Lake Surface Water Temperatures Research Articles

Analysis of Temporal and Spatial Variation Process of Dianchi Lake Surface Water Temperature Based on MODIS Remote Sensing Images

Lake surface water temperature (LSWT) is an important physical attribute that affects the ecological environment of lakes. It plays an important role in the ecological process of the water environment and directly affects human production and life, the ecological environment system of the water area, and the diversity of organisms. In order to adjust and improve the ecological conditions of the watershed, it is necessary to obtain a large amount of effective lake surface water temperature data, and use this as a basis to analyze the temporal and spatial change process of the LSWT. This study uses MODIS (MOD11A2) surface temperature product data to obtain the LSWT of Dianchi Lake, and then analyzes the temporal and spatial variation characteristics of the LSWT of Dianchi Lake on different time scales (year, season, month) during the day and night. The research results show that the annual, seasonal and monthly average values of the LSWT during the day and night are all increasing from 2001 to 2017. The average annual temperature rise rate of Dianchi LSWT during the day and night is 0.09°Cyr−1 and 0.05°Cyr−1, respectively. The spatial analysis results show that the LSWT increase area of Dianchi Lake is expanding.

Chlorophyll-a concentrations in 82 large alpine lakes on the Tibetan Plateau during 2003–2017: temporal–spatial variations and influencing factors

ABSTRACT As essential parts of the unique ecosystem of Tibetan Plateau (TP), the sizes and associated physical properties of alpine lakes have long been investigated. However, little is known about one of the most critical biogeochemical properties, i.e. the Chlorophyll-a (Chl-a) concentrations. Here, for the first time, we presented a comprehensive investigation of the temporal–spatial variations in Chl-a in 82 lakes (>50 km2) across the entire TP region, based on MODIS observations in the period of 2003–2017. The results showed that the 82 lakes exhibited an average long-term mean Chl-a of 3.3 ± 4.3 mg m−3, with high Chl-a lakes concentrated in the eastern and southern inner TP basin and northeastern parts of the TP. An interannual trend analysis revealed that lakes exhibiting (significantly) decreasing Chl-a trends and (significantly) increasing Chl-a trends were comparable in numbers but differed in distribution patterns. A correlation analysis indicated that at least 70% of the interannual variability in Chl-a values of lakes was significantly correlated with one of the four environmental factors (wind speed, ice cover duration, lake water surface temperature and surface runoff) and lake size. In addition, glacier meltwater tended to reduce lake Chl-a while salinity levels showed minor influences.

Precipitation events impact on urban lake surface water temperature under the perspective of macroscopic scale.

Lake surface water temperature (LSWT) is an important factor affecting a lake's ecological environment. In recent decades, LSWT worldwide has shown an increasing trend in the context of global climate change. This rising trend has been more evident in urban lakes. With the rapid development of urbanization, urban lakes are affected not only by climate warming but also by human activities. Among these factors, due to the increase in impervious surface coverage (ISC), the impact of thermal runoff pollution caused by precipitation events on urban lakes cannot be ignored. Therefore, this study used the Dianchi Lake watershed as a study area, and the surface water temperature of Dianchi Lake, the precipitation data, and the land use data were collected and analyzed. Based on these data, the influence of precipitation events on the surface water temperature of Dianchi Lake was analyzed. The research results show that under the background of different ISC levels and different growth rates of impervious surface area (ISA), precipitation events have different effects on the LSWT. When ISC is low and the growth rate of ISA is slow, the annual precipitation is negatively correlated with the annual average surface water temperature of Dianchi Lake (r = - 0.183). When ISC is high and the growth rate of ISA is fast, the annual precipitation is positively correlated with the average annual surface water temperature of Dianchi Lake (r = 0.65). With the increase in ISC, the correlation between seasonal precipitation and the average surface water temperature in Dianchi Lake changed from negative to positive in spring and autumn. Under the action of impervious surfaces, precipitation events have a warming effect on the surface water temperature of the lake, and this effect will be intensified with the increase in ISC.

