SUPERSLUG will push the frontiers of scientific knowledge and technical innovation to reveal new fundamental insights into the legacies of catastrophic sediment-rich flows (SRF) in mountain landscapes, such as landslides, rock-ice avalanches and glacial lake outburst floods. Catastrophic SRFs are hypothesised to become more frequent this century due to climate warming, and often affect vulnerable communities and assets in least developed countries the most. SRFs can entrain, mobilise, and deposit vast quantities of sediment, which can blanket valley floors to depths of tens of metres. The subsequent re-working and transport of these sediments by rivers can generate large-scale and fast-moving 'superslugs', which is a so-called 'legacy' impact of an SRF. Such legacy impacts are poorly understood, mostly due to observational challenges which have persisted for over a hundred years. However, improving our understanding of these impacts is of vital importance: enhanced fluvial transport of sediment following an SRF can affect flood hazard (by altering river channel bed elevation), infrastructure (e.g. by scouring bridge footings and damaging hydropower turbines), and can disrupt water quality, reducing water and energy security in regions that experience increasingly unstable and hazardous hydrological regimes. With SUPERSLUG we seek to encourage a paradigm shift framed around our argument that the landscape legacies of catastrophic SRFs should be quantified in as much detail as an initial event. To do this we will springboard from recent UKRI-funded pilot work by our international team to develop and apply a new multi-method and widely applicable suite of tools for quantifying the geomorphological evolution of SRF-affected catchments over multi-decade timeframes that are relevant for decision makers, in turn generating new insights into the fundamental behaviour, and impacts, of sediment superslugs. We will focus on a ~150 km-long exemplar system in the Indian Himalaya that has recently experienced a catastrophic SRF; the so-called 'Chamoli disaster'. This catchment arguably represents the most data-rich landscape of its type globally and sits within an otherwise extremely data-poor region. To deconstruct the evolution and impacts of sediment superslugs we will implement five work packages which will: (WP1) benchmark the geomorphological and sedimentological evolution of an SRF-affected system in space and time by using drone-derived observations to upscale from local- to catchment-wide observations using satellite remote sensing; (WP2) directly measure bedload motion in SRF-affected river channels using innovative wireless 'smart' cobbles, complemented with passive seismics; (WP3) develop an open-source toolkit for detecting and tracking fine-grained superslugs by leveraging cloud-based (Google Earth Engine) processing of free satellite imagery; and (WP4) integrate our novel observations from WP1-3 to upscale a powerful numerical landscape evolution-hydrodynamic model to simulate superslug mobility and the wider geomorphological evolution of our exemplar catchment. Our calibrated model, which will be a form of 'digital twin', will represent the largest of its kind and we will use it to explore catchment management decisions (e.g. HEP flushing schedules) for mitigating the worst superslug impacts. Underpinning these four WPs is a fifth WP, wherein we will adopt a Theory of Change-based approach for engaging closely with beneficiaries of this new knowledge and associated tools to translate our findings into practical outcomes and impact, including governance and disaster management professionals, hydropower operators and the wider international academic community.

There are a wide range of natural hazards that impact communities living within, and at the edge of the Himalayan mountains; these are dominated by earthquakes, landslides and floods. In order to reduce the risk from landslides and floods, communities have developed early warning systems to downstream villages and towns, enabling pre-planned responses. Early warning systems require local authorities to be aware of potential dangers. For example, a steep hillslope that is known to be unstable with evidence of past landslides should be monitored, particularly during periods of heavy rainfall. However, in order for the local District Disaster Management Authorities (DDMAs) to know where to monitor, medium-term forecasts of the likely risk from different hazards need to be known. For example, certain areas are more prone to earthquakes, and others to landslides and flashfloods. If these risks from different hazards remain constant through time, then the forecasts and monitoring for each community remains steady. However, hazards do not act in isolation, but form cascades, each event triggering another. As a result, the risk from multiple hazards is not stable, but dynamic, and changes in response to upstream triggers. For example a landslide, that leads to a dam that breaks out to form a debris flow that then increases subsequent risk to floods due to choking of river channels with sand and gravel. This project aims to provide the first fully quantitative forecasts of multihazard cascades using a range of new modelling techniques constrained by a history of field observations from the Garwhal Himalaya, Uttarakhand. This area has been devastated by recent landslides and flashfloods such as the Kedarnath disaster in 2013 and the Chomli landslide in 2021. Thick accumulations of sediment in these steep mountain valleys are known as 'sediment bombs' as they pose a danger to downstream communities; such sediment bombs may form where glaciers retreat or where landslides block valleys. In this project, the Indian and UK teams will combine to integrate new methodologies from digital topography, remote sensing, computer models and field monitoring to understand how sediment yield from glaciers and landslides initiate sediment bombs, and how these accumulations are then mobilised to form debris flows, flash floods and downstream flooding. Through understanding the distribution and rates involved in these processes, we will generate medium term forecasts that feed into early warning systems developed in the communities of the Alaknanda Valley. The approach as outlined above suggests that the physical science models will be the sole input into consideration of dynamic risk; but it can't be as simple as that. The communities that live with this risk, and the DDMAs that manage the early warning systems have to be involved in the generation and iteration of the scientific methodology. Consequently, we are working with social scientists in the UK and India who have experience working with communities in the Himalaya through workshops and interviews that respect the diverse cultural, ethnic and gender-based perspectives. By the end of the project, we will have generated a decisional workflow for district authorities that integrates dynamic risk into their medium term forecasts in response to cascading hazards. Having demonstrated this process in the Garwhal Himalaya, we intend to work with the National Disaster Management Authorities in India and Nepal to promote national strategies for dynamic risk assessment.

