Search

Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant-survey-based approaches applied along chronosequences provide invaluable information on plant communities, the "space-for-time" approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonization dynamics using a so-called "real-time" approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the southwestern European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonization by plants and in subsequent growth rates and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., neighboring vegetation cover and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, which is perhaps due to insufficient spatial resolution of predictor variables. Notably, the identity of pioneer plant species was highly variable, and initial plant community composition had a much stronger influence on plant assemblages than elapsed time since deglaciation. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather determined by local ecological context. We discuss the importance of scale in deciphering the complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefield systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant survey-based approaches applied along chronosequences provide invaluable information on plant communities, the "space-for-time" approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonisation dynamics using a so-called "real-time" approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the south- western European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonisation by plants and in subsequent growth rates, and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., local vegetation surrounding the forefields and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, perhaps due to insufficient spatial resolution of predictor variables. Notably, elapsed time since deglaciation showed no consistent relationship to plant assemblages, i.e., we did not identify a consistent order of successional species across forefields as a function of time. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather deterministic. We discuss the importance of scale in deciphering the unique complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefields systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields. [ABSTRACT FROM AUTHOR]

The long‐term increase in satellite‐based proxies of vegetation cover is a well‐documented response of seasonally snow‐covered ecosystems to climate warming. However, observed greening trends are far from uniform, and substantial uncertainty remains concerning the underlying causes of this spatial variability. Here, we processed surface reflectance of the moderate resolution imaging spectroradiometer (MODIS) to investigate trends and drivers of changes in the annual peak values of the Normalized Difference Vegetation Index (NDVI). Our study focuses on above‐treeline ecosystems in the European Alps. NDVI changes in these ecosystems are highly sensitive to land cover and biomass changes and are marginally affected by anthropogenic disturbances. We observed widespread greening for the 2000–2020 period, a pattern that is consistent with the overall increase in summer temperature. At the local scale, the spatial variability of greening was mainly due to the preferential response of north‐facing slopes between 1900 and 2400 m. Using high‐resolution imagery, we noticed that the presence of screes and outcrops locally magnified this response. At the regional scale, we identified hotspots of greening where vegetation cover is sparser than expected given the elevation and exposure. Most of these hotspots experienced delayed snow melt and green‐up dates in recent years. We conclude that the ongoing greening in the Alps primarily reflects the high responsiveness of sparsely vegetated ecosystems that are able to benefit the most from temperature and water‐related habitat amelioration above treeline. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this shifting mechanism to climate, as well as the role played by the cryosphere in the Earth energy budget, has motivated the development of a broad spectrum of predictive models. Such models rarely combine a high degree of physical realism in both the seasonal snow and glaciers, and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present S3M v5.1, a spatially explicit and hydrology-oriented cryospheric model that successfully reconstructs seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol. Comparatively novel aspects of S3M with respect to the existing literature are an explicit representation of the spatial patterns of snow liquid-water content, an hybrid approach to snowmelt that decouples the radiation- and temperature-driven contributions, the implementation of the ∆h parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the Italian north-western Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises an user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km -1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide. [ABSTRACT FROM AUTHOR]

Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density. [ABSTRACT FROM AUTHOR]

The input of mineral dust from arid regions impacts snow optical properties. The induced albedo reduction generally alters the melting dynamics of the snowpack, resulting in earlier snowmelt. In this paper, we evaluate the impact of dust depositions on the melting dynamics of snowpack at a high-elevation site (2160 m) in the European Alps (Torgnon, Aosta Valley, Italy) during three hydrological years (2013–2016). These years were characterized by several Saharan dust events that deposited significant amounts of mineral dust in the European Alps. We quantify the shortening of the snow season due to dust deposition by comparing observed snow depths and those simulated with the Crocus model accounting, or not, for the impact of impurities. The model was run and tested using meteorological data from an automated weather station. We propose the use of repeated digital images for tracking dust deposition and resurfacing in the snowpack. The good agreement between model prediction and digital images allowed us to propose the use of an RGB index (i.e. snow darkening index – SDI) for monitoring dust on snow using images from a digital camera. We also present a geochemical characterization of dust reaching the Alpine chain during spring in 2014. Elements found in dust were classified as a function of their origin and compared with Saharan sources. A strong enrichment in Fe was observed in snow containing Saharan dust. In our case study, the comparison between modelling results and observations showed that impurities deposited in snow anticipated the disappearance of snow up to 38 d a out of a total 7 months of typical snow duration. This happened for the season 2015–2016 that was characterized by a strong dust deposition event. During the other seasons considered here (2013–2014 and 2014–2015), the snow melt-out date was 18 and 11 d earlier, respectively. We conclude that the effect of the Saharan dust is expected to reduce snow cover duration through the snow-albedo feedback. This process is known to have a series of further hydrological and phenological feedback effects that should be characterized in future research. [ABSTRACT FROM AUTHOR]