Research on the Lake Surface Water Temperature Downscaling Based on Deep Learning

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

Baseline Water Temperature: Estimation of the Annual Cycle of Surface Water Temperature in Lakes in North-Central Poland over the 1951–1968 Period

Water temperature is an important ecological variable that affects the functioning of lakes. Unfortunately, for many lakes there are no long-term observations enabling the assessment of changes in water temperatures. This makes it difficult to include this aspect in research into the biology, ecology and chemistry of such lakes. This paper presents a literature review related to changes of surface water temperatures in lakes and in particular describing the response of water temperatures and stratification to changing climate in Polish lakes. On this basis, a model based on the available data on water temperature in 931 Polish lakes in the years 1951–1968 was proposed, which allows to estimate the baseline water temperature on any day of the year. This model is calculated using the complementary error peak function on the 0–3 m water temperature dataset, which provides the best reduction of diurnal temperature fluctuations. It can be an alternative to the average temperature of surface waters, which are calculated on the basis of systematically collected data. Based on the average water temperature data obtained from 56 thermal profiles in 10 lakes in 2010–2019, the equation was analogically calculated. The average monthly water temperatures in June, July, August and September and the change in water temperature (0.24–0.30 °C decade−1) in the period 1951–1968/2010–2019 were estimated then. Similar regional or single lake trends have been found in studies by other authors covering a similar or longer period of time. The proposed method, which is suitable for simulating temperatures, especially in summer, enables the determination of the value of changes in surface water temperature in Polish lakes when only thermal profiles data from different dates are available, which can be especially helpful when analyzing hydrobiological results.

Atmospheric stilling and warming air temperatures drive long‐term changes in lake stratification in a large oligotrophic lake

AbstractLake surface temperatures are warming in many regions and have the potential to alter seasonal thermal stratification. However, the effects of climate change on thermal stratification can be difficult to characterize because trends in thermal stratification can be regulated by changes in multiple climate variables and other characteristics, such as water clarity. Here, we use long‐term (1993–2017) data from near‐pristine Crater Lake (Oregon) to understand long‐term changes in the depth and strength of summer stratification, measured by the center of buoyancy and Schmidt Stability, respectively. The depth of stratification has shoaled significantly (2.4 m decade−1), while stratification strength exhibited no long‐term trend. Empirical observations and modeling scenarios demonstrate that atmospheric stilling at Crater Lake is associated with the 25‐year shoaling trend as spring wind speeds declined over the observation period. While summer lake surface water and air temperatures warmed during the study period, spring air temperatures were variable and correlated with summer Schmidt Stability. Our results indicate that warmer spring air temperature resulted in earlier onset of stratification and stronger summer stratification. The observed shoaling of stratification depth at Crater Lake may have important ecological consequences, especially for non‐motile primary producers who can become constrained within a thinner epilimnion and exposed to higher solar radiation and reduced upwelling of nutrients. Driven by climate changes, many large lakes may be experiencing similar trends in seasonal stratification.

Deeper waters are changing less consistently than surface waters in a global analysis of 102 lakes

Globally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade−1, comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m−3 decade−1). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade−1), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade−1 to + 0.65 °C decade−1. The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences.

Lake surface water temperature prediction and changing characteristics analysis - A case study of 11 natural lakes in Yunnan-Guizhou Plateau

Lake surface water temperature (LSWT) is an important factor for lake ecological environment. Under the influence of natural and human factors, LSWT is generally on the rise. LSWT is affected by lake’s geographical location, climate diversity, biodiversity and cultural diversity of the plateau. To this end, the study used the natural factor (near-surface air temperature) that characterized climate change, the human factors (impermeable surface area, gross domestic product, and population) that characterized human activities, and the physical factors that characterized the lake inherent properties (lake area, watershed area, and water storage) as the dataset, and a hybrid prediction model (ε-SVR-AHP-BPANN) was proposed by combining three algorithms which were ε-support vector regression (ε-SVR), analytic hierarchy process (AHP), and back propagation artificial neural network (BPANN). This model was used to estimate and simulate the LSWT of 11 lakes in the study area from January 2018 to December 2019. Then conducted on the variation characteristics of LSWT for 11 lakes from 2001 to 2019. The results showed that: (1) The hybrid prediction model had a strong predictive power with low error and high generalization (RMSELSWT-day = 0.11, RMSELSWT-night = 0.10, RLSWT-day2 = 0.77, RLSWT-night2 = 0.90). (2) The temporal change analysis showed that, overall, the change rate of LSWT-day showed a warm-cold-warm trend from north to south in Yunnan-Guizhou Plateau, while LSWT-night showed a warm-cold trend. (3) The spatial visualization analysis revealed that there was a general warming trend of LSWT, and the LSWT of northern Erhai Lake were higher than that in southern Erhai Lake, and the distribution of LSWT was regional with the change of latitude.

