STRADIVARI Project III: Plant Hydraulics and Water Use Strategies: From Optimization to Resilience

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Two centuries after Darwin's experiments (Darwin 1898; Scarth 1927), the role of stomatal kinetics in climate, atmospheric, hydrologic, agricultural, and ecosystem sciences remains pivotal (Hetherington and Woodward, 2003). Stomatal aperture dynamically regulates the exchange of water vapor and CO2 between plants and the atmosphere, influencing processes like atmospheric CO2 concentration, water cycling feedback on air temperature (Katul et al., 2012), sensible heat flux, and boundary layer dynamics tied to rainfall predisposition (Siqueira et al., 2009; Manoli et al., 2016). For every CO2 molecule absorbed during photosynthesis, hundreds of water vapor molecules are lost, the leaf water potential becomes more negative and this lifts the water column connecting the soil reservoir to the leaf, creates tension in the xylem, increases vulnerability to cavitation and embolism spread in a resulting feedback that further reduce leaf water potential, potentially leading to "runaway" cavitation. Over five decades, optimization theories describing stomatal kinetics have advanced significantly, incorporating soil-plant hydraulics, soil water availability, and energy constraints. However, critical gaps remain in integrating existing optimization schemes and explicitly linking schemes to plant water use strategies (WUS). WUS reflects balance between instantaneous and delayed gains, with isohydric plants prioritizing delayed gains while anisohydric plants favor immediate benefits (Manzoni et al., 2013).

Kruse

D'Amato and Rigon (2025) present a plant hydraulics framework emphasizing simplified mathematical approaches that initially omit plant capacitance. They propose replacing algebraic equations with partial differential equations analogous to the R2 equation to capture time lags observed in sap-flow experiments (Kume et al., 2008; Ferraz et al., 2015) and dynamic phenomena including water storage, discharge, and refilling (Phillips et al. 2009; Oliva Carrasco et al., 2015; Wang et al., 2019), processes fundamental to plant resilience under water stress: isohydric plants prioritize delayed gains, while anisohydric plants favor immediate benefits (Manzoni et al., 2013). In this context, D'Amato and Rigon (2025) challenges conventional wisdom by questioning whether plants evolved for resilience over optimality suggesting that plants may prioritize homeostasis amid fluctuating conditions rather than maximizing efficiency. Their work, using the water potential as a unifying variable, examines resilience as a fundamental evolutionary strategy, arguing that stability, not optimization, governs plant-water dynamics. Testing this hypothesis could transform our understanding of plant water management under climate change.
Paradigm-Shifting Innovation: D'Amato and Rigon (2025) challenge conventional wisdom by questioning whether plants evolved for resilience over optimality suggesting that plants may prioritize homeostasis amid fluctuating conditions rather than maximizing efficiency. Their work, using the water potential as a unifying variable, examines resilience as a fundamental evolutionary strategy, arguing that stability, not optimization, governs plant-water dynamics. Testing this hypothesis could transform our understanding of plant water management under climate change.
STRADIVARI breakthrough: Advancing the 1D Prospero model to 3D Rosalia and plant hydraulics modeling by providing tools to investigate resilience-based stomatal control versus optimization theories through virtual experiments. Rosalia model will implement complete Richards-like equations for plant water transport (following D'Amato and Rigon, 2025 theoretical framework) coupled with allometric scaling laws (Oleson et al., 2014) to bridge individual plant behavior to ecosystem-scale responses. The Rosalia component enables systematic comparison of plant hydraulic responses with and without water capacitance, coupled to allometric studies on vegetation populations. Rather than definitively resolving the resilience vs. optimization debate, STRADIVARI provides researchers with computational tools to explore conditions where different strategies emerge, enabling hypothesis testing through controlled virtual experiments that complement field observations.

