Search Results (36)

The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R² = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R² = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems. Full article

Lake surface water area (LSWA) and lake surface water temperature (LSWT) are critical indicators of climate change, responding rapidly to global warming. However, studies on the synergistic variations of LSWA and LSWT are scarce, and the coupling relationships among lakes with different environmental characteristics remain unclear. In this study, the relative growth rate of LSWA (RK_LSWA); the absolute growth rates of annual maximum, mean, and minimum LSWTs (i.e., K_{LSWT_max}, K_{LSWT_mean}, K_{LSWT_min}); and the absolute growth rates of the difference between maximum and minimum LSWT (LSWT_mmd) (K_{LSWT_mmd}) were investigated across more than 4000 lakes in China using long-term Landsat data, and their coupling relationships among different lake types (i.e., permafrost and non-permafrost recharge, endorheic or exorheic lakes, and natural and artificial lakes) were comprehensively analyzed. Results indicate significant differences in the trends of LSWA and LSWT, as well as their interrelationships across various regions and lake types. In the Qinghai–Tibet Plateau (QTP), 57.8% of lakes showed an increasing trend in LSWA, with 2.4% of the lakes showing moderate expansion (RK_LSWA values of 0.1–0.2), while over 27.5% of lakes in the South China (SC) region displayed shrinkage in LSWA (RK_LSWA values were between −0.1~0%/year). Regarding LSWTs, 49.8% of lakes in the QTP exhibited a K_{LSWT_max} greater than 0, and 47.9% of lakes showed a K_{LSWT_mean} greater than 0. In contrast, 48.1% of lakes in the Middle and Lower Yangtze River Plain (MLYP) had a K_{LSWT_max} less than 0, and 48.5% of lakes had a K_{LSWT_mean} less than 0. Additionally, lakes supplied by permanent permafrost demonstrated more significant growth in both LSWA and LSWT than those supplied by non-permanent permafrost. Further analysis revealed that approximately 20.2% of the lakes experienced a concurrent increase in both mean LSWT and LSWA, whereas around 18.9% of the lakes exhibited a simultaneous rise in both LSWT_mmd and LSWA. This suggests that the expansion of lakes in China is correlated with both rising temperatures and greater temperature differences. This study provides deeper insights into the response of Chinese lakes to climate change and offers important references for lake resource management and ecological conservation. Full article

One of the fundamental features of lakes is water temperature, which determines the functioning of lake ecosystems. However, the overall range of information related to the monitoring of this parameter is quite limited, both in terms of the number of lakes and the duration of measurements. This study addresses this gap by reconstructing the lake surface water temperature (LSWT) of six lakes in Poland from 1994 to 2023, where direct measurements were discontinued. The reconstruction is based on the Air2Water model, which establishes a statistical relationship between LSWT and air temperature. Model validation using historical observations demonstrated high predictive accuracy, with a Nash–Sutcliffe Efficiency exceeding 0.92 and root mean squared error ranging from 0.97 °C to 2.13 °C across the lakes. A trend analysis using the Mann–Kendall test and Sen’s slope estimator indicated a statistically significant warming trend in all lakes, with an average increase of 0.35 °C per decade. Monthly trends were most pronounced in June, September, and November, exceeding 0.50 °C per decade in some cases. The direction, pace, and scale of these changes are crucial for managing individual lakes, both from an ecological and economic perspective. Full article

