Climate change is reshaping the species composition, distribution and extent of forests worldwide. Across vast areas in Central Europe widespread Norway spruce (Picea abies) has exhibited large-scale decline, primarily due to its vulnerability to drought events. Forest management is thus facing important questions related to the replacement of Norway spruce, especially in areas where it was introduced due to its high economic value.

This study investigates the potential of Douglas fir (Pseudotsuga menziesii), a drought- and pest-tolerant non-native species, as a more resilient alternative for use in production forests. At the experimental plot in Jable, central Slovenia where both species coexist, we monitored the xylogenesis of five Douglas firs and five Norway Spruces from March to October 2024 by sampling phloem, cambium and xylem tissue every two weeks using the Trephor tool. Additionally, we collected tree cores from 20 trees of each species to perform dendrochronological analyses. These analyses aim to assess climate-growth correlations and growth-based resilience indicators (resilience, resistance, recovery and recovery period).

 The main objective of this study is to determine whether Douglas fir is to compare 1) interannual growth dynamics, 2) intra-annual growth dynamics of xylem and phloem, 3) climate-growth relationships, and 4) resilience components of both species. We hypothesize that non-native Douglas fir will exhibit greater growth rates and better resilience indicators and could thus be considered as a replacement for Norway Spruce at similar forest sites in central Slovenia and beyond. By addressing critical knowledge gaps regarding the responses of these species to climate variability, this research can provide important insights to support the strategic adaptation of forestry practices and improve the resilience of ecosystems in the face of environmental change.

How to cite: Balzano, A., Partemi, R., Jevšenak, J., Krže, L., and Merela, M.: Douglas Fir (Pseudotsuga menziesii) as an alternative species for the declining Norway spruce (Picea abies) in central Europe: Dendrochronological and xylogenetic insights from Slovenia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4269, https://doi.org/10.5194/egusphere-egu25-4269, 2025.

Fires can alter the properties of soil and other material via heat transfer. The identification of soil heating effects in hearths, for example, has long been a cornerstone in archaeological investigations. However, wildfires can also alter soils, and there is a surprising level of uncertainty into what degree soils are heated and to which depth this occurs in wildfires. This can lead to erroneous assumptions regarding the potential impact of wildfires when attributing heat induced changes in the soil, especially when laboratory heating results are extrapolated to field conditions.

To address this research gap, we compiled and examined new and published field data on maximum temperatures and heating durations for mineral soils during wildfires and prescribed burns in forests, shrublands and grasslands around the globe; and compared these to data obtained from laboratory heating experiments.

Most fires heated only the uppermost centimetres of the mineral soil, rarely exceeding 300 °C below 1 cm depth. Their heat pulses were shorter (<500 s) than those often applied in laboratory studies (1800-3600 s). The highest near-surface temperatures occurred in shrubland wildfires, whereas the longest heating durations in forests with deep organic layers and high fuel loads.

While it is clear that smouldering logs, tree trunks and root systems, or slash pile burns can impart intense heating to substantial depths akin to that under hearths, most landscape-scale fires generate short and shallow heat pulses that are unlikely to lead to detectable lasting changes in the mineral soil. 

How to cite: Doerr, S., Badia-Villas, D., Bryant, R., Matthew, D., Antonio, G.-G., Jorge, M.-S., Jessica, M., Carmen, S.-G., Cristina, S., Cathelijne, S., and Pete, R.: Soil heating under wildfires and prescribed burns and their relevance to archaeological investigations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15465, https://doi.org/10.5194/egusphere-egu25-15465, 2025.

Climate change is expected to exacerbate heat stress, particularly in urban areas where the urban heat island (UHI) effect tends to amplify warming compared to surrounding rural regions. Due to the heterogeneity of urban environments, heat stress can vary significantly within cities. Heat vulnerability maps, which combine data on heat sensitivity, heat exposure and adaptive capacity, are valuable tools for identifying areas that should be prioritized for heat stress mitigation measures. One important component of such heat vulnerability maps is data on the spatial distribution of heat. The present study explores the use of satellite data to generate high-resolution temperature maps, addressing two key challenges in the process.

