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The temporal concentration of snowfalls has direct implications on the management of water resources as well as on the economic activity of mountain areas, conditioning, for example, the seasonal performance of ski resorts. This work uses the daily concentration index (CI) for analysing the frequency concentration of snowfalls in the Iberian Peninsula Mountain ranges. First, we provide a spatiotemporal analysis of the CI patterns and trends for the 1980–2014 period. Subsequently, we determine the atmospheric circulation patterns that control the CI variability. In addition, we determine the geographical and low-frequency climate modes that control the CI for this mid-latitude area. In addition, we have estimated the partial dependence relationship between the CI and several geographical factors by fitting a multiple linear regression. The results from these analyses show that elevation as well as the distance from the Atlantic are the main geographical pattern that controls the CI in the Iberian Peninsula Mountain ranges. These geographical factors also reflect the role of the main atmospheric circulation patterns in the Iberian Peninsula in controlling the spatial dynamics of the CI. The Cantabrian, Iberian, and northern slopes of the Pyrenees show the lowest CI due to their exposition to northern and Atlantic circulation weather types. On the contrary, the highest CI values are found in the southern and eastern slopes of the Pyrenees, eastern slopes of Sierra Nevada, and southern slopes of the Central system. Trend analysis shows a slight increase of CI in the Central system and in the western Sierra Nevada. However, eastern Sierra Nevada, Cantabrian, Central, and Iberian show a downward CI trend. CI is principally driven by the East-Atlantic/Western Russia pattern and the North Atlantic Oscillation (NAO) in the Cantabrian, Iberian, and northern slopes of the Central range. The CI values in the Pyrenees show a different relationship with the Western Mediterranean

Climate warming is changing the magnitude, timing, and spatial patterns of mountain snowpacks. A warmer atmosphere may also induce precipitation phase shifts, resulting in a decreased snowfall fraction (Sf). The combination of Sf and snowpack directly influences the frequency and intensity of rain-on-snow (ROS) events, a common cause of flash-flood events in snow-dominated regions. In this work, we investigate ROS patterns and their sensitivity to temperature and precipitation changes in the Pyrenees by modeling ROS through a physically based snow model. This model is forced with reanalysis climate data for elevations of 1500, 1800, and 2400 m perturbed using a range of temperature and precipitation values consistent with 21st century climate projections. ROS patterns are characterized by their frequency, rainfall quantity, and snow ablation. The highest ROS frequency for the historical climate period (1980–2019) is found in the 2400 m zones of the southwest Pyrenees (17 d yr−1). The maximum ROS rainfall amount is detected in 1800 m areas of the southeast (45 mm d−1, autumn), whereas the highest ROS ablation is found in the 2400 m zones of the northwest (−10 cm d−1, summer). When air temperature increases from 1 to 4 ∘C compared to the historical climate period, ROS rainfall amount and frequency increase at a constant rate during winter and early spring for all elevation zones. For the rest of the seasons, non-linear responses of ROS frequency and ablation to warming are found. Overall, ROS frequency decreases in the shoulders of the season across eastern low-elevation zones due to snow cover depletion. However, ROS increases in cold, high-elevation zones where long-lasting snow cover exists until late spring. Similarly, warming induces greater ROS ablation (+10 % ∘C−1) during the coldest months of the season, 2400 m elevations, and northern sectors, where the deepest snow depths are found. In contrast, small differences in ROS ablation are found for warm and margin

A marginal snowpack is a recent concept that refers to snow cover that is relatively shallow and transient but still has important environmental and socioeconomic functions. This class of snow cover influences environments over large areas of the world and is very common in the Mediterranean climates, but also in the transition zone between persistent seasonal and ephemeral snowpacks all over the world. However, a globally-accepted definition to identify these environments has not been reached yet. Most of the modelling and monitoring tools for snow have been developed in cold and alpine areas with persistent snowpacks. The study and modelling of shallower snowpacks in milder environments are complicated by their high temporal variability, frequently patchy nature and typical coexistence with shrubs and forest. However, in recent years new remote sensing tools and more complex modelling approaches and specific parametrizations are facilitating their study. This paper provides a definition for marginal snowpacks, identifying them as those with: (i) seasonal peak depths that only occasionally surpass 1.5 m in favorable deposition zones such as wind drifts, ii) having typical seasonal durations ranging from 60 to 120 days, and (iii) that are predominantly in an isothermal state (on >75% of days) with high densification rates and several cycles of accumulation-ablation during each snow season. We review the main existing research challenges to improve scientific capabilities for their study and we discuss their hydrological and environmental relevance at different spatial scales.