The Patagonia Icefield, the largest ice mass in the Southern Hemisphere outside Antarctica, has experienced significant growth and expansion of ice-contact lakes in recent decades, with lake surface water temperature (LSWT) being one of the key influencing factors. LSWT affects glacier melting at the waterline and accelerates glacier mass loss. However, the observations of ice-contact LSWT are often limited to short-term, site-based field measurements, which hinders long-term, whole-lake monitoring. This study examines LSWT for the three largest ice-contact lakes in the Patagonia Icefield—Lake Argentino, Lake Viedma, and Lake O’Higgins, each exceeding 1000 km²—and the three largest nearby non-ice-contact lakes for comparison using MODIS data between 2002 and 2022. In 2022, the mean LSWTs for Lake Argentino, Lake Viedma, and Lake O’Higgins were 7.2, 7.0, and 6.4 °C, respectively. In summer, ice-contact lakes exhibited wider LSWT ranges and more pronounced cooling near glacier termini and warming farther away compared to other seasons, demonstrating glacier melt cooling and its seasonal variability. Over the past 20 years, both Lake Viedma and Lake O’Higgins showed a warming rate of +0.20 °C dec⁻¹, p > 0.1, with slower warming near the glacier, reflecting glacier contact suppression on the LSWT trend. Conversely, Lake Argentino displayed a significant warming rate of +0.43 °C dec⁻¹ (p < 0.05), with faster rates near the glacier terminus, possibly linked to a prolonged and large (>64 km²) iceberg accumulation event from March 2010 to October 2011 in Glacier Upsala’s fjord. Iceberg mapping shows that larger events caused more pronounced short-term (24 days) LSWT cooling in Lake Argentino’s ice-proximal region. This study highlights the role of glacier–lake interactions including calving events in regulating ice-contact lake water temperature. Full article

In this study, long-term lake surface water temperature (LSWT) data were used to investigate the impact of climate change on thermal conditions in 25 Polish lowland lakes. The results show that the warming rate of the annual mean LSWT ranges from 0.14 °C per decade to 0.69 °C per decade with an average value of 0.44 °C per decade. The annual maximum LSWT presented the strongest warming trend, with the warming rate varying between 0.26 °C per decade and 1.06 °C per decade (average value of 0.65 °C per decade). Warming rates were observed in all seasons but with different intensities, with warming rates increasing from spring to autumn and then to summer. The warming rate of the summer LSWT varied between 0.29 °C per decade and 0.87 °C per decade with an average value of 0.56 °C per decade. Conversely, winter and annual minimum LSWTs did not present clear increasing trends. The increase in the annual maximum, annual average, and seasonal LSWTs correlated well with the inter-annual variability in air temperature. To understand the relationship between LSWT and air temperature, the non-linear regression model (S-curve) was used in this study. The results indicate that the non-linear regression model can help to present the relationship between LSWT and air temperature in the studied lakes (the average values of the root mean squared error (RMSE), the mean absolute error (MAE), and the Nash–Sutcliffe efficiency coefficient (NSE) are 1.68 °C, 1.28 °C, and 0.95, respectively). The warming trends of LSWTs observed for the studied lakes in Poland are coherent and in some cases larger than the data from other lakes worldwide, and should be seriously considered by policy makers. Full article

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article

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. Full article

Wetlands are valuable natural resources that provide many services to both the environment and humans. Over the past several decades, climatic change and human activities have had a considerable impact on the water level of wetlands. Zarivar Lake, located in the northwestern region of Iran, represents a significant ecological unit and aquatic ecosystem. In this study, from 2015 to 2022, the relationship between seasonal changes in Zarivar Lake’s waterbody (LWB) area and weather factors like precipitation, evapotranspiration, and the temperature of the lake’s surface water (LSWT) were examined. For this purpose, the Google Earth Engine (GEE) cloud platform, a powerful and fast tool for processing the time series of images, was used. The LWB was extracted by utilizing the average images of the dual-polarized SAR Sentinel-1 imagery for each season. Furthermore, meteorological parameters encompass the utilization of the Landsat-8 satellite’s thermal band to determine LSWT by using statistical mono-window (SMW), the CHIRPS rainfall model data for assessing precipitation levels, and the employment of MODIS evapotranspiration products in the form of 8-day data. The study revealed significant correlations between variations in Zarivar Lake’s waterbody area and meteorological factors. Correlation coefficients indicated a positive relationship between LWB area and precipitation during the winter (r = 0.67) and spring (r = 0.73), while weaker positive correlations were observed in the summer (r = 0.29) and fall (r = 0.30). Conversely, the LWB area showed a relative relationship with LSWT, with positive correlations in winter (r = 0.10) and spring (r = 0.26), and negative correlations in summer (r = −0.30) and fall (r = −0.07). Additionally, evapotranspiration parameters aligned with precipitation changes throughout the seasons, highlighting the significant influence of climate on Zarivar Lake. Full article

