Despite a long history of vegetation modelling, robustly simulating vegetation dynamics remains a complex but highly needed task. There is a growing consensus that vegetation models need to better integrate forest structure, diversity and ecosystem functioning to tackle this research challenge. However, this has long been hindered by a coarse-grained representation of vegetation and subsequent difficulties to assimilate field data. Here I will present recent developments in an individual- and trait-based model of forest dynamics, TROLL 4.0. I will discuss the modelling choices we made to jointly simulate carbon and water fluxes, leaf phenology, as well as individual tree size and trait distribution, and evaluate them against a range of field-based and remotely-sensed data at two Amazonian sites. Based on this example, I will finally discuss several challenges that remain to fully bridge the gap between plant ecology, vegetation remote sensing and ecosystem modelling, and to improve our understanding and predictive ability of vegetation contribution to the Earth’s system.
How to cite: Maréchaux, I., Fischer, F. J., Schmitt, S., and Chave, J.: The need and challenges of jointly simulating vegetation structure, diversity and ecosystem functioning: a tropical forest perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10623, https://doi.org/10.5194/egusphere-egu25-10623, 2025.
Recent climate warming has been much faster in the Arctic than in the rest of the world, and is expected to accelerate in the future. These rapid changes will affect arctic biodiversity, threaten certain species and shift their distribution ranges. With a focus on dispersal abilities, we here aim to better understand the dynamics of plant species distributions over the next century, and how these changes may impact the composition and distribution of biomes across the terrestrial Arctic. We developed climate-driven species distribution models (SDM) to predict the emerging climate niches for 1174 plant species under different climate scenarios. The model was parameterized using field observations stored in the Global Biodiversity Information Facility database (GBIF) and temperature and bioclimatic variables from the CHELSA climate data set. Trait-based dispersal rates were assigned to each species according to Lososová et al. (2023) and were implemented and used to predict future habitat with a distance-based probability over time. Our results indicated that given the dispersal constraints, only 15 % of the emerging climate niche would be in reach for colonization of plant species until 2100. Characteristic “boreal forest”-biomes were predicted to gain area while the "tundra"-biome became squeezed between the “boreal forest”-biomes and the sea. The “Palearctic boreal forest”-biome was predicted to colonize more area while the "Nearctic boreal forest"-biome showed the largest spatial displacement to the north. The species composition of the vegetation biomes was predicted to change over time and habitat suitability declined overall. We find that the response of arctic plant species to climate change is not simply a straight migration towards the north but rather a complex interaction of different mechanisms leading to altered distribution ranges. The differences in species’ dispersal abilities could lead to compositional changes within the biomes, which can subsequently result in biome shifts from tundra to boreal forest. Extinction lag and establishment lag might substantially delay the predicted range shifts. For future studies, we recommend to include dispersal constraints as we could show that they substantially impact species distributions.
How to cite: Schwenkler, R., Herzschuh, U., Farkas, L. Z., Schröder, B., and Lisovski, S.: Different dispersal abilities of plant species impact the future composition and distribution of biomes across the Arctic, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-439, https://doi.org/10.5194/egusphere-egu25-439, 2025.
Hydro-climatic conditions control the spatial distribution of many plant species, and with a changing climate, shifts in distribution patterns are foreseen. Beyond affecting species distribution, climate change at high elevation is altering land cover with processes such as glacier retreats providing new terrains for plant colonization and succession. Predicting plant distribution shifts under climate change has led to the development of various models in different communities, including species distribution models (SDMs) and dynamic global vegetation models (DGVMs). SDMs are predominantly data-driven and DGVMs often simplify processes representation of energy and water budget or look at very large scales. At finer scales, ecohydrological models comprehensively reproduce key components of hydrological cycle and vegetation dynamics but typically cannot explicitly simulate plant distribution dynamics. To address these limitations, we incorporate a seed dispersal and establishment kernel into the T&C mechanistic ecohydrological model. The model features the migration and interaction of plant species while maintaining accurate representations of water and energy budgets alongside plant physiological properties. Two catchments that have experienced substantial vegetation shifts over the past decades are chosen as evaluation sites. This model exhibits good quantitative agreement with historical vegetation records and provides insights through sensitivity analysis into the environmental factors driving rapid shifts in plant distribution. We show that ecohydrological models, enhanced with seed dispersal and establishment mechanisms, could be potentially used to investigate plant distribution dynamics at the catchment scale and can deepen our understanding of how climate change influences plant encroachment and disappearance.
How to cite: Jiang, F., Fatichi, S., Losapio, G., and Peleg, N.: Exploring plant distribution shifts in a non-stationary climate with an ecohydrological model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9296, https://doi.org/10.5194/egusphere-egu25-9296, 2025.
Climate change likely accelerates the global hydrological cycle, which poses escalating impacts on human health and the social economy. Soil and groundwater water flow influence vegetation processes by affecting the timing and amount of plant available water. However, most models do not comprehensively represent the interactions between vegetation dynamics and lateral surface-subsurface water processes, which hinders a full understanding of catchment and regional water and carbon fluxes in a changing climate. This study incorporated a fully integrated three-dimensional groundwater flow and overland flow model ParFlow into the dynamic vegetation model LPJ-GUESS to investigate the influence of lateral water connection on vegetation composition and ecosystem carbon cycle. We conducted the stand-alone LPJ-GUESS and the fully coupled LPJ-GUESS-ParFlow simulations in the Yangtze River and the Danube River Basin to assess lateral water flow on simulated hydrological variables, vegetation composition and carbon cycles, as well as their response to climate change. This fully coupled model showed improved performance in simulating catchment soil moisture and runoff, especially for the areas with steep slopes. The coupled model offers a mechanistic framework encompassing well-defined vegetation dynamics, surface-subsurface water interactions, and ecosystem biogeochemical processes, which could be tested in many other catchments to thoroughly study climate-induced modification on plant-water-carbon interactions.
How to cite: jia, Z., H. fu, Y., Chen, S., and Tang, J.: Coupling LPJ-GUESS with ParFlow for Integrated Vegetation and Surface-subsurface Hydrology simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-765, https://doi.org/10.5194/egusphere-egu25-765, 2025.
