Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km²), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

Natural hazard models need accurate digital elevation models (DEMs) to simulate mass movements on real-world terrain. A variety of platforms (terrestrial, drones, aerial, satellite) and sensor technologies (photogrammetry, lidar, interferometric synthetic aperture radar) are used to generate DEMs at a range of spatial resolutions with varying accuracy. As the availability of high-resolution DEMs continues to increase and the cost to produce DEMs continues to fall, hazard modelers must often choose which DEM to use for their modeling. We use satellite photogrammetry and topographic lidar to generate high-resolution DEMs and test the sensitivity of the Rapid Mass Movement Simulation (RAMMS) software to the DEM source and spatial resolution when simulating a large and complex snow avalanche along Milford Road in Aotearoa/New Zealand. Holding the RAMMS parameters constant while adjusting the source and spatial resolution of the DEM reveals how differences in terrain representation between the satellite photogrammetry and topographic lidar DEMs (2 m spatial resolution) affect the reliability of the simulation estimates (e.g., maximum core velocity, powder pressure, runout length, final debris pattern). At the same time, coarser representations of the terrain (5 and 15 m spatial resolution) simulate avalanches that run too far and produce a powder cloud that is too large, though with lower maximum impact pressures, compared to the actual event. The complex nature of the alpine terrain in the avalanche path (steep, rough, rock faces, treeless) makes it a suitable location to specifically test the model sensitivity to digital surface models (DSMs) where both ground and above-ground features on the topography are included in the elevation model. Considering the nature of the snowpack in the path (warm, deep with a steep elevation gradient) lying on a bedrock surface and plunging over a cliff, RAMMS performed well in the challenging conditions when using the high-resolution 2 m lidar DSM, with 99 % of the simulated debris volume located in the documented debris area.

Predicting wildfires using Machine Learning models is relevant and essential to minimize wildfire threats to protect human lives and reduce significant property damage. Reliance on traditional wildfire indices for forecasting wildfires has failed to provide the expected prediction outcomes, resulting in limited application of these models. Thus, this research compares the outcome of wildfire forecasting using fire danger rating indices against Machine Learning model outcomes. Furthermore, the performance effectiveness of the fire danger rating indices and Machine Learning model outcomes are assessed using the same wildfire incidents. The One-class Machine Learning algorithms used are Support Vector Machine, Isolation Forest, Neural network-based Autoencoder and Variational Autoencoder models. The two global wildfire indices investigated were the US National Fire Danger Rating System for California and the McArthur Forest Fire Danger Index for Western Australia, using similar features. For the same data sets, the National Fire Danger Rating System and the McArthur Forest Fire Danger Index prediction outcomes were compared with Machine Learning model outcomes. Higher wildfire prediction accuracy was achieved by the One-class models, exceeding the performance of the two wildfire danger indices by at least 20%. The implications of our research findings have the potential to influence both these wildfire indices and state-of-the-art methods in wildfire prediction by proposing alternative ML methods to model the onset of wildfires.

An exceptional July 2022 winter storm brought 550 mm of precipitation to the Southern Alps of New Zealand. A series of alpine mass movements occurred during the storm, including a widespread snow avalanche cycle, debris flows, and erosion from rain runoff. We detail the sequence of events in the Kitchener avalanche path. Here, two large snow avalanches were followed by a debris flow. Substantial erosion of deposition and the underlying alluvial fan were induced by runoff from over 300 mm of rain falling after the first avalanche. The Kitchener path saw the largest avalanche since 1986, testing the utility of a diversion berm constructed for a 1:100‐year event. Results from a unmanned aerial vehicle lidar survey and numerical modeling characterize the rain‐on‐snow hazard sequence. In particular, the rain‐on‐snow event occurred on a deep mid‐winter snowpack, offering insights into future hazards posed by increasingly frequent extreme alpine precipitation.

In New Zealand, camping is a popular accommodation alternative when touring through its islands. The Rugby World Cup (RWC) 2011 is, to date, the largest event ever hosted in the country, and it redefined the way the authorities approach camping. In preparation for the RWC, special legislation was crafted to guarantee the right to practice ‘freedom camping’ while reducing the annoyance associated with doing so for the hosting communities. Counter to the original objective, these regulations ended up intensifying mobility restrictions. Framed against the theory of exception, this paper uses a spatial narrative to analyse the historical development of camping regulations in the country. The paper reveals the role of the RWC as one of a few catalysts that have refined the operation of a long-standing network of camp-spaces efficiently managing mobility through New Zealand's large and sparsely populated territory, since its colonial origin.