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The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from ∼ 125 to ∼ 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau–Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use.

Aim: Humid tropical alpine environments are crucial ecosystems that sustain biodiversity, biological processes, carbon storage and surface water provision. They are identified as one of the terrestrial ecosystems most vulnerable to global environmental change. Despite their vulnerability, and the importance for regional biodiversity conservation and socio-economic development, they are among the least studied and described ecosystems in the world. This paper reviews the state of knowledge about tropical alpine environments, and provides an integrated assessment of the potential threats of global climate change on the major ecosystem processes. Location: Humid tropical alpine regions occur between the upper forest line and the perennial snow border in the upper regions of the Andes, the Afroalpine belt and Indonesia and Papua New Guinea. Results and main conclusions: Climate change will displace ecosystem boundaries and strongly reduce the total area of tropical alpine regions. Displacement and increased isolation of the remaining patches will induce species extinction and biodiversity loss. Drier and warmer soil conditions will cause a faster organic carbon turnover, decreasing the below-ground organic carbon storage. Since most of the organic carbon is currently stored in the soils, it is unlikely that an increase in above-ground biomass will be able to offset soil carbon loss at an ecosystem level. Therefore a net release of carbon to the atmosphere is expected. Changes in precipitation patterns, increased evapotranspiration and alterations of the soil properties will have a major impact on water supply. Many regions are in danger of a significantly reduced or less reliable stream flow. The magnitude and even the trend of most of these effects depend strongly on local climatic, hydrological and ecological conditions. The extreme spatial gradients in these conditions put the sustainability of ecosystem management at risk.

Electricity generated by hydro power is the most widely used form of renewable energy, and as such, its vulnerability to climate change is of great interest. The aim of this work is to estimate the change in river discharge characteristics in the Alpine region due to possible impacts of climate and the related changes in the power generation of run-of-river hydro power plants up to 2050. Four representative bias-corrected climate simulations from the ENSEMBLES project are chosen based on the SRES greenhouse gas emission scenario pathway A1B. Data of these simulations serve as input for a lumped-parameter rainfall-runoff model at a monthly time step, which is calibrated on discharge data of gauging stations along important rivers in the Alpine region. A power plant model fed with runoff data generated by the hydrological model is used to compute changes in the long-term average annual net electrical energy output of hydro power plants for the whole Alpine region; while the model for Austria is based on known technical parameters of the power plants, a more simplified approach is employed elsewhere. The general warming trend observed in all four climate scenarios causes to various degrees a seasonal shift towards earlier runoff. However, more diverse changes in precipitation for the different climate scenarios and time periods result in diverging hydrological projections. Although the annual runoff is found to decrease in some scenarios, the generally observed shift of runoff towards the winter season that typically shows higher energy consumption in the Alpine region suggests that the overall impact for the electricity sector tends to be positive rather than negative. Estimated changes in the average annual electricity generation of run-of-river plants are generally found to be within a single-digit percentage range but can be either positive or negative depending on the climate scenario. The estimated ranges reflect the diversity (uncertainty) of the climate models; the total bandwidth of possible changes in the water availability and hydro power generation in the Alpine region up to 2050 is assumed to be even higher, because of other uncertainties in the model chain that are not explicitly considered here. Nevertheless, as the general regional trends and bandwidth of changes in runoff and hydro power production strongly depend on the future changes in precipitation, the results of this work provide reasonable orders of magnitude of expected changes and are seen as a first step towards an improved understanding of climate impacts on hydro power production within the entire Alpine region.

Question: Global change is likely to strongly affect alpine and sub-alpine regions, in which bryophytes are important components. Global change effects on sub-alpine vegetation, bryophytes in particular, however, have been addressed in few studies. We ask if global warming and increased nitrogen (N) deposition, two of the most important components of global change, will have different effects on bryophyte communities and species in sub-alpine coniferous and shrubland ecosystems. Location: Eastern slope of the Tibetan Plateau. Methods: We established a warming by N deposition experiment, using a 2 × 2 factorial design, replicated three times, in each of two sub-alpine ecosystems. Effects on bryophytes at the community and species levels were evaluated after 4 (shrubland) and 5 (coniferous forest) years of warming and N deposition treatments. Results: Bryophyte cover increased in the first two growing seasons and thereafter decreased until the end of the experiment in all treatments, most strongly in warming plots in both ecosystems and in N deposition plots in the coniferous forest. At the species level, the pleurocarpous bryophyte Pleurozium schreberi was resilient to warming but sensitive to N deposition, while the acrocarpous bryophytes Rhizomnium tuomikoskii and Racomitrium japonicum were resilient to N addition but sensitive to warming. Conclusions: Effects of warming and increased N deposition on bryophytes were species-and to some extent also ecosystem-specific in the experiment in the sub-alpine region, indicating that bryophytes do not respond to global change as one single functional group. The observed species replacements in response to warming and N deposition may affect ecosystem processes.

