Lake Surface Water Temperatures Research Articles

Lake Surface Water Temperature in high altitude lakes in the Pyrenees: Combining satellite with monitoring data to assess recent trends

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.

Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.

Functional principal component analysis for incomplete space–time data

AbstractEnvironmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space–time data, when data may be affected by complex missing data structures. We formalize the problem in the framework of functional data analysis, proposing an innovative method of functional principal component analysis (fPCA) for incomplete space–time data. The functional nature of the proposed method permits to borrow information from measurements observed at nearby spatio-temporal locations. The resulting functional principal components are smooth fields over the considered spatio-temporal domain, and can lead to interesting insights in the spatio-temporal dynamic of the phenomenon under study. Moreover, they can be used to provide a reconstruction of the missing entries, also under severe missing data patterns. The proposed model combines a weighted rank-one approximation of the data matrix with a roughness penalty. We show that the estimation problem can be solved using a majorize–minimization approach, and provide a numerically efficient algorithm for its solution. Thanks to a discretization based on finite elements in space and B-splines in time, the proposed method can handle multidimensional spatial domains with complex shapes, such as water bodies with complicated shorelines, or curved spatial regions with complex orography. As shown by simulation studies, the proposed space–time fPCA is superior to alternative techniques for Principal Component Analysis with missing data. We further highlight the potentiality of the proposed method for environmental problems, by applying space–time fPCA to the study of the lake water surface temperature (LWST) of Lake Victoria, in Central Africa, starting from satellite measurements with large gaps. LWST is considered one of the fundamental indicators of how climate change is affecting the environment, and is recognized as an essential climate variable.

An analysis of the Lake Surface Water Temperature evolution of the world’s largest lakes during the years 2003-2020 using MODIS data

The Lake Surface Water Temperature (LSWT) evolution is analysed in ten of the largest lakes in the world: Caspian Sea, Superior, Victoria, Huron, Michigan, Tanganyika, Baikal, Great Slave Lake, Erie and Ontario. The time span selected is 2003-2020 and the satellite product, MODIS Level 3 SST Thermal IR 8 Day 4km V2019.0. Results show warming trends ranging from 0.012⁰C/yr. in the Victoria Lake to 0.083 ⁰C/yr in the Baikal Lake. Results have been validated with the product MOD11L2 LSWT estimations for the years 2003-2014 in the Laurentian Great Lakes, obtaining correlations between 0.962 and a 0.998. The validation has been enlarged by considering Sentinel 3 observations from the Issyk-Kul lake, with a 0.99 correlation. The validation shows that the MODIS SST product is capable of estimating the LSWT parameter with a high precision.

Warming surface and Lake heatwaves as key drivers to harmful algal Blooms: A case study of Lake Dianchi, China

Extreme heat events such as marine and atmospheric heatwaves, have catastrophic impacts on marine and terrestrial ecosystems. Recent studies have shown that the continuous warming of lake surfaces and enhancement of lake heatwaves (LHWs) have generated great research interest globally. However, the effects of LHWs on lake ecosystems, especially cyanobacterial harmful blooms (cHABs), remain unclear. This study reconstructed the daily Lake surface water temperature (LSWT) time series in Lake Dianchi from 1951 to 2020 using random forest. Trends and characteristics of LSWT and LHWs were identified, and their critical driving effects on cHABs were analyzed. Results showed that the LSWT in Lake Dianchi significantly increased with a rate of 0.26 °C/10a and with an abrupt changing point in 1992, coinciding with the first report of severe cHABs in Lake Dianchi. From 1951 to 2020, 141 LHWs were detected, of which 94.3 % occurred after 1992. Among all the potential climatic and water quality driving factors on Chlorophyll-a (Chla) variation, LSWT is the most important. Chla concentration and the intensity of LHWs were significantly correlated. The complex and strong interactions between LHWs and cHABs enable the suppression of cHABs only when both remain low. A case study in Lake Dianchi highlighted the critical driving effects of LSWT and LHWs on cHABs, suggesting that continuously warming surface and enhancing LHWs in the future will further challenge more specific and effective lake management strategies to suppress cHABs.