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References - Plant Hydraulics and Water Use Strategies

• Darwin, F. 1898. "IX. Observations on Stomata." Philosophical Transactions of the Royal Society of London 190(0): 531-621.
• D'Amato, Concetta, and Riccardo Rigon. 2025. "Elementary Mathematics Helps to Shed Light on the Transpiration Budget under Water Stress." Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology 18(2).
• 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.
• Ferraz, T. M., et al. 2015. "Relationships Between Sap-Flow Measurements, Whole-Canopy Transpiration and Reference Evapotranspiration in Field-Grown Papaya." Theoretical and Experimental Plant Physiology 27(3): 251-262.
• Hetherington, Alistair M., and F. Ian Woodward. 2003. "The Role of Stomata in Sensing and Driving Environmental Change." Nature 424(6951): 901-8.
• Javaux, Mathieu, et al. 2013. "Root Water Uptake: From Three-Dimensional Biophysical Processes to Macroscopic Modeling Approaches." Vadose Zone Journal 12(4): 0-16.
• Katul, Gabriel G., et al. 2012. "Evapotranspiration: A Process Driving Mass Transport and Energy Exchange in the Soil-Plant-Atmosphere-Climate System." Reviews of Geophysics 50(3): 1083.
• Kennedy, D., et al. 2019. "Implementing plant hydraulics in the community land model, version 5." Journal of Advances in Modeling Earth Systems 11: 485-513.
• Kume, T., et al. 2008. "Less Than 20-min Time Lags Between Transpiration and Stem Sap Flow in Emergent Trees in a Bornean Tropical Rainforest." Agricultural and Forest Meteorology 148(6): 1181-1189.
• Manoli, Gabriele, et al. 2016. "Soil-Plant-Atmosphere Conditions Regulating Convective Cloud Formation above Southeastern US Pine Plantations." Global Change Biology 22(6): 2238-54.
• Manzoni, Stefano, et al. 2013. "Hydraulic Limits on Maximum Plant Transpiration and the Emergence of the Safety-Efficiency Trade-Off." The New Phytologist 198(1): 169-78.
• Oleson, Mark E., et al. 2014. "Universal Hydraulics of the Flowering Plants: Vessel Diameter Scales with Stem Length across Angiosperm Lineages, Habits and Climates." Ecology Letters 17(8): 988-97.
• Oliva Carrasco, L., et al. 2015. "Water Storage Dynamics in the Main Stem of Subtropical Tree Species Differing in Wood Density, Growth Rate and Life History Traits." Tree Physiology 35(4): 354-365.
• Phillips, N. G., et al. 2009. "Using Branch and Basal Trunk Sap Flow Measurements to Estimate Whole-Plant Water Capacitance: Comment on Burgess and Dawson (2008)." Plant and Soil 315(1): 315-324.
• Scarth, G. W. 1927. "Stomatal Movement: Its Regulation and Regulatory rÔle a Review." Protoplasma 2(1): 498-511.
• Wang, H., D. Tetzlaff, and C. Soulsby. 2019. "Hysteretic Response of Sap Flow in Scots Pine (Pinus sylvestris) to Meteorological Forcing in a Humid Low Energy Headwater Catchment." Ecohydrology 12(6): e2125.

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STRADIVARI Project II: Atmospheric Boundary Layer (ABL) Dynamics

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The equations modeling the ABL are extensively covered under various aspects (Stull, 1988; Yin and Porporato, 2022, Honnert at al., 2020; Canché-Cab et al., 2024) and implemented in various software. However, the interaction between soil moisture, land surface fluxes, and convection initiation, leading to rainfall, remains challenging (Dirmeyer et al., 2006; Koster et al., 2004; Santanello et al., 2007). This stems from the complexity of the Soil-Plant-Atmosphere Continuum interactions across multiple spatial and temporal scales. The ABL, influenced by mechanical and thermal turbulence, links surface processes with synoptic phenomena, while plant physiology regulates sensible and latent heat fluxes. These fluxes affect the energy needed for convection and ABL growth, as well as the transfer of water vapor from the root zone to the atmosphere and determine the Lifting Condensation Level (LCL) (Siqueira et al., 2009; Cuxart et al., 2020), and its intersection with the ABL, critical for rainfall initiation. The height of this crossing, visible as cloud base, highlights the soil-plant system's control over hydrological self-regulation, which implies that drier soils may increase sensible heat flux, enhancing convection and raising ABL depths, thus elevating the likelihood of ABL-LCL crossing and rainfall, an example of negative feedback. Conversely, reduced latent heat flux can lower ABL water vapor concentration, raising the LCL above the ABL, leading to sustained dry conditions, exemplifying positive feedback.

