STRADIVARI Project VI: Informatics and Small Language Models Revolution

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Complex Earth System problems demand sophisticated computational architectures, yet current modeling frameworks face significant limitations in balancing scientific rigor with accessibility. While Rigon et al. (2022) demonstrated that Modeling By Components strategies offer promising approaches for tackling such challenges, true implementation remains constrained by existing software architectures and knowledge transfer barriers across disciplinary boundaries. The interdisciplinary nature of coupled Earth System modeling creates substantial knowledge transfer barriers: effective research requires integration of informatics, software engineering, numerical methods, soil science, plant physiology, and atmospheric physics, competencies rarely unified in standard curricula. Traditional documentation approaches, written manuals, video tutorials, and workshops, fail to provide the interactive, contextual assistance needed for complex modeling frameworks.

Gould 

OMS3 framework (David et al., 2013) represents one of the most advanced implementations of component-based environmental modeling, yet several critical gaps persist. ML integration within existing frameworks remains rudimentary. While Serafin et al. (2021) demonstrated basic ML capabilities in OMS3, current implementations lack integration with modern ML libraries necessary for hybrid physics-ML approaches. This limitation prevents effective handling of massive datasets typical in large-scale studies and restricts development of computationally efficient surrogate models where full physics becomes intractable. The NET3 parallelization subsystem (Serafin et al., 2021) can represent complex systems as directed acyclic graphs, but performance limitations and inflexibility restrict its application to the dynamical systems pervasive in coupled ESS modeling.

Domain-specific SML trained on hydrological literature and extensive GEOframe documentation address this fundamental bottleneck by providing accessible interfaces to sophisticated modeling capabilities. Keeping in mind that technologies in this sector are rapidly evolving and breakthrough could change the technological approach, STRADIVARI will implement an innovative knowledge management system utilizing current compact language models (3-8B parameters) such as Phi-3.5-mini or Qwen2.5, fine-tuned on domain-specific content through parameter-efficient methods like LoRA (Hu et al., 2021). The system will integrate multiple knowledge sources: STRADIVARI GitHub repositories, approximately 1000 GEOframe tutorial videos, and the complete AboutHydrology blog archive (900+ posts spanning 15 years). Using retrieval-augmented generation architecture with vector embeddings and modern frameworks like LangChain, the system will provide interactive documentation and contextualized assistance. This democratization infrastructure is essential for community adoption: without lowering technical barriers, sophisticated coupling frameworks remain accessible only to specialists, limiting scientific validation opportunities.

STRADIVARI breakthrough: Modernizes the proven OMS3 framework to OMS4, implementing enhanced Service-Oriented Architecture with machine learning integration and domain-specific small language model (SML) creating an intelligent modeling platform trained on hydrological literature and extensive GEOframe documentation. This infrastructure will provide intelligent assistance for model configuration, parameter selection, and results interpretation. This democratizes access to complex Earth System modeling while maintaining computational rigor, enabling researchers worldwide to contribute to dynamic Earth System understanding through interfaces providing contextual assistance and automated workflow guidance. The OMS4 improvements address critical computational architecture limitations in three key areas. First, enhanced parallelization capabilities unify disparate computational paradigms within a coherent framework: NET3 improvements enable efficient handling of large-scale systems of ordinary differential equations governing biota population dynamics and vegetation processes, while seamlessly integrating with grid-based partial differential equation solvers for soil-atmosphere transport. Second, the Service-Oriented Architecture redesign facilitates dynamic coupling between previously isolated computational domains—population dynamics models can now exchange state variables with spatially distributed hydrological processes in real-time, enabling feedback mechanisms between biological activity and physical transport that were computationally prohibitive in OMS3. Third, the integration of domain-specific Small Language Models represents a fundamental shift toward intelligent modeling infrastructure: rather than requiring users to navigate complex parameter spaces and component interactions manually, the SML provides contextual guidance for model configuration, interprets results, and suggests optimization strategies based on the extensive hydrological literature and GEOframe documentation corpus.

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References - Informatics and Small Language Models

• Belcak, Peter, et al. 2025. "Small Language Models Are the Future of Agentic AI." arXiv [Cs.AI]. arXiv.
• Chen, Min, et al. 2020. "Position Paper: Open Web-Distributed Integrated Geographic Modelling and Simulation to Enable Broader Participation and Applications." Earth-Science Reviews 207(103223): 103223.
• David, O., et al. 2013. "A Software Engineering Perspective on Environmental Modeling Framework Design: The Object Modeling System." Environmental Modelling & Software: With Environment Data News 39(c): 201-13.
• Hu, Edward J., et al. 2021. "LoRA: Low-Rank Adaptation of Large Language Models." arXiv [Cs.CL]. arXiv.
• Moore, R. V., and A. G. Hughes. 2017. "Integrated Environmental Modelling: Achieving the Vision." Geological Society, London, Special Publications 408(1): 17-34.
• Rigon, R., et al. 2022. "HESS Opinions: Participatory Digital Earth Twin Hydrology Systems (DARTHs) for Everyone: A Blueprint for Hydrologists." Hydrology and Earth System Sciences, January, 1-38.
• Serafin, Francesco, et al. 2021. "Bridging Technology Transfer Boundaries: Integrated Cloud Services Deliver Results of Nonlinear Process Models as Surrogate Model Ensembles." Environmental Modelling and Software[R] 146(105231): 105231.