Analysis on driving factors of lake surface water temperature for major lakes in Yunnan-Guizhou Plateau

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.

Bayesian prediction of spatial data with non-ignorable missingness

In spatial data, especially in geostatistics data where measurements are often provided by satellite scanning, some parts of data may get missed. Due to spatial dependence in the data, these missing values probably are caused by some latent spatial random fields. In this case, ignoring missingness is not logical and may lead to invalid inferences. Thus incorporating the missingness process model into the inferences could improve the results. There are several approaches to take into account the non-ignorable missingness, one of them is the shared parameter model method. In this paper, we extend it for spatial data so that we will have a joint spatial Bayesian shared parameter model. Then the missingness process will be jointly modeled with the measurement process and one or more latent spatial random fields as shared parameters would describe their association. Bayesian inference is implemented by Integrated nested Laplace approximation. A computationally effective approach is applied via a stochastic partial differential equation for approximating latent Gaussian random field. In a simulation study, the proposed spatial joint model is compared with a model that assumes data are missing at random. Based on these two models, the lake surface water temperature data for lake Vanern in Sweden are analyzed. The results of estimation and prediction confirm the efficiency of the spatial joint model.

Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN

Prediction of rivers and lakes water temperature plays an important role in hydrology, ecology, and water resources planning and management. Recently, machines learning approaches have been widely used for modelling water temperature, and the obtained results vary depending on the kind of models and the selections of the appropriates predictors. In the present paper, a new family of machines learning are proposed and compared to the famous air2stream model, using a large data set collected at 25 lakes in the northern part of Poland. The proposed models were: (i) the extremely randomized trees (ERT), (ii) the multivariate adaptive regression splines (MARS), (iii) the M5 Model tree (M5Tree), (iv) the random forest (RF), and (v) the multilayer perceptron neural network (MLPNN). The models were developed using the air temperature as input variables and the component of the Gregorian calendar (year, month and day) number. Results obtained were evaluated using several statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), correlation coefficient (R) and Nash-Sutcliffe efficiency coefficient (NSE). Obtained results reveals that the air2stream model outperformed all other machines learning models and worked best with high accuracy at all the 25 lakes, and none of the ERT, MARS, M5Tree, RF and MLPNN models was able to provides an improvement of the water temperature prediction compared to the air2stream.

Correction to: Global reconstruction of twentieth century lake surface water temperature reveals different warming trends depending on the climatic zone

Correction to: Global reconstruction of twentieth century lake surface water temperature reveals different warming trends depending on the climatic zone

Variability of lake surface water temperature: a case study during El Nino and La Nina events over the Himalayan lake region

ABSTRACT Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes. This paper investigates the association of LSWT with meteorological parameters and its impact during El Nino and La Nina events over the Himalayan lake region, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data from 2001 to 2017. The observed daytime rates of change of LSWT was −0.05°C/year whereas it was +0.01°C/year for nighttime. The cooling of lake during daytime could be attributed to increase in rainfall and decrease in solar radiation, whereas increase in nighttime LSWT is due to increasing lowest temperature. The variability in LSWT with respect to meteorological variables during El Nino Southern Oscillation period was highest for cold phase than warm phase. There was only 1% increase in LSWT in the El Nino years (2002 and 2009), due to decreased rainfall by 95% and 56% respectively. On the other hand, the LSWT decreased in strong La Nina years of 2007 (2%) and 2010 (6%) due to the drastically increased rainfall, decreased maximum temperature and solar radiation. The correlation revealed that, in addition to the increase in temperature, the LSWT warming is consistent with the decrease in rainfall and increase in solar radiation.