Under future climate change, plants are expected to experience increased water stress. Most terrestrial biosphere models use empirical soil moisture stress factors to capture the impacts of droughts on stomatal conductance and photosynthesis. However, this empirical approach lacks a mechanistic representation of water flow in the soil-plant-atmosphere continuum (SPAC) and causes uncertainties in simulated carbon and water fluxes. In this study, a plant hydraulic module was developed and integrated into the process-based Biosphere-atmosphere Exchange Process Simulator (BEPS-EcoHydro). The plant hydraulic module considers three mechanisms of water uptake: water supply driven by the water potential gradient between soil and leaf, water demand due to potential transpiration, and water storage within the plant. Finally, the effect of water stress on photosynthesis is quantified via a linkage to leaf water potential. BEPS-EcoHydro and original BEPS were run to simulate water and carbon fluxes in a drought-prone temperate deciduous forest located in the Ozark region of central Missouri, USA, during 2005-2019. The results showed that BEPS-EcoHydro effectively captured variations in predawn leaf water potential at the ecosystem scale, and also outperformed the original BEPS in simulating soil moisture. Additionally, BEPS-EcoHydro performed better than the original model in simulating evapotranspiration (ET) and gross primary production (GPP), especially at the hourly scale. Importantly, BEPS-EcoHydro captured drought impact better than the original BEPS. These results suggest that consideration of plant hydraulics in process-based ecosystem models is needed to better understand vegetation responses to climate extremes.
How to cite: Hu, L., Wu, M., and Ju, W.: Improved drought impacts detection with the novel implementation of plant hydraulics into an ecosystem model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15237, https://doi.org/10.5194/egusphere-egu25-15237, 2025.
“Greenness” is a key indicator of the functional state of vegetation. However, physiological processes behind seasonal patterns in greenness are diverse and incompletely understood, hindering the predictability of climate-driven shifts in global foliage phenology. Optimality principles suggest plants invest in canopy architecture to maximize light capture. Therefore, we hypothesize, irrespective of specific physiological mechanisms, greenness (fAPAR: fractional canopy light absorption) tracks seasonal dynamics of potential production (A0: theoretical canopy carbon uptake with all light absorbed). In other words, plants everywhere display foliage when it is most productive. We show that observations confirm this hypothesis, and develop a model predicting fAPAR from the seasonal cycle of A0 with a time-lag increasing (from two weeks to three months) with moisture. This model captures 81% of observed variations in fAPAR and shows that light and environmentally regulated biophysical constraints drive global patterns of vegetation greenness, its seasonal cycle, and its recent increase.
How to cite: Zhu, Z., Wang, H., Zhou, B., Cai, W., Harrison, S. P., De Kauwe, M. G., and Prentice, I. C.: Optimal light use strategy explains seasonal dynamics and trends in vegetation greenness, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14367, https://doi.org/10.5194/egusphere-egu25-14367, 2025.
Savannas, characterized by scattered trees with a grass layer, are key ecosystems in semi-arid regions. They profoundly influence global carbon (C) and water fluxes through high seasonal and inter-annual variations. Understanding these dynamics at the ecosystem scale is essential for better representing their impacts on the Earth’s climate system. Terrestrial ecosystem models (TEM) such as QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), is a new generation TEM that integrates C, nitrogen (N), and phosphorus (P) cycles, are essential for assessing ecosystem responses to climate variability and extremes. However, these models' complexity and reliance on site-specific parameters can limit predictive accuracy, especially in complex ecosystems such as savannas.
Remote sensing (RS) images can be leveraged to improve TEM predictions (e.g., assimilation of RS data) when radiative transfer models (RTM) are coupled with TEMs. In semi-arid grasslands and savannas, the mixture of green and senescent vegetation challenges RS-based vegetation property retrieval. To address this, we integrated senSCOPE, an advanced version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) RTM that separately simulates green and senescent leaves, with QUINCY to improve the representation of absorbed photosynthetically active radiation (aPAR) and therefore photosynthesis and ecosystem dynamics using RS data.
To leverage the high computational demands of complex RTMs such as senSCOPE, we further developed simplified RTMs based on a two-leaf approach to maintain computational efficiency within QUINCY. These submodels can improve the representation of senescent material in nutrient cycling, thereby improving our understanding of ecosystem processes such as biomass production and litter decomposition. We evaluated the outputs, including gross primary productivity, aPAR, and albedo, against the standard QUINCY model over green and senescent material leaf area fractions using the goodness of fit measures (root mean square error, mean error, and mean absolute error).
By integrating the two-leaf-based advanced RTM and computationally efficient submodels within QUINCY, we achieved a more accurate and cost-effective representation of senescent material in grasslands, respectively.
How to cite: Gauns, A., Pacheco-Labrador, J., Prikazuik, E., van der Tol, C., Zaehle, S., and Lee, S.-C.: Integrating Radiative Transfer with Ecosystem Models to Reflect Litter Dynamic and Optical Vegetation Properties in Semi-Arid Grasslands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2150, https://doi.org/10.5194/egusphere-egu25-2150, 2025.
Wetlands are the largest and most uncertain natural contributor of atmospheric methane (CH4) with water table fluctuations being a key factor controlling spatial and temporal variations. Natural dry summer months are here linked to low water table, increased oxygen diffusing into the soil and corresponding carbon dioxide (CO2) and CH4 fluxes in a Danish temperate wetland north of Copenhagen. We used the process-based model LPJ-GUESS to quantify the dynamic changes in CO2 and CH4 fluxes in the past 17 years, with an improved methane algorithm considering both methane production and oxygen transport influenced by water table level, CH4 consumption rates with the vertical distribution of methanotrophs. Our findings show that the model successfully can reproduce the temporal of pattern five-year (2007-2011) oxygen concentration in the soil profile with water fluctuation and corresponding CO2 and CH4 fluxes. For 2007-2023, the calibrated model simulates the site as a net CO2 sink of -77 ± 81 g C-CO2 m-2 year-1 and a CH4 source of 1.48 ± 0.84 g C-CH4 m-2 year-1. For changes in seasonal pattern, precipitation has a significant declining trend in early- and mid-growing season (March to July) (-9.2 mm per year, p < 0.05), with the largest reduction in June (-4.8 mm per year, p < 0.01), encountering the growth peak of vegetation. Such reduced precipitation mitigates methane emission (-0.04 g C-CH4 m-2 per year, p < 0.01) and increases net ecosystem exchange (+5.1 g C-CO2 m-2 per year, p < 0.05) in early and mid of the growing season with the interplay of a lower water table. The average budget of radiative balance with a lower annual mean water table (-0.31 m) was enhanced to -58 g C-eq m-2 year-1, while -556 g C-eq m-2 year-1 with a higher annual mean water table (-0.14 m), which is mainly explained by that lower water table decreased C assimilation and increased soil respiration.