The first challenge arises from the fact that satellites measure land surface temperature (LST) rather than air temperature (AT), whereas the latter is needed as input for most heat stress indicators. While linear models calibrated with weather station data are frequently used to estimate AT from LST, there are cities where the availability of weather stations is insufficient for calibrating models with multiple control variables. Additionally, the LST-AT relationship depends on the prevailing atmospheric conditions. The second challenge of using satellite data is that satellite images are usually not available on an hourly or daily basis due to factors such as satellite scheduling or excessive cloud cover.

To address the first challenge, we adopt a technique introduced by the ECOSTRESS mission, which leverages reanalysis data (GEOS-5) to estimate AT using LST, the normalized difference vegetation index (NDVI), and albedo. We apply this method to spatially downscaled LST data (100m) from the VIIRS instrument aboard the Suomi NPP satellite, AT reanalysis data from ERA5-Land (9km), as well as NDVI and albedo derived from Harmonized Landsat Sentinel (HLS) data (aggregated to 100m). Applying the method to individual satellite images enables day-specific adjustments for varying atmospheric conditions. To overcome the second challenge, we utilize high-resolution AT maps derived from LST images to calculate spatial patterns of air temperature distribution, which are then used to downscale ERA5-Land AT data for those times without satellite images available.

To evaluate the approach described, it is exemplary applied to various cities, whereby the downscaled temperature estimates are validated against (i) temperature estimates based on alternative methods than the ECOSTRESS technique to derive AT from LST, (ii) weather station data, and (iii) existing results from urban climate models.

How to cite: Kortschak, D., Gallaun, H., Kernitzkyi, M., Köberl, J., Miletich, P., and Strohmaier, M.: Spatial downscaling of urban temperatures: evaluation of an approach using satellite and reanalysis data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1411, https://doi.org/10.5194/egusphere-egu25-1411, 2025.

The aim of this study was to find the connection between the large-scale atmospheric circulation in the winter season and the occurrence of extreme precipitation in the spring months at the regional scale. For the large-scale circulation, climate indices (GBOI and NAOI) associated with the Greenland-Balkan Oscillation and the well-known North Atlantic Oscillation were considered, and for the regional scale, certain representative stations for the middle and lower Danube basins were considered. The tests were carried out for a 120-year interval (1901-2020), by applying the extreme value theory (EVT). The modelling of maximum precipitation was carried out through the generalized extreme value (GEV) distribution. In order to see the impact of the large-scale circulation, the results obtained by incorporating NAOI as covariate into the location parameter of GEV distribution, were compared with the results obtained considering GBOI as covariate. For extreme precipitation in the lower basin area, the influence of GBOI is much more significant than that of NAOI, while for the middle basin area, the differences between the two indices are not so significant.

How to cite: Mares, I., Dobrica, V., Mares, C., and Demetrescu, C.: On the links between large-scale atmospheric circulation and extreme precipitation in the middle and lower Danube basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9608, https://doi.org/10.5194/egusphere-egu25-9608, 2025.