Forecasting surface water temperature in lakes: A comparison of approaches

Accurate water temperature forecasting in lake systems is important for environmental impact assessment and fisheries management, among others. In this study, two models are developed and applied for water temperature forecasting in lake systems: (1) the multi-layer perceptron neural network (MLPNN) model; and (2) the wavelet transform and MLPNN integrated model (WT_MLPNN). The models are applied to forecast daily lake surface water temperature (LSWT) of eight lowland Polish lakes. Long-term daily LSWT from eight lakes and daily air temperatures from seven meteorological stations are used for daily LSWT forecasting. The results from the two models are compared with those obtained from two other widely used models: the physically-statistically based hybrid air2water model and a non-linear regression model (S-curve). The modelling results show that the air2water model performs the best, followed by the WT_MLPNN, MLPNN, and the non-linear regression model. Overall, the air2water, WT_MLPNN, and MLPNN models reproduce well the seasonal and inter-annual variations of the LSWT dynamics in the eight lakes. The non-linear regression model, although providing the lowest accuracy, can still provide good preliminary estimates of the LSWT for the eight lakes. The outcomes of the present research can provide references for forecasting lake surface water temperature and sustainable management of lake ecosystems.

Impact of the 2018 European heatwave on lake surface water temperature

ABSTRACT In 2018, Europe experienced the warmest May–October (Northern Hemisphere warm season) since air temperature records began. In this study, we ran model simulations for 46 557 lakes across Europe to investigate the influence of this heatwave on surface water temperature. We validated the model with satellite-derived lake surface temperatures for 115 lakes from 1995 to 2018. Using the validated model, we demonstrated that, during May–October 2018, mean and maximum lake surface temperatures were 1.5 and 2.4 °C warmer than the base-period average (1981–2010). A lake model experiment demonstrated that, on average, the increase in air temperature was the dominant driver of surface water temperature change. However, in some lake regions, other meteorological forcing had a greater influence. Notably, higher than average solar radiation and lower than average wind speed exacerbated the influence of the heatwave on lake surface temperature in many regions, particularly Fennoscandia and Western Europe. To place our results in the context of projected 21st century climate change, we then ran the lake model with input data from state-of-the-art climate model projections under 3 emissions scenarios. Under the scenario with highest emissions (Representative Concentration Pathway 8.5), we demonstrated that by the end of the 21st century, the lake surface temperatures that occurred during the heatwave of 2018 will become increasingly common across many lake regions in Europe.

Reconstructing Six Decades of Surface Temperatures at a Shallow Lake

Lake surface water temperature (LSWT) plays a fundamental role in the lake energy budget. However, direct observations of LSWT require considerable effort for acquisition and hence are rare relative to a large number of lakes. In lakes where LSWT has not been covered sufficiently by in situ measurements, remote sensing and lake modeling can be used to produce a fine spatio-temporal record of LSWTs. In our study, the Moderate-Resolution Imaging Spectroradiometer (MODIS) LSWT was used to compare with in situ data at the overpass times over the six sites in Lake Chaohu, a large shallow lake in China. MODIS-derived LSWT reflected the variation of lake surface temperature well, with a correlation coefficient of 0.96 and a cool bias of 1.25 °C. The bias was modified by an “Upper Envelop” smoothing method and then employed to evaluate the general lake model (GLM) performance, a one-dimensional hydrodynamic model. The GLM simulations showed good performance compared with MODIS LSWT data at an interannual time scale. A 57-year record of simulated LSWT was hindcast by the well-calibrated GLM for Lake Chaohu. The results showed that LSWT decreased by 0.08 °C/year from 1960 to 1981 and then increased by 0.05 °C/year. These trends were most likely caused by a cooling effect of decreased surface incident solar radiation and a warming effect of reduced wind speed. Our study promoted the use of MODIS-derived LSWT as an alternative data source, and then combined with a numerical model for inland water surface temperature, and also further provided an understanding of climate warming effect on such a shallow eutrophic lake. Key points: (1) Moderate-Resolution Imaging Spectroradiometer (MODIS) lake water surface temperature (LSWT) was validated with real-time in situ data collected at Lake Chaohu with high accuracy; (2) MODIS LSWT was modified by the bias correction and employed to evaluate a one-dimensional lake model at interannual and intraannual scale; The LSWT hindcast by a well-calibrated model at Lake Chaohu decreased by 0.08 °C/year from 1960 to 1981 and increased by 0.05 °C/year from 1982 to 2016.