How to cite: Zhao, B., zhang, W., Wang, P., Gustafson, A., Olin, S., and Elberling, B.: Quantifying CO2 and CH4 fluxes and their seasonal dynamics in a temperate wetland with water table fluctuations , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15573, https://doi.org/10.5194/egusphere-egu25-15573, 2025.
Grasslands, as one of Earth’s major ecosystems, are critical for sustaining biodiversity, ecosystem services, and global food security. However, their nitrogen cycles are increasingly influenced by climate change, including elevated atmospheric CO2 (eCO2), warming, and shifting precipitation regimes. These changes significantly affect grassland productivity and nitrogen dynamics, with substantial regional variations. Using a synthesis of over 5,000 experimental observations coupled with multiple ecosystem models, we investigated the impacts of climate drivers on nitrogen dynamics under the SSP2-4.5 scenario. Elevated CO2 alone is projected to enhance global grassland net primary productivity (NPP) by 10% while reducing leaf nitrogen content by 8%, resulting in a net increase of 4 Tg yr-1 increase in nitrogen harvest by 2050. Enhanced nitrogen use efficiency (+29%) and biological nitrogen fixation (+66%) under eCO2 would reduce nitrogen surplus (-29 Tg yr-1) and fertilizer demand (-9 Tg yr-1), potentially mitigating nitrogen pollution and yielding economic benefits of 235 billion USD. Warming, as another driver, is projected to increase nitrogen inputs by 17 Tg yr-1 and nitrogen harvest by 12 Tg yr-1 but may exacerbate reactive nitrogen losses by 5 Tg yr-1. Adaptation measures to minimize nitrogen leakage could deliver economic gains of 121 billion USD by 2050. Precipitation shifts further complicate nitrogen dynamics. Regions with increased precipitation, such as the United States and mid-to-high latitude Asia, could see nitrogen harvest rise by 16 Tg yr−1, while areas facing reduced precipitation, including Sub-Saharan Africa and South Asia, risk a 9 Tg yr−1 harvest decline. These imbalances could worsen global inequalities in nitrogen cycles and food security. Our findings highlight the need for improved representation of these complex interactions in ecosystem models to guide climate adaptation strategies. Timely, targeted interventions can help balance benefits and risks, safeguard ecosystem health, and support sustainable, equitable grassland management in a changing climate.
How to cite: Zheng, M., Cui, J., Wang, X., Zhang, X., Cheng, L., Reis, S., Lam, S. K., Wang, S., Xie, Z., Zhang, R., Xu, X., Xu, J., and Gu, B.: Climate Change effects on Nitrogen Cycles in Global Grasslands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2673, https://doi.org/10.5194/egusphere-egu25-2673, 2025.
Abstract：Crop growth phenology and leaf area index (LAI) are essential monitoring indicators in precision agriculture, playing a key role in crop management, yield prediction, and assessing responses to environmental changes. Traditional agricultural monitoring methods are constrained by limitations such as low temporal resolution and poor spatial resolution. This study proposes a synchronous monitoring model (SMM) for maize phenological stages and LAI using Unmanned Aerial Vehicle (UAV) imagery, leveraging deep learning techniques to improve inversion accuracy. More exactly, the input variables include multispectral images, thermal infrared images, solar radiation (SRT), evapotranspiration (ETP), and effective accumulated temperature (Tsum). To effectively extract image features, the Vision Transformer (ViT) and ResNetV2_34d models were employed. These deep learning models effectively leverage the spatial information within the images, significantly improving the prediction accuracy of crop phenology (BBCH) and LAI. The results demonstrate that the SMM outperforms traditional methods, achieving substantial improvements in BBCH and LAI inversion accuracy. By integrating deep convolutional neural networks (CNN) with self-attention mechanisms, the ViT captures long-range dependencies in remote sensing images, while ResNetV2_34d enhances the model's ability to extract detailed features. Furthermore, the SMM exhibits superior robustness in spatial information extraction and feature fusion. This study presents an innovative deep learning framework for crop growth monitoring, integrating remote sensing data and climatic factors to facilitate more precise agricultural production management and regulation, thereby contributing to sustainable agricultural development.
Key words: Crop growth phenology; Leaf area index; Deep learning; Vision Transformer; Precision agriculture
How to cite: Zhang, H., Zeng, W., Ma, T., Huang, J., Liu, Y., Ren, Z., and Ao, C.: Synchronous monitoring of maize phenology stages and leaf area index by integrating Vision Transformer and ResNetV2_34d, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4898, https://doi.org/10.5194/egusphere-egu25-4898, 2025.
Autumn phenology plays a critical role in shaping the carbon sequestration capacity of temperate forests. Notable local-scale variations in autumn phenology have drawn increasing attention recently, potentially introducing substantial uncertainty when predicting temperate forest productivity. Yet, the underpinning mechanisms driving these variations remain inadequately elucidated. While macroclimate conditions are traditionally recognized as primary determinants of autumn phenology, they fail to explain inter-crown variations occurring within the same macroclimate environment. Here, we hypothesize that canopy structure serves as a key determinant of the local-scale variations of autumn phenology in temperate forests by mediating microclimate conditions. To test this hypothesis, we develope microForest, a novel lightweight forest microclimate model capable of efficiently and accurately predicting under-canopy air temperature at high temporal and spatial resolutions using readily available remote sensing data and meteorological reanalysis products as inputs. Our results reveal significant and consistent relationships between canopy structure and autumn phenology across six temperate forest sites, induced by the regulation effect of canopy structure on microclimate conditions. Incorporating the identified “canopy structure-microclimate-autumn phenology” pathway into existing autumn phenology models significantly improves the prediction accuracy and reduces the projected delay in the start of autumn over the remainder of the century. These findings offer a new perspective for interpreting the local variations of autumn phenology in temperate forests and emphasize the urgent need to integrate the identified pathway into Earth system and vegetation models, especially considering the asynchronous changes of macroclimate and microclimate conditions.
How to cite: Su, Y., Wu, X., Niu, C., Liu, X., Hu, T., Feng, Y., Zhao, Y., Liu, S., Liu, Z., Dai, G., Zhang, Y., Meerbeek, K., Wu, J., Liu, L., and Guo, Q.: Canopy structure regulates autumn phenology by mediating microclimate in temperate forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5554, https://doi.org/10.5194/egusphere-egu25-5554, 2025.
