Eurac Research

Climate change imposes increasing stress on ecosystems worldwide through rising temperatures, altered precipitation regimes, and more frequent extreme events. In high-elevated environments, these pressures are often compounded by geomorphological disturbances, which represent a major stress factor shaping vegetation dynamics and biodiversity. Understanding how plant traits and functional diversity respond to such disturbances is therefore essential for predicting ecosystem responses to ongoing environmental change.In this study, we investigated how geomorphic disturbance influences plant functional traits and biodiversity along elevational gradients in subalpine to alpine ecosystems. We hypothesised that: (i) disturbance-driven differences in species composition leads to functional differentiation at the community level, (ii) disturbance acts as a stressor inducing intraspecific variability in functional traits, and (iii) the proportion of thermophilic species increases on disturbed plots, particularly at higher elevations. Across three study sites, we sampled the five most abundant species per plot to test the interspecific variability as well as the three most frequent species shared across plots to test the intraspecific variability along elevation gradients. Key plant functional traits (leaf area, leaf dry weight, SLA, plant height) were measured and analysed using t-tests and non-parametric statistical approaches. For explaining the differences found Generalise Additive Models were performed.Our results showed that, at the community level, only Specific Leaf Area (SLA) differed significantly between disturbed and undisturbed plots for the five most common species (interspecific variability). Furthermore, we could observe significant differences for the relative cover of bryophytes, lichens, dwarf shrubs, and trees. For herbs and graminoids the climate-induced growth (RC1) and the improved edaphic conditions (RC2) revealed to be more important. At the species level, disturbance-related stress led to significant intraspecific trait variability in several species, highlighting flexible trait responses under changing environmental conditions. Contrary to our expectations, the proportion of thermophilic species was consistently lower on disturbed plots compared to undisturbed plots across the entire elevational gradient, although it decreased with elevation in both plot types as expected.Overall, differences in SLA likely reflect shifts in functional group composition under disturbance stress. Observed intraspecific trait variability along abiotic and disturbance gradients provides valuable insight into the capacity of alpine plant species to adjust their morphology and physiology in response to environmental stress, with important implications for biodiversity and ecosystem resilience under climate change.

Irrigation strongly influences land-atmosphere interactions and the terrestrial water cycle, yet its representation in land surface models (LSMs) remains highly uncertain. These uncertainties arise from both the scarcity of reliable irrigation benchmarks and the challenge of representing heterogeneous irrigation practices within coarse model grid cells (e.g., kilometer-scale resolutions). In this study, we examine structural limitations in the representation of irrigation within the Noah-MP LSM, implemented in the NASA Land Information System, by testing different calibration strategies. A sprinkler irrigation scheme is optimized using Sentinel-1-derived irrigation estimates and a genetic algorithm over an intensively irrigated region of northeastern Spain at a 0.01° spatial resolution. Two calibration approaches are evaluated: (i) adjusting the soil moisture threshold (Thirr) that triggers irrigation, and (ii) introducing a Scale Irrigation Coefficient (SIC) to account for sub-grid heterogeneity in irrigated area and applications’ timing. Results show that calibrating Thirr alone provides limited flexibility, resulting in unrealistic irrigation peaks and excessive water application. By contrast, the optimized SIC-based parameterization substantially improves irrigation dynamics, reduces model errors relative to benchmark in situ observations, and better captures interannual variability in surface soil moisture. Findings demonstrate that assuming uniform, full-grid irrigation at resolutions of ~1 km or coarser is physically unrealistic due to both operational constraints on irrigation practices and the fragmented structure of agricultural landscapes. Comparisons with satellite-based evapotranspiration and gross primary production datasets also reveal inconsistencies in simulated vegetation responses, highlighting remaining limitations in vegetation parameterization. Overall, this work underscores the importance of explicitly accounting for scaling effects in irrigation schemes and points toward future integration of satellite data assimilation to enhance representation of irrigation-water-carbon interactions.

The magnificent but complex topography of the Alps makes them Europe's indispensable water tower, channeling vital precipitation into rivers that sustain communities, economies, and ecosystems downstream. Therefore, gaining an understanding of how precipitation behaves in this mountain region is crucial. These mountain regions are home to a great variety of climatic regimes, from Mediterranean and Atlantic maritime to strongly continental, and yet, hydro-meteorological extremes pose an increasingly disastrous threat, heightening pressure on water security, infrastructure resilience, and transboundary risk management.There has been much research into precipitation extremes that has taken place over various parts of the Alps, bringing to light their intensity, frequency, and dynamics. However, these studies are confined to specific regions of the mountain range. The novelty of this work is threefold: a) It is based on a consistent, high-resolution, observation-based, recently published alpine-wide database of High-Impact Precipitation Events (HIPEs), observed for the period 1961-2022 and developed through a homogeneous transnational approach (Lemus-Canovas et al., 2025); b) It is based on a multi-metric analysis to draw a comprehensive picture of changes in the HIPE features, including the assessment of their spatial extent, which has not been addressed before; and c) The variations in the different characteristics of these events, as well as the responsible drivers, are addressed by considering the contribution of the annual frequency of weather types.By combining Regression Analysis with the modeling of the Generalized Extreme Value distribution, the study was able to identify significant patterns of increase in HIPE occurrences in subregions of the Alps, along with different contributions from the main weather types when explaining the annual variability of the different characteristics of HIPEs. Such techniques offer a nuanced view of the contribution of atmospheric dynamics and local factors in determining the observed variability of precipitation extremes.Overall, our results indicate that the emerging shifts in HIPE behavior will most likely result from a nonlinear interaction of thermodynamic intensification and dynamics of circulation. The findings are intended to emphasize the emerging regional vulnerabilities and provide science-based support for cross-border preparedness for flooding and adaptation to climate change in Alpine environments.Keywords: High-Impact Precipitation Events (HIPEs), European Alps, trend analysis, Alpine-wide observationsReference: Lemus-Canovas, M. (2025). High-Impact Precipitation Events in the European Alps (1961–2022) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17047822