Mountain landscapes experience sudden and violent geohazards, such as landslides, lake outburst floods, and debris flows. The size and frequency of such events is anticipated to increase due to climate change, enhancing landscape instability. These landscapes are also experiencing rapid population growth, directly exposing people and assets to geohazards, but also exposing them to legacy impacts which manifest after an event and are commonly overlooked and unquantified. A legacy impact of many mountain geohazards is enhanced coarse sediment transport in rivers. This is a problem because sediment travelling as 'bedload' is the primary driver of river channel adjustment. These adjustments affect: 1) flood hazard, by modifying channel bed elevation; 2) the integrity of riparian infrastructure, e.g. hydropower, by blocking intakes and rapidly filling reservoirs, and 3) fluvial ecology, by reorganising channel substrate. It is therefore vital to generate well-constrained knowledge of the pace and manner in which the bedload transport regime evolves in mountain rivers after extreme disturbances. However, due to technical limitations and challenges associated with working in unstable, post-flood landscapes, we have little first-hand information on the behaviour of such systems, which this project aims to address. This new project will consolidate a new international partnership of leading researchers from the UK and India. The team is led by the University of Plymouth, working in close collaboration with the Indian Institute of Technology Roorkee (IITR) and the Wadia Institute of Himalayan Geology (WIHG), the University of Exeter, and Newcastle University. The diverse team bring complementary expertise in geomorphology, hydrology, and environmental sensor networks, and the work would not be possible without the regional knowledge, technical competencies, and field experience of the international partners. The project also features prominent early- and early-to-mid-career researchers in leading roles. Working together we will apply a suite of innovative environmental monitoring and modelling tools to characterise the hydrological and bedload transport regime of the Alaknanda river, Uttarakhand, India, which experienced an extreme debris flow in February 2021 which killed >200 people and triggered enhanced sediment transport as a legacy impact, evidenced through pilot work. To achieve our aim, we will: 1) Develop a new hydrological model of the Alaknanda catchment, enabling us to identify and disentangle the key components of flow (e.g. snowmelt, rainfall). This information will be used to better understand the hydrological drivers of sediment transport; 2) Quantify the grain size characteristics of channel bars using drone- and satellite-based observations and modelling. This information will allow us to explore downstream transitions in grain size through time and examine the influence of the Chamoli event; 3) Deploy innovative, low-cost 'smart' tags to track the motion of cobbles and boulders travelling as bedload. We will supplement these data with measurements of the timing and relative magnitude of bedload transport using low-cost passive seismics. We will effect skills and knowledge transfer in-person via joint fieldwork and discussions at IITR and WIHG), and a regular series of virtual project meetings and seminars. We will publish results in peer-reviewed open-access journals and will produce a technical summary report which we will disseminate to local stakeholders. Project success will lead to future joint funding bids which will appraise the role of hydropower as a disruptor to coarse sediment transport in mountain rivers and explore operational practices that can mitigate the immediate and legacy impacts of extreme floods. In doing so we will further consolidate a wider research network involving regional academics and practitioners, whilst supporting the development of early career researchers in both countries.