Ongoing fragmentation puts increasing parts of the world’s forests in proximity to edges. Forest edges are known to differ from interiors for many ecosystem variables like biomass and species diversity, but also microclimate. While it is documented that temperatures change with presence and absence of forests, our knowledge of edge effects in temperatures (temperature change with distance to forest edge) is largely restricted to local studies and shorter timeframes. Here, we use satellite data to investigate edge effects in surface temperature of forests across the globe and across seasons. We find that edge effects indeed exist, with the forest interior generally being cooler, though effect sizes differ with biome and season. Worryingly, summer temperatures at forest edges lie above the optimal temperature for ecosystem level vegetation productivity across the globe, especially in the tropics. Our analyses suggest that the creation of forest edges through fragmentation reduces the ability of remnant forest patches to regulate their local temperatures, leading to hotter edges, which may hamper ecosystem productivity.
How to cite: Reek, J., Crowther, T., Lauber, T., Schemm, S., Parastatidis, D., Chrysoulakis, N., Huang, M., Piao, S., and Smith, G.: Temperatures at global forest edges exceed vegetation productivity optima, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17149, https://doi.org/10.5194/egusphere-egu25-17149, 2025.
Quantifying forest microclimate dynamics is vital for improving our understanding of ecosystem processes, biodiversity patterns, and carbon sequestration. While existing mechanistic microclimate models effectively simulate conditions within forest cores, they often fail to capture the complexities inherent to forest edges. This limitation is increasingly critical as forest fragmentation creates more edge environments, profoundly influencing microclimate gradients.
To address this gap, we developed a high-resolution microclimate model capable of simulating temperature and radiation gradients from forest core to edge. This novel model integrates 3D heat transfer and 2D radiative processes for 3D explicit forest scenes constructed from terrestrial laser scanning, allowing to account for the unique spatial patterns of microclimate in forest edges. By integrating these mechanisms in full 3D, our model provides a realistic representation of fragmented forest microclimates.
The initial site for applying our model is a 135 m transect in a temperate forest in Gontrode, Belgium. Along this transect, various microclimate sensors are installed, including TMS-4 sensors and an ultrahigh-resolution (25 cm) distributed temperature sensor using optical fiber technology. The model successfully simulates the observed spatial gradients along the transect for different times of the day and across seasons. Moreover, we observe that without including lateral radiation or horizontal heat transfer, microclimate gradients cannot be modelled accurately. These processes are, therefore, essential for simulating microclimates near forest edges.
How to cite: Van de Walle, E., De Hertog, S., Meunier, F., Calders, K., De Frenne, P., Li, Y., Stock, M., wyffels, F., Terryn, L., Sanczuk, P., Verhelst, T. E., Yang, Z., and Verbeeck, H.: Microclimate modelling from forest core to edge, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4029, https://doi.org/10.5194/egusphere-egu25-4029, 2025.
Information on microclimatic conditions beneath canopies is key to understanding small-scale ecological processes, especially concerning the response of biodiversity to climate change. In north-western Patagonia, where data on climate-driven species distribution are scarce, our study provides valuable insights by providing microclimatic models covering spatiotemporal dynamics at 30 x 30 m resolution. Applying in-situ data from 2022 to 2024, we employed a random forest-based regression to assess the impact of several biophysical predictor variables describing terrain and vegetation properties on four microclimatic response variables at three vertical levels within forests. We also interpolated this data spatiotemporally, using statistical downscaling of ERA5 data. Our analysis reveals that the influence of the predictor variables varies strongly by month and response variable. Moreover, significant variability was observed between the models and months regarding their explanatory power and error range. For instance, the model predicting maximum air temperature at a 2 m height achieved an R² of 0.88 and an RMSE of 1.5°C, while the model for minimum air temperature resulted in an R² of 0.73 and an RMSE of 1.8°C. Our model approach provides a benchmark for spatiotemporal projections in this data-scarce region, aligned with the climate normal from 1981 to 2010. Future refinement could benefit from data on snow cover, land use and land cover, soil, as well as structural information on vegetation over an extended period, to enhance the dynamical aspects of microclimatic modelling. We are confident that our present model will substantially enhance possibilities to analyse species distribution across vegetation types and terrain-related features within the area.
How to cite: Fierke, J., Putzenlechner, B., Simon, A., Gowda, J., Reiter, E., Walentowski, H., and Kappas, M.: Modelling microclimatic variability in Andean forests of northern Patagonia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8571, https://doi.org/10.5194/egusphere-egu25-8571, 2025.
Accurately assessing the impacts of climate change on forest ecosystems requires understanding how macroclimate and microclimate interact over time. Forest microclimates, strongly influenced by canopy cover and terrain, often deviate significantly from regional macroclimate. Recent research has highlighted the role of forest microclimate buffering for understory plant communities, which seem to be less impacted by global climate change. However, the increasing magnitude and frequency of macroclimatic extremes and associated forest disturbances could still threaten these communities, potentially overwhelming the forest buffering capacity and significantly altering plant community composition. Addressing these uncertainties requires long-term microclimate time-series. Yet, long-term datasets on forest microclimatic dynamics remain scarce.
Here, we aim to fill this gap by leveraging detailed historical microclimate measurements from the 1950s in central European forests. These unique data serve as a foundation for developing and validating mechanistic microclimate models, enabling the reconstruction of long-term microclimate dynamics. Using biophysical principles, mechanistic modeling provides a robust approach to simulating near-ground temperature and humidity conditions based on macroclimatic inputs and local landscape and vegetation characteristics. By integrating macroclimate data with our in situ microclimate measurements, this research paves the way for exploring how forest microclimates influence plant community composition over time and disentangling the relative contributions of micro- and macroclimatic drivers to long-term vegetation change.
How to cite: Finocchiaro, M., Man, M., Macek, M., Wild, J., and Kopecký, M.: Reconstructing long-term forest microclimate dynamics: validating mechanistic modeling with historical measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20310, https://doi.org/10.5194/egusphere-egu25-20310, 2025.
Microclimate ecology conveys fundamental information about how organisms react to and feedback to influence climate change. Evidence shows that vegetation and its spatial variation modify microclimate temperature and relative humidity, mediating thermal regulation and energy exchange with the atmosphere by affecting vapour pressure deficit (VPD) [2]. This process influences crucial eco-physiological processes such as carbon capture, nutrient cycling, and flower visitation, promoting ecosystem productivity. Diverse communities typically display complex canopies due to functionally dissimilar species that spatially complement each other. The differences in diverse communities' canopy have the potential to modulate energy exchange and affect canopy surface temperature [1].
However, microclimate measurements are typically made at the coarse spatial scale using climate means based on meteorological stations or satellites, which ignore the bounded exchange between upper and lower canopy layers. Our approach integrates sub-canopy sensors with remotely sensed products and reveals that microclimate is driven by plant functional traits, groups, and the diversity of plant communities [1, 2, 3]. In biodiversity experiments, we assessed microclimate using under- and upper-canopy microclimate sensors and estimated plant canopy structure with a high spatial resolution (proximal sensing such as terrestrial laser scanning).