Lake surface water temperature (LSWT) has significantly increased over China and even globally in recent decades due to climate change. However, the responses of LSWTs to climate warming in various climatic regions remain unclear due to the limited lake observations. Satellite-observed LSWT data from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset were extended using the air2water model. This research aimed to investigate summer LSWT trends across various climatic zones in China, shedding light on the complex interplay between surface air temperatures and LSWT from 1950 to 2020. The results demonstrate robust model performance, with high Nash–Sutcliffe efficiency coefficients, affirming its capability to simulate LSWT variability. Regional disparities in LSWT patterns are identified, revealing notable warming trends in dryland lakes, particularly in central Inner Mongolia. Notably, the study unveils a substantial increase in the intensity and duration of lake heatwaves, especially in semi-arid regions. Dryland lake heatwaves emerge as dominant contributors to intensified LSWT warming, showcasing stronger and longer-lasting events than humid regions. The research highlights a positive feedback loop between lake warming and heatwaves, further amplifying dryland LSWT warming. These findings underscore the vulnerability of dryland lakes to climate change and signal the potential ramifications of increased greenhouse gas concentrations. Full article

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. Full article

The thermodynamics of many lakes around the globe are shifting under a warming climate, affecting nutrients and oxygen transportation within the lake and altering lake biota. However, long-term variation in lake heat and water balance is not well known, particularly for regions like the Tibetan Plateau. This study investigates the long-term (1963–2019) variation in the heat balance of a large lake in the Tibetan Plateau (Nam Co) by combining the strengths of modeling and remote sensing. Remotely sensed lake surface water temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) are used to calibrate and validate a conceptual model (air2water) and a thermodynamic model (LAKE) for the studied lake, for which in situ observation is limited. The results demonstrate that remotely sensed lake surface water temperature can serve as a valuable surrogate for in situ observations, facilitating effective calibration and validation of lake models. Compared with the MODIS-based lake surface water temperature (LSWT) for the period 2000–2019, the correlation coefficient and root mean square error (RMSE) of the LAKE model are 0.8 and 4.2 °C, respectively, while those of the air2water model are 0.9 and 2.66 °C, respectively. Based on modeling, we found that the water temperature of Nam Co increased significantly (p < 0.05) during the period of 1963–2019, corresponding to a warming climate. The rate of water temperature increase is highest at the surface layer (0.41 °C/10a). This warming trend is more noticeable in June and November. From 1963 to 2019, net radiation flux increased at a rate of 0.5 W/m²/10a. The increase in net radiation is primarily responsible for the warming of the lake water, while its impact on changes in lake evaporation is comparatively minor. The approaches developed in this study demonstrate the flexibility of incorporating remote sensing observations into modeling. The results on long-term changes in heat balance could be valuable for a systematic understanding of lake warming in response to a changing climate in the Tibetan Plateau. Full article

The RC propeller performance under steady and sinusoidally time-varying freestream (stream-wise or longitudinal gust) was investigated in the University of Dayton Low-Speed Wind Tunnel (UD-LSWT) in the open-jet configuration. The propellers were tested at varying incidence angles and reduced frequencies. The streamwise gust was created by actuating the shuttering system located at the test section exit and was characterized using hot-wire anemometry. A system identification model was developed for the shuttering system to determine the shutter actuation profile that would result in a sinusoidal gust in the test section. Changes in propeller thrust, power, and pitching moment were observed with an increase in propeller incidence angle under the steady freestream. The propeller’s steady freestream performance was then used to predict response under periodic streamwise gusts in edgewise flight. Below a reduced frequency of 0.2, the propeller response agrees with the prediction model, suggesting that the propeller response is quasi-steady. At reduced frequencies higher than 0.2, a reduction in mean thrust and pitching moment and significant phase lag was observed. Full article