Snow patterns in ice-free areas of Greenland play important roles in ecosystems. Within a changing climate, a comprehensive understanding of the snow responses to climate change is of interest to anticipate forthcoming dynamics in these areas. In this study, we analyze the future snowpack evolution of a polar maritime Arctic location, Qeqertarsuaq (Disko Island, Central-Western Greenland). A physically-based snow model (FSM2) is validated and forced with CMIP6 projections for SSP2–4.5 and SSP5–8.5 greenhouse gasses emission scenarios, using two models: CanESM5 and MIROC6. The future snowpack evolution is assessed through four key seasonal (October to May) snow climate indicators: snow depth, snow days, snowfall fraction and ablation rate. Comparison against the observed air temperature for the reference climate period demonstrates superior accuracies for MIROC6 SSP2.4–5, with anomalies at 19 %, compared to CanESM5 SSP5.8–5 (25 %) and CanESM5 SSP2.4–5 (78 %). In terms of precipitation, CanESM5 SSP2.4–5 and SSP2.4–5 exhibit smaller anomalies against the observed data (5 %) in contrast to MIROC6 SSP2.4–5 (15 %) and MIROC6 SSP2.8–5 (17 %). Results demonstrate distinct snowpack responses to climate change depending on the model and emission scenario. For CanESM5, seasonal snow depth anomalies with respect to the reference period range from – 38 % (SSP2–4.5, 2040–2050 period) to – 74 % (SSP5–8.5, 2090–2100 period). MIROC6 projects lower snowpack reductions, with a decrease ranging from – 38 % (SSP2–4.5, 2040–2050 period) to – 57 % (SSP5–8.5, 2090–2100 period). Similar reductions are anticipated for snowfall and snow days. Changes in the snowpack evolution are primarily driven by positive trends in downwelling longwave radiation and air temperature. The projected increase in precipitation by the mid to late 21st century will lead to more frequent rain-on-snow events, intensifying snowpack melting. These findings help enhance the comprehension of future snow dynamics in the

The Mediterranean Basin has experienced one of the highest warming rates on earth during the last few decades, and climate projections predict water scarcity in the future. Mid-latitude Mediterranean mountain areas, such as the Pyrenees, play a key role in the hydrological resources for the highly populated lowland areas. However, there are still large uncertainties about the impact of climate change on snowpack in the high mountain ranges of this region. Here, we perform a snow sensitivity to temperature and precipitation change analysis of the Pyrenean snowpack (1980–2019 period) using five key snow–climatological indicators. We analyzed snow sensitivity to temperature and precipitation during four different compound weather conditions (cold–dry (CD), cold–wet (CW), warm–dry (WD), and warm–wet (WW)) at low elevations (1500 m), mid elevations (1800 m), and high elevations (2400 m) in the Pyrenees. In particular, we forced a physically based energy and mass balance snow model (FSM2), with validation by ground-truth data, and applied this model to the entire range, with forcing of perturbed reanalysis climate data for the period 1980 to 2019 as the baseline. The FSM2 model results successfully reproduced the observed snow depth (HS) values (R2>0.8), with relative root mean square error and mean absolute error values less than 10 % of the observed HS values. Overall, the snow sensitivity to temperature and precipitation change decreased with elevation and increased towards the eastern Pyrenees. When the temperature increased progressively at 1 ∘C intervals, the largest seasonal HS decreases from the baseline were at +1 ∘C. A 10 % increase in precipitation counterbalanced the temperature increases (≤1 ∘C) at high elevations during the coldest months because temperature was far from the isothermal 0 ∘C conditions. The maximal seasonal HS and peak HS max reductions were during WW seasons, and the minimal reductions were during CD seasons. During WW (CD) seasons, the seas

The Aneto glacier, although it may be considered a very small glacier (<0.5 km2), 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.

