Pre-collapse motion of the February 2021 Chamoli rock-ice avalanche, Indian Himalaya

On the 7th of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, leaving over 200 dead or missing. The rock-ice avalanche originated from a steep, glacierized north-facing slope. In this work, we assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event DEMs, which we use to evaluate elevation change over the same area. Both datasets show that the 26.9 Mm3 collapse block moved over 10 m horizontally and vertically in the five years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow-loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Our observation of a clear precursory signal highlights the potential of satellite imagery for monitoring the stability of high-risk slopes. We find that the timing of the Chamoli rock-ice avalanche could likely not have been forecast from satellite data alone.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Resolving pre-collapse slope motion at the February 2021 Chamoli rock-ice avalanche via feature tracking of optical satellite imagery

<p>On the 7<sup>th</sup> of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, resulting in over 200 dead or missing and widespread infrastructure damage. The rock-ice avalanche originated from a steep, glacierized north-facing slope with a history of instability, most recently a 2016 ice avalanche. In this work, we assess whether the slope exhibited any precursory displacement prior to collapse. We evaluate monthly slope motion over the 2015 and 2021 period through feature tracking of high-resolution optical satellite imagery from Sentinel-2 (10 m Ground Sampling Distance) and PlanetScope (3-4 m Ground Sampling Distance). Assessing slope displacement of the underlying rock is complicated by the presence of glaciers over a portion of the collapse area, which display surface displacements due to internal ice deformation. We overcome this through tracking the motion over ice-free portions of the slide area, and evaluating the spatial pattern of velocity changes in glaciated areas. Preliminary results show that the rock-ice avalanche bloc slipped over 10 m in the 5 years prior to collapse, with particularly rapid slip occurring in the summer of 2017 and 2018. These results provide insight into the precursory conditions of the deadly rock-ice avalanche, and highlight the potential of high-resolution optical satellite image feature tracking for monitoring the stability of high-risk slopes.</p>

Challenges in the predictive simulation of cascading landslide processes

<p>History has shown that cascading landslides, such as the debris avalanches from Huascarán in 1962 and 1970, the Kolka-Karmadon rock-ice-avalanche in 2002, or the rock avalanche-debris flow event of Bondo in 2017, can be very destructive due to their high energies, velocities and volumes. They can lead to large numbers of fatalities, huge material damage, and disruption of critical infrastructure.</p><p>Cascading landslides are a specific class of multi-hazard events in which one type of motion transforms into another or an initial, primary movement triggers a secondary process. High-mountain areas are particularly prone to this type of landslides due to their dynamic, rapidly changing environments and their high relief. For example, an initial rock fall can reach snow or ice masses and transform into a rock-snow- or rock-ice-avalanche, or into a debris flow. Physically-based numerical modelling is often used for the attempt to predict such events as a basis for the design of risk management strategies such as early warning systems. However, we identify at least two specific types of challenges making accurate and reliable predictions highly difficult:</p><ul><li>(a) The dynamic behaviour of such process chains, especially in the transition phase, is not yet fully understood. Existing models are either developed for (i) fall or (ii) flow processes. Whereas substantial progress has been made in previous years in the integrated simulation of flow-type movements, no software which fully and directly considers the transformation of fall to flow processes is known to the authors. Therefore, it is not yet possible to simulate fall-flow sequences of cascading landslide events with one single tool. Model chains have to be used instead, which have a limited capacity for appropriately representing the transition phase between the two types of processes.</li> <li>(b) Limited knowledge on the initial conditions and input parameters represents another severe limitation. Model input relies on available information on previous events and on certain characteristics of the (possible) release and impact area. Obviously, the quality of the data set is significantly influencing the model results. Whereas the scientific community is far away from exact predictions of landslide impact, an important objective should consist in better constraining the definition of possible scenarios to be considered for hazard and risk management.</li> </ul><p>For the reasons highlighted, it remains highly challenging to adequately predict the impact areas, energies, and travel times of cascading landslides in space and time. Nevertheless, stakeholders require such predictions for decisions on sustainable hazard and risk management strategies. Therefore, the aims of this study are (i) to evaluate possibilities to appropriately combine models for fall and flow processes and (ii) to examine data acquisition methods for the model input. Furthermore, (iii) appropriate strategies to present and to communicate simulation results need to be discussed.</p>

Modelling the impact of a GLOF scenario at Parón lake, Cordillera Blanca, Perú, using a novel multi-phase topographical and geological procedure

<p>The Cordillera Blanca is undergoing rapid deglaciation due to climatic warming, especially since the late 20th century. This process has resulted in the formation of new glacial lakes and an increase in the volume of existing lakes, some of which pose a risk in the form of Glacier Lake Outburst Floods (GLOF); such as Parón lake in the Cordillera Blanca, which represents a significant hazard to the Caraz city and smaller populations located in the Llullán-Parón sub-basin. Here, we model a potential dam breach and GLOF generation scenario at Parón lake using a novel numerical modelling procedure that, amongst other factors, considers the geological structure of the natural dam. Overall, this procedure includes four distinct phases: (1) estimation of the potential for ice avalanche impact on Parón lake sourced from surrounding glacial cirques; (2) modelling of subsequent impulsive wave generation and propagation; (3) analysis of the hydraulic parameters of a possible breach of the natural dam, considering the non-erodible material within empirical estimations of the hydrograph where the composition of the dam is interpreted based on surface geological mapping and drill sampling carried out in the area; and (4) simulation of a potential GLOF using the FLO-2D model with input data from the previous phases. Modelling results indicate that Parón lake is most at risk from ice avalanches that originate from the adjacent Hatunraju glacier and that such events have the potential to generate impulse waves that could initiate erosion and a subsequent breach of the natural dam. Considering a worst-case ice avalanche scenario, our results indicate the potential generation of a GLOF with average peaks flow of 25,264.22 m<sup>3</sup>/s. This GLOF event would reach the urban area of the  Caraz city in around 36 - 42 minutes with now rates and flood heights fluctuating between 11.2 m/s to 22.4 m/s and 9.9 m to 19.7 m, respectively.</p>

Response of downstream lakes to Aru glacier collapses on the western Tibetan Plateau

The lower parts of two glaciers in the Aru range on the western Tibetan Plateau (TP) collapsed on 17 July and 21 September 2016, respectively, causing fatal damage to local people and their livestock. The giant ice avalanches, with a total volume of 150 × 106 m3, had almost melted by September 2019 (about 30 % of the second ice avalanche remained). The impact of these extreme disasters on downstream lakes has not been investigated yet. Based on in situ observation, bathymetry survey and satellite data, we explore the impact of the ice avalanches on the two downstream lakes (i.e., Aru Co and Memar Co) in terms of lake morphology, water level and water temperature in the subsequent 4 years (2016–2019). After the first glacier collapse, the ice avalanche slid into Aru Co along with a large amount of debris, which generated great impact waves in Aru Co and significantly modified the lake's shoreline and underwater topography. An ice volume of at least 7.1 × 106 m3 was discharged into Aru Co, spread over the lake surface and considerably lowered its surface temperature by 2–4 ∘C in the first 2 weeks after the first glacier collapse. Due to the large amount of meltwater input, Memar Co exhibited more rapid expansion after the glacier collapses (2016–2019) than before (2003–2014), in particular during the warm season. The melting of ice avalanches was found to contribute to about 23 % of the increase in lake storage between 2016 and 2019. Our results indicate that the Aru glacier collapses had both short-term and long-term impacts on the downstream lakes and provide a baseline in understanding the future lake response to glacier melting on the TP under a warming climate.