Increased Warming Efficiencies of Lake Heatwaves Enhance Dryland Lake Warming over China

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.

A new model to estimate shallow lake nitrogen removal rate based on satellite derived variables

Lake nitrogen (N) removal, mainly resulting from bacterial denitrification that converts nitrate (NO3 −) to gaseous N (N2), is important for lake water quality and eutrophication control. However, quantifying lake N removal is challenging due to the high background atmosphere N2 concentration and the heavy burden of field surveys, leading to a decoupling of watershed N management and water quality improvement. Here, we developed and validated an innovative nonlinear model for lake N removal rate estimation by linking the N removal rates with remote sensing-derived variables (chlorophyll-a, chromophoric dissolved organic matter, and lake surface water temperature). The model was validated in shallow eutrophic Lake Taihu in the Yangtze River basin and at the global scale. Based on the new N removal model, we estimated that an annual average of 3.21 × 104 t N yr‒1 was removed in Lake Taihu from 2011 to 2020, accounting for 53%–66% of the total lake N loading. The remaining N loading after denitrification removal in Lake Taihu would be approximately 2.37 mg N l‒1, and 0.79 × 104 t N y‒1 of lake N loading still needs to be removed to meet the target of class IV water quality (1.5 mg N l‒1). This is the first study linking lake N removal in sediment microcosm incubations with reach-scale remote sensing derived variables, providing timely-much insights into lake N removal. This approach can be easily applied in other lakes with satellite derived data, to better understand lake N budget, drivers of eutrophication control, and watershed N management.

Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data

The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.

Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Lake Surface Water Temperature (LSWT) is essential for understanding and regulating various processes in lake ecosystems. Remote sensing for large-scale aquatic monitoring offers valuable insights, but its limitations call for a dynamic LSWT monitoring model. This study developed multiple machine learning models for LSWT retrieval of four representative freshwater lakes in the Yangtze River Basin using Himawari-8 (H8) remote sensing imagery and in-situ data. Based on the in situ monitoring dataset in Lake Chaohu, the dynamic LSWT retrieval models were effectively configured and validated to perform H8-based remote sensing inversion. The test results showed that six models provided satisfactory LSWT retrievals, with the Back Propagation (BP) neural network model achieving the highest accuracy with an R-squared (R2) value of 0.907, a Root Mean Square Error (RMSE) of 2.52°C, and a Mean Absolute Error (MAE) of 1.68°C. Furthermore, this model exhibited universality, performing well in other lakes within the Yangtze River Basin, including Taihu, Datonghu and Dongtinghu. The ability to derive robust LSWT estimates confirms the feasibility of real-time LSWT retrieval using synchronous satellites, offering a more efficient and accurate approach for LSWT monitoring in the Yangtze River Basin. Thus, this proposed model would serve as a valuable tool to support the implementation of more informed policies for aquatic environmental conservation and sustainable water resource management, addressing challenges such as climate change, water pollution, and ecosystem restoration.

The impact of extreme heat on lake warming in China

Global lake ecosystems are subjected to an increased occurrence of heat extremes, yet their impact on lake warming remains poorly understood. In this study, we employed a hybrid physically-based/statistical model to assess the contribution of heat extremes to variations in surface water temperature of 2260 lakes in China from 1985 to 2022. Our study indicates that heat extremes are increasing at a rate of about 2.08 days/decade and an intensity of about 0.03 °C/ day·decade in China. The warming rate of lake surface water temperature decreases from 0.16 °C/decade to 0.13 °C/decade after removing heat extremes. Heat extremes exert a considerable influence on long-term lake surface temperature changes, contributing 36.5% of the warming trends within the studied lakes. Given the important influence of heat extremes on the mean warming of lake surface waters, it is imperative that they are adequately accounted for in climate impact studies.