Small glaciers are one of the best indicators of climatic variations and their short-term effects. Located in the Spanish Pyrenees, the Maladeta is one of these glaciers. Its systematic observation began in the 1980s, being one of the few Pyrenean glaciers with a tongue-shaped front. This study presents the evolution of the Maladeta glacial tongue over a decade (2010–2020) through multiple geomatic techniques. Surveys have ranged from Total Stations and Global Navigation Satellite System devices to massive data capture techniques such as Terrestrial Laser Scanners or Unmanned Aerial Vehicles photogrammetry. The aim is to analyze in detail the loss of surface area and thickness of the glacier and its transition from being a glacier with a tongue partially determined by climate to a topoclimatically determined cirque glacier. The results reveal a tongue retreat of over 5 m/yr and area losses of over 0.2 ha/yr, along with ice thickness and volume losses of −1.7 m/yr and over −21 × 103 m3/yr, respectively. If this trend continues, the tongue, and possibly the Maladeta glacier, could disappear by the end of the 2030s.

Data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high-resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a deterministic ensemble smoother with multiple data assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase the capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r=0.13) and root mean square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r=0.63 and RMSE = 0.89 m) and observations (Experiments III, r=0.92 and RMSE = 0.44 m) largely outperform the simulated patterns in Experiment I (r=0.3

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 Iz

Mountain forests affect spatial and temporal variability of snow processes through snow interception and by modifying the energy balance of snowpack. The high sensitivity of snow cover to seasonal temperatures in mid–latitude mountains is well known and is of particular interest with regard to a future warmer climate. The snowpack in the Pyrenees is expected to be the most impacted by climate change in the Mediterranean mountains, where future climate trends project rising temperatures and decreasing precipitation. This study analyzes how changes in temperature and precipitation can affect current forest–snow interactions in four forests, located near each other but under contrasting topographic settings, in the Spanish Pyrenees. This understanding will allow us to anticipate the future hydrological responses of Pyrenean forested mountain basins. The research was accomplished by performing a sensitivity analysis using simulations from the Cold Regions Hydrological Model (CRHM) and by comparing forest canopy sites (F) vs. openings (O). The CRHM platform focuses on the incorporation of physically based descriptions of snow–dominated regions hydrological processes. It was found that forest cover induced different snowpack sensibility to climatic change conditions in the studied forests. Delayed onset of snow accumulation (F: 13 days·°C−1; O: 5 days·°C−1) and reduced snowpack duration (F: 28 %·°C−1; O: 23 %·°C−1) under warmer temperatures were more intense in areas beneath the forest canopy compared to openings. A lower annual peak of snow water equivalent (SWE) (F: 81 mm·°C−1; O: 129 mm·°C−1), earlier melt-out date (F: 8 days·°C−1; O: 10 days·°C−1) and slower melting rates (F: 0.4 mm·day−1·°C−1; O: 0.5 mm·day−1·°C−1) with increasing temperatures were more intense in forest openings. The forest–driven reduction in snowpack duration (40%) was significantly enhanced with warming (10% per °C). Lower precipitation (20% precipitation reduction) could increase the response of

[EN] Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes., [ES] Dada la variabilidad de la nieve en áreas de montaña complejas, un reto importante de las aplicaciones de modelado a gran escala es representar con precisión la variabilidad de las propiedades de la capa de nieve a escala de subcuadrícula. El coeficiente de variación (CVds) del espesor de la nieve es una medida útil para caracterizar la distribución de la nieve en subcuadrículas, pero no ha sido bien definido mediante una parametrización para entornos montañosos. Este estudio utiliza datos de espesor de la nieve derivados de LIDAR en áreas montañosas alpinas y subalpinas de Colorado, EE. UU. La finalidad es evaluar la variabilidad de la distribución de la nieve a escala de subcuadrícula dentro de un tamaño de cuadrícula de una resolución de 1000 m habitual para modelos hidrológicos y de superficie del terreno. Los CVds de la subcuadrícula mostraron un amplio rango de variabilidad en el área de estudio de 321 km2 (0,15 a 2,74) y fueron significativamente mayores en las áreas alpinas en comparación con las áreas subalpinas. El espesor medio de la nieve fue el factor determinante de la variabilidad del CVds tanto en áreas alpinas como subalpinas, ya que el CVds disminuyó de forma no lineal con el incremento del espesor de la nieve. Esta correlación negativa se atribuye al tamaño estático de los elementos rugosos (topografía y dosel) que influyen fuertemente en la variabilidad estacional de la nieve. El CVds de la subcuadrícula también estuvo muy relacionado con la topografía y las variables forestales. Los controladores determinantes del CVds fueron la variabilidad a escala de subcuadrícula de la exposición del terreno al viento en áreas alpinas y la media y variabilidad de las métricas forestales en áreas subalpinas. Se desarrollaron dos modelos estadísticos (alpino y subalpino) para predecir el CVds a escala de subcuadrícula que muestran estadísticamente rendimientos razonables. La metodología presentada aquí puede ser utilizada para caracterizar la variabilidad

Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM’s capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations.

