On 3 October 2023, a multihazard cascade in the Sikkim Himalaya, India, was triggered by 14.7 million cubic meters of frozen lateral moraine collapsing into South Lhonak Lake. The impact generated an ~20-meter tsunami-like impact wave, which breached the moraine and drained ~50 million cubic meters of the lake’s water. The ensuing glacial lake outburst flood (GLOF) eroded ~270 million cubic meters of sediment, which overwhelmed infrastructure, including hydropower installations along the Teesta River. The physical scale and human and economic impacts of this event prompt urgent reflection on the role of climate change and human activities in exacerbating such disasters. Insights into multihazard evolution are pivotal for informing policy development, enhancing early warning systems (EWS), and spurring paradigm shifts in GLOF risk management strategies in the Himalaya and other mountain environments.

Modelling complex mass flow processes, such as glacial lake outburst floods (GLOFs), for hazard and risk assessments requires extensive data and computational resources. Researchers often rely on low-resolution, open-access datasets and parameters derived from plausibility due to the difficulty involved in conducting direct measurements. This results in considerable uncertainties in forward modelling, potentially limiting the accuracy and reliability of predictions. To determine the sensitivity of the model outputs stemming from input parameter uncertainties in the forward modelling, we selected 9 parameters relevant to GLOF modelling and performed a total of 84 simulations, each representing a unique GLOF scenario in the physically based r.avaflow model. Our results indicate that mass-movement-triggered moraine-dammed GLOF modelling outputs are notably sensitive to five parameters, which are, in order of importance: (1) volume of mass movement entering the lake, (2) DEM datasets, (3) origin of mass movement, (4) entrainment coefficient, and (5) basal friction angle. The GLOF output parameter resulting from the volume of mass movement entering the lake has the greatest coefficient of variation (CV) (47 %), while the internal friction angle had the lowest CV (0.4 %). For future GLOF modelling, we recommend carefully considering the output uncertainty stemming from the sensitive input parameters identified here, some of which cannot be constrained before a GLOF and which must be addressed using statistical approaches.

Hazard and risk from glacial lake outburst floods (GLOFs) in Bhutan have traditionally been assessed with limited consideration of the downstream exposure and vulnerability associated with individual lakes. However, exposure and vulnerability are key components of risk, and when explicitly attributed to each lake, can provide a more robust basis for prioritising hazard investigations and mitigation efforts. We modelled hypothetical GLOF scenarios for all glacial lakes with an area greater than 0.05 km² and located within 1 km of a glacier terminus. We then determined GLOF risk by explicitly accounting for downstream impacts using depth–velocity outputs at each exposed element affected by the simulated GLOF from each lake, as well as the vulnerability of the affected community. Our study shows that approximately > 11 000 people, > 2500 buildings, > 250 km of road, > 400 bridges and ∼ 20 km² of farmland are exposed to potential GLOF in Bhutan. We classified lake130 (Thorthormi Tsho) as a very high hazard glacial lake in Bhutan, five lakes as high hazard and 22 other lakes as moderate hazard. Among these high hazard glacial lakes, three of them: lake93 (Phudung Tsho), lake251, and lake278 (Wonney Tsho) were not recognised as being high hazard in previous studies. Five downstream local government administrative units (LGUs) were associated with very high GLOF risk, while eight others are associated with high GLOF risk. Five of these very high and high risk LGUs had not been previously documented as being at risk from GLOF. Our study underscores the significance of integrating potential inundation mapping and downstream exposure data to define high hazard glacial lakes. We recommend strengthening and expanding the existing GLOF preparedness and risk mitigation efforts in Bhutan, particularly in the LGUs, as having high GLOF risk identified in this study, to reduce potential future damage and loss.

During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 10⁶ m³. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers.

Modelling complex mass flow processes like glacial lake outburst floods (GLOFs) for hazard and risk assessments involves substantial data and computational resources, often leading researchers to use low-resolution, open-access data and parameters based on plausibility rather than direct measurement, which, although effective in back analysis, introduces significant uncertainties in forward modelling. To determine the sensitivity of the model outputs stemming from input parameter uncertainties in the forward modelling, we selected nine parameters relevant to GLOF modelling and performed a total of 78 simulations in the physically-based r.avaflow model. Our results indicate that GLOF modelling outputs are notably sensitive to six parameters, which are, in order of importance: 1) volume of mass movements entering lakes; 2) DEM datasets; 3) the origin of mass movements; 4) mesh size; 5) basal frictional angle; and 6) entrainment coefficient. The volume of mass movement impacting lakes has the greatest impact on GLOF output, with an average coefficient of variation (CV) = 47 %, while the internal friction angle had the least impact (CV=0.4 %). We recommend that future GLOF modelling should carefully consider the output uncertainty stemming from the sensitive input parameters identified here, some of which cannot be constrained before a GLOF and must be considered only statistically.