Waterborne Virus Transport and Risk Assessment in Lake Geneva Under Climate Change

AbstractClimate changes influence lake hydrodynamics and radiation levels and thus may affect the fate and transport of waterborne pathogens in lakes. This study examines the impact of climate change on the fate, transport, and associated risks of four waterborne viruses in Lake Geneva. We used a coupled water quality‐microbial risk assessment model to estimate virus concentrations and associated risks to recreational water users for each month in 2019 and 2060. Long‐term hydrodynamic simulations suggested that although the annual hydrodynamic transport pattern of Lake Geneva will remain relatively stable, a 1.9°C increase of lake surface water temperature can be expected, while a slight decrease in lake current velocity may occur. The subsequent effect on the fate and transport of the four enteric viruses was found to vary by time of year. During warmer periods, the increase of virus inactivation due to higher water temperature and stronger solar radiation at the earth's surface will compensate for the additional virus discharge brought about by population growth over the time period considered, whereas during winter the virus concentration near the lake shore and the associated infection probabilities risks are likely to increase due to population growth. Additionally, the current estimation of virus inactivation rate shows significant variability, which has a more substantial effect on enteric virus concentrations in the lake compared with changing climate parameters. Overall, the study suggests that future risks posed by enteric viruses with recreational water users near popular beaches around Lake Geneva will likely remain similar to current risks and accurate estimation of the environmental inactivation of viruses is crucial for predicting the fate of enteric viruses in the aquatic system.

Impact of a Severe Dust Event on Diurnal Behavior of Surface Water Temperature in Subtropical Lake Kinneret

Dust impact on lake surface water temperature (SWT) over lakes, located in the Eastern Mediterranean, has not yet been discussed in previous publications. We investigated the effect of an extreme dust intrusion on the diurnal behavior of SWT in Lake Kinneret, appearing from 7–9 September 2015. This was carried out using METEOSAT and in-situ observations of SWT. In the presence of dust, METEOSAT SWT decreased along with increasing dust pollution both in the daytime and nighttime. This contradicted in-situ measurements of SWT at a depth of 20 cm which increased to 1.2 °C in the daytime and to 1 °C in the nighttime, compared to SWT on clear-sky September 6. The in-situ radiometer measurements of upwelling longwave radiation (ULWR) provided us with a criterion for assessing the reliability of METEOSAT and in-situ observations of SWT. Using this criterion, we found that, in the presence of dust, in-situ SWT was in line, whereas METEOSAT SWT contradicted in-situ ULWR. Considering in-situ ULWR is determined by actual SWT, we concluded that, in the presence of dust, in-situ SWT were capable of reproducing Kinneret SWT, while METEOSAT was incapable of doing so. An observed increase in daytime air temperature during the dust intrusion contributed to an increase in daytime Kinneret SWT. In the presence of maximal dust pollution on September 8, atmospheric humidity (ρv) exceeded by 30% that on clear-sky September 6. This increase in ρv was observed in the absence of moisture advection indicating that dust intrusion can cause additional evaporation from Lake Kinneret and, consequently, intensify its drying up.

Attribution of Lake Surface Water Temperature Change in Large Lakes Across China Over Past Four Decades

AbstractLake surface water temperature (LSWT) is a key parameter in lake energy budget and is highly vulnerable to climate change. However, the long‐term trends in LSWT across China and their driving factors remain uncertain. Here, we used a calibrated lake model to simulate LSWT over 1979–2018 for 91 large lakes (&gt;100 km2) across China. Simulations reveal an overall LSWT warming trend (0.040°C yr−1, p &lt; 0.05), but with large spatial variations. The majority of these lakes show significant warming trends (84%, 0.053°C yr−1), while a significant cooling trend is found in the seven lakes in the northwestern Tibetan Plateau (−0.064°C yr−1). LSWT of approximately 42% of the lakes increases more rapidly than the corresponding ambient air temperature. Regionally, the warming trend is highest for lakes in the Eastern Plain (0.049°C yr−1) and the lowest in the Yunnan‐Guizhou Plateau (0.016°C yr−1). The increases in simulated LSWT also vary across seasons, with a higher rate in winter and spring than in summer and autumn. Changes in air temperature, downward longwave radiation, and wind speed are the most important climatic drivers for LSWT changes. Lake surface warming could be more rapid under future global warming, necessitating greater attention to lake‐atmosphere interactions.

