1.       Mapping supraglacial ponds using SAR data during the monsoon

Supraglacial ponds have been identified as an important component of the hydrological cycle of Himalayan debris-covered glaciers (Miles et al, 2016, 2017; Benn et al, 2017; Irvine-Fynn et al, 2017) and as key contributors to mass loss in the ablation area of these glaciers (Sakai et al, 2000; Salerno et al, 2017; Miles et al, 2018).  However, observation of these features during the ablation season has been precluded by the South Asian Monsoon, which brings consistent cloud cover and heavy rainfall, preventing access to field sites and inhibiting optical satellite observations. Satellite-based Synthetic Aperature Radar observations, now readily available at moderate resolution thanks to the ESA Sentinel program, are insensitive to cloud cover and offer the possibility for routine observation of supraglacial hydrology and novel insights into processes.  This project aims to develop and validate a method for identifying supraglacial ponds from publicly-available SAR imagery.

2.       Examining the debris cover anomaly at Nyanang Phu Glacier, Tibet

Supraglacial debris has been widely recognized to greatly reduce surface melt by inhibiting the transfer of radiative and atmospheric energy to the ice (e.g. Ostrem, 1959; Nicholson et al, 2006).  Yet, paradoxically, glaciers across High Asia appear to be thinning at comparable rates regardless of the presence of debris (e.g. Kaab et al 2012; Gardelle et al 2013). Studies have implicated reduced ice flux, ice cliffs and supraglacial ponds, and glacier elevation differences as contributing factors for this surprising result, but the effects are difficult to disentangle due to differences in setting, size, dynamics, and source areas of glaciers exhibiting thick surface debris or clean ice at the terminus.  In this study, we will examine the thinning rates of an unusual glacier in the monsoon-influenced region of Tibet.  Nyanang Phu Glacier has two major flow units – one, fed by avalanches, is covered by thick debris, while the other is sourced simply from snowfall and remains clear of debris. The study will determine historic and contemporary thinning rates from satellite stereo imagery, patterns and differences in ice velocity, and specific changes in surface features for this glacier for the last 40 years, then integrate these results to advance our understanding of the effects of surface debris on glacier melt.

3.       Ice-cliff detection from high-resolution multispectral satellite imagery

The backwasting of bare-ice cliffs accounts for a disproportionate amount of mass loss for Himalayan debris-covered glaciers (Immerzeel et al, 2014; Thompson et al, 2016; Brun et al, 2018). However, our ability to understand the formation and dynamics has been limited by time-consuming manual delineation over successive images. The manual digitization process is not only time-intensive, but reliant on highly subjective expert knowledge as well as patience. In this project, you will use spectral mixing and object-oriented techniques to develop a probabilistic method to map these features from commercial high-resolution multispectral satellite imagery (i.e. Pleiades or SPOT6/7 data) and moderate resolution public data (e.g. Sentinel-2). The results will be compared to a DEM-based topographic method and used to assess spatial variability of cliffs’ key geometric characteristics (aspect, slope, shape-factor) for multiple sites.

6.       Deriving debris thickness on Oberaletsch glacier (Swiss Alps)

     Numerous models aim to predict glacier evolution in a warming climate by determining the total melt based on energy balance calculations over the glacier surface. Supraglacial rock debris has a major impact on glacier melt by increasing the heterogeneity and process complexity of surface melt. Indeed, a very thin layer of debris will enhance melt by absorbing shortwave radiation that will be directly conducted to the ice, while even a few centimeters of debris are enough to insulate the ice from the incoming radiation and therefore results in reduced melt (Oestrem, 1959; Mattson et al., 1993). Debris thickness is very heterogeneous as debris accumulate by riding the glacier surface from different sources, or being melted out from the ice (McCarthy et al., 2017; Nicholson et al., 2018). Debris thickness therefore difficult to constrain and is one of the major unknowns in glacier melt modeling. This project will progress methods to accurately map debris-thickness using on-site high-resolution UAV thermal imagery, with manually excavated debris pits as a validation. The project comprises a few days of fieldwork on Oberaletsch Glacier (Valais, Switzerland) during which the student will use a FLIR T530 thermal camera to measure debris thickness variations and excavate a number of pits for validation. The data will further be inverted and analyzed to infer debris thickness based on near-surface meteorology, and will later be used to inform models assessing the future thinning of Oberaletsch Glacier.