STRADIVARI Project V : Carbon Cycles: Integration Challenges

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Despite ongoing refinement of Land Surface Model processes and spatial resolution up to 1 km (Niu et al., 2011) regarding carbon cycles (Friedlingstein et al., 2023), vegetation remains the atmosphere's lower boundary layer or detailed soil hydrological models' upper layer (Maxwell et al., 2015). It is thus treated parsimoniously and only somewhat dynamically (Sitch et al., 2003). Conversely, detailed forest ecosystem models working at smaller spatial and temporal scales aim at simulating ecosystem services like carbon sequestration and wood supply, including population and species dynamics, management and natural disturbances (Bugmann et al., 2022) at the expense of soil hydrological processes or micrometeorology. Yet such forest models inherently include dynamics and plant trait subsets translating to functional features, mirroring tree phenological plasticity (Mastrotheodoros et al., 2017). These models are suitable for studying forest ecosystem resilience and adaptability at different time scales, responding to meteorological variability and climate change (Vangi et al., 2024). Only recently have these models been applied at large scales (Dalmonech et al., 2024) or coupled with hydrological watershed models (Speich et al., 2020) to investigate how vegetation responses and dynamics influence precipitation and its partitioning into green and blue water across timescales, while ensuring the long-term sustainability of the modeling framework (Nyenah et al., 2024). Examples of easily integrable models include 3D-CMCC-FEM (Collalti et al., 2016) and Tethys-Chloris for forests (Fatichi et al., 2012), and ARMOSA for crops (Valkama et al., 2020).

Gould

Among others, C3DF (Collalti et al., 2016) and T&C (Fatichi et al., 2012) offer good examples of easily integrable models that can be used to work in the desired direction. The ARMOSA model (Valkama et al., 2020) is an example of the same concept applied to crops. They are distinguished for their functionalities but lack the detailed and algorithmic-safe implementation of the hydrology promoted by the GEOframe infrastructure.

Critical Gap: A critical coupling emerges through plant physiology, where carbon dynamics fundamentally govern water fluxes in ways that current hydrological models inadequately represent. The mechanistic link between photosynthesis and transpiration, captured in Ball-Berry-Leuning formulations (Dewar et al., 2002), reveals stomatal conductance as an emergent property of carbon assimilation rather than a parameter to be prescribed. Yet most hydrological models, lacking carbon cycle representation, resort to the empirical Jarvis (1976) approach, relating conductance directly to environmental drivers through fitted response curves that obscure the underlying biochemical mechanisms. When carbon dynamics drive structural changes, leaf area expansion during favorable periods, height growth altering the aerodynamic profile, root proliferation modifying soil water access, these become active agents in shaping both the energy balance and atmospheric turbulence regime. The feedback loop closes when these structural changes, in turn, alter the very environmental conditions that regulate carbon assimilation. Without representing this co-evolution of vegetation structure and function, models miss the slow variables that determine system resilience and the thresholds where ecosystems shift between alternative stable states. The challenge lies not merely in adding a carbon module, but in reformulating the coupled system where vegetation emerges as both product and architect of its hydrological environment.

STRADIVARI breakthrough: Integrates established forest ecosystem models (3D-CMCC-FEM, T&C) through component-based architecture, enabling simultaneous resolution of hydrological processes and carbon dynamics. This coupled framework provides the foundation for systematically investigating catchment metabolism by tracking energy and matter fluxes across vegetation-soil-atmosphere interfaces, revealing how local biogeochemical processes scale up to emergent landscape-level patterns.

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References - Carbon Cycles

• Bugmann, Harald, and Rupert Seidl. 2022. "The Evolution, Complexity and Diversity of Models of Long-Term Forest Dynamics." The Journal of Ecology 110(10): 2288-2307.
• Collalti, A., et al. 2016. "Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against Eddy Covariance Data for 10 European Forest Sites." Geoscientific Model Development 9(2): 479-504.
• Dalmonech, D., et al. 2024. "Regional Estimates of Gross Primary Production Applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing Multiple Datasets." European Journal of Remote Sensing 57(1).
• Dewar, R. C. 2002. "The Ball-Berry-Leuning and Tardieu-Davies Stomatal Models: Synthesis and Extension Within a Spatially Aggregated Picture of Guard Cell Function." Plant, Cell & Environment 25(11): 1383-1398.
• Fatichi, S., V. Y. Ivanov, and E. Caporali. 2012. "A Mechanistic Ecohydrological Model to Investigate Complex Interactions in Cold and Warm Water-Controlled Environments: 1. Theoretical Framework and Plot-Scale Analysis." Journal of Advances in Modeling Earth Systems 4(2).
• Friedlingstein, Pierre, et al. 2023. "Global Carbon Budget 2023."
• Jarvis, P. G., J. L. Monteith, and P. E. Weatherley. 1976. "The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field." Philosophical Transactions of the Royal Society B: Biological Sciences 273(927): 593-610.
• Mastrotheodoros, Theodoros, et al. 2017. "Linking Plant Functional Trait Plasticity and the Large Increase in Forest Water Use Efficiency: WUE Increase Revisited." Journal of Geophysical Research. Biogeosciences 122(9): 2393-2408.
• Maxwell, R., L. Condon, and S. Kollet. 2015. "A High-Resolution Simulation of Groundwater and Surface Water over Most of the Continental US with the Integrated Hydrologic Model ParFlow V3." Geoscientific Model Development 8(3): 923-37.
• Niu, Guo-Yue, et al. 2011. "The Community Noah Land Surface Model with Multiparameterization Options (Noah-MP): 1. Model Description and Evaluation with Local-Scale Measurements." Journal of Geophysical Research 116(D12).
• Nyenah, Emmanuel, et al. 2024. "Software Sustainability of Global Impact Models." Geoscientific Model Development 2024: 1-29.
• Sitch, S., et al. 2003. "Evaluation of Ecosystem Dynamics, Plant Geography and Terrestrial Carbon Cycling in the LPJ Dynamic Global Vegetation Model: LPJ DYNAMIC GLOBAL VEGETATION MODEL." Global Change Biology 9(2): 161-85.
• Speich, Matthias J. R., et al. 2020. "FORests and HYdrology under Climate Change in Switzerland v1.0: A Spatially Distributed Model Combining Hydrology and Forest Dynamics." Geoscientific Model Development 13(2): 537-64.
• Valkama, Elena, et al. 2020. "Can Conservation Agriculture Increase Soil Carbon Sequestration? A Modelling Approach." Geoderma 369(114298): 114298.
• Vangi, E., et al. 2024. "Stand age diversity (and more than climate change) affects forests' resilience and stability, although unevenly." Journal of Environmental Management 366: 121822.

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