Lake Surface Temperature Research Articles

Lake temperature and morphometry shape the thermal composition of recreational fishing catch

AbstractObjectiveManaging freshwater fisheries in warming lakes is challenging because climate change impacts anglers, fish, and their interactions.MethodsWe integrated recent models of current and future lake temperatures with recreational fisheries catch data from 587 lakes in three north‐central U.S. states (Michigan, Minnesota, and Wisconsin) to evaluate how the thermal composition of recreational fisheries catch varied as a function of temperature, ice coverage, and lake morphometry.ResultWe found that warmwater catch share (WCS), defined as the proportion of fish in recreational angling catch that belonged to the warmwater thermal guild (final temperature preferendum [FTP] > 25°C), increased with average annual lake surface temperature and decreased with survey ice coverage. However, we also found that WCS decreased with increased lake area and depth. Using mid‐century (2040–2060) water temperature and ice projections while holding all other variables constant, we predicted that WCS will likely increase as the climate warms but that significant thermal heterogeneity will persist.ConclusionLakes that are large (>100 ha) and deep (>10 m) and those with cooler (<3700 annual growing degree‐days) predicted future temperatures will likely hold thermal refugia for coolwater (FTP = 19–25°C) and coldwater (FTP < 19°C) fish even as average lake temperatures rise, creating the potential for management actions to resist the shift from coolwater to warmwater fisheries. Managers of smaller and more rapidly warming lakes may want to consider strategies that accept or direct emerging warmwater fishing opportunities. We suggest that the most viable path to climate adaptation in landscapes of diverse lakes may be to resist warmwater shifts where possible and to accept or direct the rise of warmwater fishing opportunities where necessary.

Long-term trends in water transparency of Tibetan Plateau lakes and the response to extreme climate events

Water transparency, as indicated by the Secchi disk depth (Zsd), is a key parameter for assessing the quality of aquatic environments, reflecting the ability of light to penetrate through the water column. In the Tibetan Plateau (TP), where lakes are abundant yet remote and challenging to access, remote sensing techniques offer a promising approach for monitoring Zsd over large spatial scales. In this study, we used the semianalytical -based Zsd algorithm to study the temporal and spatial dynamics of water transparency over TP during the period from 2003 to 2022. The results show that the 173 lakes have a mean value of Zsd is 3.64 ± 2.4 m for long term, and generally with the significantly increasing change trends in the past 20 years. In the central Tibetan Plateau (CTP) region, lake transparency showed a positive correlation with lake surface temperature (r = 0.73) and a negative correlation with precipitation (r = −0.54), highlighting the region’s heightened sensitivity to meteorological changes compared to other areas. The spike in water clarity observed in the CTP region may be linked to alterations in lake hydrodynamics driven by the extremely climate events (i.e., El Niño). These results indicated the importance of considering regional climatic factors when interpreting fluctuations in water transparency.

Lake Surface Temperature Predictions under Different Climate Scenarios with Machine Learning Methods: A Case Study of Qinghai Lake and Hulun Lake, China

Lake Surface Temperature Predictions under Different Climate Scenarios with Machine Learning Methods: A Case Study of Qinghai Lake and Hulun Lake, China

Emergence of lake conditions that exceed natural temperature variability

Lake surface temperatures are projected to increase under climate change, which could trigger shifts in the future distribution of thermally sensitive aquatic species. Of particular concern for lake ecosystems are when temperatures increase outside the range of natural variability, without analogue either today or in the past. However, our knowledge of when such no-analogue conditions will appear remains uncertain. Here, using daily outputs from a large ensemble of SSP3-7.0 Earth system model projections, we show that these conditions will emerge at the surface of many northern lakes under a global warming of 4.0 °C above pre-industrial conditions. No-analogue conditions will occur sooner, under 2.4 °C of warming, at lower latitudes, primarily due to a weaker range of natural variability, which increases the likelihood of the upper natural limit of lake temperature being exceeded. Similar patterns are also projected in subsurface water, with no-analogue conditions occurring first at low latitudes and occurring last, if at all, at higher latitudes. Our study suggests that global warming will induce changes across the water column, particularly at low latitudes, leading to the emergence of unparalleled climates with no modern counterparts, probably affecting their habitability and leading to rearrangements of freshwater habitats this century.

Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management.