Crop yield is important for agricultural productivity and country’s economy. Accurate crop yield estimation is critical for policymakers, farmers, and governments because it allows better management techniques, decision making and the implementation of practicable agricultural policies. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. In this study, we used the Food and Agriculture Organization (FAO) of the United Nations developed AquaCrop model to estimate the crop yields of corn and soybean crops in Illinois, United States (US). Data of various meteorological parameters as precipitation, maximum and minimum temperature, relative humidity, wind speed, solar radiation datasets were collected from NASA Prediction of Worldwide Energy Resources (POWER), for a period of 25-years from 2000 to 2024. Whereas, reference evapotranspiration was calculated by using the modified Hargreaves method. The objective of this study is to assess the accuracy of yield estimation of corn and soybean by using the AquaCrop model. The AquaCrop model was simulated for the growing period of corn and soybean from May to September. Using the AquaCrop model, the maximum and minimum corn yields were found to be 14.49 tons/ha in the year 2022 and 7.60 tons/ha in the year 2005, respectively. Similarly, the maximum yield of soybean was found to be 4.33 tons/ha in the year 2022, while the minimum yield was 2.26 tons/ha in the year 2012. The coefficient of determination (R²) values of 0.72 for maize and 0.76 for soybean, gives a satisfactory level of model accuracy. The model's performance can be improved further by incorporating more ground-truth data and appropriate parameters. This study demonstrates the AquaCrop model's ability to estimate crop production with few input parameters, as well as suggest opportunities for improvement. To improve prediction accuracy and promote informed agricultural planning and food security, future study might use sophisticated methodologies, localized farming practices, crop phenology, and specific soil data. 

Keywords:  AquaCrop, Crop yield, Illinois, Yield Predictions.

How to cite: Gautam, V. and Pathak, S.: Optimizing Corn and Soybean Yield Predictions in Illinois Using the AquaCrop Model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3943, https://doi.org/10.5194/egusphere-egu25-3943, 2025.

Monitoring key parameters that drive land-surface processes, such as surface soil moisture (SSM)), is essential for understanding global biogeochemical cycles, including those of water, energy, and carbon. While Earth Observation (EO)-based SSM products have demonstrated significant potential, their practical application is often limited by coarse spatio-temporal resolution. Therefore, downscaling these operational products is a critical scientific challenge for enabling their effective use in regional and local-scale applications.

This study’s aims at presenting an innovative approach for downscaling operational soil moisture products using a variant of the so-called “triangle” method, named the “simplified” triangle. The use of the proposed technique is demonstrated herein using the European Space Agency's (ESA) operational soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) and optical data from ESA’s Sentinel-3 platform. The enhanced spatial SSM estimates are compared against near collocated reference ground data from multiple validated experimental sites across Europe. The results obtained indicate a satisfactory agreement, confirming the proposed approach's promising potential to accurately estimate key land-surface interaction parameters. Conceptually the proposed herein methodological framework is applicable to any operational product, a topic of further investigation.

How to cite: Detsikas, S. E., Petropoulos, G. P., Saviolakis, P., Lekka, C., Karymbalis, E., Katsafados, P., and Georgaki, F.: Downscaling Earth Observation Operational Soil Moisture Products Using multi-sensor Satellite Data: “A Triangle Inversion Approach", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19591, https://doi.org/10.5194/egusphere-egu25-19591, 2025.

Advances in geo-information technologies, including Earth Observation (EO), GIS, cloud computing and software tool development, have shown great potential towards addressing key societal challenges faced today associated with the study of land-atmosphere interactions. Accurate information on spatially explicit, distributed estimates of land-atmosphere fluxes and soil surface moisture is essential in a wide range of disciplines, including meteorology, hydrology, agriculture and ecology.

Use of simulation process models has played a key role in extending our abilities to study Earth system processes and enhancing our understanding on how different components of it interplay. A special category of such models includes the so-called Soil Vegetation Atmosphere Transfer (SVAT) models. Those are deterministic simulation models that describe the physical processes controlling energy and mass transport in the soil/vegetation/atmosphere system.

SimSphere is such a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth’s land surface. Its use is at present continually expanding worldwide both as an educational and as a research tool for scientific investigations. It is being used either as a stand-alone application or synergistically with EO data and important advancements particularly in the recent years have been implemented to the model.

Herein, we present state of the art advancements introduced recently to SimSphere SVAT model aiming at making its use more robust when integrated with EO data via the so-called “triangle” method. Use of the recently developed add-on to SimSphere is illustrated herein using a variety of examples that involve both satellite and UAV data. The presented work  is of key significance to the users' community of the model and very timely, given that variants of the so-called “triangle” method being considered for deriving operationally regional estimates of energy fluxes and soil moisture from EO data provided by non-commercial vendors.