Stradivari's Hellier

Beyond the conceptual framework, a critical implementation challenge emerges from the fundamental mismatch between hydrological and atmospheric process scales and their characteristic timescales. Figure below illustrates the multi-scale coupling mechanisms central to STRADIVARI, adapted from Miralles et al. (2025). 

Vegetation controls surface energy, water, and carbon fluxes at local scales through processes governed by soil moisture dynamics and groundwater table fluctuations. These surface controls propagate to the atmosphere via turbulent fluxes, driving convective and mechanical instability that alters the diurnal evolution of the atmospheric boundary layer (ABL). The ABL growth dynamics regulate moisture and heat entrainment processes, determining the lifting condensation level (LCL) and subsequent convective cloud formation—the critical link between local surface processes and regional precipitation patterns. While contemporary atmospheric models can resolve these multi-scale interactions, their representation of surface phenomena remains heavily parameterized, obscuring the mechanistic coupling that STRADIVARI seeks to capture. 

Critical Gap: Current Land Surface Models rely predominantly on bulk aerodynamic formulations and Monin-Obukhov Similarity Theory parameterizations that treat the ABL as a prescribed boundary condition rather than solving governing transport equations (Santanello et al., 2018). PLUMBER-2 analysis shows systematic LSM failures in water-limited regions where soil-plant coupling becomes critical, while TRENDY simulations reveal persistent discrepancies in vegetation-atmosphere CO2 exchange (Friedlingstein et al., 2023). These failures stem from models using parameters as "garbage collectors" (sensu Beven, 2006) rather than physically meaningful quantities corresponding to independently measurable soil, plant, and atmospheric properties. The most widespread conceptual framework in treating these issues reduces the SPAC complexity to an electrical circuit analogy (Monson & Baldocchi, 2015; Bonan, 2019), conflating aerodynamic transport, physiological regulation, and soil physics into parameterized "resistances" that obscure actual mechanisms at play. While full resolution of precipitation recycling mechanisms remains beyond current capabilities, the hydrological modeling community lacks tools to explore even minimal surface-atmosphere processes complexity.

STRADIVARI innovation: Rather than claiming to resolve precipitation recycling, STRADIVARI deconstructs the resistance framework by resolving atmospheric turbulence through governing equations rather than parameterizations. This allows temperature, wind velocity, and humidity profiles to emerge naturally from ABL physics, creating boundary conditions at leaf and soil surfaces that couple directly with plant hydraulic solutions while isolating plant conductance as a purely physiological phenomenon rather than a catch-all for system-level behaviors we fail to resolve mechanistically. The approach bridges the gap between hydrological and micrometeorological communities, providing tools for collaborative investigation of coupled processes at scales where both communities can contribute observational constraints and process understanding. STRADIVARI addresses this challenge by starting the implementation of hierarchical ABL modeling framework with four levels of increasing complexity: The framework implements a hierarchical ABL approach: (1) Wood (2000) statistical-dynamical corrections for basic terrain effects, (2) spectral methods for turbulent scalar transport following Katul et al. (2011) and Poggi et al. (2004), (3) multi-scale atmospheric boundary layer modeling incorporating canopy-atmosphere interactions (Finnigan et al., 2009; Brunet & Irvine, 2000), and (4) machine learning-enhanced parameterizations for complex terrain effects (Cheng et al., 2021; Rasp et al., 2018). Validation against Alpine meteorological stations determines minimum complexity required for meaningful surface-atmosphere coupling.

