Wind wave and water level dataset for Hornsund, Svalbard (2013–2021)

Underwater pressure sensors were deployed near-continuously at various locations of the nearshore (8–23 m depth) Hornsund fjord, Svalbard, between July 2013 and February 2021. Raw pressure measurements at 1 Hz were used to derive mean water levels, wave spectra and bulk wave parameters for 1024 s bursts at hourly intervals. The procedure included subtracting atmospheric pressure, depth calculation, fast Fourier transform, correction for the decrease of the wave orbital motion with depth and adding a high-frequency tail. The dataset adds to the sparse in situ measurements of wind waves and water levels in the Arctic, and it can be used, for example, for analysing seasonal wind wave conditions and inter-annual trends and calibrating/validating wave models. The dataset is stored in the PANGAEA repository (https://doi.org/10.1594/PANGAEA.954020; Swirad et al., 2023).</p

National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal

Nepal is one of the most landslide-prone countries in the world, with year-on-year impacts resulting in loss of life and imposing a chronic impediment to sustainable livelihoods. Living with landslides is a daily reality for an increasing number of people, so establishing the nature of landslide hazard and risk is essential. Here we develop a model of landslide susceptibility for Nepal and use this to generate a nationwide geographical profile of exposure to rainfall-triggered landslides. We model landslide susceptibility using a fuzzy overlay approach based on freely-available topographic data, trained on an inventory of mapped landslides, and combine this with high resolution population and building data to describe the spatial distribution of exposure to landslides. We find that whilst landslide susceptibility is highest in the High Himalaya, exposure is highest within the Middle Hills, but this is highly spatially variable and skewed to on average relatively low values. Around 4 × 106 Nepalis (∼15\% of the population) live in areas considered to be at moderate or higher degree of exposure to landsliding (>0.25 of the maximum), and critically this number is highly sensitive to even small variations in landslide susceptibility. Our results show a complex relationship between landslides and buildings, that implies wider complexity in the association between physical exposure to landslides and poverty. This analysis for the first time brings into focus the geography of the landslide exposure and risk case load in Nepal, and demonstrates limitations of assessing future risk based on limited records of previous events

Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023

The Sentinel-1A/B synthetic aperture radar (SAR) imagery archive between 14 October 2014 and 29 June 2023 was used in combination with a segmentation algorithm to create a series of binary ice/open-water maps of Hornsund fjord, Svalbard, at 50 m resolution for nine seasons (2014/15 to 2022/23). The near-daily (1.57 d mean temporal resolution) maps were used to calculate sea ice coverage for the entire fjord and its parts, namely the main basin and three major bays: Burgerbukta, Brepollen and Samarinvågen. The average length of the sea ice season was 158 d (range: 105–246 d). Drift ice first arrived from the southwest between October and March, and the fast-ice onset was on average 24 d later. The fast ice typically disappeared in June, around 20 d after the last day with drift ice. The average sea ice coverage over the sea ice season was 41 % (range: 23 %–56 %), but it was lower in the main basin (27 %) compared to in the bays (63 %). Of the bays, Samarinvågen had the highest sea ice coverage (69 %), likely due to its narrow opening and its location in southern Hornsund protecting it from the incoming wind-generated waves. Seasonally, the highest sea ice coverage was observed in April for the entire fjord and the bays and in March for the main basin. The 2014/15, 2019/20 and 2021/22 seasons were characterised by the highest sea ice coverage, and these were also the seasons with the largest number of negative air temperature days in October–December. The 2019/20 season was characterised by the lowest mean daily and monthly air temperatures. We observed a remarkable interannual variability in the sea ice coverage, but on a nine-season scale we did not record any gradual trend of decreasing sea ice coverage. These high-resolution data can be used to, e.g. better understand the spatiotemporal trends in the sea ice distribution in Hornsund, facilitate comparison between Svalbard fjords, and improve modelling of nearshore wind wave transformation and coastal erosion.</p

Constraining the ice-content and timing of deposition of ice-rich deposits in Utopia Planitia: SHARAD, stratigraphy and crater counting

International audienc