KEYWORDS: land surface interactions, geoinformation, earth observation, triangle, SimSphere   Acknowledgements The research presented herein has been conducted in the framework of the project LISTEN-EO (DeveLoping new awareness and Innovative toolS to support efficient waTer rEsources man- agement Exploiting geoinformatiOn technologies), funded by the Hellenic Foundation for Research and Innovation programme (ID 15898). 

How to cite: Gkatzios, G., Petropoulos, G. P., Detsikas, S. E., Lekka, C., Karymbalis, E., and Katsafados, P.: Advancing our understanding of land surface interactions via the development of innovative geoinformation tools , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21175, https://doi.org/10.5194/egusphere-egu25-21175, 2025.

Assessing the risks posed by climate change to cultural heritage (CH) is crucial for developing effective strategies to preserve this non-renewable heritage. This study provides a comprehensive approach to assess climate change-related risks to cultural heritage across five selected sites in Europe: Choirokoitia, Aegina, Epidaurus, Kalapodi, and Ventotene. By applying the Heritage Outdoor Microclimate (HMR_out) and Predicted Risk of Damage (PRD) indices, the study quantifies potential damage to inorganic materials due to long-term changes in temperature and relative humidity (RH). Climate projections are based on high-resolution EURO-CORDEX Regional Climate Model (RCM) simulations under three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5) for the periods 2021–2050, and 2071–2100. Results reveal a significant increase in temperature and the related indices under all emission scenarios highlighting a warming trend and intensified heat stress across the CH sites. The projected rise in temperature leads to an increase in the HMR_out index across all the CH sites, with the rate of change differing between time periods and scenarios. This rise in the HMR_out index suggests an increase in the predicted risk of damage (PRD) to monuments made of inorganic materials due to heat stress. In contrast, RH and the associated PRD index are expected to decrease. Overall, the projected changes in the HMR_out and PRD indices provide a deeper insight into how climate change may influence preservation of cultural heritage sites constructed from stone and marble.

This work is based on procedures and tasks implemented within the project “Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage - TRIQUETRA”, which is a Project funded by the EU HE research and innovation program under GA No. 101094818.

How to cite: Tringa, E., Georgoulias, A. K., Akritidis, D., Feidas, H., and Zanis, P.: Climate Change and Cultural Heritage: Assessing Future Risks of Damage at Selected European Cultural Heritage Sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3882, https://doi.org/10.5194/egusphere-egu25-3882, 2025.

Fires are among the most significant causes leading to significant alterations, both at the level of the natural and built landscape. These in fact induce significant alterations not only on the vegetation cover, but also on fauna, soil, atmosphere, artifacts and, inevitably, economic losses as well. In the context of the archaeological heritage, fires are a cause of extensive damage especially at the territorial scale, on sites and fragments not yet subject to either excavation or reconnaissance campaigns, but also on known sites that suffer from insufficient protection actions.

Traditional methods of assessing fire severity and property damage incur costs in terms of money and time because of the necessary field survey activities. A combination of geodata science and remote sensing, on the other hand, turns out to be an inexpensive and effective tool for modeling fires, understanding their causes and fire evolution.

In this work we use the potential of geodata science methods applied to spatial and satellite data, to analyse past trends and its correlation with environmental and anthropic factors and to forecast fire risk in the context of climate change, considering the evolution of environmental parameters stated from the Intergovernmental Panel on Climate Change (IPCC, 2022). These findings can be the starting point for the development of forecasting models also with a view to proposing prevention and protection strategies for the archaeological heritage of the Basilicata Region.

Reference

IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.

How to cite: Danese, M., Florio, V., Masini, N., and Lasaponara, R.: Impact of fire risk on archaeological heritage in the Age of climate change. Geodata science for prediction and development of strategies for protection., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16429, https://doi.org/10.5194/egusphere-egu25-16429, 2025.

When discussing climate change and cultural heritage, the focus often lies exclusively of the vulnerability aspects of the latter. However, cultural heritage can also play an active role in activating strategies and actions to increase its sustainability and mitigate environmental impacts.