On 7th February 2021 a massive rock-ice avalanche originating from a mountain ridge in Chamoli District, Uttarakhand, Indian Himalaya, transformed into a fast-moving and catastrophic debris flow which travelled along the Rishiganga, Dhauliganga, and Alaknanda rivers. The flow killed hundreds of people, destroyed or damaged mature and under-construction hydropower projects, and caused severe modification to the channel and wider valley floor landscape, including the destabilising of steep valley sides. Once the flood subsided, rapid post-event analysis revealed that sediments deposited by the debris flow were more than 20 m thick in places, and that the flow was capable of transporting boulders exceeding 20 m in diameter. The next 12 months are a crucial period for this river system because this is when we predict that newly deposited sediments will be eroded and transported in vast quantities, and we believe that most of this activity will occur within a distance of around 50 km from the avalanche source, which includes four hydropower facilities and riverside settlements and infrastructure. This 're-activation' of sediments left behind by the flood has implications for local hydropower operators, who need to anticipate these elevated sediment loads and plan accordingly to reduce the risk of blockage to dam outlets and tunnels, avoid reduced discharge capacity, and damage to mechanical equipment. In addition, there is a high risk of further valley flank instability as this new drape of sediment is removed and banks that were undercut by the initial flow become more unstable, or undercutting is initiated in new areas. We also anticipate that sediment deposition could also present a hazard where these deposits intersect with valley floor energy and transport infrastructure. To urgently predict rates and patterns of post-flood channel modification we will use a computer model that is capable of simulating river flow and the erosion, transport, and deposition of sediment. We will run this model for an initial period of one year (including the summer monsoon, which brings an order-of-magnitude increase in river discharge) and we will generate critical summary datasets that can be rapidly communicated to in-country end users. We already have access to most of the data that we require to set up and run the model, and project partners are well-placed to provide missing data that we need to perform initial runs and perform regular checks on model performance. The work will be carried out by an international team comprised of experts in extreme floods and numerical flood modelling, the hydrology of high mountain landscapes, and community adaptation to (rapid) environmental change. The team includes researchers from the UK, India, Canada and the USA with a collective track record of delivering high quality science to inform real-world decision-making. Follow-on work will broaden the scope of the work to look at sediment transport and deposition over a much larger area: analysis of satellite imagery shows that the initial sediment plume generated by the flood travelled >150 km in ~24 h and we anticipate that annual re-activation of flood sediment will have significant impacts on the hazard posed by this extreme event.

A global trend of glacier loss is leading to the development of high-mountain glacial lakes that can exceed kilometres in length and over 200 m in depth, therefore storing vast quantities of water. However, their poorly constrained estimates of current and future water stores restricts assessments of water resource availability and potential downstream flood risks. Glacial lakes can drain seasonally and catastrophically, leading to downstream flooding with high socio-economic impacts, particularly across High-Mountain Asia in countries such as Nepal. These flood events cause widespread concern, spanning mountain communities to development agencies and government departments. However, historical records suggest that most glacial lakes are inherently stable. To ensure that disaster risk-reduction resources are targeted to deliver maximum benefit, and that water resource trends are understood, it is therefore essential to develop a robust evidence base in the context of climate change, accelerating lake development, and urban expansion into mountain regions. High-mountain glacial lake water storage is measured for a small proportion of lakes globally due to logistically challenging survey requirements and the inability to derive depth observations using satellite data. Instead, glacial lake bathymetry datasets are produced through field surveys, and are subsequently used to inform empirical scaling relationships that relate lake area to volume. Estimating water storage using these relationships that are based on few datapoints globally (n~100) contains large uncertainties (>20-50%). Glacial lakes also promote a positive feedback, whereby the thermal energy stored in lake water and buoyancy forces acting on the glacier can accelerate glacier recession. However, similarly sparse observations of lake-glacier interactions mean these mechanisms are not parameterised in models predicting glacier evolution and downstream runoff trends. This Fellowship presents an integrated, interdisciplinary approach to assess both glacial lake development and downstream floods in topographically complex catchments. I will develop an innovative survey methodology to derive the bathymetry of glacial lakes using both a single beam sonar, combined with a cutting edge multibeam sonar system. The latter will produce complete maps of lakebed morphology and reveal the subaqueous glacier structure. Extensive bathymetry surveys in Nepal will underpin numerical modelling and machine learning approaches that conceptualise glacial lake geometry and development trajectories, and quantify current and future water resource trends. The models derived from these data will also provide a scalable solution to robustly estimate dynamic water storage at unsurveyed lakes, therefore reducing the requirement for costly and difficult field surveys. I will also address the critical requirement for high-resolution topographic data to enable robust flood modelling downstream of glacial lakes. These models will identify socio-economic exposure of buildings and infrastructure to flood events caused by precipitation extremes or glacial lake drainage events. The Fellowship's outputs will be operationalised in an online open access Glacial Lake Observatory (GLO) platform that will underpin a new era of collaborative glacial lake research by removing barriers to data access and knowledge exchange. The GLO will catalogue glacial lakes globally and will monitor near-real-time lake dynamics using optical, radar, and altimetry satellite data. Our research culture will advocate for ethical and inclusive overseas fieldwork practices that strengthen partnerships, research collaborations, and knowledge exchange, therefore maximising the long-term benefits of the Fellowship's outputs. Collaboration with leading academics, development agencies, and government departments in Nepal will enable co-production of knowledge that addresses global water resource challenges.