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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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STRADIVARI Project IV: Soil Hydrology and Biota Interactions: Beyond Static Properties

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While certain aspects of soil hydrology, regulated by Soil Water Retention Curves (Tubini and Rigon, 2022), are relatively well understood, even extending to their ability to describe soil structure beyond soil texture (Kosugi 1999), other areas remain less explored: the behavior of soils under non-equilibrium (Zehe et al., 2010), the evolution of hydraulic properties with soil evolution (Meurer et al., 2020), and the impact of underground vegetation on infiltration (Jones et al., 2022). According to Zehe et al. (2010), water transiently distributes across all pore sizes, moving more slowly toward the smallest pores, a dynamic which does not fit within the Mualem/vanGenuchten theory. A compromise approach often involves accounting for at least two independent pore domains, which exchange water while conveying it largely independently. However more physically sound mechanisms could be envisioned, such as allowing the SWRC to evolve dynamically over time. A key focus in understanding vegetation restoration effects requires comprehending soil biota impacts on hydraulic properties. Earthworm studies and preliminary modeling approaches classify porosity modifications using ordinary differential equations akin to population dynamics models or Hydrological Dynamical Systems (Meurer et al., 2020; Bancheri et al., 2019). Such equations could integrate into Richards-Richardson formulations using methods like Kosugi (1999), with improved modelling of root growth (Vanderborght et al., 2024).

A key focus in understanding effects of vegetation restoration lies in understanding the effects of soil biota on the hydraulic properties of soils. The actions of earthworms have already been studied (e.g. Meurer et al., 2020), and preliminary methods to incorporate these effects into hydrological models have been proposed. These approaches often classify porosity modifications induced by biota using systems of ordinary differential equations akin to population dynamics models or HDS (Calabrese and Porporato, 2015; Bancheri et al., 2019). Such equations could, in turn, be integrated into R2 formulations using methods extending the theory proposed by Kosugi (1999) that relates pore size with SWRC. The effects of root growth on soil structure and hydraulic properties has been already addressed (D'Amato and Rigon, 2025) and can be further improved (Vanderborght et al., 2024). A related aspect regards soil cover which is profoundly impacted by decaying plant material, particularly leaves, which contribute to mulching and alter soil moisture evaporation rates (Villegas et al., 2010). Often neglected is the energy budget of the soil which relates to all of the previously mentioned processes but, especially for what regards the soil evaporation, is often implemented in ways that do not coincide with the current understanding of the phenomena (Or et al., 2013).

Critical Gap: The modeling community faces a double challenge: despite mounting evidence that soil biota fundamentally alter hydraulic properties through macropore creation and soil aggregation modification (Weber et al., 2024; Fraccica et al., 2025), these biological agents remain absent from equations. Additionally, the energetic relationship between water potential and content needs revisiting in light of biological activity, extending beyond static parameter approaches. Furthermore, roots themselves occupy significant soil volume (Garré et al., 2011), creating flow patterns that diverge dramatically from bulk soil behavior, yet most models still treat root water uptake as a distributed sink term rather than acknowledging the three-dimensional flow fields roots create (Vanderborght et al., 2024).

STRADIVARI breakthrough: Extends proven WHETGEO from 1D and 2D to 3D with dynamically evolving hydraulic properties. Unlike episodic studies (Meurer et al., 2020) that suggest biota effects without providing implementable frameworks, STRADIVARI develops operational population dynamics equations coupled to Richards-Richardson formulations, creating the first systematic tool for investigating soil evolution effects on watershed hydrology under different soil management conditions. Experimental soil scientists can validate dynamic SWRC modifications through tomographic techniques (e.g., Yang et al., 2018) and controlled laboratory experiments, while STRADIVARI provides the modeling tools to explore the hydrological implications of observed biota-induced changes, under different soil management conditions. This collaborative approach enables hypothesis testing without requiring STRADIVARI to independently validate all biota-soil interactions.

