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In modeling species distributions and population dynamics, spatially‐interpolated climatic data are often used as proxies for real, on‐the‐ground measurements. For shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite‐derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high‐resolution global maps of air‐LSWT offsets, corresponding uncertainty measures and derived LSWT‐based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature‐based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab‐measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically‐meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.

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

The determination of the rational minimum ecological water level is the base for the protection of ecosystems in shrinking lakes and wetlands. Based on the lake surface area method, a simplified lake surface area method was proposed to define the minimum ecological lake level from the lake level-logarithm of the surface area curve. The curve slope at the minimum ecological lake level is the ratio of the maximum lake storage to the maximum surface area. For most practical cases when the curve cannot be expressed as a simple analytical function, the minimum ecological lake level can be determined numerically using the weighted sum method for an equivalent multi-objective optimization model that balances ecosystem protection and water use. This method requires fewer data of lake morphology and is simple to compute. Therefore, it is more convenient to use this method in the assessment of the ecological lake level. The proposed method was used to determine the minimum ecological water level for one freshwater lake, one saltwater lake, and one wetland in China. The results can be used in the lake ecosystem protection planning and the rational use of water resources in the lake or wetland basins.

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration.

Using the WRF-Lake model, this study tried to improve the prediction of lake surface water temperature (LSWT) and how it affects the atmospheric conditions over the hypersaline Urmia Lake (UL). Several changes were made to the lake model to fix its problems with showing how the temperature changes in UL. Because the salinity was abnormally high in this lake, changes had to be made to the formulas for water density, freezing point, and saturation vapor pressure. A dynamic data assimilation method was also used to use in-situ observations to keep the lake surface temperature up to date. For the cold season of 2016–2017, model simulations were run and the model’s performance was assessed using field observations, reanalysis data, and satellite retrievals. The results indicated significant discrepancies between the results from model with default configuration and in-situ observations. The model underestimated LSWT and had cold biases, particularly during nighttime. After trying out different combinations, the SLake model, which included all of the changes, worked the best. It enhanced the accuracy of LSWT prediction and eliminated thermal stratification patterns. The model was still not able to reproduce the lake’s complicated thermal behavior; hence, a dynamic LSWT assimilation method was employed that used in-situ data to keep the lake surface temperature up to date. This new method cut down the cold bias in LSWT by a large amount and improved the simulation of near-surface air temperatures significantly. The dynamic LSWT updates also improved simulations of lake evaporation, with the SLake_LSWT configuration outperformed other models and GLEAM predictions. The study shows importance of inclusion of physical properties of hypersaline lakes in to numerical models to have realistic results.

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.

Predicting the thermal response of lakes to cyclic environmental forcing and their expected global changes requires precise mathematical expressions. We apply a hybrid approach, based on theoretical analysis and direct measurements, including the role of lake's thermal stratification. We explore the equilibrium solution of the energy balance equation, achieved when changes in stored heat are negligible, governed by environmental conditions. As environmental forcing varies, lake surface temperature depart from equilibrium, with a time delay and reduced amplitude, depending on the ratio between the forcing period and the lake's thermal response time, and its dependency on lake/thermocline depth and wind speed. These formulations were tested based on 2 years of eddy covariance data from two neighboring Mediterranean lakes differing in physical structure, a deep and shallow lake. Finally, we provide tools for estimating surface temperature and evaporation rates across various timescales, stratification patterns, and global environmental changes.