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References - Atmospheric Boundary Layer

• Anderson, M. C., et al. 2003. "A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales." Remote Sensing of Environment 90(4): 521-531.
• Best, M. J., et al. 2015. "The Plumbing of Land Surface Models: Benchmarking Model Performance." Journal of Hydrometeorology 16(3): 1425-42.
• Beven, Keith. 2006. "A Manifesto for the Equifinality Thesis." Journal of Hydrology 320(1-2): 18-36.
• Bonan, Gordon. 2019. Climate Change and Terrestrial Ecosystem Modeling. Cambridge University Press.
• Brunet, Y., and M. R. Irvine. 2000. "The Control of Coherent Eddies in Vegetation Canopies." Boundary-Layer Meteorology 94(1): 139-63.
• Canché-Cab, Linda, et al. 2024. "The Atmospheric Boundary Layer: A Review of Current Challenges and a New Generation of Machine Learning Techniques." Artificial Intelligence Review 57(12).
• Cheng, Y. 2021. "Machine Learning Methods Turbulence Modeling Atmospheric Boundary Layer Flows." Physics Fluids 33.
• Cuxart, Joan, et al. 2020. "Current Challenges in Evapotranspiration Determination, GEWEX News."
• Dirmeyer, Paul A., et al. 2006. "GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface." Bulletin of the American Meteorological Society 87(10): 1381-98.
• Finnigan, John J., Roger H. Shaw, and Edward G. Patton. 2009. "Turbulence Structure above a Vegetation Canopy." Journal of Fluid Mechanics 637: 387-424.
• Foken, Thomas. 2006. "50 Years of the Monin–Obukhov Similarity Theory." Boundary-Layer Meteorology 119(3): 431-47.
• Friedlingstein, Pierre, et al. 2023. "Global Carbon Budget 2023."
• Honnert, Rachel, et al. 2020. "The Atmospheric Boundary Layer and the 'Gray Zone' of Turbulence: A Critical Review." Journal of Geophysical Research Atmospheres 125(13).
• Jiménez, Pedro A., et al. 2012. "A Revised Scheme for the WRF Surface Layer Formulation." Monthly Weather Review 140(3): 898-918.
• Katul, Gabriel G., et al. 2011. "A mixing-layer theory for flow resistance in shallow streams." Water Resources Research 47(11).
• Koster, Randal D., et al. 2004. "Regions of Strong Coupling between Soil Moisture and Precipitation." Science 305(5687): 1138-40.
• Lawrence, David M., et al. 2019. "The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty." Journal of Advances in Modeling Earth Systems 11(12): 4245-87.
• Miralles, Diego G., et al. 2025. "Vegetation-Climate Feedbacks across Scales." Annals of the New York Academy of Sciences 1544(1): 27-41.
• Monson, Russell, and Dennis Baldocchi. 2015. Terrestrial Biosphere-Atmosphere Fluxes. Cambridge University Press.
• Poggi, D., G. G. Katul, and J. D. Albertson. 2004. "A Note on the Contribution of Dispersive Fluxes to Momentum Transfer within Canopies." Boundary-Layer Meteorology 111(3): 615-21.
• Rasp, Stephan, Michael S. Pritchard, and Pierre Gentine. 2018. "Deep Learning to Represent Subgrid Processes in Climate Models." Proceedings of the National Academy of Sciences 115(39): 9684-89.
• Santanello, Joseph A., et al. 2018. "Land–Atmosphere Interactions: The LoCo Perspective." Bulletin of the American Meteorological Society 99(6): 1253-72.
• Siqueira, Mario, Gabriel Katul, and Amilcare Porporato. 2009. "Soil Moisture Feedbacks on Convection Triggers." Journal of Hydrometeorology 10(1): 96-112.
• Stull, R. B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers.
• Wood, Eric F., et al. 2011. "Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water." Water Resources Research 47(5).
• Yin, Jin, and Amilcare Porporato. 2022. Ecohydrology: Dynamics of Life and Water in the Critical Zone. Cambridge University Press.

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