This study extends knowledge of the evolution of glacier shrinkage in the Cocuy-Güican Mountains since the maximum glacier extent of the Little Ice Age (LIA). Mass balance data for the Ritacuba Glacier since 2009 were acquired and compared with available data for the Conejeras Glacier (Los Nevados National Park). This study also investigated the hydrological significance of Colombian glaciers, which is still largely unknown because the available information is very limited. Glaciers in the Cocuy-Güican Mountains covered 13.2 km2 in 2019 compared with 127.8 km2 during the maximum LIA, representing a shrinkage of 89.7%. Analysis of glacier cover observations made in 1955, 1994, 2010, and 2019 revealed that the rate of ice loss was greatest from 1994 to 2010 (0.59 km2 yr−1), and was then almost halved from 2010 to 2019 (0.34 km2 yr−1). This slowing of glacier retreat is consistent with the moderate negative mass balance measured for 2009–2019, and an accumulated loss of 1766 mm w.e. (mm water equivalent). The progressive confinement of glaciers to higher elevations, ice accumulation in topographic locations providing shelter from solar radiation, and an absence of recent marked climatic anomalies could explain why the Cocuy-Güican glaciers have temporally reached a near equilibrium state conditions. This contrasts with the Conejeras Glacier, where 47,000 mm w.e. has been lost in the same period. The available data on runoff and isotope tracers of streamflow and precipitation suggest that precipitation rather than glacier melt water exerts primary control over the hydrological variability at high elevation sites.

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019–2020 and 2020–2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.

Mid-latitude mountain snowpacks are highly vulnerable to climate warming. Past and future hydroclimate changes require a thorougout knowledge of snow ablation physical processes and the associate climate drivers. In this work we provide the first spatio-temporal characteritzation of the energy available for snow ablation (Qm) in the Pyrenees for the period 1959–2020, during the main ablation season (March to June) for low (1200 m), mid (1800 m) and high (2400 m) elevations. We analyze the role of the the main Circulation Weather Types (CTs) in the Qm components for the entire mountain range. Finally, we train and tune a machine learning algorithm, the Random Forest, with atmospheric data (Surface Level Pressure and 500 hPa Geopotential Height) as an independent variable and Qm as the dependent one, in order to determine how much of the observed changes in Qm can be related with atmospheric circulation variability. The largest contribution of Qm is Net Radiation (Rn), increasing with elevation. Qm has shown a statistically significant increase since the 1980s. The comparison between the period 1959–1980 and the 2000–2020 revealed that positive Qm fluxes have been anticipated 22 and 12 days at mid and high elevations, respectively, showing evidence of an advance in the timing of the ablation season and faster snow ablation in high-elevation areas of the Pyrenees. The Qm is principally driven by Rn during the prevailing antyciclonic situations, characterized by the extension of high-pressure systems, low-barometric gradients and the SE advection of hot and dry air masses. The positive frequency of these anticyclonic CTs explains the majority (75%) of the Qm variability, the upward Qm trends and the earlier snow ablation onset since the 1980s.

The duration of the seasonal snowpack determines numerous aspects of the water cycle, ecology and the economy in cold and mountainous regions, and is a balance between the magnitude of accumulated snow and the rate of melt. The contribution of each component has not been well quantified under contrasting topography and climatological conditions although this may provide useful insights into how snow cover duration could respond to climate change. Here, we examined the contribution of the annual peak snow water equivalent (SWE) and the seasonal melt rate to define the duration of the snowpack over temperate mountains, using snow data for mountain areas with different climatological characteristics across the Iberian Peninsula. We used a daily snowpack database for the period 1980–2014 over Iberia to derive the seasonal peak SWE, melt rate and season snow cover duration. The influence of peak SWE and melt rates on seasonal snow cover duration was estimated using a stepwise linear model approach. The stepwise linear models showed high R-adjusted values (average R-adjusted = 0.7), without any clear dependence on the elevation or geographical location. In general, the peak SWE influenced the snow cover duration over all of the mountain areas analysed to a greater extent than the melt rates (89.1%, 89.2%, 81.6% 93.2% and 95.5% in the areas for the Cantabrian, Central, Iberian, Pyrenees and Sierra Nevada mountain ranges, respectively). At these colder sites, the melt season occurs mostly in the spring and tends to occur very fast. In contrast, the areas where the melt rates dominated snow cover duration were located systematically at lower elevations, due to the high interannual variability in the occurrence of annual peak SWE (in winter or early spring), yielding highly variable melt rates. However, in colder sites the melt season occurs mostly in spring and it is very fast in most of the years. The results highlight the control that the seasonal precipitation patterns, i

The Aneto Glacier, 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 surface and ice thickness 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 reconstructed for 1981 and 2011 and 15.0 m 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.