We demonstrate that examining trait-microclimate relationships reveals the potential of diverse communities and communities dominated by species with particular traits to buffer ecosystems from the negative effects of warming and air dryness. Fundamentally, we propose that future work focuses on the facilitative effects of vegetation microclimate, indicating how plant community composition and diversity feedback on vegetative cooling and air humidification under more frequent and intense climate change events.
[1] Guimarães-Steinicke et al. (2021) J Ecol. 109: 1969–1985, http://doi.org/10.1111/1365-2745.13631
[2] Wright et al. (2024) Journal of Ecology, 112, 2462–2470. https://doi.org/10.1111/1365-2745.14313
[3] English et al. (2022) Frontiers, 10, https://doi.org/10.3389/fevo.2022.921472
How to cite: Guimaraes-Steinicke, C., English, J., Sookhan, N., and Wright, A.: Biodiversity affects microclimate - plant diversity and functional traits driving temperature and humidity using proximal sensing., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18924, https://doi.org/10.5194/egusphere-egu25-18924, 2025.
Renewable energy developers are highlighting the co-benefits of agrivoltaic systems for energy production and crop production. To quantify these co-benefits, an essential step is to understand the impact of photovoltaic structures on the micro-climate provided to the crop. This study investigates the impact of a tracking agrivoltaic structure, referred to as a canopy, on local micro-climate over a nine-year period. This presentation highlights the first-year results at four sites commissioned in France. Micro-climate monitoring includes 15min data for air and soil temperature and humidity, wind speed, irradiation, and precipitation for each site. These measurements are taken under the canopy and on a control plot adjacent to the plant. The differences between the canopy and the control micro-climates are analysed, with a particular focus on quantifying the adaptation potential of the canopy for crops regarding climate change. Hydro-meteorological indicators are also studied, in order to understand more precisely the impact of micro-climate on crops. The following observations were made :
- The canopy mitigated extreme air temperatures by an average of -2°C and +1.5°C for respectively extreme high and low temperatures. It reduced soil temperatures by an average of 1°C during summer hot days;
- Evapotranspiration decreased under the canopy, while air moisture levels were, on average, 1% higher than in the control plot;
- Sharing light resources under the canopy is a major challenge especially for specific phenological stages of the crop. Adaptations of tracking angles are considered to combine the protective effect of the structures with the crops' need for light at these stages. To implement such adaptations, modelling tools are developped, including irradiance model. Caracterization of the irradiance models regarded to the in-situ measurments is presented.
This study determines that agrivoltaic systems such as canopies can increase the resilience of agricultural systems, notably by absorbing shocks due to extrem events. This can only be true if we think of a synergistic tracking system that optimizes food and energy productions. At that condition, those systems then have the capacity to grow a farm’s sustainability regarding climate change and economic swings.
How to cite: Trolliet, M., Duchalais, A., Lorieau, L., and Vialle, Q.: Impact of agrivoltaic systems on crop microclimate in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20506, https://doi.org/10.5194/egusphere-egu25-20506, 2025.
Climate change has the influence on the functioning of natural ecosystems, even those barely affected by human activity. Some of the endangered ecosystems, such as pine bogs, strictly depend on groundwater availability, which, due to progressive dry climatic conditions, may be limited in the future (IPCC 2022). Furthermore, the current microclimatic conditions of pine bogs remain poorly understood, making the future of these ecosystems difficult to predict. Having accurate microclimate datasets would enable the identification of relationships between individual components of this ecosystem, leading to increased accuracy in forecasting the impacts of climate change on it.
The objective of the study was to investigate microclimatic functioning of the pine bogs of the Bialowieza Primeval Forest, which is the largest area of old-growth forest in Europe. The research was conducted at the eight study sites between 2023 and 2024.
In order to gain insight into the microclimatic functioning of the pine bogs, a series of air temperature and humidity measurements in the near ground air layer were conducted. Furthermore, comparisons were made between soil temperature and moisture at the sites, as well as with the reference station situated outside the forest. Additionally, groundwater level was recorded at each site and peat thickness was mapped. Analyses of vegetation composition and horizon obscuration were also performed.
The results indicate that the microclimate of the pine bogs in the Bialowieza Primeval Forest differs significantly from the climate of the open areas outside the forest, with the scale of these differences being seasonally determined. The sites exhibited notable differences in water conditions, peat thickness and vegetation, which influenced the microclimatic functioning. The subsequent step will be to attempt to model the microclimatic conditions of the pine bogs based on the collected data, which will facilitate the prediction of shifts occurring in these ecosystems in the context of climate change.
How to cite: Kaszkiel, A., Pilch, K., Czarnecka, K., Klisz, M., Kowalczyk, P., Słowiński, M., Zin, E., and Słowińska, S.: Microclimatic functioning of pine bogs in the Bialowieza Primeval Forest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-463, https://doi.org/10.5194/egusphere-egu25-463, 2025.
Land Surface Models (LSMs) are key components of Earth system models as they describe the spatial and temporal dynamics of vegetation and how it exchanges energy, momentum, water and CO2 with the atmosphere. These models have been greatly improved over the last fifty years to include more and more processes such as carbon and nutrient cycling, plant hydraulics or vegetation dynamics. However, in most LSMs, the vertical structure of vegetation canopies is taken into account only implicitly, through surface parameters such as displacement height or roughness height. This implicit representation of vegetation canopies prevents the estimation of the microclimate within and below the canopy and its influence on the energy, water and carbon exchanges. The representation of below-canopy microclimate seems to be a key development in order to improve energy and water balance descriptions in LSMs and our knowledge on forest response to a changing climate. This need justifies the current efforts of the land surface community to explicitly represent canopy microclimate in LSMs. This poster presents the main developments introduced in the ORCHIDEE LSM to address this need. A multi-layer energy budget representation previously implemented in an earlier version of ORCHIDEE (Ryder et al. (2015)) has been re-implemented, adapted and improved in the main ORCHIDEE version (i.e. Trunk) in order to represent the exchange of water and energy between vegetation and the atmosphere, and the turbulent transport of mass, energy and momentum within vegetation canopies. This model is evaluated against in situ observations, and compared with a more complex ecosystem model, MuSICA (Ogée et al. (2003)). To provide a comprehensive understanding of the strengths and drawbacks of the two models, and to pave the way to future improvements to the ORCHIDEE LSM, this model inter-comparison is carried out over a dataset that gathers measurements of intra-canopy temperature and humidity profiles and fluxes from 10 forest sites. In addition to the results of this inter-comparison, new perspectives for the ORCHIDEE community and general thoughts on microclimate modelling in LSMs induced by these developments will be drawn.