Globally the lake surface water temperature (LSWT) has shown an upward trend and exhibits significant spatial heterogeneity,but revious studies have indeed delved it under current period. Here, we utilized the characteristics of LSWT variation in major lakes of China over two centuries. First we used the AIR2WATER model to constructed the data set of LSWT in Chin Chinese major lakes based on the data from CMIP6. Considering the rapid urbanization and climate change, the year of 1900–2014 can be divided into two phases: A stable period (1900–1970‐Phase I, 0.01°C/10a) and a warming period (1971–2014‐ Phase II, 0.16°C/10a). For the future (2015–2100), under the low emission scenario model (SSP1‐RCP2.6, 0.12°C/10a); under the medium emission scenario model (SSP2‐RCP4.5, 0.18°C/10a); and under the high emission scenario model (SSP5‐RCP8.5, 0.38°C/10a). We have designed three spatial classes to analyze the characteristics of LSWT of China major lake. Particularly, when analyzing the spatial pattern based on China's famous population‐economic demarcation line (the Hu Huanyong Line), we found that the LSWT growth rate in lakes east of the Hu Huanyong Line is higher than that in lakes to the west in Phase II, as well as under the SSP1‐RCP2.6 and SSP2‐RCP4.5 scenario models. However, in Phase I and under the SSP5‐RCP8.5 scenarios, the LSWT growth rate in lakes east of the Hu Huanyong Line is lower than that in the west. This study helps improve our understanding of China's major lakes and their changing mechanisms under the warming climate. Plain Language Summary Recent trends in lake surface water temperature (LSWT) indicate a warming pattern, there is needed a comprehensive understanding of the past, present, and future dynamics with LSWT. Here, we utilized the AIR2WATER model to reconstruct LSWT data set of Chinese major lakes under 1900–2100. Considering the rapid urbanization and climate change, the warming rate is Phase I (1900–1970,0.08°C/10a), Phase II (1971–2014, 0.16°C/10a), and under the low emission scenario model (SSP1‐RCP2.6, 0.12°C/10a); under the medium emission scenario model (SSP2‐RCP4.5, 0.18°C/10a); and under the high emission scenario model (SSP5‐RCP8.5, 0.38°C/10a). Interestingly, when we analyzed the spatial variation characteristics of LSWT based on China's population‐economic demarcation line (the Hu Huanyong Line), we found that the LSWT growth rate in lakes east of the Hu Huanyong Line is higher than that in the west during Phase II, as well as under the SSP1‐RCP2.6 and SSP2‐RCP4.5 scenario models. However, in Phase I and under the SSP5‐RCP8.5 scenarios, the LSWT growth rate in lakes east of the Hu Huanyong Line is lower than that in the west. Future research may focus on understanding how changes in LSWT affect the habitat suitability for aquatic species. Key Points We constructed a data set of lake surface water temperature for Chinese lakes during 1900–2100 LSWT warming west of the Hu Huanyong Line is increasingly exceeding that in the east Deep lake LSWT warming will influence and dominate the regional LSWT changes

Satellite remote sensing is widely used to monitor lake surface water temperature (LSWT) due to its global coverage and relatively long‐term record. A common practice in previous studies is to exclude observations during cloudy periods, as most satellite‐based LSWT products rely on optical sensors that cannot penetrate cloud cover. Using synthetic LSWT data sets, we demonstrate that cloud cover tends to coincide with specific thermal conditions, introducing geographically structured biases in annual mean LSWT estimates. We further conducted global‐scale synthetic numerical experiments to examine the impact of cloud cover propagates into model predictions when satellite‐derived LSWTs are used for model development. Two modeling approaches were evaluated: a physically‐based model (Air2water) and a data‐driven model based on a Long Short‐Term Memory (LSTM) neural network. While the LSTM model achieved an overall higher predictive accuracy, it exhibited significantly amplified biases when trained on cloud‐affected data sets, particularly in the warmest‐month temperature and ice duration. In contrast, Air2water showed relatively stable bias patterns, reflecting its resistance to data gaps due to its physical constraints. Our findings underscore the importance of accounting for cloud‐induced selection bias in optical satellite remote sensing and environmental modeling.

Previous research has identified significant and highly variable summertime (July–August–September) warming trends across the Great Lakes, with critical implications for aquatic ecosystems. However, these analyses of long-term warming trends have generally been constrained by the short duration or coarse spatial resolution of available observational datasets. Here, we integrate two existing datasets of Great Lakes surface temperature (LSWT) to evaluate long-term warming trends during 1982–2012 at fine spatial scales and understand the roles of lake bathymetry and climatic factors in regulating the spatially heterogeneous warming rates with the aid of regional climate modeling. Our results show amplified warming in Lake Superior, central-northern Lake Michigan, and central Lake Huron, and muted lake warming elsewhere. This spatial heterogeneity in summertime lake warming is primarily ascribed to the interplay of lake bathymetry and climatological springtime (April–May–June) air temperature. The climatological air temperature strongly influences the relationship between lake warming rates and bathymetry, as the summertime warming rates increase markedly with greater lake depth in the relatively cold environment of Lake Superior but change little in the warmer environment of Lake Ontario. This conditional dependence on background temperature has important implications for understanding and predicting global lake temperature trends.