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The Aneto glacier, although it may be considered a very small glacier (<0.5 km 2), is the largest glacier in the Pyrenees. Its surface and thickness loss have been continuous in recent decades, and there have been signs of accelerated melting in recent years. In this study, thickness and surface losses of the Aneto glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne lidar point clouds and unoccupied aerial vehicle (UAV) imagery. A ground-penetrating radar (GPR) survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glacierised area has decreased by 64.7 %, and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thickness of 32.9, 19.2 and 15.0 m reconstructed for 1981 and 2011 and observed in 2020, respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. [ABSTRACT FROM AUTHOR]

The Aneto Glacier, although it may be considered very small (<0.5 km2), is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the area and volume of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m and 15.0 m reconstructed for 1981 and 2011 and observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021-2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e. the number of particles and their covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including the particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost. [ABSTRACT FROM AUTHOR]

We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database. [ABSTRACT FROM AUTHOR]

This paper analyzes the evolution of the Monte Perdido Glacier, the third largest glacier in the Pyrenees, from 1981 to the present. We assessed the evolution of the glacier's surface area by analysis of aerial photographs from 1981, 1999, and 2006, and changes in ice volume by geodetic methods with digital elevation models (DEMs) generated from topographic maps (1981 and 1999), airborne lidar (2010) and terrestrial laser scanning (TLS, 2011, 2012, 2013, and 2014) data. We interpreted the changes in the glacier based on climate data from nearby meteorological stations. The results indicate that the degradation of this glacier accelerated after 1999. The rate of ice surface loss was almost three times greater during 1999-2006 than during earlier periods. Moreover, the rate of glacier thinning was 1.85 times faster during 1999-2010 (rate of surface elevation change = -8.98±1.80 m, glacier-wide mass balance = -0.73±0.14 m w.e. yr-1) than during 1981-1999 (rate of surface elevation change = -8.35±2.12 m, glacier-wide mass balance = -0.42±0.10 m w.e. yr-1). From 2011 to 2014, ice thinning continued at a slower rate (rate of surface elevation change = -1.93±0.4 m yr-1, glacier-wide mass balance = -0.58±0.36 m w.e. yr-1). This deceleration in ice thinning compared to the previous 17 years can be attributed, at least in part, to two consecutive anomalously wet winters and cool summers (2012-2013 and 2013-2014), counteracted to some degree by the intense thinning that occurred during the dry and warm 2011-2012 period. However, local climatic changes observed during the study period do not seem sufficient to explain the acceleration of ice thinning of this glacier, because precipitation and air temperature did not exhibit statistically significant trends during the study period. Rather, the accelerated degradation of this glacier in recent years can be explained by a strong disequilibrium between the glacier and the current climate, and likely by other factors affecting the energy balance (e.g., increased albedo in spring) and feedback mechanisms (e.g., heat emitted from recently exposed bedrock and debris covered areas). [ABSTRACT FROM AUTHOR]