Globally, climate warming is increasing air temperatures and changing river flows, but few studies have explicitly considered the consequences for lake temperatures of these dual effects, or the potential to manage lake inflows to mitigate climate warming impacts. Using a one-dimensional model, we tested the sensitivity of lake temperatures to the separate and interacting effects of changes in air temperature and inflow on a small, short-residence time (annual average ≈ 20days), temperate lake. Reducing inflow by 70% increased summer lake surface temperatures 1.0-1.2°C and water column stability by 11-19%, equivalent to the effect of 1.2°C air temperature warming. Conversely, similar increases in inflow could result in lake summer cooling, sufficient to mitigate 0.75°C air temperature rise, increasing to more than 1.1°C if inflow temperature does not rise. We discuss how altering lake inflow volume and temperature could be added to the suite of adaptation measures for lakes.

Lake surface and downstream river temperature response to the retreat of a lake‐terminating glacier

AbstractGlacier contributions to streamflow moderate summer water temperatures and help keep rivers cool during periods of hot, dry weather. As glaciers retreat, the length of channel exposed to solar radiation above a given point of interest would increase, which should ultimately result in higher water temperatures. However, formation of proglacial lakes may complicate river temperature response to glacier retreat. This study examined river and lake temperature response to the retreat of lake‐terminating Bridge Glacier, British Columbia, using a combination of manual spot and continuous river temperatures at a stream gauge, lake surface temperatures derived from Landsat imagery, and in situ lake temperatures made during a field campaign during summer 2013. Temperature data spanned the period from 1984 to 2022. Lake and river temperatures were relatively constant prior to and during a period of rapid retreat, but exhibited an upward trend following 2012, when iceberg density declined due to an apparent decrease in calving rate, resulting in an increase in lake surface area and river temperature of about 5°C even though the glacier remained lake terminating. In addition to providing the first longitudinal study of water temperature response to the retreat of a lake‐terminating glacier, this study adds to a body of literature that demonstrates that routine spot river temperatures, if collected in a consistent manner over a number of years, can provide valid information about river and stream temperature trends.

Thermal Response of Large Seasonally Ice‐Covered Lakes Over Tibetan Plateau to Climate Change

AbstractIn this study, a process‐based lake model is used to investigate the influence of climate change on the thermodynamics of 30 large lakes over Tibetan Plateau (TP). The lake model was driven by the atmospheric forcing derived from the bias‐corrected projections of three global climate models in the twenty‐first century under three Shared Socioeconomic Pathways (SSPs). The hindcasts during 2000–2014 can reasonably capture the seasonality and magnitude of satellite retrieved lake surface temperature (LST). Future projections during 2015–2100 suggest a widespread increased LST, declined ice cover, and prolonged stratification, with the severity of changes in line with the climate driver shifts under different SSPs. Under the scenario with the highest level of anthropogenic radiative forcing (SSP5‐8.5), the end‐of‐century (2086–2100) changes of LST, ice thickness, and stratification duration averaged across all studied lakes reach 4.90°C, −0.43 m, and 65.46 days, respectively. Note that the positive ice‐albedo feedback can cause excess lake warming by accelerating ice break‐up (30.07 days earlier) and stratification onset (46.83 days earlier). By the end of this century, more frequent, multi‐seasonal thermal extremes are anticipated to push nearly half of the studied lakes into a permanent heatwave state. Together with the remarkable LST increase and winter ice loss, the lakes will mix less frequently and may shift from a dimictic to warm monomict mixing regime. Hopefully, the irreversible thermal changes can be avoided if the anthropogenic radiative forcing is controlled within the envelope outlined by the stringent climate mitigation scenario SSP1‐2.6.

Progress of remote sensing inversion research on surface temperature of lakes and reservoirs

Surface temperature of lakes and reservoirs is one of the important indicators of temperature change on the earth's surface, which not only reflects the impact of earth's climate change, but also has great significance for water resource management and ecological environmental protection. However, traditional temperature observation methods are limited by factors such as time, space and economic cost, which make it difficult to provide comprehensive and continuous temperature data. Therefore, the quantitative inversion of the surface temperature of lakes and reservoirs using remote sensing technology has become one of the hot spots in research. Based on this, this paper summarizes the relevant data sources and various research methods of remote sensing quantitative inversion of surface temperature of lakes and reservoirs in recent years, and elaborates and analyzes them, in order to provide reference for the ecological protection of lakes.