How to cite: Alléon, J., Ottlé, C., Ogée, J., Bouwen, K., Lemaire-Patin, R., and Peylin, P.: Forest microclimate representations in land surface and ecosystem models: an inter-comparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17385, https://doi.org/10.5194/egusphere-egu25-17385, 2025.
Vegetation phenological shifts impact the terrestrial carbon and water cycle and affect the local climate system through biophysical and biochemical processes. Dynamic global vegetation models (DGVMs), serving as pivotal simulation tools for investigating climate impacts on terrestrial ecosystem processes, incorporate representations of vegetation phenological processes. Nevertheless, it is still a challenge to achieve an accurate simulation of vegetation phenology in the DGVMs. Here, we developed and implemented spring and autumn phenology algorithms into one of the DGVMs, LPJ-GUESS. The new phenology modules are driven by temperature and photoperiod and are parameterized for deciduous trees and shrubs by using remotely sensed phenological observations and the reanalysis data from ERA5. The results show that the LPJ-GUESS with the new phenology modules substantially improved the accuracy in capturing the start and end dates of growing seasons. For the start of the growing season, the simulated RMSE for deciduous trees and shrubs decreased by 8.04 and 17.34 d, respectively. For the autumn phenology, the simulated RMSE for deciduous trees and shrubs decreased by 22.61 and 17.60 d, respectively. Interestingly, we have also found that differences in the simulated start and end of the growing season also alter the simulated ecological niches and competitive relationships among different plant functional types (PFTs) and subsequentially influence the terrestrial carbon and water cycles. Hence, our study highlights the importance of accurate phenology estimation to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.
How to cite: Chen, S., Fu, Y. H., and Tang, J.: A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-692, https://doi.org/10.5194/egusphere-egu25-692, 2025.
The forest carbon sink is crucial to climate change mitigation efforts. At the same time forests are threatened by climate-change induced extremes. In Europe, increasingly frequent and severe droughts are one of the main culprits endangering the forest carbon sink. Understanding how droughts may alter water- and carbon-cycle dynamics of forests is essential to preparing for an almost inevitably hotter and drier future. Here, dynamic vegetation models (DVMs) serve as useful tools for studying how extremes can impact the carbon and water cycles. In recent years, many DVMs have consequently begun including mechanistic representations of plant hydraulic architecture.
We use a version of LPJ-GUESS with plant hydraulic architecture, water-potential regulation strategies, and hydraulic failure mortality (LPJ-GUESS-HYD) and extend its capabilities by including aspects of turgor-driven growth dynamics to better simulate the impact of drought on the water and carbon cycles for common European forest tree species. We evaluate the performance of LPJ-GUESS for 12 European tree species across a network of 37 eddy-covariance flux sites covering a wide climatic and geographic gradient.
Our results indicate that LPJ-GUESS-HYD is able to simulate observed patterns of evapotranspiration more accurately than the standard version of LPJ-GUESS. Additionally, we show that LPJ-GUESS-HYD can simulate a wide range of species-specific evapotranspiration and canopy conductance in response to increasing VPD in line with established theories on the isohydric-anisohydric continuum. Lastly, our results suggest that the currently implemented model processes responsible for governing the response of water fluxes to drought are not as crucial in regulating the simulated carbon response to drought. This indicates that a shift toward more sink-driven model processes may be necessary to capture the full effect of drought on both the water and carbon cycles.
Given these results, future model development should focus on the interplay of source and sink processes in regulating tree response to extreme events such as drought. In particular, our results suggest that reliably modeling drought impacts entails improving the representation of water limitation not only on photosynthesis but, independently, also on growth. Here, we present results for the impact of drought on the modeled water cycle and discuss concepts and ideas related to improving the simulated effect of drought on the carbon cycle.
How to cite: Meyer, B. F., Darela-Filho, J. P., Buras, A., and Rammig, A.: Simulating the drought response of water and carbon cycle in European forests with a dynamic vegetation model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10911, https://doi.org/10.5194/egusphere-egu25-10911, 2025.
The threat of large-scale pollinator decline is escalating globally due to multiple anthropogenic pressures. The physiological impacts of warming scenarios on terrestrial ectotherms often intensify with the increasing rate of warming. These effects also depend on the network structure of plant-pollinator networks and the physiological sensitivities of ectotherms to temperature changes over time. Previous conservation approaches have predominantly focused on applying consistent strategies over extended periods within single ecosystems. However, in practice, conservation funding is typically allocated for shorter durations. To implement effective conservation strategies, it is essential to first assess the health of a network and then devise an appropriate approach for the next 5-10 years. In this study, we present a new approach for designing region-specific dynamic management strategies tailored to individual networks, accounting for anthropogenic stressors like warming scenarios. Our approach uses sampled plant-pollinator network data from various climatic zones and temperature projections from Earth system models under different future scenarios. We found that plant-pollinator networks with low connectance respond more effectively to species-focused management strategies, such as pollinator management. Conversely, networks with high connectance show greater resilience under habitat-focused management approaches. These findings emphasize the need for dynamic assessment and the development of tailored management strategies for each region. This framework provides a strategic plan for conserving plant-pollinator networks by integrating network structure and regional warming scenarios. It bridges the gap between mutualistic network research and practical conservation ecology, offering a comprehensive approach to safeguarding these critical ecosystems.
How to cite: Datta, A., Kumar, A., Dubey, S., and Bhatia, U.: Dynamic Management Strategies for Plant Pollinator Networks under Anthropogenic Warming Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-779, https://doi.org/10.5194/egusphere-egu25-779, 2025.
The degradation and loss of natural habitats caused by human activities have become a serious threat to the survival of wildlife. Existing habitat models struggle to represent the complex interactions of landscape matrices that drive biogeographical processes, limiting their ability to simulate and analyze habitats on a large scale. Therefore, conducting in-depth interdisciplinary research between GIS and zoogeography is of significant practical value for wildlife and habitat conservation. By leveraging GIS’s advantages in integrating updated high-precision data sources and spatial data analysis, a habitat analysis model based on composite spatial networks is developed to study the likely consequences of surrounding changes. The model constructs a high-dimensional heterogeneous spatial network to comprehensively simulate the effects of surrounding geographical environments on habitats, providing a more reasonable and comprehensive evaluation of species’ potential habitats under the influence of human activities at the macro level, and offering technical methods for empirical research in animal geography. Simulations and empirical tests of the model show that it performs steadily with changes in parameters and effectively characterizes the variations in the simulated environment as parameters change. Additionally, in habitat restoration and wildlife conservation practices, the composite spatial network offers more complete and scientific scenario simulations, providing technical support for balancing economic development and wildlife protection.