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References - Soil Hydrology and Biota Interactions

• Bancheri, Marialaura, Francesco Serafin, and Riccardo Rigon. 2019. "The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN)." Water Resources Research 55(11): 8895-8921.
• Brinkmann, Pernilla E., et al. 2010. "Plant-Soil Feedback: Experimental Approaches, Statistical Analyses and Ecological Interpretations." The Journal of Ecology 98(5): 1063-73.
• Calabrese, Salvatore, and Amilcare Porporato. 2015. "Linking Age, Survival, and Transit Time Distributions." Water Resources Research 51(10): 8316-30.
• Fraccica, Alessandro, Enrique Romero, and Thierry Fourcaud. 2025. "Effects of Vegetation Growth on Soil Microstructure and Hydro-Mechanical Behaviour." Géotechnique 75(3): 293-307.
• Garré, S., et al. 2011. "Three-Dimensional Electrical Resistivity Tomography to Monitor Root Zone Water Dynamics." Vadose Zone Journal: VZJ 10(1): 412-24.
• Jones, Julia, et al. 2022. "Forest Restoration and Hydrology." Forest Ecology and Management 520(120342): 120342.
• Kosugi, K. 1999. "General Model for Unsaturated Hydraulic Conductivity for Soils with Lognormal Pore-size Distribution." Soil Science Society of America Journal 63(2): 270-77.
• Meng, Xia, et al. 2022. "The Current and Future Role of Biota in Soil-Landscape Evolution Models." Earth-Science Reviews 226: 103945.
• Meurer, Katharina, et al. 2020. "A Framework for Modelling Soil Structure Dynamics Induced by Biological Activity." Global Change Biology 26(10): 5382-5403.
• Or, D., et al. 2013. "Advances in Soil Evaporation Physics, A Review." Vadose Zone Journal 12.
• Tubini, Niccolò, and Riccardo Rigon. 2022. "Implementing the Water, HEat and Transport Model in GEOframe (WHETGEO-1D v.1.0)." Geoscientific Model Development 15(1): 75-104.
• Vanderborght, Jan, et al. 2024. "Combining Root and Soil Hydraulics in Macroscopic Representations of Root Water Uptake." Vadose Zone Journal: VZJ 23(3): e20273.
• Villegas, Juan Camilo, et al. 2010. "Ecohydrological Controls of Soil Evaporation in Deciduous Drylands." Journal of Arid Environments 74(5): 595-602.
• Weber, Tobias Karl David, et al. 2024. "Hydro-Pedotransfer Functions: A Roadmap for Future Development" 28(14): 3391-3433.
• Yang, Yonghui, et al. 2018. "Assessment of the Responses of Soil Pore Properties to Combined Soil Structure Amendments Using X-Ray Computed Tomography." Scientific Reports 8(1): 695.
• Zehe, Erwin, Theresa Blume, and Günter Blöschl. 2010. "The Principle of 'Maximum Energy Dissipation': A Novel Thermodynamic Perspective on Rapid Water Flow in Connected Soil Structures." Philosophical Transactions of the Royal Society of London 365(1545): 1377-86.

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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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The STRADIVARI Project - I : Objectives

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STRADIVARI leverages established physical foundations, infiltration influenced by soil characteristics evolving through biotic activity (Brinkmann et al., 2010; Meng X et al., 2002), plant hydraulics with detailed transpiration processes (Kennedy et al., 2019; D'Amato and Rigon, 2025), soil and surface evaporation with proper energy partitioning (Or et al., 2013), and atmospheric boundary layer dynamics creating feedback loops with surface processes (Anderson et al., 2003; Siqueira et al., 2008), as launching points for investigating emergent behaviors from coupled system interactions. The modular component architecture, interconnected through supporting software layers (David et al., 2013; Moore and Hughes, 2017), enables exploration of cross-compartmental feedback loops that amplify or dampen climate responses through nonlinear dynamics. While such dynamics are well-documented at regional scales (Gross et al., 2018; Gröger et al., 2021), their investigation at catchment and local scales remains limited. The project's scope, while ambitious in vision, does not pretend to solve all coupling challenges directly but opens pathways toward their resolution. STRADIVARI will concentrate mainly on soil-plant interactions, water and carbon cycle dynamics, and plant-atmosphere exchanges. For atmospheric boundary layer description, the project will develop a hierarchy of models with increasing realism to bridge current modeling capabilities. By dynamically linking hydrology with soil biotic evolution (Meurer et al., 2020) and vegetation dynamics, STRADIVARI transcends traditional approaches through inter-compartmental feedback analyses.