Multi-decadal change in summer mean water temperature in Lake Konnevesi, Finland (1984–2021)

Depth-resolved water temperature data on the thermal environment of lakes are often hindered by sparse temporal frequency, limited depth resolution, or short duration that create many challenges for long-term analysis. Where high frequency and depth-resolved data exist, they can provide a wealth of knowledge about how lakes are responding to a changing climate. In this study, we analyzed around 950 profiles of summer mean water temperature (July to September), which includes about 30,600 unique observations, from a subarctic lake (Lake Konnevesi, Finland) to understand the changes in lake surface water temperature (LSWT), lake deepwater temperature (LDWT), and lake volumetrically weighted mean temperature (LVWMT) from 1984 to 2021. Statistical analysis of this dataset revealed a substantial warming of LSWT (0.41 °C decade−1) and LVWMT (0.32 °C decade−1), whilst LDWT remained unchanged (0.00 °C decade−1). Our analysis using a generalized additive model suggested the inter-annual variability in LSWT and LVWMT correlated significantly with the upward trends of summer mean air temperature and solar radiation, but suggested no significant effect of observed changes in ice departure dates and near-surface wind speed. None of the investigated predictors correlated with the change in the LDWT. Due to the variable response of lake surface and bottom water temperature to climate change in this subarctic lake, our data suggest a substantial increase in lake thermal stability. Our study supports the growing literature on lake thermal responses to climate change, and illustrates the unique contrast of climate change impacts at the surface and at depth in lake ecosystems, with deep waters acting as a potential thermal refuge to aquatic organisms within a warming world.

A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave

In 2018, Europe experienced one of the most severe heatwaves ever recorded. This extreme event's impact on lake surface water temperature (LSWT) in Polish lakes has largely remained unknown. In this study, the impact of the 2018 European heatwave on LSWT in 24 Polish lakes was investigated based on a long-term observed dataset (1987–2020). To capture the LSWT dynamics during the heatwave period and reproduce lake heatwaves, a novel BO-NARX-BR model was developed and evaluated. This model combines the capabilities of the Nonlinear Autoregressive network with Exogenous Inputs (NARX) neural network, the Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values, and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training. The results showed that from April to October 2018, the mean and maximum LSWTs were 2.35 and 3.38 °C warmer than the base-period average (1987–2010) due to the impact of the extreme heatwave. The NARX-based model outperformed another widely used model called air2water in calibration and validation periods. The results also revealed that the BO-NARX-BR model produced significantly better results in capturing lake heatwaves, with computed duration and intensity of lake heatwaves close to the in-situ data. Additionally, LSWT anomaly significantly impacted the duration and intensity of heatwaves that occurred in lakes. Extreme climatic events are gaining increasing importance for the functioning of various elements of the hydrosphere. Such a situation encourages the search for more accurate methods and tools for their prediction. The model applied in the paper corresponds with these assumptions, and its good performance allows for its adaptation to lakes in other regions.

How Do Extreme Lake Water Temperatures in Poland Respond to Climate Change?

Lakes are vital components of the Earth’s hydrological cycle and are susceptible to the impacts of climate change. Understanding the changes in terms of minimum and maximum lake surface temperatures is crucial for assessing the effects of climate change on freshwater ecosystems. This study focuses on ten lakes in Poland to investigate the impacts of climate change on lake temperatures in different geographical regions. The Mann–Kendall (MK) and Sen tests were employed to analyze trends and changes in minimum and maximum water temperatures, respectively. The results reveal significant increases in the minimum and maximum temperatures, particularly in May and June. Different lakes exhibit varying trends and variability in temperature changes over time, indicating the vulnerability of these ecosystems. The current study also examines the magnitude of annual temperature changes and classifies them into different levels. This analysis highlights the complex relationship between air temperature, seasonal cycles, and lake morphometric characteristics in shaping variations in lake surface water temperature. These findings contribute to understanding the impacts of climate change on Poland’s lakes and provide valuable insights for developing conservation strategies and adaptive measures to protect freshwater resources.