Lake chlorophyll-a (Chl-a) is one of the important components of the lake ecosystem. Numerous studies have analyzed Chl-a in ocean and inland water ecosystems under pressures from climate change and anthropogenic activities. However, little research has been conducted on lake Chl-a variations in the Tibet Plateau (TP) because of its harsh environment and limited opportunities for in situ data monitoring. Here, we combined 95 in situ measured lake Chl-a concentration data points and the Landsat reflection spectrum to establish an inversion model of Chl-a concentration. For this, we retrieved the mean annual Chl-a concentration in the past 35 years (1986–2021) of 318 lakes with an area of > 10 km² in the TP using the backpropagation (BP) neural network prediction method. Meteorological and hydrological data, measured water quality parameters, and glacier change in the lake basin, along with geographic information system (GIS) technology and spatial statistical analysis, were used to elucidate the driving factors of the Chl-a concentration changes in the TP lakes. The results showed that the mean annual Chl-a in the 318 lakes displayed an overall decrease during 1986–2021 (−0.03 μg/L/y), but 63%, 32%, and 5% of the total number exhibited no significant change, significant decrease, and significant increase, respectively. After a slight increase during 1986–1995 (0.05 μg/L/y), the mean annual lake Chl-a significantly decreased during 1996–2004 (−0.18 μg/L/y). Further, it decreased slightly during 2005–2021 (−0.02 μg/L/y). The mean annual lake Chl-a concentration was significantly negatively correlated with precipitation (R² = 0.48, p < 0.01), air temperature (R² = 0.31, p < 0.01), lake surface water temperature (LSWT) (R² = 0.51, p < 0.01), lake area (R² = 0.42, p < 0.01), and lake water volume change (R² = 0.77, p < 0.01). The Chl-a concentration of non-glacial-meltwater-fed lakes were higher than those of glacial-meltwater-fed lakes, except during higher precipitation periods. Our results shed light on the impacts of climate change on Chl-a variation in the TP lakes and lay the foundation for understanding the changes in the TP lake ecosystem. Full article

Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST). However, the influence of LCC near waterbodies outside of an urban environment remain less understood. Waterbodies serve as local climate moderators where nearby LCC has the potential to decrease their cooling ability. Altered water surface temperatures can lead to altered species migration and distribution in aquatic species depending on a given species thermal boundary. In this study, using remotely sensed land cover and surface temperature data, we investigate the role that LCC around small lakes (500 m) plays on the surface water temperature change of nine small lakes in the Cataraqui Region Conservation Authority’s watershed, located in Eastern Ontario, from 1985 to 2020. The Continuous Change Detection Classification (CCDC) algorithm was used alongside the Statistical Mono-Window (SMW) algorithm to calculate LCC and LST, respectively. Results indicated a strong positive relationship (R2 = 0.81) between overall LCC and lake surface water temperature (LSWT) trends, where LSWT trends in all inland small lakes investigated were found to be positive. The land cover class sparse vegetation had a strong positive correlation with water temperature, whereas dense vegetation displayed a strong negative correlation. This 35-year study contributes to the broader understanding of the impact that LCC has on the surface water temperature trends of inland lakes. Full article

Lake surface water temperature (LSWT) is a key parameter in understanding the variability of lake thermal conditions and evaporation. The MODIS-derived LSWT is widely used as a reference for lake model validations and process studies in data-scarce regions. In this study, the accuracy of the MODIS LSWT was examined on the Tibetan Plateau (TP). In-situ subsurface temperatures were collected at five large lakes. Although the observation period covers from summer to winter, only the observations during the lake turnover period (from October to freeze-up), when the lakes are well mixed, can be used as ground truth. The MODIS LSWT agrees well with the selected in-situ data for the five large lakes, with root mean square error (RMSE) < 1 °C at nighttime and <2 °C in the daytime, indicating a high accuracy of the MODIS LSWT data. Before the turnover period, the water is thermally stratified and the surface water is warmer than the subsurface water, and thus the in-situ subsurface water temperature data and the MODIS LSWT have different representativeness. In this case, if the observations are used as a validation basis, the MODIS errors could be much magnified. This in turn indicates the importance of period selection for the validation. Full article