How to cite: Qian, T. and Wang, J.: A Composite Spatial Network Model for Delineating Interpatch Influence in Habitat Analysis , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19013, https://doi.org/10.5194/egusphere-egu25-19013, 2025.
Improving agricultural sustainability is a global challenge, particularly for China’s high-input and low-efficiency cropping systems with environmental trade-offs. Although national strategies have been implemented to achieve Sustainable Development Goals in agriculture, the potential contributions of crop switching as a promising solution under varying future climate change are still under-explored. Here, we optimize cropping patterns spatially with the targets of enhancing agriculture production, reducing environmental costs, and achieving sustainable fertilization across the different climate scenarios. Compared with that maintains the historical cropping patterns, the optimal crop distributions under different climate scenario consistently suggest allocating the planting areas of maize and rapeseed to the other crops (rice, wheat, soybean, peanut and potato). Such crop switching can consequently increase crop production by 14.1%, with the reduction in environmental impacts (8.2% for leached nitrogen and 24.0% for irrigation water use) across three representative Shared Socio-economic Pathways (SSPs) from 2020 to 2100. The sustainable fertilization rates vary from 148-173 kg N ha-1 in 2030 to 213-253 kg N ha-1 in 2070, significantly smaller than the current rate (305 kg N ha-1). These outcomes highlight large potential benefits of crop switching and fertilizer management for improving China’s future agricultural sustainability.
How to cite: Guan, Q., Tang, J., Davis, K. F., Kong, M., Feng, L., Shi, K., and Schurgers, G.: Improving future agricultural sustainability by optimizing crop distributions in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21668, https://doi.org/10.5194/egusphere-egu25-21668, 2025.
Terrestrial ecosystems play a key role in the global carbon cycle, and drylands cover most of the land area and are very likely to expand as climate change causes evaporation to increase faster than precipitation. At the same time, they are often overlooked, and currently models cannot reproduce seasonal patterns of the vegetation in drylands.
The main reasons for this problem are the complexity and diversity of the processes that govern the terrestrial ecosystems' functioning; they may be missing or under-represented in the models. Among such processes, one can first emphasize the response of vegetation to water availability and its asymmetry, as well as soil and plant hydraulics, fires, and additional groundwater sources.
This study uses the terrestrial biosphere model QUINCY, that simulates the energy, water and carbon balance and vegetation dynamics of global terrestrial ecosystems. Our simulations cover 15 sites in the USA, Australia and Europe, which were chosen as arid sites based on water balance and temperature. The results are validated with data from the FLUXNET database and the Copernicus projects.
It was found that, on average, the QUINCY model overestimates gross primary productivity (GPP) and sensible heat (SH) values in dry areas. Also, water use efficiency (WUE) according to the model is higher compared to observations, and evaporation fraction (EF) is lower at most of the studied sites. Sensitivity analysis improved model performance at only a few sites, suggesting inadequate representation of key dryland vegetation dynamics. These results can be used to understand what processes need to be modified or added to improve performance.
How to cite: Lipavskii, A., Papastefanou, P., Orth, R., and Zaehle, S.: Modeling Seasonal Carbon and Water Dynamics in Dryland Ecosystems: Challenges for Process-Based Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10771, https://doi.org/10.5194/egusphere-egu25-10771, 2025.
The planting and growth of trees and forests are a major component of meeting international net zero emissions targets. A more passive and cost-effective approach to meeting these targets is the natural regeneration or colonisation of native woody species, including shrubs. There has been much research into how trees and forests sequester carbon and increase in biomass, with a wealth of biomass calculations and allometric equations available. However, there is a contrasting lack of this information for shrubs. Biomass equations for shrubs need to differ to those for trees due to their inherent structural differences, and also recognise that we do not have the same levels of data to train models on this massively diverse vegetation type. Diameter at breast height (DBH) is often cited as the single most influential metric to derive biomass, but this can be impractical or unclear for many shrubs: a different metric must be used. Hawthorn (Crataegus monogyna; Jacq.) is a multi-stemmed, multi-branched thorny shrub native to Europe, North Africa and western Asia. Despite its broad geography, there is little information describing this species’ allometry or biomass.
Our aim was to generate an allometric equation for the above-ground biomass of distinct hawthorn shrubs by adapting existing allometric equations and using field-collected measurements to generate a coefficient that accounts for the diverse structural relationships brought about by the multi-stemmed nature of hawthorn compared to trees.
Our study site was a 150ha rewilding site in Bedfordshire, East of England. In the 35 years since farm management was withdrawn, shrubs and other vegetation have been allowed to naturally colonise the previously arable fields. Hawthorn is common here in an unmanaged distinct form, as well as in overgrown hedgerows and merged canopies with other shrubs and species. In a first visit, we measured shrub height and crown diameter measurements for 36 distinct hawthorn shrubs up to 5m in height. In a second visit, we destructively sampled 27 of these and an additional 55 hawthorn shrubs and measured wet weight in situ.
To analyze this data, we employed a Maximum Entropy allometric model using Bayesian inference. This approach enabled us to quantify the likely range of biomass values based on our allometric measurements while accounting for the inherent uncertainty caused by incomplete or missing data. Preliminary results from this model are promising, and we are working towards linking these findings with Earth Observation (EO) data to extend the application of shrub allometry to larger spatial scales. By integrating field-derived measurements with EO techniques, we aim to estimate biomass not only for individual shrubs but also for entire shrubland areas, providing a broader perspective on carbon storage potential.
Additionally, we welcome feedback on the types of allometric relationships we have assumed and invite discussions on how our newly developed models might find applications in other fields. These efforts could pave the way for improved understanding and management of shrubland ecosystems in the context of global carbon accounting.
How to cite: Upcott, E., Kelley, D., George, C., Broughton, R., Barbedo, R., Hall, J., and Gerard, F.: Biomass allometry for shrubs at a UK rewilding site, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11605, https://doi.org/10.5194/egusphere-egu25-11605, 2025.
