IAP-24-059

Landslide-flood interactions in the Himalayan mountain range in a warming climate

Rivers sourced in actively uplifting mountains support some of the world’s most densely populated and poorest communities: for example, more than 600 million people live within the drainage basins of Himalayan rivers. Mountain regions also tend to be prone to earthquakes and extreme rainfall events, with steep slopes and high erosion rates, leading to exposure of people and communities to hazardous surface processes such as seismic shaking, landslides, and floods. However, these processes do not work in isolation but are part of a downstream hazard cascade: for example, landslides may supply sediment to rivers at the base of hillslopes, or in some cases dam rivers completely (e.g. Bennett et al., 2024). This ponded sediment is unstable, and may be mobilised in future flood events, which in the Himalaya are commonly triggered by extreme rainfall events or glacial lake outburst floods. As floods travel downstream, they can also entrain sediment stored within the valley floor. This causes sediment-rich flows, which travel vast distances compared to clearwater flows, causing loss of life and damage to infrastructure (Westoby et al., 2014). This cascade means that the risk to communities living downstream in mountain catchments can be dynamically controlled by processes occurring in the highest-elevation parts of the catchment. In concert, flood frequency and magnitude are expected to increase in the Himalaya due to the strengthening of the Indian Summer Monsoon with climate change (e.g. Fahad et al., 2023), and fresh glacial sediment sources are at risk of mobilisation in floods as the freezing isotherm rises in elevation, causing rainfall instead of snowfall at high elevations (Hunt et al., 2020)

Many knowledge gaps exist regarding the links between landslide-derived and glacial sediment, the geometry of river valleys, and the transport of sediment during subsequent extreme flood events. For example, it is unclear how changes in flood frequency with climate change might impact channel stability and sediment transport as rivers exit the mountain front. This project will be focused on the Indian and Nepal Himalaya, where there have been many damaging sediment-rich floods, such as the Kedarnath disaster in 2013, the Chamoli landslide of 2021, and the Melamchi disaster in central Nepal in 2021. The student will combine large-scale inventories of landslide locations with AI-based river sediment mapping from satellite imagery (Carbonneau and Bizzi, 2024), topographic analysis of valley floor geometry (Clubb et al., 2023), and reach-scale models of sediment transport to determine under what conditions landslide-derived sediment is mobilised through catchments, and how landslide-flood interactions impact channel and valley geometry and stability in mountain regions.

The student will compile existing landslide inventories from across India and Nepal, as well as mapping shallow landslides automatically from Landsat and Sentinel-2 satellite data using automated landslide detection algorithms (ALDI, Milledge et al., 2022). These will be supplemented by manual mapping from higher-resolution satellite imagery in places which are harder to detect landslides automatically (e.g. glacial zones above the tree line).

They will compare landslide locations to sediment storage and valley geometry across the Himalaya, using techniques to map sediment storage from topographic data and measure valley widths (Clubb et al., 2017; 2022). They will assess the relationship between glacier extent, landslide inventories, and these sediment storage data to determine how the mechanism of sediment storage varies across the Himalaya (i.e. determining zones of glacial vs. landslide/tectonic controlled sediment storage). The student will also generate time series of observed erosion and deposition of active channel sediment by applying machine learning (ML) models to detect channel sediment from Sentinel-2 or Planet imagery (Carbonneau and Bizzi, 2024). They will train the ML model by manual mapping and field data. This will result in a network-wide time series of erosion and deposition: they will test whether erosion of sediment sources is expanding to higher elevations as glaciers retreat, and whether channel stability is affected by increasing flood frequency.

The student will also focus on high-resolution river modelling of specific areas of high sediment mobility, which will be highlighted by the catchment-scale automated analysis. They will model patterns of sediment cover and exposed bedrock following landsliding and flood events into river reaches, to determine under what conditions landslide-derived sediment is mobilised through catchments, and how landslide-derived sediment supply impacts channel geometry and migration rates (e.g. Dingle et al., 2020). They will explore how different models of sediment transport impact the transit time of landslide-derived sediment following flood events. The student will conduct a field season to the Alaknanda catchment in the Indian Himalaya to collect data on sediment properties (i.e. grain size, sorting) to parameterise these models.

Year 1

Reading of relevant literature and determination of key research questions; compilation of landslide inventories and application of automated mapping techniques across the study area. Field season to the Alaknanda catchment in the Indian Himalaya in March-April.

Year 2

Extract valley geometry and pattern of sediment storage from topographic data; detection of time series of sediment erosion and deposition from satellite data using machine learning. Comparison of landslide and channel sediment data. Attend UK geomorphology conference.

Year 3

Modelling of specific regions of high sediment mobility in the Alaknanda catchment. Present outcomes at international conference. Begin to draft papers and thesis.

Year 3.5

Write up and submit thesis. Finalise publication manuscripts. Attend international conference.

Techniques in topographic analysis, machine learning, Python, and numerical modelling will form the core of this project. Development of the necessary skills will be facilitated through in-house expertise in Durham and Newcastle, via NERC researcher training events, and via internationally recognised summer schools, such as the Community Surface Dynamics Modelling System (CSDMS) Spring School which provides training in modelling, software version control, Python programming, and high-performance computing.

Further training in transferable skills, including project management, scientific writing, oral and written communication, and media and public engagement, is available via the award-winning Durham Centre for Academic Development (DCAD). The student will also benefit from cross-disciplinary training provided as part of the IAPETUS2 DTP.

The student will be encouraged to present their work at conferences and seminars at Durham and attend relevant national and international conferences throughout their PhD research. The student is also encouraged to apply for small grants from appropriate funding agencies such as the British Society for Geomorphology, and the Royal Geographical Society, which will allow them to gain experience writing funding proposals.