Energy rehabilitation and reuse of existing buildings hold the potential to contribute to sustainable heritage conservation while embracing new energy efficiency principles.

According to literature, energy rehabilitation and retrofitting of the building envelope need to be carried out with respect to historic and cultural features and the protection of cultural heritage. This applies as much to listed buildings as to those that, although not formally protected, are part of the historical heritage and define the identity and the skyline of the place (Magrini, Franco, 2016).

In this work, starting from the spatial modeling of the territory and use of satellite data thank to the free-cloud application Google Earth Engine (GEE), it is possible to perform some preliminary analysis. These ones are useful to derive some formal characteristics that directly influence both the energy requirements and the choice of some technological solutions for integrating renewable energy sources (Forster et al.,2025).

References

Forster et al.,2025: Forster, J., S. Bindreiter, B. Uhlhorn, V. Radinger‐peer, and A. Jiricka‐pürrer. 2025. 'A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change', Urban Planning, 10.

Magrini, Franco, 2016: Magrini, A., and G. Franco. 2016. 'The energy performance improvement of historic buildings and their environmental sustainability assessment', Journal of Cultural Heritage, 21: 834-41.

How to cite: Florio, V., Danese, M., and Biscione, M.: Preliminary analysis for energy efficiency assessment. Deriving technical parameters with spatial analysis and GEE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16568, https://doi.org/10.5194/egusphere-egu25-16568, 2025.

The construction and analysis of daily temperature data series in long enough a time period is important to understand decadal to multi-decadal variability and changing trends in extreme temperature events. This paper reports a new analysis of extreme temperature indices over the last 140 yr in Wuhan, China, with an emphasis on changes in extreme high temperature changes. The daily temperature data from 9 stations from 1881 to 1950 and 1 modern station from 1951 to 2020 were used for the analysis. Based on the data, and the commonly used extreme temperature indices, the variations and long-term trends of extreme high temperature events in Wuhan since 1881 were analyzed. The results show that there was no clear warming trend in maximum temperature, but a quite large inter-annual and inter-decadal fluctuation. In contrast, there was a very significant increase in minimum temperature, with a large upward trend overall. The extreme temperature indices exhibit a periodic variability, and frequent extreme heat events have been experienced over the last 140 yr in Wuhan. Most extreme temperature indices did not exhibit remarkable changes during the first half of the period analyzed. However, the majority of the extreme temperature indices showed significant upward trends over the latter half of the 140 yr period. The possible causes of the observed changes in the extreme high temperature events in the different time periods are also discussed.

How to cite: Zheng, X., Ren, G., He, J., Zhao, Y., Ren, Y., and Yang, G.: Temporal characteristics of extreme high temperatures in Wuhan since 1881, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-456, https://doi.org/10.5194/egusphere-egu25-456, 2025.

Coastal sea level changes have profound impacts on coastal ecosystems, infrastructure, and communities. Interannual sea level variations along the U.S. East Coast are influenced by a combination of dynamic and thermodynamic processes, including local wind forcing, Gulf Stream variability, regional ocean circulation changes, and thermosteric contributions. These processes are interconnected and strongly modulated by large-scale climate modes such as the North Atlantic Oscillation (NAO), El Niño-Southern Oscillation (ENSO), and Atlantic Multi-decadal Oscillation (AMO). This study leverages machine-learning-based predictive models to quantify and forecast interannual sea level variability by integrating diverse climate indicators. By incorporating indices of large-scale climate modes alongside local and regional oceanographic parameters, the model quantifies the relative contributions of each factor and identifies the dominant processes driving observed variability. The results demonstrate the potential of machine-learning approaches to capture complex nonlinear relationships between climate modes and regional sea level changes. NAO-driven atmospheric forcing and ENSO-related ocean-atmosphere interactions emerge as key predictors, with the models successfully replicating observed variability along different sections of the U.S. East Coast. The findings highlight the importance of integrating large-scale climate dynamics into regional sea level prediction frameworks and suggest new opportunities for improving forecast accuracy at interannual timescales.

