Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 degrees C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R-2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Lakes in permafrost regions are highly sensitive to changes in air temperature, snowmelt, and soil frost. In particular, the Qinghai-Tibetan Plateau (QTP) is one of the most sensitive regions in the world influenced by global climate change. In this study, we use retracked Enivsat radar altimeter measurements to generate water level change time series over Lake Qinghai and Lake Ngoring in the northeastern QTP and examine their relationships with precipitation and temperature changes. The response of water levels in Lake Qinghai and Lake Ngoring is positive with regards to precipitation amount. There is a negative relationship between water level and temperature change. These findings further the idea that the arid and high-elevation lakes in the northeastern QTP are highly sensitive to climate variations. Water level increases in Lake Qinghai in winter may indicate inputs of subsurface water associated with freeze-thaw cycles in the seasonally frozen ground and the active layer.

The area of desertified land has increased by 27.3% from 1987 to 2000 in Maduo County, northeastern Qinghai-Tibet Plateau. Driving forces of land degradation has been extensively studied in the region. Using Factor Analysis (FA), we evaluate contribution of human activity and natural environmental change to land degradation. Four common factors were extracted in this study. The result shows that climate related other than human-related factors, are the major inducing factors of land degradation in Maduo County. Climate change and consequent change of permafrost account for 70% to the land degradation. Increasing evaporation and declining precipitation in the beginning of the growing season hamper seedling establishment. Decreasing frozen days and rising active layer lower bound make surface soil loose and less soil moisture available for plant.