitoring sites from 2005 to 2016. Support vector regression (SVR), Principal Component Analysis (PCA) and Back Propagation Artificial Neural Network (BPANN) methods were applied to form a hybrid forecasting model. A geospatial analysis was conducted to observe historical LSWTs and water quality changes for Dianchi Lake from 2005 to 2016. Based on the constructed model, LSWTs and changes in water quality were simulated for 2017 to 2020. The relationship between LSWTs and water quality thresholds was studied. The results show limited errors and highly generalized levels of predictive performance. In addition, a spatial visualization analysis shows that from 2005 to 2020, the chlorophyll-a (Chla), chemical oxygen demand (COD) and total nitrogen (TN) diffused from north to south and that ammonia nitrogen (NH3-N) and total phosphorus (TP) levels are increases in the northern part of Dianchi Lake, where the LSWT levels exceed 17°C. The LSWT threshold is 17.6-18.53°C, which falls within the threshold for nutritional water quality, but COD and TN levels fall below V class water quality standards. Transparency (Trans), COD, biochemical oxygen demand (BOD) and Chla levels present a close relationship with LSWT, and LSWTs are found to fundamentally affect lake cyanobacterial blooms.