How to cite: Ye, Z., Ye, Z., and Zhao, J.: Predicting Interannual Sea Level Variations Along the U.S. East Coast Using Machine Learning and Climate Indicators, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7561, https://doi.org/10.5194/egusphere-egu25-7561, 2025.

Environmental and socioeconomic factors increase small islands' exposure and vulnerability to climate change. This study reviews issues related to current and future climatic change and its impacts on the small island environments in the Canary Islands. Convection-permitting regionalized projections driven by data from three global climate models included in the Coupled Model Intercomparison Project (CMIP5) have been performed, covering the recent past (1980–2009) and future (2070–2099) periods, under two Representative Concentration Pathways, 4.5 and 8.5. The impact analysis includes water resources, energy, ecosystems and biodiversity, natural hazards, and health issues. We provide a succinct review of sectors that warrant particular attention, due to their weight in the gross domestic product, agriculture and tourism. The concluding section discusses adaptation and response strategies, and the portfolio of research that needs to be addressed. 

How to cite: Carrillo, J., Barrancos, J., Tondreau, P. S., Pérez, J. C., González, A., Expósito, F. J., and Díaz, J. P.: A review of climate change impacts in the Canary Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8577, https://doi.org/10.5194/egusphere-egu25-8577, 2025.

Green alder (Alnus alnobetula (Ehrh.) K. Koch) is a tall, multi-stemmed deciduous shrub widespread in the Central European Alps across the treeline ecotone. The focus of this study was (i) to determine key dates of intra-annual radial stem growth (RG), and (ii) to assess environmental factor most closely related to daily RG along an elevational transect. For this purpose, RG was continuously recorded by dendrometers mounted on shoots (n=16–20 dendrometer records per year) at three study plots on Mt. Patscherkofel (1940–2150 m asl) during the growing seasons 2022–2024, and in potted saplings (n=3) at 600 m asl (Botanical garden in Innsbruck, Tyrol, Austria). The Gompertz function was applied to determine phenological dates of intra-annual RG dynamics, i.e., onset, end, duration and time of maximum RG. Daily radial stem increments were extracted from dendrometer traces, and Spearman correlations (ρ) with environmental variables were determined. Results revealed that within the treeline ecotone RG started and ceased around end of June (doy 177±7) and end of August (233±9), respectively. The mean growth duration was found to be 56 ± 9 days. Within the treeline ecotone mild temperatures in May and cold spells during the growing period led to an earlier start and end of RG, respectively. The time of maximum RG was observed in early July (doy 192±8), with about 60 % of the annual increment developing during this month. Although RG in the valley already started in mid-May (doy 134±2) and lasted until mid-October (doy 286±8; duration: 153±7 days), time of maximum RG in 2024 was reached at a similar point in time as within the treeline ecotone (doy 194±13). The environmental factor most closely related to intra-annual RG was soil temperature along the entire elevational transect (ρ=0.371–0.419; P<0.01). Air and cambial temperature were less closely related to RG at all study plots. Precipitation constrained RG at the forest line (ρ=0.549, P<0.001) and at the south-facing treeline site (ρ=0.426, P<0.001). Soil water content and vapour pressure deficit of the air had no significant influence on RG. Results of this study revealed a high degree of plasticity in RG of A. alnobetula, with the RG period spanning two and five months within the treeline ecotone and in the valley, respectively. The greater significance of soil temperature compared to air and cambial temperature for RG is most likely attributable to a non-linear relationship between RG and temperature.

This research was funded by the Austrian Science Fund (FWF), P34706-B.

How to cite: Oberhuber, W., Wieser, G., and Gruber, A.: Effects of environmental variables on intra-annual dynamics of radial growth of green alder (Alnus alnobetula) along an elevational transect, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4019, https://doi.org/10.5194/egusphere-egu25-4019, 2025.