The canopies of mature trees establish a vertical microclimatic gradient, especially in light availability. The decreasing irradiance from top to bottom of canopies lead to significant differences in the seasonal C assimilation between the uppermost and lowest branches of mature tree crowns. But whether this translates also to differences in the seasonal net C-balance of sun- vs. shade branches remains unclear. Here, we present in-situ measurements of upper and lower branches from mature canopies of three conifer species and 6 broadleaved tree species at the mixed temperate forest of the Swiss Canopy Crane II facility. We combined a light-driven model of the seasonal photosynthesis with branch functional growth analyses to test whether the relative C investment in structural biomass and C reserves (starch) of one-year-old branches differ between the uppermost, sun-exposed and lowest, most shaded branches.
We found that amortization times for the C costs of one-year-old branches varied widely among species, but only in a few species also between sun and shade branches. Interestingly however, expressed as a percentage of the total branch C uptake, the structural C-costs were surprisingly similar across species and crown positions between 15 and 25 % of the total seasonal C assimilation per branch. Key shade acclimations included SLA, dark respiration rates and photosynthetic low-light efficiency. We further found that a similar proportion of the total C assimilation is required for the seasonal starch build-up in sun and shade branches. Our results thus show that the balance of assimilation and both structural and non-structural C costs at the branch-level is finely tuned along the vertical light gradient, suggesting a high degree of C autonomy even in the most shaded branches of our investigated trees.
How to cite: Hoch, G., Zehnder, M., Kahmen, A., and Zahnd, C.: Relative carbon costs for growth and starch formation of young branches are similar along the vertical microclimatic gradient of mature forest canopies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9558, https://doi.org/10.5194/egusphere-egu25-9558, 2025.
Atmospheric vapor pressure deficit (VPD) is a critical climate variable influencing vegetation productivity and the global carbon cycle. With climate warming, VPD has been increasing globally, but its effects on gross primary production (GPP) remain poorly understood, leading to uncertainties in predicting terrestrial ecosystem responses. This study classified the causes of VPD increase into three different types: temperature-driven, combined temperature and relative humidity-driven, and relative humidity-driven, to investigate the spatial heterogeneity and drivers of VPD impacts on GPP using three datasets-FLUXCOM_GPP, GOSIF_GPP, and VPM_GPP-spanning 2000 to 2018. By integrating trend analysis, partial correlation techniques, and random forest models, the results reveal a distinct latitudinal gradient in the relationship between VPD and GPP, characterized by a "Z-shaped" pattern. Near the equator and at high latitudes, VPD positively influences GPP, whereas in mid-latitudes, the relationship is predominantly negative. This variation is shaped by the climatic background and the interplay of water and energy-related factors. For example, in regions with synchronous changes in temperature and humidity, VPD effects on GPP tend to be neutral or positive. Conversely, asynchronous changes exacerbate negative effects, particularly in humidity-driven regions. This study provides a mechanistic understanding of the drivers of interannual GPP variability across different climatic contexts, implying the importance of biodiversity in shaping vegetation responses to climate extremes and affecting the overall ecosystem vulnerability and global carbon cycle under future warming.
How to cite: Chen, S. and Wu, M.: Drivers and Divergent Impacts of Rising Vapor Pressure Deficit on Global Vegetation Productivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17750, https://doi.org/10.5194/egusphere-egu25-17750, 2025.
Ericaceous dwarf-shrubs are a key actor of the boreal, arctic and alpine biomes in which they are locally dominant and extensively spread, affecting biodiversity, ecology and ecosystem functioning. In particular, they play significant roles in ecosystem carbon sequestration and long-term storage through interactions with belowground fungal networks. These effects may have an important influence on feedback mechanisms between terrestrial ecosystems and the global climate system. This role may be noticeably affected by climate change. However, the dwarf-shrub interactions with climate are still poorly understood, and this plant functional type is currently underrepresented in climate-biosphere science.
The objective of the DURIN project is to explore the role of dwarf-shrubs in climate responses and feedbacks across biomes and habitats in Norway, and to provide new insight on the direct and indirect effects of climate change on this plant functional type and its ecosystem functions and services. Land-surface models offer a particularly convenient framework to explore and quantify the complexity of the relationship between the climate system and dwarf-shrub plant functional type, as well as the impacts of climate change on related ecosystems across Norway.
As part of this project, we propose here to implement dwarf-shrub plant functional type within the Community Land Model (CLM) coupled with the Ecosystem Demography model FATES (Functionally Assembled Terrestrial Ecosystem Simulator). By taking advantage of the enhanced understanding achieved through the other work packages of the project, we will first parametrize the new plant functional type and integrate it into the CLM-FATES model. We will use the field observations from the other work packages to calibrate the CLM-FATES model at site-level across environmental gradients of temperature, precipitation and light availability. Using the new implementation of dwarf-shrubs into the CLM-FATES model, we will ultimately assess the role of dwarf-shrubs in both biochemical and biophysical climate feedbacks.
How to cite: Rezsöhazy, J., Fisher, R. A., Geange, S. R., Halbritter, A. H., Tang, H., and Vandvik, V.: A new implementation of dwarf-shrub plant functional type within the CLM-FATES land-surface model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13087, https://doi.org/10.5194/egusphere-egu25-13087, 2025.
Understanding and accurately representing ecological processes between land reservoirs and the atmosphere is crucial for predicting climate responses. However, the terrestrial carbon cycle in Earth system models remains a source of uncertainty. Particularly boreal soils, which store large amounts of organic matter, are an important player in the global carbon cycle, and are therefore a key component in terrestrial models.
Recent advancements, such as microbial-explicit models, have improved the modeling of carbon cycling and soil decomposition processes. Further, is the symbiosis between mycorrhizal fungi and vegetation is a critical ecological process influencing climate dynamics. The exchange of nutrients between the symbionts not only affects carbon storage and vegetation growth, it also impacts biogeophysical aspects of the vegetation, such as albedo, surface roughness, and transpiration. Incorporating and refining those biogeochemical processes in the terrestrial carbon cycle in Earth system models is essential for enhancing predictions of soil and vegetation responses to global warming.
Coupling the microbial-explicit soil decomposition model, MIMICS+, into the Earth system model CTSM, and connecting the mycorrhizal component of MIMICS+ to the above-ground vegetation will enable feedback mechanisms between soils and vegetation. This integration aims to improve the representation of ecosystem-climate feedbacks and provide a more robust tool for understanding the impacts of climate change on terrestrial ecosystems.
How to cite: Wörner, E., Debolskiy, M. V., Fisher, R., Parmentier, F.-J., and Berntsen, T. K.: Modeling carbon cycling in boreal soils under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4297, https://doi.org/10.5194/egusphere-egu25-4297, 2025.