Climate extremes can have tremendous impacts on the terrestrial biosphere and their frequency is very likely going to increase in the coming years. In this study we examine the impact of the 2015 summer heat wave on a mountain grassland in the Western European Alps by jointly analyzing phenocam greenness (GCC) trajectories, proximal sensing, CO 2 flux data and structural canopy traits. Phenocam effectively tracked the impact of the heat wave, showing 39% of reduction in maximum canopy greenness and a senescence advance of 32 days compared to mean values. The same patterns ( i.e. reduction of maximum values and senescence advance) were observed for all considered canopy traits and photosynthetic ecosystem functional properties, in particular the maximum light-saturated rate of CO 2 uptake ( A max ), LAI and PRI. Pixel-level analysis of phenocam images allowed us to further highlight that forbs were more heavily impacted than grasses. Moreover the effect of the extreme event on greenness seasonal course was evaluated testing new formulations of the Growing Season Index (GSI) model. Results demonstrate that a combination of water and high temperature stress was responsible for the observed reduction of canopy greenness during the heat wave. [ABSTRACT FROM AUTHOR]

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice. [ABSTRACT FROM AUTHOR]

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps. [ABSTRACT FROM AUTHOR]

The quantification of rock glacier dynamics has gained increasing importance in recent years. In this study, the spatial and temporal flow patterns of perennially frozen debris in the active Gran Sometta rock glacier (Western Italian Alps) were investigated with repeated Unmanned Aerial Vehicle (UAV) surveys (2016–2019), Global Navigation Satellite System (GNSS) campaigns (2012−2020), geophysical prospections (2015) and ground surface temperature data (2014–2020). UAV data were used to generate maps of changes and elevation differences of the rock glacier surface by 3D point cloud comparison to evaluate surface lowering and accumulation processes. Horizontal velocities were quantified by an automatic image correlation technique and the results were then compared with horizontal surface velocities from GNSS measurements on selected points. The horizontal velocities estimated with the automatic method agree well with the GNSS velocities with an R2 = 0.99 and a RMSE lower than 0.07 m/y. Point cloud comparisons show surface lowering in the orographic left-hand side of the terminal part and in the central body of the rock glacier. The upper part exhibits almost absence of subsidence and any movement. This is explained by the lack of permafrost in this sector due to its overriding by the development of a small glacier during the Little Ice Age. As a result of the downslope movement, zones of surface rising occurred at the advancing front and at the moving ridge and furrow complexes. Surface velocity decreases from the orographic left to the right-hand side of the rock glacier tongue, where a thaw subsidence of up to 0.05 m/y was also observed. According to the GNSS measurements, the range of flow velocity of the rock glacier increased from 0.17–1.1 m/y in 2013 to 0.21–1.45 m/y in 2015 and then decreased until 2018 when the smallest surface velocity is detected. Since 2018, the creep velocities gradually started to increase again reaching values of 0.23 m/y up to a maximum of 1.9 m/y in the orographic left-hand side of the rock glacier tongue. This agrees with observations from other rock glaciers in the European Alps in recent decades. The complex Gran Sometta rock glacier dynamics can be explained by the heterogeneous distribution of permafrost and related subsurface perennially frozen ground which is thick enough (about 20–30 m) for permafrost creep to occur. Creep rates of the rock glacier permafrost depend also on the ground thermal regime: annual warmer surface conditions promote an acceleration of the creep rates within the rock glacier permafrost, whereas ground surface cooling causes a slight deceleration. • High-resolution point clouds and orthomosaics are generated from UAV surveys. • Rock glacier flow velocity increased from 2012 until 2015 and from 2018 to 2020. • Velocities estimated by feature-tracking algorithm and GNSS data are highly correlated. • Permafrost kinematics is influenced by thermal regime and internal structure. [ABSTRACT FROM AUTHOR]

The Alps are very sensitive to climate and land cover changes. In the past years, a growing interest towards understanding the water and carbon exchanges in the mountains led to an increasing number of studies. However, the complexity of these environments determines many uncertainties from a methodological and ecological point of view. Therefore, there is a need to compute and compare flux data collected at different, high-altitude and complex sites. Such studies will improve the knowledge about the impact of local morphology on measurements. In this study we compare different alpine sites regarding their momentum and scalar fluxes.Three eddy covariance sites in the Aosta Valley (Italy) are considered. The first is located on a 33° slope in a small lateral valley having a South-East aspect at 1730 m a.s.l. The land cover is characterised by an abandoned pasture. The second site is on an almost flat area along a milder slope. The area has a South-East aspect and it is located at 2610 m a.s.l. and the land cover is a grassland. The third (namely the ICOS associated ecosystem site IT-Tor) is located on an almost flat (less than 5°) area. The site has a South aspect and its altitude is 2160 m a.s.l. The area is characterised by an unmanaged subalpine grassland.Daytime measurements of sensible and latent heat and carbon dioxide fluxes collected at the three sites are compared during the growing season. Quality control procedure, energy balance closure, mean diurnal and cumulative fluxes are presented for each site. Potential reasons for differences among the investigated sites are discussed. [ABSTRACT FROM AUTHOR]