Stradivari Francesca

Building on earlier visions (Rigon et al., 2006; Rigon et al., 2022), STRADIVARI couples water and energy budgets with dynamic soil and vegetation processes and atmospheric boundary layer transport equations. This addresses a critical gap: micrometeorological models often lack detailed soil and plant descriptions, while hydrological models miss key atmospheric interactions. Existing efforts suffer from architectural limitations and inflexible frameworks (Telteu et al., 2021). STRADIVARI's component-based architecture overcomes these constraints by enabling seamless process integration while maintaining computational efficiency and addressing theoretical and computational challenges in SPAC modeling, particularly regarding soil-plant, plant-atmosphere, and water-carbon cycle couplings. STRADIVARI adopts a "luthier" approach to ESS modeling, crafting instruments that enable virtuoso scientific performance rather than attempting to resolve all coupling challenges directly. Like a violin maker who provides musicians with tools for artistic expression, STRADIVARI provides researchers with computational infrastructure that embodies new conceptual frameworks for investigating process interactions. This philosophy recognizes that ESS coupling involves phenomena across multiple disciplines, temporal scales, and spatial domains that no single project can fully resolve. Instead, STRADIVARI creates a modular research infrastructure that enables systematic investigation of individual coupling mechanisms, virtual experiments to test competing hypotheses, community collaboration across traditional disciplinary boundaries, and progressive advancement as understanding develops. The project's success lies not in solving ESS coupling but in democratizing access to tools that enable meaningful investigation of these challenges. To accelerate adoption, STRADIVARI integrates Small Language Models with AI agents that provide intelligent assistance to new users and developers, making the modeling framework more accessible and reducing the learning curve for complex Earth system investigations.

References 

• 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.

• Brinkmann, Pernilla E., Wim H. Van der Putten, Evert-Jan Bakker, and Koen J. F. Verhoeven. 2010. "Plant-Soil Feedback: Experimental Approaches, Statistical Analyses and Ecological Interpretations." The Journal of Ecology 98(5): 1063-73.

• 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).

• David, O., J. C. Ascough II, W. Lloyd, T. R. Green, K. W. Rojas, G. H. Leavesley, and L. R. Ahuja. 2013. "A Software Engineering Perspective on Environmental Modeling Framework Design: The Object Modeling System." Environmental Modelling & Software: With Environment Data News 39(c): 201-13.

• Gross, Markus, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, et al. 2018. "Physics–Dynamics Coupling in Weather, Climate, and Earth System Models: Challenges and Recent Progress." Monthly Weather Review 146(11): 3505-44.

• Gröger, Matthias, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, et al. 2021. "Coupled Regional Earth System Modeling in the Baltic Sea Region." Earth System Dynamics 12(3): 939-73.

• Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Lola da Costa, A. C., and Gentine, P. 2019. "Implementing plant hydraulics in the community land model, version 5." Journal of Advances in Modeling Earth Systems 11: 485-513.

• Meng, Xia, Annemieke M. Kooijman, Arnaud J. A. M. Temme, and Erik L. H. Cammeraat. 2022. "The Current and Future Role of Biota in Soil-Landscape Evolution Models." Earth-Science Reviews 226: 103945.

• Meurer, Katharina, Jennie Barron, Claire Chenu, Elsa Coucheney, Matthew Fielding, Paul Hallett, Anke M. Herrmann, et al. 2020. "A Framework for Modelling Soil Structure Dynamics Induced by Biological Activity." Global Change Biology 26(10): 5382-5403.

• Moore, R. V., and A. G. Hughes. 2017. "Integrated Environmental Modelling: Achieving the Vision." Geological Society, London, Special Publications 408(1): 17-34.

• Or, D., P. Lehmann, E. Shahraeeni, and N. Shokri. 2013. "Advances in Soil Evaporation Physics, A Review." Vadose Zone Journal 12.

• Rigon, R., G. Bertoldi, and T. Over. 2006. "GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets." Journal of Hydrometeorology 7: 371-88.

• Rigon, R., G. Formetta, Marialaura Bancheri, Niccolò Tubini, Claudia d'Amato, O. David, and C. Massari. 2022. "HESS Opinions: Participatory Digital Earth Twin Hydrology Systems (DARTHs) for Everyone: A Blueprint for Hydrologists." Hydrology and Earth System Sciences, January, 1-38.

• Siqueira, Mario, Gabriel Katul, and Amilcare Porporato. 2008. "Onset of Water Stress, Hysteresis in Plant Conductance, and Hydraulic Lift: Scaling Soil Water Dynamics from Millimeters to Meters." Water Resources Research 44(1): 1-14.

• Telteu, Camelia-Eliza, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, et al. 2021. "Understanding Each Other's Models: An Introduction and a Standard Representation of 16 Global Water Models to Support Intercomparison, Improvement, and Communication." Geoscientific Model Development 14(6): 3843-78.

• Wilkinson, Mark D., Michel Dumontier, I. Jsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." Scientific Data 3: 160018. https://doi.org/10.1038/sdata.2016.18.

 

The STRADIVARI project - STudy of the teRrestriAl Hydrology and its feeDback wIth the lower atmosphere and the carbon cycle and Its VARIation under vegetation changes at various scales - 0

Project STRADIVARI represents the culmination of what I hope to accomplish in the coming years. Its contents draw from twenty years of preparation and experience. While I'm uncertain whether it will receive funding, I'm committed to pursuing its components regardless. The scope may expand or contract based on circumstances, but I remain dedicated to the work. I hope you find it engaging to read and perhaps discover something valuable within it.