This article explores how small mountain tourism destinations (MTDs) in the European Alps are beginning to translate climate change adaptation strategies into concrete, context-specific measures. Drawing on the strategies of 5 Pilot Working Areas (PWAs)—Ala di Stura and Balme (Italy), Werfenweng (Austria), Bohinj (Slovenia), Sattel-Hochstuckli (Switzerland), and Balderschwang (Germany)—of the Interreg Alpine Space project “BeyondSnow,” the study includes one of the first structural analyses of adaptation measures of MTDs. By applying the framework method for applied policy research, a matrix for the structural assessment of these measures was developed, including 3 analytic dimensions: overarching objectives, structural categories, and the distinction between implementation instruments (means) and the destination elements addressed (targets). Regarding overarching objectives, Year-round tourism emerged as the primary objective, followed by Destination cooperation and Sustainable development. Regarding structural categories, Product/concept development predominated as the implementation means, followed by Communication & marketing and Intradestination cooperation, with targets concentrated on Activity infrastructure and Activities & entertainment and complemented by Accommodation & gastronomy, Mobility & accessibility, and Destination community. Reading across the PWAs, means, and targets revealed 3 recurring implementation pathways: offer-led diversification, cooperation-led system alignment and portfolio extension, and distributed activation centered on specific audiences, placing low-barrier actions ahead of selective capital-intensive steps. The article concludes that, due to tourism's multidimensional, cross-sectoral nature, effective adaptation in small MTDs requires systemic approaches spanning products, community participation, and enabling infrastructure. Sequencing feasible, scalable measures, aligned with challenges identified, local governance, and sustainability visions, lays the groundwork for the broader systemic changes that are necessary for the creation of robust adaptation pathways, aimed at safeguarding both the competitiveness and viability of MTDs.

• Introduces CircularB-DfC, a design-stage framework for assessing and prioritising circularity in buildings • Integrates 35 circularity indicators and 20 enabling factors across design and value-chain processes • Provides Delphi-weighted scoring and a practitioner-oriented Excel tool for decision support • Demonstrates framework applicability through three contrasting regional and structural scenarios • Reveals how design strategies and enabling conditions jointly shape circular performance Circularity is increasingly recognised as a critical paradigm for sustainability in the built environment, yet existing efforts to assess it—whether focused on material flow analysis, design-for-disassembly strategies, durability metrics, or carbon accounting—remain fragmented and operate at different scales. Despite numerous indicator sets, the literature lacks an integrated framework that combines both technical design factors and the enabling organisational conditions required to support circular outcomes at the building level. This paper introduces CircularB-DfC (CircularB COST Action – Design for Circularity), a decision-Support Tool with a structured matrix for prioritising building design factors to enhance circular Material flows. The framework consolidates insights from a systematic literature review and a multi-stage expert engagement process, resulting in 35 technical indicators and 20 enabling factors . These are organised into four technical categories: Material Selection; Design for Disassembly; Embodied Energy and Carbon Footprint; Waste Minimisation, and one enabling category, Circular Construction Management, including Governance, Certification, Stakeholder Engagement, Digitalisation, and Socio-economic aspects. Indicators and enablers are aggregated into a Design Score and an Enabler Score to support early decision-making. The tool was applied to three illustrative scenarios: a reinforced-concrete industrial hall in the Western Balkans, a steel office building in Central Europe, and a timber residential project in East London. The steel scenario achieved the highest Design and Enabler Scores, the concrete scenario performed strongest in Waste Minimisation through prefabrication and site-based strategies, and the timber scenario scored lowest overall due to limited reuse and disassembly provisions in the original design. While CircularB-DfC offers a simple and transparent basis for integrating circularity in design, it is limited by the subjectivity of expert-based weighting and its static structure. Future research will focus on dynamic modelling, integration with digital tools, and broader validation to enhance applicability.