Lake Surface Temperature Retrieval Study Based on Landsat 8 Satellite Imagery—A Case Study of Poyang Lake

Poyang Lake is the largest freshwater lake in China and forms an essential component of the hydrological, nutrient, and carbon cycles, providing various ecosystem services to the local environment. Since changes in Poyang Lake’s water temperature can significantly affect the surrounding environment and social development, continuous monitoring of lake temperature changes is required. Traditional water monitoring methods are resource intensive and cannot simultaneously conduct extensive water monitoring. Remote sensing of temperature inversion has the advantages of all-weather, efficient, and large-scale real-time monitoring. Six Landsat 8 images from August to October in 2020 and 2021 were utilized to extract lake surface temperature (LST), and the variations in LST over the two years were analyzed to determine the impact of global climate anomalies on inland lakes. The results indicate that the LST in August and October 2021 was significantly higher than that in the same periods of the previous year, and the temperature difference in October reached 8 °C. In contrast to the overall normal distribution pattern of the water temperature in 2020, 2021 exhibited a relatively concentrated, unimodal distribution pattern. A trend analysis of the driving factors suggests that the LST of Poyang Lake is influenced by the global climate, and the artificial heat sources around the lake clearly alter the distribution characteristics of the LST simultaneously.

Functional principal component analysis for incomplete space–time data

Environmental 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.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Historical and projected response of Southeast Asian lakes surface water temperature to warming climate

The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts.Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986–2020) and future (2020–2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT.Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century.This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.

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.

Evaporation and sublimation measurement and modeling of an alpine saline lake influenced by freeze–thaw on the Qinghai–Tibet Plateau

Abstract. Saline lakes on the Qinghai–Tibet Plateau (QTP) affect the regional climate and water cycle through water loss (E, evaporation under ice-free conditions and sublimation under ice-covered conditions). Due to the observational difficulty over lakes, E and its underlying driving forces are seldom studied when targeting saline lakes on the QTP, particularly during ice-covered periods (ICP). In this study, the E of Qinghai Lake (QHL) and its influencing factors during ice-free periods (IFP) and ICP were first quantified based on 6 years of observations. Subsequently, three models were calibrated and compared in simulating E during the IFP and ICP from 2003 to 2017. The annual E sum of QHL is 768.58±28.73 mm, and the E sum during the ICP reaches 175.22±45.98 mm, accounting for 23 % of the annual E sum. E is mainly controlled by the wind speed, vapor pressure difference, and air pressure during the IFP but is driven by the net radiation, the difference between the air and lake surface temperatures, the wind speed, and the ice coverage during the ICP. The mass transfer model simulates lake E well during the IFP, and the model based on energy achieves a good simulation during the ICP. Moreover, wind speed weakening resulted in an 7.56 % decrease in E during the ICP of 2003–2017. Our results highlight the importance of E in ICP, provide new insights into saline lake E in alpine regions, and can be used as a reference to further improve hydrological models of alpine lakes.

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.

Warming inland water in peninsular Spain revealed by landsat 5 analysis

Lakes and reservoirs provide habitat for a wide range of species and are essential components of numerous ecosystems. In Spain, these water bodies are particularly important and sensitive, so studying the impact of climate change critical for their long-term management and maintenance. This study aims to identify warming trends in these water bodies and determine whether they vary by season or geographical location by processing time series data of surface temperatures from Landsat 5 images to identify changes in the surface temperature of lakes and reservoirs between 1994 and 2011. This analysis revealed that 87% of water bodies exhibit a warming trend, with an average increase in water surface temperature of +0.037 °C per year. The Ebro and Guadalquivir depressions exhibited the highest temperature increases. However, temperature changes vary across the peninsula, generally associated with air surface temperatures, and are influenced by altitude in the mountain areas and Atlantic winds in the west depressions. The trends indicate a seasonal pattern: in summer, the number of water bodies with high temperatures increases, while in autumn there are more lakes and reservoirs with intermediate temperatures. In winter and spring, the number of water bodies with low temperatures decreases and they are located further north. The influence of factors such as climate and local conditions should be investigated to better understand the warming of these water bodies.

