Glacial lake outburst floods (GLOFs) are a major concern in the Himalaya and on the Tibetan Plateau (TP), where several disasters occurring over the past century have caused significant loss of life and damage to infrastructure. This study responds directly to the needs of local authorities to provide guidance on the most dangerous glacial lakes across TP where local monitoring and other risk reduction strategies can subsequently be targeted. Specifically, the study aims to establish a first comprehensive prioritisation ranking of lake danger for TP, considering both the likelihood and possible magnitude of any outburst event (hazard), and the exposure of downstream communities. A composite inventory of 1,291 glacial lakes (>0.1 km²) was derived from recent remote sensing studies, and a fully automated and object assessment scheme was implemented using customised GIS tools. Based on four core determinates of GLOF hazard (lake size, watershed area, topographic potential for ice/rock avalanching, and dam steepness), the scheme accurately distinguishes the high to very high hazard level of 19 out of 20 lakes that have previously generated GLOFs. Notably, 16% of all glacial lakes threaten human settlements, with a hotspot of GLOF danger identified in the central Himalayan counties of Jilong, Nyalam, and Dingri, where the potential trans-boundary threat to communities located downstream in Nepal is also recognised. The results provide an important and object scientific basis for decision-making, and the methodological approach is ideally suited for replication across other mountainous regions where such first-order studies are lacking.

With new cycles of global environmental assessments (GEAs) recently starting, including GEO-7 and IPCC AR7, there is increasing need for artificial intelligence (AI) to support in synthesising the rapidly growing body of evidence for authors and users of these assessments. In this article, we explore recent advances in AI and connect them to the different stages of GEAs showing how some processes can be automatised and streamlined. The meticulous and labour-intensive nature of GEAs serves as both a valuable strength and a challenge to staying pertinent and current in today’s era of urgency and the pursuit of the latest knowledge. Utilising AI tools for reviewing and synthesizing scientific literature holds the evident promise of substantially lessening the workload for experts and expediting the assessment process. This, in turn, could lead to more frequent report releases and a smoother integration of the latest scientific advancements into actionable measures. However, successful outcomes can only be achieved if domain experts co-develop and oversee the deployment of such tools together with AI researchers. Otherwise, these tools run the risk of producing inaccurate, incomplete, or misleading information with significant consequences. We demonstrate this through a few examples that compare recently deployed large language models (LLMs) based tools in their performance in capturing nuanced concepts in the context of the reports of the Intergovernmental Panel on Climate Change (IPCC). We recommend establishing ethical committees and organising dedicated expert meetings to develop best practice guidelines, ensuring responsible and transparent integration of AI into GEAs.

Widespread retreat of glaciers has accelerated over recent decades in most mountain regions as a consequence of global warming, leading to rapid expansion of glacial lakes, bringing related risks.When water is suddenly released, Glacial Lake Outburst Floods (GLOFs) can devastate lives and livelihoods up to hundreds of kilometres downstream of their source. This threat is most apparent in High Mountain Asia (HMA), home to >200 million inhabitants, and where >150 GLOFs have been recorded from moraine dammed lakes alone. Here we reflect on our recent experience working across HMA to outline key learnings, challenges and perspectives in applying GLOF hazard and risk assessment at various scales, with an emphasis on how results have or can inform local response planning.The number of large-scale assessment studies has increased exponentially over recent years, often giving inconsistent results in terms of what are considered potentially dangerous lakes. This makes it difficult for authorities and funding agencies to identify where more detailed hazard mapping and risk management strategies should be targeted, especially in cases where the science may not be aligned with local understanding and experience. We therefore recommend a consensus approach, drawing across multiple studies, and including the knowledge of local authorities to arrive at a final listing of high priority lakes which may be subject to further monitoring, Early Warning Systems and other response strategies. In our stakeholder interactions, we have particularly emphasised that GLOFs from even relatively small lakes can lead to significant damages when combined with other hazardous processes, e.g., the case of 2013 Chorabari GLOF combining with monsoon flooding and landslides in Northern India, or the 2016 outburst from Gongbatongshaco, Chinese Himalaya, Tibet, where erosion and bulking was significantly enhanced as a consequence of the Gorkha earthquake occurring a year earlier.Looking to the future, several assessment studies have now combined modelling of glacier bed topography to identify where new lakes could emerge in the future, and even combined this information with changing exposure levels (e.g., planned hydropower development). However, there are challenges around communicating these uncertain future hazards and risks, and to what extent they should be considered in planning. In the transboundary Poiqu basin originating in Tibet, we have focussed on worst-case scenario modelling for such a future lake, demonstrating that flow depths and velocities would exceed the threat from current lakes, and the peak wave would reach the border with Nepal up to 20 minutes faster. Open questions remain around how triggering processes will evolve in the future. Most assessments currently focus on cascading process chains triggered by ice or rockfall, whereas under a wetter and warmer future climate, heavy rainfall and snowmelt as a direct or indirect trigger could become increasingly important. Further, major uncertainties arise from socio-economic developments and related changes in exposure and vulnerability, that could, in some regions, be the most significant drivers of future GLOF risk. Ultimately, forward-looking, GLOF hazard and risk assessment must ensure that response strategies remain robust in the face of ongoing environmental and societal change.