In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice.

Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modeling, we have produced an integrated daily LSWT for 160 lakes across the Tibetan Plateau where in situ observation is limited. The MODIS-based lake-wide mean LSWT in the integrated dataset includes the daytime, nighttime, and daily mean for the period 2000–2017. The MODIS-based daily mean LSWT is used to calibrate a simplified physically based model (i.e., modified air2water model), upon which a complete and consistent daily LSWT dataset is reconstructed for the period 1978–2017. The reconstructed LSWT dataset is validated by comparing it with both the satellite-based and in situ observations. The validation shows that the reconstructed LSWT is in good agreement with the observations. According to the reconstructed LSWT dataset, the annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with an increase rate ranging from 0.01 to 0.47 ^∘C per 10 years. The warming rate is higher in winter than in summer. The integrated dataset is unique for its relatively large temporospatial span (1978–2017) and high temporal resolution. The dataset together with the methods developed can contribute to research in exploring water and heat balance changes and the consequent ecological effects at the Tibetan Plateau. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.6637526 (Guo et al., 2022).

Lake ice phenology is a sensitive indicator reflecting global warming. In this article, the long-term changes in lake ice phenology of the second-largest lake at Tibetan Plateau (i.e., the Nam Co) in response to climate change are investigated based on mathematical modeling. The model has been testified capable of reproducing daily surface temperature of the lake in both frozen and unfrozen seasons. The lake ice phenology determined according to the simulated lake surface temperature is found consistent with that derived from satellite observations. For the Nam Co during the period 1978-2017, the freezing date has been delayed (reached up to 0.57 days/year), whereas the thawing date became earlier (-0.23 days/year). The trend of lake ice phenology is significantly correlated to the annual minimal lake surface and air temperature. Modeling experiment suggests that lake ice phenology of the Nam Co is very sensitive to a warmer climate. Under warmer future, much later freezing date and earlier thawing date are expected; hence the ice-covering duration would be significantly shortened.

Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modelling, we have produced an integrated daily lake surface water temperature for 160 lakes across the Tibetan Plateau where in-situ observation is limited. The satellite-based lake-wide mean LSWT in the integrated dataset includes that for the daytime, night-time and for the daily mean for the period 2000–2017. The dataset is comparable with other satellite-based LSWT products (e.g., LSWT from AVHRR and ARC-Lake) and unique for its tempo-spatial span and resolution. Calibrated and validated against the satellite-based LSWT, complete and consistent daily LSWT dataset have been reconstructed and extended to the period 1978–2017 basing on the modified air2water model. According to the reconstructed LSWT dataset, it is found that annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with increase rate ranging at 0.01 to 0.4 °C 10 a⁻¹. The warming trends of the lakes are more evident in winter than in summer. The integrated dataset together with the methods introduced herein can contribute to the research community to explore water and heat balance changes and the consequent ecological effects at the Tibetan Plateau in the future researches. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.5111400 (Guo et al., 2021).

Understanding the regulatory effects of lakes on land surface temperature is critical for assessing regional climatological and ecological dynamics on the Tibetan Plateau (TP). This study investigates the spatiotemporal variability in the thermal effect of lakes across the TP from 2000 to 2022 using the MODIS land surface temperature product and a model-based lake surface water temperature product. Our results show that the lake–land temperature difference (LLTD) within 10 km buffer zones surrounding lakes ranges from −2.8 °C to 3.4 °C. A declining trend in 79.2% of the lakes is detected during 2000–2022, with summer contributing most significantly to this decrease at a rate of −0.56 °C per decade. Assessments of the spatial extent of lake thermal effects show that the “warm island” effect in autumn (5.5 km) influences a larger area compared to the “cold island” effect in summer (1.3 km). Furthermore, southwestern lakes exhibit stronger warming intensities, while northwestern lakes show more pronounced cooling intensities. Correlation analyses indicate that lake thermal effects are significantly related to lake depth, freeze-up start date, and salinity. These findings highlight the importance of lake thermal regulation in heat balance changes and provide a foundation for further research into its climatic and ecological implications on the Tibetan Plateau.