Global reconstruction of twentieth century lake surface water temperature reveals different warming trends depending on the climatic zone

Lake surface water temperatures (LSWTs) are sensitive to climate change, but previous studies have typically focused on temperatures from only the last few decades. Thus, while there is good appreciation of LSWT warming in recent decades, our understanding of longer-term temperature change is comparatively limited. In this study, we use a mechanistically based open-source model (air2water), driven by air temperature from a state-of-the-art global atmospheric reanalysis (ERA-20C) and calibrated with satellite-derived LSWT observations (ARC-Lake v3), to investigate the long-term change in LSWT worldwide. The predictive ability of the model is tested across 606 lakes, with 91% of the lakes showing a daily root mean square error smaller than 1.5 °C. Model performance was better at mid-latitudes and decreased towards the equator. The results illustrated highly variable mean annual LSWT trends during the twentieth century and across climatic regions. Substantial warming is evident after ~ 1980 and the most responsive lakes to climate change are located in the temperate regions.

Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions

ABSTRACTModel-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a–nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic–eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.

Exploring and quantifying the impact of climate change on surface water temperature of a high mountain lake in Central Europe.

Lake surface water temperature (LSWT) is a key indicator which drives ecosystem structure and function. Quantifying the impact of climate change on LSWT variations is thus of great significance. In this study, observed data of LSWT during the period 1969-2018 in a high mountain lake (Morskie Oko Lake, Central Europe) were analyzed. The results showed that the prominent warming of the LSWT and air temperature began around 1997. A logistic non-linear S-curve function was used to model monthly average LSWT. The non-linear model performed well to capture monthly average LSWT and air temperature relationships (Nash-Sutcliffe efficiency coefficient 0.86 and the root mean squared error 1.63°C). Using the 2009-2018 period as base scenario, a sensitivity analysis was conducted. The results showed that the annual mean LSWT will likely increase about + 1.29°C and + 2.64°C with air temperature increases of + 2°C and + 4°C respectively at the end of the twenty-first century. If realized, such a scenario will cause serious consequences on lake ecosystem.

The Importance of Systematic Spatial Variability in the Surface Heat Flux of a Large Lake: A Multiannual Analysis for Lake Geneva

AbstractThe spatiotemporal surface heat flux (SurHF) distribution over Lake Geneva, the largest lake in Western Europe, was estimated for a 7‐year period (2008–2014). Data sources included hourly maps of over‐the‐lake assimilated meteorological data from a validated numerical weather model and lake surface water temperature (LSWT) from satellite imagery. A set of bulk algorithms, previously optimized and calibrated at two locations in Lake Geneva, was used. Results indicate a systematic long‐term average spatial range of >40 Wm‐2 between different parts of the lake and little year‐to‐year variability. This variability is mainly due to topographically induced wind sheltering over parts of the lake, which in turn produces spatial variability in the sensible and latent heat fluxes. These results are supported by a systematic spatial heat content variability obtained from long‐term temperature profile measurements in the lake. During spring, a lower SurHF spatial range was evident. Unlike other seasons, the spring spatial variability of air‐water temperature differences and, to a lesser extent, the global radiation variability resulting from sheltering by the mountainous topography were the main drivers of the SurHF spatial variability. Analysis of the atmospheric thermal boundary layer showed stable conditions from March to early June and unstable conditions for the rest of the year. This regime change can explain the low SurHF spatial variability observed during spring. The results emphasize that spatial variability in meteorological and LSWT patterns, and consequently in the spatiotemporal SurHF data, should be considered when assessing the time evolution of the heat budget of large lakes.