© Springer-Verlag Berlin Heidelberg 2015. The authors wish to acknowledge Ricardo Trigo and one anonymous reviewer for their detailed and helpful comments to the original manuscript. The data for this paper are available at Spanish National Research Council repository (http://sac.csic.es/spei/database.html), at Climate Research Unit (http://www.cru.uea.ac.uk/cru/data/nao/) and the Climate Prediction centre of the NOAA (http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml). This work was supported by the research project CGL2014-52135-C3-1-R and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT) financed by the Spanish Commission of Science and Technology and FEDER, Ephyslab (UVIGO-CSIC Associated Unit) and "LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)" financed by the LIFE programme. C. A-M received a postdoctoral fellowship #JCI-2011-10263. Iberdrola Renovable provided partial support from contracts., This paper analyses the influence of different atmospheric circulation indices on the multi-scalar drought variability across Europe by using the Standardized Precipitation Evapotranspiration Index (SPEI). The monthly circulation indices used in this study include the North Atlantic oscillation (NAO), the East Atlantic (EA), the Scandinavian (SCAN) and the East Atlantic-Western Russia (EA-WR) patterns, as well as the recently published Westerly Index (WI), defined as the persistence of westerly winds over the eastern north Atlantic region. The results indicate that European drought variability is better explained by the station-based NAO index and the WI than by any other combination of circulation indices. In northern and central Europe the variability of drought severity for different seasons and time-scales is strongly associated with the WI. On the contrary, the influence of the NAO on southern Europe droughts is stronger than that exerted by the WI. The correlation patterns of the NAO and WI with the SPEI show a spatial complementarity in shaping drought variability across Europe. Lagged correlations of the NAO and WI with the SPEI also indicate enough skill of both indices to anticipate drought severity several months in advance. As long as instrumental series of the NAO and WI are available, their combined use would allow inferring European drought variability for the last two centuries and improve the calibration and interpretation of paleoclimatic proxies associated with drought., Ministerio de Economía e Innovación (MICINN), Red de variabilidad y cambio climático (RECLIM), Iberdrola, LIFE Programme (UE), Comisión Interministerial de Ciencia y Tecnología (CICYT), Estrategia Española de Ciencia y Tecnología y de Innovación 2013-2020, Fondo Europeo de Desarrollo Regional (FEDER), Environmental Physics Laboratory EPHYSLAB, Universidad de Vigo, Consejo Superior de Investigaciones Científicas (CSIC), España, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

This research examines the atmospheric patterns associated with damages caused by extreme snowfalls during a 115-year period, using newspapers as the source to document the events and performing then a synthetic damage index. Between 1900 and 2015 there was 180 days with personal and material damages due to snowfalls (without considering associated hazards as snow avalanches or landslides): Apart from the numerous material damages recorded, 60 people were killed and 254 injured. Advections from the north represented a 30% of damaging days (followed by cyclonic and northeastern) accumulating the greatest number of days in the highest deciles of damage, although cyclonic days were the most damaging ones on average. The analysis of data from five weather stations in the area, allowed to relate weather types with temperature and precipitation extremes, showing that northern and cyclonic types led to the lowest temperature and the highest precipitation. Both the number of events and the damage index decreased throughout the study period, with the most serious events (those that caused deaths and injuries) highly concentrated in the first half of the 20th century. This decrease trend is parallel to that observed in the frequency of two of the most damaging advections, northern and northeastern (responsible for a 46.7% of damage), which have gone from representing 7.3% of days before 1930 to 5.8% after 1990. Futhermore, the relationship between the NAO phases and the days of damage has been examined, noting that damages are less frequent and les intense during the NAO's positive phase. Finally, non-atmospheric drivers such as land-use intensity decline in rural areas and Spanish regulations on roof snow loads standards (initiated in the 1960s and refined subsequently, thus mitigating roof collapses and associated casualties), could have partially contributed to the decline in snowfall damage in the area.

Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.

The unprecedented precision of satellite laser altimetry data from the NASA ICESat-2 mission and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in mountains, a critical variable for ecosystem and water resource monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for 3 years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off digital elevation models. Snow depth derived from ATL06 data only (snow-on and snow-off) offers a poor temporal and spatial coverage, limiting its potential utility. However, using a digital terrain model from airborne lidar surveys as the snow-off elevation source yielded a snow depth accuracy of ∼ 0.2 m (bias) and precision of ∼ 1 m (random error) across the basin, with an improved precision of 0.5 m for low slopes (< 10∘), compared to eight reference airborne lidar snow depth maps. Snow depths derived from ICESat-2 ATL06 and a satellite photogrammetry digital elevation model have a larger bias and reduced precision, partly induced by increased errors in forested areas. These various combinations of repeated ICESat-2 snow surface elevation measurements with satellite or airborne products will enable tailored approaches to map snow depth and estimate water resource availability in mountainous areas with limited snow depth observations.

[EN] Snow observations are important in many mountain areas to quantify the water stored in snowpacks and to predicting runoff during the melting period. In this study we compare the performance of five different regional-scale gridded snow products to reproduce snow water equivalent (SWE) in the Upper Euphrates region (Karasu Basin, 10,275 km2), with observations from automatic weather stations in the catchment through Taylor diagrams. The products compared are the ERA5, ERA5-Land, MERRA-2, snow data from a dynamical downscaling of ERA-5 (period 2000-2018) and SWE generated from microwave satellite data (SWE-E(H13) period 2013-2015 product of the EUMETSAT H SAF project). The H13 product presented deficiencies in terms of not being able to reproduce the spatial and temporal variability of the snowpack. ERA-5 and, in particular, ERA-Land products, at 30 and 9 km grid size, respectively, showed good performance in reproducing snow evolution compared to four available observation sites. MERRA2 at 50 km resolution showed lower skills compared to the above-mentioned products. Resulting snow data from WRF at 10 km resolution did not show any improvement with respect to the global datasets. The impossibility of testing different configurations due to the lack of observations to compare and the computational constraints to test different parametrizations may be the reasons to explain the low performance although they remain speculative. All the gridded datasets showed good performance in reproducing snow duration over the basin, compared to remotely sensed data. Results highlight ERA-Land dataset as a very promising tool for regional snow studies in mountainous regions with limited observations, in a cost-effective way., [ES] La observacion del manto de nieve es importantes para cuantificar el agua almacenada y predecir la escorrentía durante el período de deshielo. En este trabajo comparamos el rendimiento de cinco productos de nieve en rejilla a escala regional diferentes para reproducir el equivalente de agua de nieve (SWE) en la región del Alto Éufrates (Cuenca de Karasu, 10.275 km 2 ), con observaciones de estaciones meteorológicas automáticas en la cuenca a través de diagramas de Taylor. Los productos comparados han sido ERA5, ERA5-Land, MERRA-2, un dowscaling dinámico de ERA-5 (período 2000-2018) e información del SWE generado a partir de datos satelitales de microondas (SWE-E(H13) período 2013-2015 resultado del proyecto EUMETSAT H SAF). El producto H13 presentó deficiencias en cuanto a no poder reproducir la variabilidad espacial y temporal de la capa de nieve. ERA-5 y, en particular, los productos ERA-Land, con un tamaño de cuadrícula de 30 y 9 km, respectivamente, mostraron un buen rendimiento en la reproducción de la evolución de la nieve en comparación con cuatro sitios de observación disponibles. MERRA2 con una resolución de 50 km mostró un menor rendimiento en comparación con los productos mencionados anteriormente. Los datos de nieve resultantes de WRF a una resolución de 10 km no mostraron ninguna mejora con respecto a los conjuntos de datos globales. La imposibilidad de probar diferentes configuraciones debido a la falta de observaciones para comparar y las limitaciones computacionales, explican el bajo rendimiento del downscalling que precisa de una configuracion especifica. Todos los productos procedentes de simulaciones numéricas mostraron un buen rendimiento en la reproducción de la duración de la nieve sobre la cuenca, en comparación con los datos de detección remota. Los resultados destacan el conjunto de datos ERA-Land como una herramienta muy prometedora para los estudios regionales de nieve en regiones montañosas con observaciones limitadas.

The 2018-2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.