Abstract Quantifying irrigation is a key challenge in hydrology due to its impact on water and carbon cycles. This is compounded by the scarcity of benchmark irrigation data and limitations of land surface models (LSMs), which hinder accurate gridscale irrigation estimation. In this study, we optimize a sprinkler irrigation scheme within the Noah-MP LSM, as part of the NASA Land Information System, using Sentinel-1-based irrigation estimates and a genetic algorithm. Experiments in an intensively irrigated region of northeastern Spain (0.01° resolution) compare two calibration approaches: one adjusting the root-zone soil moisture threshold (Th irr ) which triggers irrigation and another introducing a scale irrigation coefficient (SIC) parameter to account for spatial heterogeneity in irrigation practices. The Th irr calibration shows limitation in the system’s flexibility, causing sparse irrigation applications with excessive water amounts that optimization cannot correct. In contrast, SIC calibration improves irrigation dynamics, reduces model errors, and better represents interannual surface soil moisture anomalies, outperforming the default scheme against in situ data. Results highlight that assuming full irrigation at resolutions equal or beyond 1 km is unrealistic due to two reasons: First, farmers cannot irrigate all fields within a grid cell simultaneously; second, the heterogeneous field mosaic further complicates uniform irrigation. Comparisons with satellite-based evapotranspiration (ET) and gross primary production (GPP) datasets highlight inconsistencies between model estimates and satellite ET products, revealing persistent vegetation dynamics issues. Future efforts could leverage the calibrated scheme with satellite data assimilation to improve soil moisture and vegetation conditions, capturing complex interactions between irrigation and the water–carbon cycles. Significance Statement Optimizing irrigation schemes in land surface models (LSMs) is crucial for improving hydrological predictions. This study addresses the scarcity of in situ irrigation data by leveraging satellite-based irrigation estimates to calibrate a sprinkler irrigation scheme coupled to the Noah-MP LSM. Findings reveal that default irrigation schemes, running at 1-km spatial resolution or coarser, rely on unrealistic assumptions. By calibrating a scale irrigation coefficient accounting for spatial heterogeneity in irrigation practices, the model better captures irrigation dynamics and reduces errors compared to conventional approaches. Nonetheless, results underscore the need for refining vegetation dynamics representation to enhance our understanding of the irrigation’s role in the water–carbon cycle.

The degradation of permafrost and the higher number of heavy rainfall events increase geomorphic disturbance (GMD), affecting the vegetation establishment in high-elevation belts. To evaluate the effect of GMDs on vegetation development, it is essential to understand how species or plant groups respond to disturbances. In this study, we investigated vegetation establishment in undisturbed and disturbed plot pairs along elevational transects in the Austrian and Italian Central Alps. Differences in total vegetation cover, species diversity, the cover of different plant groups and the community weighted means of the Landolt indicator values were analysed using the Kruskal-Wallis test for non-parametric data and paired t-test for parametric data. Generalised additive models combined with a principal component analysis were applied to identify the significant environmental variables (e.g., inclination or precipitation) explaining the differences found. To assess species' plasticity, the three most abundant species (five individuals per plot) per undisturbed-disturbed pairs were collected on-site, and functional traits measured in the laboratory. Disturbed sites exhibited lower total vegetation cover, species number, cover of competitive species, dwarf shrubs, herbs and lichens. The cover of stress-tolerant, cryophilic species and herbs was higher in disturbed sites. The observed variations can be mainly explained by climatic, edaphic and topographic variables. The Stream Power Index as a disturbance proxy had a significant negative influence on the total vegetation cover and herb cover and a positive influence on bryophyte, dwarf shrub, and tree cover. Most collected species showed high trait plasticity, with disturbance primarily reducing plant height and specific leaf area. Synthesis: GMD was the key driver in relation to both vegetation cover and species richness. The cover of most functional plant groups as well as species plasticity was primarily affected by climatic factors, soil conditions and the presence of less acidic debris than by GMD.

Mobility of Care (MoC) encompasses all trips made for unpaid care work, including caregiving and domestic responsibilities. Previous studies have shown the prevalence of the MoC in daily mobility, its disproportionate impact on women, and its key characteristics. However, research has mostly focused on urban residents, while rural dwellers are underexplored, despite their challenges in accessing care services. In this study, we aim to explore the role of the MoC in rural areas through three steps. First, we conduct a literature review to derive the main findings from previous MoC studies. Second, we perform an exploratory analysis of the MoC in a rural case study by investigating its daily prevalence, who undertakes it, and how. Third, we compare the nuances of our case study with the literature. In line with previous research, our exploratory analysis shows that care trips represent about 35% of all daily trips in our rural case study. Women, seniors, and non-working people are the main caregivers in the sample. Private cars and active modes account for 90% of the care-related modal split. Notably, 30% of care trips are 5-15 km long, 50% take place during off-peak hours, and 60% are destinated for villages. Although our results cannot be generalised due to the limited sample size, they provide novel nuances of the potential rural specificities of MoC, which may be explored in future research. Moreover, they highlight the potential need for more flexible collective transport options for rural caregivers, beyond the peak-hour requirements of commuters and students.