The diffuse presence of small copper ore deposits in the Alpine area, mostly exploited since Late Medieval times, led most scholars to assume that these deposits may actually be active much earlier and that many of the circulating prehistoric metal objects found in the area were produced with local copper sources. This assumption was recently validated for the Recent Bronze Age through the use of lead isotope tracers, and well supported by the archaeometallurgical evidences found in the South-Eastern Alps. However, the scarcity of available lead isotope data for pre-Bronze Age metals precluded to date the reconstruction of the metal flow through the Late Neolithic and Eneolithic (or Copper Age). Based on 49 new analyses of important archaeological objects from the Alpine region, the Po River Valley and Central Italy, mostly axes dated from the Late Neolithic to the Late Eneolithic, here we show that the diffusion of copper in Northern Italy (approximately 4500–2200 BC) includes three major periods of metal use and/or production, each related to specific ore sources. The South Alpine copper was massively used only starting from the middle of the 3rd millennium BC, in connection or slightly earlier than the Beaker event.

The increasing threat of Glacial Lake Outburst Floods (GLOFs), intensified by climate change, underscores the urgency for developing advanced early warning systems. The near-annual, cyclical outbursts of Lake Merzbacher in the Tien Shan mountains present a severe downstream threat, yet its remote location and lack of instrumentation pose a significant challenge to traditional monitoring. To bridge this gap, we develop and validate a dynamic risk assessment framework driven entirely by remote sensing data. Methodologically, the framework introduces an innovative Ice-Water Composite Index (IWCI) to resolve the challenge of lake area extraction under mixed ice-water conditions. This is coupled with a high-fidelity 5 m resolution Digital Elevation Model (DEM) of the lake basin, autonomously generated from GF-7 Dual-Line Camera (DLC) imagery, which enables accurate daily volume retrieval. Through systematic feature engineering, nine key hydro-thermal drivers are quantified from MODIS and other products to train a Random Forest (RF) machine learning model, establishing the non-linear relationship between catchment processes and lake volume. The model demonstrates robust predictive performance on an independent validation set (2023–2024) (R2 = 0.80, RMSE = 5.15 × 106 m3), accurately captures the complete lake-filling cycle from initiation to near-peak stage. Furthermore, feature importance analysis quantitatively confirms that Positive Accumulated Temperature (PAT) is the dominant physical mechanism governing the lake’s storage dynamics. This end-to-end framework offers a transferable paradigm for GLOF hazard management, enabling a critical shift from static, regional assessments to dynamic, site-specific early warning in data-scarce alpine regions.

PurposeSoil organic carbon (SOC) in mountainous regions is characterized by strong topography-induced heterogeneity, which may contribute to large uncertainties in regional SOC stock estimation. However, the quantitative effects of topography on SOC stocks in semiarid alpine grasslands are currently not well understood. Therefore, the purpose of this research study is to determine the role of topography in shaping the spatial patterns of SOC stocks.Materials and methodsSoils from the summit, shoulder, backslope, footslope, and toeslope positions along nine toposequences within three elevation-dependent grassland types (i.e., montane desert steppe at ~ 2450 m, montane steppe at ~ 2900 m, and subalpine meadow at ~ 3350 m) are sampled at four depths (0–10, 10–20, 20–40, and 40–60 cm). SOC content, bulk density, soil texture, soil water content, and grassland biomass are determined. The general linear model (GLM) is employed to quantify the effects of topography on the SOC stocks. Ordinary least squares regressions are performed to explore the underlying relationships between SOC stocks and the other edaphic factors.Results and discussionIn accordance with the present results, the SOC stocks at 0–60 cm show an increasing trend in respect to the elevation zone, with the highest stock being approximately 37.70 g m−2 in the subalpine meadow, about 2.07 and 3.41 times larger than that in the montane steppe and montane desert steppe, respectively. Along the toposequences, it is revealed the SOC stocks are maximal at toeslope, reaching to 14.98, 31.76, and 49.52 kg m−2, which are also significantly larger than those at the shoulder by a factor of 1.38, 2.31, and 1.44, in montane desert steppe, montane steppe, and subalpine meadow, respectively. Topography totally is seen to explain about 84% of the overall variation in SOC stocks, of which 70.61 and 9.74% are attributed to elevation zone and slope position, while the slope aspect and slope gradient are seen to plausibly explain only about 1.84 and 0.01%, respectively.ConclusionsThe elevation zone and the slope position are seen to markedly shape the spatial patterns of the SOC stocks, and thus, they may be considered as key indicating factors in constructing the optimal SOC estimation model in such semiarid alpine grasslands.

The low risk awareness of the residents living in flood-prone areas is usually considered among the main causes of their low preparedness, which in turns generates inadequate response to natural disasters. In this paper, we challenge this assumption by reporting on the results of a sociological research in four communities exposed to flood risk in the Eastern Italian Alps. The research design included semi-structured interviews and focus groups with key local stakeholders and a standardized questionnaire submitted to 400 residents. Results revealed that residents felt both slightly worried about flood risk and slightly prepared to face an event. Considerable differences were found between the evaluations of individual subjects as opposed to overall communities. There was also a clear discrepancy between the actual adoption of household preparatory measures and the willingness to take self-protection actions. Overall, the risk awareness was significantly higher among those residents who had been personally affected by a flood in the past, were living in isolated (vs. urban) communities, in the most risky areas or had a lower level of trust in local authorities. The improvement of residents’ knowledge about their environment and the residual risk seemed to be crucial to increase risk awareness, and the same was true for the strengthening of local support networks to foster preparedness. The link between risk awareness and preparedness was not at all straightforward. Results revealed instead the complexity of residents’ perspectives, attitudes, behaviours and decisions about risk-related issues.

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r2 = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.