This project was foreshadowed in earlier blog posts, particularly "The man who planted trees" and many others. These posts are part of a broader initiative I recently submitted for review. I hope the reviewers won't dismiss it as merely incremental science—though I invite readers to form their own judgment after examining the work. I will be adding two additional sections to the posts, not included in the project, which I am currently developing. I am interested in hiring postdocs and Ph.D. students for all aspects of this project. If you're interested, please contact me. While no positions are currently available, I recommend starting early training with GEOframe to improve your candidacy for future opportunities. 

Index

• I      Objectives
• II    AtmosphericBoundary Layer
• III   Plant's Hydraulics
• IV   Soil Hydrology and Biota
• V    Carbon Cycle Integration
• VI   Informatics and small language models
• VII - Snow and Permafrost
• VIII - Scale Processes

Scientific Challenge and Innovation

The poetic novel "The Man Who Planted Trees" (Giono, 1953) depicts the transformation of a barren wasteland into an ecosystem with a complex hydrological cycle through decades of tree planting. While inspirational, the story raises unresolved hydrological questions about feedbacks between soil, vegetation, climate, hydrology, and ecosystems, questions current models cannot fully address. Current Earth System Models fail to capture critical feedbacks between soil evolution, plant hydraulics, and atmospheric processes required for understanding coupled hydrological and ecosystem functioning (e.g., Miralles et al., 2025) because Earth's system compartments are often treated as silos or parameterized in crucial aspects of their dynamics. This leaves fundamental questions unanswered: Can the compelling ecosystem transformation depicted in Giono's story be quantitatively validated through dynamic modeling?

Stradivari's Antonius

References

Giono, Jean. 1953. "L'homme qui plantait des arbres." Vogue, Paris.

Giono, Jean. 2015. The Man Who Planted Trees. New York, NY: Random House.

Miralles, Diego G., Jordi Vilà-Guerau de Arellano, Tim R. McVicar, and Miguel D. Mahecha. 2025. "Vegetation-Climate Feedbacks across Scales." Annals of the New York Academy of Sciences 1544(1): 27-41. https://doi.org/10.1111/nyas.15286.

From Parameterized to Dynamic Earth System Coupling

Current ESS Models fail to capture critical feedbacks between soil evolution, plant hydraulics, and atmospheric processes required for understanding coupled hydrological and ecosystem functioning (e.g., Miralles et al., 2025) because Earth's system compartments are often treated as silos or heavily parameterized in crucial aspects of their dynamics. This leaves fundamental questions unanswered: Can the compelling ecosystem transformation depicted in Giono's (1953) "The Man Who Planted Trees" be quantitatively validated through dynamic modeling? STRADIVARI aims to fill this knowledge gap by developing an integrated modeling framework that couples dynamic soil-biota interactions, plant hydraulic strategies, and atmospheric boundary layer processes, replacing the fixed BCs that constrain current models with dynamical feedbacks.

The GEOSPACE framework (D'Amato et al., 2025) demonstrates this integration philosophy through operational coupling of soil heat-water transport (WHETGEO) with transpiration processes (Prospero). In that approach, the dynamic root water uptake responds to evolving soil moisture while simultaneously influencing soil energy balance. This proof-of-concept validates that meaningful process coupling emerges from component interactions without sacrificing individual model integrity and represents a blueprint for the project.

The Figure illustrates the complexity of coupled Earth system interactions using Extended Petri Net notation (Bancheri et al., 2019). Even this simplified representation, which misses the feedback with the atmosphere, reveals multiple interdependencies across water, energy, and carbon budgets. The loops represent dynamic feedback, while solid arrows show water, carbon and energy fluxes that must be tracked simultaneously. Traditional models typically fix the quantity inside a triangle as boundary conditions rather than allowing them to evolve dynamically. For a full explanation of the symbols, please see the cited paper.

STRADIVARI aims to fill this knowledge gap by developing an integrated modeling framework that couples dynamic soil-biota interactions, plant hydraulic strategies, and atmospheric boundary layer processes, replacing the fixed boundary conditions that constrain current models with dynamical feedbacks.

Core Innovation: STRADIVARI represents a fundamental methodological paradigm shift: moving from model-constrained science to science-driven modeling. Traditional Earth System modeling forces researchers to adapt scientific questions to existing tool capabilities, while STRADIVARI inverts this relationship by providing computational infrastructure that adapts to scientific inquiry by design. Rather than solving all Earth System coupling challenges directly, STRADIVARI creates tools facilitating the investigation of process interactions, answering the question "what tools do we need to investigate this process?" This paradigm shift transforms Earth system modeling from isolated research efforts into collaborative knowledge construction. Individual researchers are enabled to contribute specialized process knowledge while the modeling infrastructure integrates these contributions into system-level understanding, creating a positive feedback loop where broader participation accelerates discovery across interconnected Earth system processes.

