The entire cascade of scenario generation, global and regional climate modeling, as well as concrete measures towards climate adaptation are subject to uncertainties. An exact prediction of how the climate will change in the coming years, and how it will affect land use, is not possible. There is thus a perceived need to identify ways via which uncertainties can be addressed. Based on the need to address the research gap in this area, this paper reports the findings of a study on uncertainty in a climate change adaptation context, and how it is perceived. It consists of a multi-stakeholder survey among climate change professionals, including academic staff at universities, representatives from international agencies, members of NGOs, policymakers, and representatives of industry from 50 countries, including a balanced representation of industrialized and developing nations. The results obtained suggest that uncertainties are often a hindrance to engagement in climate change adaptation efforts, and to land management. Furthermore, there is a range of tools to reduce climate change adaptation uncertainties, whose deployment may help to address them. The paper concludes by providing a list of lessons learned and suggestions as to how uncertainty can be better communicated, and by doing so, how a reduction in the levels of climate change vulnerability may be achieved, and how land management may be fostered.

Using GIMMS NDVI3g+ data (1982–2022) together with ERA5-Land temperature and precipitation, this study examined long-term vegetation dynamics in the Indus River Basin (IRB) and used a residual trend framework for cautious first-order attribution. Basin-averaged NDVI increased significantly at 0.0061 per decade (p < 0.05), and 65.5% of the basin showed greening, mainly in irrigated croplands and river-adjacent agricultural zones, whereas 12.6% showed degradation concentrated in rapidly urbanizing areas, cryosphere margins, and desert fringes. Partial correlation and residual analyses indicate that climate-related enhancement was most evident in upper-elevation cryosphere transition zones and some lower-basin barren lands, whereas non-climatic residual effects were especially important in intensively managed agricultural landscapes. Because the attribution model includes only temperature and precipitation, the residual component is interpreted here as a non-climatic residual rather than a direct measure of human activity. The study, therefore, provides a spatially explicit basin-wide assessment of vegetation change while highlighting the uncertainty and interpretation limits of residual-based attribution.