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• Predict lake level under climate scenarios by assimilating model and observation. • Incorporate the advantages of last ICESat-2 observations and hydrological model. • Integrated by Prophet forecasting procedure and Variational Bayesian Monte Carlo. • Enhance both short-term and long-term prediction accuracy. • Successfully predict lake level turning points driven by climate changes. Predicting future lake levels under climate change is critical for advancing our understanding of hydrological processes in a changing environment. However, continuous and long-term prediction of lake levels is challenging due to discrepancies in multi-source data and the lack of integration of hydrological models and climate scenarios. Physical and statistical models have been used for lake levels prediction, however, physical models are difficult to calibrate and statistical models often fail to account for the effects of climate changes. In this study, a lake level prediction model was proposed by assimilating the hydrophysical model based on water balance and the ICESat-2 observations using Variational Bayesian Monte Carlo. The model can predict monthly lake levels combining CMIP6-SWAT climate-driven projections and ICESat-2 observations. Short-term validation over 24 months showed the R2 was 0.91 and the RMSE was 5 cm between the proposed model and the ICESat-2 observation. The accuracy is superior to both the hydrophysical model based on water balance and the Prophet time series model. Long-term validation from 1978 to 2021 showed the proposed model has the potential to enhance prediction accuracy within 2 to 3 years compared with the hydrophysical model based on water balance and it is suitable for predicting long-term lake level trends. Notably, it successfully predicted the significant turning point around 2005 where lake levels shift from decline to increase based on past data. The water levels of Lake Qinghai were predicted to rise at a rate of 3.7 cm per year by 2050 under the SSP2-4.5 scenario. The proposed assimilation model combines the strengths of hydrological modeling based on water balance (incorporating the effects of climate change) and the latest ICESat-2 lake level observations (incorporating the effects of recent historical lake levels), improving the accuracy of short-term lake levels and long-term lake level trends prediction. Moreover, as the model is based on satellite remote sensing observations, it has the potential to be applied to any lake globally. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]