Community Innovation: The GEOframe system technologies, which form the backbone of the project and were designed in anticipation of the FAIR principles of reproducible research and community building, will be extended with AI agents. A domain-specific small language model trained on hydrological literature and extensive GEOframe documentation addresses the fundamental bottleneck by providing accessible interfaces to sophisticated modeling capabilities. This AI assistant will democratize access to complex Earth system modeling by enabling researchers without extensive technical expertise to interact naturally with the modeling framework through conversational interfaces, accelerating scientific discovery and broadening the user community.

References

• Bancheri, Marialaura, Francesco Serafin, and Riccardo Rigon. 2019. "The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN)." Water Resources Research 55(11): 8895-8921. https://doi.org/10.1029/2019wr025099.
• Giono, Jean. 1953. "L'homme qui plantait des arbres." Vogue, Paris.
• Giono, Jean. 2015. The Man Who Planted Trees. New York, NY: Random House.
• Miralles, Diego G., Jordi Vilà-Guerau de Arellano, Tim R. McVicar, and Miguel D. Mahecha. 2025. "Vegetation-Climate Feedbacks across Scales." Annals of the New York Academy of Sciences 1544(1): 27-41. https://doi.org/10.1111/nyas.15286.

Breakthrough Scientific Objectives and Technical Innovation

The overall project's goal is to avoid setting boundary conditions and build a dynamics system that couples all compartments dynamically with the capability to turn on and off parts of the interactions. This will instantiate through dynamic soil-biota-hydrology coupling extending WHETGEO to 3D with evolving hydraulic properties using population dynamics equations coupled to Richards-Richardson equations (target: demonstrate infiltration changes over 30-year restoration scenarios); plant resilience vs. optimization paradigm testing resilience-based stomatal control through virtual experiments implementing complete Richards-like equations with allometric parameterizations (target: quantify conditions where resilience strategies outperform optimization); surface-atmosphere feedback quantification implementing efficient ABL equations linking plant-level decisions to regional precipitation through LCL dynamics (target: demonstrate precipitation changes from strategic vegetation management); and integrated carbon assessment incorporating proven forest ecosystem models through

Integrating GLEAM Earth Observation data strategy usage within GEOframe

To better understand the previous discussions, let's examine the specific case of GLEAM 3.0 (Martens et al., 2017) and 4.0. GLEAM (Miralles et al., 2025) is a global evapotranspiration product built on multiple Earth Observation (EO) resources. Since evapotranspiration (ET) cannot be measured directly, it must be inferred or modeled from available data. AS you know, GEOframe is our system for doing hydrology. For GEOframe methodologies in catchments application, please see this previous post.

Actually GLEAM operates at 0.1-degree spatial resolution (approximately 11 km grid cells), which is adequate for global analyses but insufficient for our purposes. Our objective requires information at 1km (approximateli 0.01-degree resolution), particularly for applications in complex terrain such as the Alps, where significant topographic variation occurs within very small areas.

From Miralles et al. 2025. References can be recovered there

Earth Observation Resources in GLEAM

According to Miralles et al. (2025), GLEAM utilizes the EO and reanalysis resources listed in Table 1. Below is an analysis of how each resource could be integrated with GEOframe:

Radiation

Current GLEAM approach: 0.1-0.5 degree resolution GEOframe implementation: Point-wise calculations using local solar radiation, filtered through cloud interception and atmospheric scattering models, then topographically corrected using digital elevation models.

Limitations: The coarse satellite resolution is inadequate for rugged terrain. Additionally, GEOframe's current empirical methods lack reliability, often requiring radiation estimates from randomly selected points within catchments, reducing representativeness.

Potential improvements: Satellite data could be fused with ground measurements to enhance overall accuracy.

Air Temperature

GEOframe implementation: Kriging interpolation with drift using ground station data.

Assessment: Generally reliable since temperature varies gradually across space, though canopy effects may introduce complications.

Precipitation

GEOframe implementation: Ground station measurements interpolated using kriging (typically without drift, as drift was found insignificant). Event-specific variograms are employed.

Key challenge: Determining whether kriging interpolation accurately captures storm spatial patterns. Satellite and radar data could provide valuable validation and improvement opportunities. See also the last post here.

Wind Speed

Application: Required for Penman-Monteith formulations.

Potential integration: ERA5 reanalysis data could supplement ground station measurements through data fusion/assimilation approaches, pending reliability validation (see Azimi et al., 2025)

Vapor Pressure Deficit (VPD)

Current use: Input for Penman-Monteith solutions (D'Amato and Rigon, 2025).

