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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.
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Ecosistema , Lagos , Cambio Climático , Temperatura , AguaRESUMEN
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.
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Lake surface water temperature is a fundamental metabolic indicator of lake ecosystems that affects the exchange of material and energy in lake ecosystems. Estimating and predicting changes in lake surface water temperature is crucial to lake ecosystem research. This study selected Dianchi Lake, a typical urban lake in China, as the research area and used the Air2water model combined with the Mann-Kendall mutation statistical method to analyze the temporal and spatial variation in the surface water temperature of Dianchi Lake under three climate models. The research results show that, under the RCP 5-8.5 scenario model, the surface water temperature change rate for Dianchi Lake from 2015 to 2100 would be 0.28 â/10a, which was the largest change rate among the three selected scenarios. The rate of change during 2015-2100 would be 9.33 times higher than that during the historical period (1900-2014) (0.03 °C/10a). Against the background of Niulan River water diversion and rapid urbanization, the surface water temperature of Dianchi Lake experienced abrupt changes in 1992, 2016, 2017, and 2022. Against the background of urbanization, the impact of human activities on the surface water temperature of urban lakes will become greater.
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Ecosistema , Lagos , China , Monitoreo del Ambiente/métodos , Humanos , Temperatura , AguaRESUMEN
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.
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Lagos , Urbanización , China , Cambio Climático , Humanos , Temperatura , AguaRESUMEN
Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000-2018 across all the investigated lakes. Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement. All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of -0.038 °C/y during the period of 2000-2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000-2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.
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The availability of more than thirty years of historical satellite data is a valuable source which could be used as an alternative to the sparse in-situ data. We developed a new homogenised time series of daily day time Lake Surface Water Temperature (LSWT) over the last thirty years (1986-2015) at a spatial resolution of 1km from thirteen polar orbiting satellites. The new homogenisation procedure implemented in this study corrects for the different acquisition times of the satellites standardizing the derived LSWT to 12:00 UTC. In this study, we developed new time series of LSWT for five large lakes in Italy and evaluated the product with in-situ data from the respective lakes. Furthermore, we estimated the long-term annual and summer trends, the temporal coherence of mean LSWT between the lakes, and studied the intra-annual variations and long-term trends from the newly developed LSWT time series. We found a regional warming trend at a rate of 0.017°Cyr-1 annually and 0.032°Cyr-1 during summer. Mean annual and summer LSWT temporal patterns in these lakes were found to be highly coherent. Amidst the reported rapid warming of lakes globally, it is important to understand the long-term variations of surface temperature at a regional scale. This study contributes a new method to derive long-term accurate LSWT for lakes with sparse in-situ data thereby facilitating understanding of regional level changes in lake's surface temperature.