Application of a Three‐Dimensional Coupled Hydrodynamic‐Ice Model to Assess Spatiotemporal Variations in Ice Cover and Underlying Mechanisms in Lake Nam Co, Tibetan Plateau, 2007–2017

AbstractA three‐dimensional lake‐ice coupled model is used to investigate the space‐time variations of ice and underlying mechanisms in Lake Nam Co (LNC), the third largest lake over Tibetan Plateau (TP), during 2007–2017. The model reasonably reproduces the in situ measured ice thickness and water temperature profile, and satellite retrieved ice coverage and lake surface temperature. Seasonally, the lake ice first forms in the eastern basin during early January, expands from east to west during January and February, covers nearly the entire LNC in March, starts melting from west to east in April, and eventually disappears in May. The eastward drift of thin ice throughout the ice‐covered phase and the eastward water heat transport during the ice melting phase are key factors to determine the spatial variation of ice and freeze‐thaw processes. A multiple linear regression analysis confirms that the eastward drift of thin ice can be mostly attributed to the prevailing westerly. During 2007–2017, ice volume, duration, ice‐on and ice‐off dates show significant interannual variations, and they are highly correlated with the surface air temperature (T2m) averaged over January‐March, from the preceding December to May, in December and over March–May, respectively, suggesting the “cumulative effects” of T2m. Seasonal and interannual variations of ice drift are attributed to the combined effects of wind and ice volume variations. Sensitivity analysis further points out the important impacts of ice on the lake temperature and circulation structure in winter and spring, hence the necessity of hydrodynamic‐ice coupled models in large TP lakes.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Four decades of lake surface temperature in the Northwest Territories, Canada, using a lake-specific satellite-derived dataset

Study regionThis study analyzed lake surface temperature (LST) trends and spatial distribution across 535 predominantly small to medium lakes across the North Slave Region (NSR) of the Northwest Territories (NWT), Canada. Study focusThe NWT is characterized by a vast number of lakes covering a significant portion of its spatial extent. However, there is limited knowledge of how LST responds to climate warming in this region. To address this, LST was analyzed in four distinct periods: open water season (OW), ice cover season (IC), and the transitional months of May (TM) and October (TO). LSTs from 1984 to 2021 were retrieved from a lake-specific satellite-derived LST dataset (North Slave LST). LST trend distribution and relationships were analyzed using the Mann-Kendall test and a multilinear regression model. New hydrological insightsThe analysis revealed an overall increase in LST, with average rates (max) of 0.03 °C/year (0.05 °C/year), 0.03 °C/year (0.06 °C/year), and 0.13 °C/year (0.27 °C/year) for OW, TM, and TO, respectively accross study lakes. A faster rate of change was observed in October compared to other periods. Results indicated significant increases in LST for 411 lakes (77%) during OW, 418 lakes (78%) during TO, and 490 lakes (92%) during TM. The spatial distribution and magnitude of LST change were primarily influenced by geographical than morphometric properties. The analysis demonstrated later freeze-up (0.20 day/year) and earlier break-up (−0.17 day/year) of lake ice across the NSR.

Modeling the Effects of Local Atmospheric Conditions on the Thermodynamics of Sobradinho Lake, Northeast Brazil

The objective of this work was to study climate variability and its impacts on the temperature of Sobradinho Lake in Northeast Brazil. Surface weather station data and lake measurements were used in this study. The model applied in this work is FLake, which is a one-dimensional model used to simulate the vertical temperature profile of freshwater lakes. First, the climate variability around Sobradinho Lake was analyzed. Observations showed a reduction in precipitation during 1991–2020 compared to 1981–2010. To study climate variability impacts on Sobradinho Lake, the years 2013, 2015, and 2020 were selected to characterize normal, dry, and rainy years, respectively. In addition, the months of January, April, July, and October were analyzed for rainy months, rainy–dry transitions, dry months, and dry–rainy transitions. Dry years showed higher incoming solar radiation at the surface and, consequently, higher 2 m air temperatures. A characteristic of the normal years was more intense surface winds. October presented the highest incoming solar radiation, the highest air temperature, and the most intense winds at the surface. The lowest incoming solar radiation at the surface was observed in January, and the lightest wind was observed in April. To assess the effects of these atmospheric conditions on the thermodynamics of Sobradinho Lake, the FLake model was forced using station observation data. The thermal amplitude of the lake surface temperature (LST) varied by less than 1 °C during the four months. This result was validated against surface lake observations. FLake was able to accurately reproduce the diurnal cycle variation in sensible heat fluxes (H), latent heat fluxes, and momentum fluxes. The sensible heat flux depends directly on the difference between the LST and the air temperature. During daytime, however, Flake simulated negative values of H, and during nighttime, positive values. The highest values of latent heat flux were simulated during the day, with the maximum value was simulated at 12:00 noon. The momentum flux simulated a similar pattern, with the maximum values simulated during the day and the minimum values during the night. The FLake model also simulated the deepest mixing layer in the months of July and October. However, our results have limitations due to the lack of observed data to validate the simulations.