Technical note: VPD represents the temperature difference between emitting surfaces and air. Understanding EO estimation methods could enable valuable comparative analyses with the Prospero model (Bottazzi et al., 2021; D'Amato et al, 2025), where VPD emerges from energy budget calculations.

Carbon Dioxide Concentration

Application: Controls transpiration conductance in both Jarvis and Ball-Berry-Leuning parameterizations.

Current status: Available as input parameter in GEOframe for Penman-Monteith and Prospero models but not utilized in Priestley-Taylor formulations.

Snow Water Equivalent (SWE)

GEOframe approach: Calculated from precipitation, temperature, and snowpack evolution models. It should not be confused with Snow Covered Area (SCA).

Improvement opportunities: MODIS snow products offer superior resolution compared to GLEAM's 25 km resolution. For mountainous terrain with 5000 m elevation changes within 25 km, higher-resolution products could provide significant improvements.

Surface Soil Moisture

GEOframe implementation: Prognostic variable within root zone compartment.

Integration potential: Could enable GEOframe calibration if EO resolution and reliability improve. GEOframe soil moisture could be upscaled to match EO data resolution for comparative analysis.

Vegetation Optical Depth (VOD)

Definition: Proxy for vegetation biomass and cumulative transpiration (assuming linear correlation).

Current status: Not implemented in GEOframe.

Integration potential: Could be connected to Leaf Area Index (LAI), which is used in both interception and transpiration calculations.

Fraction of Absorbed Photosynthetic Radiation (fPAR)

Current status: Not used in GEOframe, which employs total radiation instead.

Advantage: Available at appropriate spatial scales for potential integration.

Leaf Area Index (LAI)

Applications: Useful for both interception and transpiration calculations (when using Penman-Monteith or Prospero models).

Integration potential: High, given its direct relevance to existing GEOframe processes.

Vegetation Height

Value: Excellent spatial resolution and direct application in aerodynamic resistance calculations.

Integration status: Not currently used but could be easily incorporated into GEOframe.

Land Cover Fraction

Current status: Not implemented in GEOframe.

Potential application: Could enhance transpiration estimations.

Soil Properties

Current status: Not utilized, as no GEOframe parameters currently depend on soil characteristics.

Future applications: Could become relevant if replacing reservoir-based root zone approaches with simplified versions of WHETGEO (Tubini and Rigon, 2022) or GEOSPACE (D'Amato and Rigon, 2025b).

GLEAM Methodological Components and GEOframe Integration

Rainfall Interception

GLEAM approach: Utilizes the van Dijk-Bruijnzeel model (van Dijk et al., 2001), developed from global experimental datasets.

GEOframe current implementation: Uses the Gash model.

Integration opportunity: Adding a van Dijk-Bruijnzeel component to GEOframe could enhance model compatibility and performance. Notably, Zhong et al. (2022) successfully constrained interception estimates using fPAR, providing valuable insights for EO integration.

Potential Evapotranspiration

GLEAM approach: Employs the Penman equation for potential ET estimation.

GEOframe compatibility: This methodology is already available in GEOframe, enabling direct reproduction of GLEAM's approach. However, D'Amato et al. (2025) implement a more sophisticated Penman-Monteith formulation than GLEAM, allowing for comparative analyses.

Aerodynamic conductance: GLEAM uses Thom's equation, which differs from GEOframe's current formulation but could be easily implemented. Both approaches require roughness length and zero displacement height parameters that can be derived from EO vegetation retrievals.

Soil Moisture Integration

GLEAM4 advancement: Incorporates data assimilation using European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture data through a Newtonian Nudging scheme. The method decomposes soil moisture into anomalies and computes uncertainties using triple collocation (Miralles et al., 2025).

GEOframe current approach: Relies solely on root zone reservoirs for ET sources.

Enhancement opportunities:

• Adding ET sources from groundwater reservoirs through minor modifications to GEOframe's groundwater component
• Implementing GLEAM4's multi-layer running water balance approach, which considers constant root depth per land cover fraction

Physical realism considerations: While GLEAM4 moved from reservoir models (similar to GEOframe) to multi-layer approaches, GEOframe could implement GEOSPACE (D'Amato et al., 2025) to achieve superior physical realism compared to GLEAM4. However, the reliability of satellite-derived soil moisture estimates requires careful validation.

Stress Function Formulations

GEOframe current approach: GEOET (GEOframe's transpiration component) employs empirical schemes following Jarvis or Ball-Berry-Leuning (BBL) parameterizations.

GLEAM4 innovation: Introduces an innovative deep neural network approach replacing traditional semi-empirical stress computations. As described by the authors: "GLEAM4 replaces the original semi-empirical computation based on soil moisture and vegetation optical depth (VOD) with the deep neural network approach presented in Koppa et al., 2022."