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

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.

Improvements and Evaluation of the FLake Model in Dagze Co, Central Tibetan Plateau

FLake has been one of the most extensively used lake models in many studies for lake thermal structure simulations. However, due to the scarcity of lake temperature observations, its applicability and performance on lakes over the Tibetan Plateau are still poorly investigated, especially in small- to medium-sized lakes. In this study, based on water profile observations in Dagze Co, a medium-sized lake on the central Tibetan Plateau, the sensitivity of lake thermal features to three key parameters in FLake was investigated. The performance of FLake in reproducing the lake thermal features was evaluated and improved by optimizing these key parameters. The results showed that the FLake model with default parameter settings can generally reproduce the thermal features of Dagze Co, but there are still significant deviations compared to observation. The sensitive experiments demonstrated that the thermal structure of the lake obviously responds to the change in the water extinction coefficient (Kd), friction velocity (u*), and ice albedo (αice). Based on previous studies and sensitive experiments, the three key parameters were set to the optimized value, which substantially improved the performance of FLake. The values of bias and RMSE of simulated lake surface water temperature decreased from 3.08 °C and 3.62 °C to 2.0 °C and 2.48 °C after parameter optimization. The integration of a simple salinity scheme further improved the ability of FLake to reproduce the observed thermal features of Dagze Co. These results will improve our understanding of thermal processes in lakes on the Tibetan Plateau, as well as the applicability of lake models.

Lake Ice Simulation and Evaluation for a Typical Lake on the Tibetan Plateau

This study aims to simulate the lake ice conditions in the Nam Co lake using a lake ice model, which is a one-dimensional physics-based model that utilizes enthalpy as the predictor variable. We modified the air density schemes within the model to improve the accuracy of the lake ice simulation. Additionally, the process of lake ice sublimation was included, and the effect of lake water salinity on the freezing point was considered. Using the improved lake ice model, we simulated lake surface water temperature, lake ice thickness, and interannual variations in lake ice phenology, and we compared these results with observations at Nam Co. The results demonstrate that the improved model better reproduces the lake surface water temperature, lake ice thickness, and lake ice phenology at Nam Co. Additionally, the thin air density affects lake processes by weakening sensible heat and latent heat, which ultimately leads to a delayed ice-on date and a slightly earlier ice-free date in Nam Co. This study contributes to an enhanced understanding of the freeze–thaw processes in Nam Co and reduces the biases in lake ice simulation on the Tibetan Plateau through the lake model improvement.

Investigating the warming effect of urban expansion on lake surface water temperature in the Dianchi lake watershed

Study regionDianchi lake (DCL) watershed, located in southwestern China, has undergone significant urbanization. Study focusDue to the accelerating urbanization in the watershed, the influence of urban expansion on lake surface water temperature (LSWT) has become increasingly significant. However, there is currently no quantitative study on the specific warming value of LSWT caused by urban expansion. This study primarily quantifies the increase in LSWT caused by urban expansion from two perspectives: the rise in near-surface air temperature (NSAT) resulting from urban expansion and the thermal runoff generated by urban expansion. New hydrological insights for the regionWe found that the DCL watershed experienced significant urban expansion between 2001 and 2018. In this context, the cumulative increase in LSWT caused by the newly added impervious surface (IS) through NSAT is about 0.25 °C. The cumulative warming of the newly added IS on the LSWT through thermal runoff is about 0.05–0.06 °C. Urban expansion mainly affects the LSWT by increasing the NSAT.