Predicting glacier accumulation area distributions

A mass balance model based on energy balance at the terrain surface was developed and used to predict glacier accumulation areas in the Jotunheimen, Norway. Spatially distributed melt modelling used local climate and energy balance surfaces to drive predictions, derived from regional climate and topographic data. Predictions had a temporal resolution of 1 month and a spatial resolution of 100 m, which were able to simulate observed glacier accumulation area distributions. Data were stored and manipulated within a GIS and spatial trends and patterns within the data were explored. These trends guided the design of a suite of geomorphologically and climatologically significant variables which were used to simulate the observed spatial organisation of climatic variables, specifically temperature, precipitation and wind speed and direction. DEM quality was found as a critical factor in minimising error propagation. A new method of removing spatially and spectrally organised DEM error is presented using a fast Fourier transformation. This was successfully employed to remove error within the DEM minimising error propagation into model predictions. With no parameter fitting the modeled spatial distribution of snowcover showed good agreement with observed distributions. Topographic maps and a Landsat ETM+ image are used to validate the predictions and identify areas of over or under prediction. Topographically constrained glaciers are most effectively simulated, where aspect, gradient and altitude impose dominant controls on accumulation. Reflections on the causes of over or under prediction are presented and future research directions to address these are outlined. Sensitivity of snow accumulation to climatic and radiative variables was assessed. Results showed the mass balance of accumulation areas is most sensitive to air temperature and cloud cover parameterisations. The model was applied to reconstruct snow accumulation at the last glacial maximum and under IPCC warming scenarios to assess the sensitivity of melt to changing environmental conditions, which showed pronounced sensitivity to summer temperatures Low data requirements: regional climate and elevation data identify the model as a powerful tool for predicting the onset, duration and rate of melt for any geographical area

Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery

Landslides are one of the most damaging natural hazards and have killed tens of thousands of people around the world over the past decade. Slow‐moving landslides, with surface velocities on the order of 10−2–102 m a−1, can damage buildings and infrastructure and be precursors to catastrophic collapses. However, due to their slow rates of deformation and at times subtle geomorphic signatures, they are often overlooked in local and large‐scale hazard inventories. Here, we present a remote‐sensing workflow to automatically map slow‐moving landslides using feature tracking of freely and globally available optical satellite imagery. We evaluate this proof‐of‐concept workflow through three case studies from different environments: the extensively instrumented Slumgullion landslide in the United States, an unstable lateral moraine in Chilean Patagonia and a high‐relief landscape in central Nepal. This workflow is able to delineate known landslides and identify previously unknown areas of hillslope deformation, which we consider as candidate slow‐moving landslides. Improved mapping of the spatial distribution, character and surface displacement rates of slow‐moving landslides will improve our understanding of their role in the multi‐hazard chain and their sensitivity to climatic changes and can direct future detailed localised investigations into their dynamics

Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal

Coseismic landslides represent the first stage of a broader cascading sequence of geohazards associated with high-magnitude continental earthquakes, with the subsequent remobilisation of coseismic landslide debris posing a long-term post-seismic legacy in mountain regions. Here, we quantify the controls on the hazard posed by landslide remobilisation and debris runout, and compare the overlap between areas at risk of runout and the pattern of post-seismic landslides and debris flows that actually occurred. Focusing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we show that the extent of the area that could be affected by debris runout remained elevated above coseismic levels 4.5 years after the event. While 150 km2 (0.6% of the study area) was directly impacted by landslides in the earthquake, an additional 614 km2 (2.5%) was left at risk from debris runout, increasing to 777 km2 (3.2%) after the 2019 monsoon. We evaluate how this area evolved by comparing modelled predictions of runout from coseismic landslides to multi-temporal post-seismic landslide inventories, and find that 14% (85 km2) of the total modelled potential runout area experienced landslide activity within 4.5 years after the earthquake. This value increases to 32% when modelled runout probability is thresholded, equivalent to 10 km2 of realised runout from a remaining modelled area of 32 km2. Although the proportion of the modelled runout area from coseismic landslides that remains a hazard has decreased through time, the overall runout susceptibility for the study area remains high. This indicates that runout potential is changing both spatially and temporally as a result of changes to the landslide distribution after the earthquake. These findings are particularly important for understanding evolving patterns of cascading hazards following large earthquakes, which is crucial for guiding decision-making associated with post-seismic recovery and reconstruction

Archaeological investigations in northern Laos : new contributions to Southeast Asian prehistory

Not applicableNational Science FoundationNational Geographic SocietyHenry Luce FoundationUniversity of Pennsylvania Museum of Archaeology and AnthropologyDepartment of Heritage, Lao PDRUniversity College Dublin Seed FundingUS Embassy Lao PDRAustralian National Universit

Archaeological investigations in northern Laos : new contributions to Southeast Asian prehistory

Not applicableNational Science FoundationNational Geographic SocietyHenry Luce FoundationUniversity of Pennsylvania Museum of Archaeology and AnthropologyDepartment of Heritage, Lao PDRUniversity College Dublin Seed FundingUS Embassy Lao PDRAustralian National Universit