Neural network advantages: The approach recognizes that actual-to-potential transpiration ratios are controlled by numerous environmental variables with non-linear interactions, including:

• Soil moisture and VOD
• Vapor pressure deficit (VPD)
• Incoming solar radiation (SWi)
• Air temperature (Ta)
• CO2 concentration
• Wind speed (u)
• Leaf Area Index (LAI)

Training methodology: The neural network learns universal transpiration stress functions using global eddy-covariance and sap flow data, with separate parameterizations for tall and short vegetation.

Implementation potential: Incorporating this neural network approach could represent a significant alternative for GEOframe, moving beyond traditional empirical formulations to data-driven, physically-informed methods.

Implementation Considerations

Full understanding and implementation of these methodological improvements requires careful examination of Miralles et al. (2025) and its supporting literature. The integration of these approaches could substantially enhance GEOframe's capabilities while maintaining compatibility with global EO datasets.

Conclusions

While GLEAM provides a comprehensive framework using multiple EO resources, significant opportunities exist for improving spatial resolution and integrating these datasets with process-based models like GEOframe. The main challenges involve resolution limitations and the need for validation of empirical methods against ground-truth data. The methodological advances in GLEAM4, particularly the neural network-based stress functions and improved data assimilation schemes, offer promising directions for enhancing GEOframe's predictive capabilities. Overall GLEAM should not be considered as a EO dataset but a modeling product. Describing it as an EO product makes it more objective that it is actually. Other global products on ET are available. Ecostress (Pierrat et al., 2025) is a recent notable example. A scrutiny similat to the one applied to GLEAM can be made with that platform too, but I let you as an exercise.

This post is part of the dissemination material of the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005.

References

• Azimi, Shima, Christian Massari, Gaia Roati, Silvia Barbetta, and Riccardo Rigon. 2025. “A New Tool for Correcting the Spatial and Temporal Pattern of Global Precipitation Products across Mountainous Terrain: Precipitation and Hydrological Analysis.” Journal of Hydrology 660 (133530): 133530. https://doi.org/10.1016/j.jhydrol.2025.133530.
• Bottazzi, M., M. Bancheri, M. Mobilia, and G. Bertoldi. 2021. “Comparing Evapotranspiration Estimates from the Geoframe-Prospero Model with Penman–Monteith and Priestley-Taylor Approaches under Different Climate Conditions.” WATER. https://www.mdpi.com/2073-4441/13/9/1221.
• 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). https://doi.org/10.1002/eco.70009.
• D’Amato, Concetta, Niccolò Tubini, and Riccardo Rigon. 2025. “A Component Based Modular Treatment of the Soil-Plant-Atmosphere Continuum: The GEOSPACE Framework (v.1.2.9).” https://doi.org/10.5194/egusphere-2024-4128.
• D’Amato, Concetta, Niccolò Tubini, and Riccardo Rigon. 2025. “A Component Based Modular Treatment of the Soil-Plant-Atmosphere Continuum: The GEOSPACE Framework (v.1.2.9).” https://doi.org/10.5194/egusphere-2024-4128.
• Dijk, A. I. J. M. van, and L. A. Bruijnzeel. 2001. “Modelling Rainfall Interception by Vegetation of Variable Density Using an Adapted Analytical Model. Part 1. Model Description.” Journal of Hydrology 247 (3–4): 230–38. https://doi.org/10.1016/s0022-1694(01)00392-4.
• Koppa, Akash, Dominik Rains, Petra Hulsman, Rafael Poyatos, and Diego G. Miralles. 2022. “A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation.” Nature Communications 13 (1): 1912. https://doi.org/10.1038/s41467-022-29543-7.
• Martens, Brecht, Diego G. Miralles, Hans Lievens, Robin Van Der Schalie, Richard A. M. De Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest. 2017. “GLEAM v3: Satellite-Based Land Evaporation and Root-Zone Soil Moisture.” Geoscientific Model Development 10 (5): 1903–25. http://www.geosci-model-dev-discuss.net/gmd-2016-162/.
• Pierrat, Zoe Amie, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Ryu, et al. 2025. “Evaluation of ECOSTRESS Collection 2 Evapotranspiration Products: Strengths and Uncertainties for Evapotranspiration Modeling.” Water Resources Research 61 (6). https://doi.org/10.1029/2024wr039404.
• Miralles, Diego G., Olivier Bonte, Akash Koppa, Oscar M. Baez-Villanueva, Emma Tronquo, Feng Zhong, Hylke E. Beck, et al. 2025. “GLEAM4: Global Land Evaporation and Soil Moisture Dataset at 0.1 Resolution from 1980 to near Present.” Scientific Data 12 (1): 416. https://doi.org/10.1038/s41597-025-04610-y.
• Zhong, Feng, Shanhu Jiang, Albert I. J. M. van Dijk, Liliang Ren, Jaap Schellekens, and Diego G. Miralles. 2022. “Revisiting Large-Scale Interception Patterns Constrained by a Synthesis of Global Experimental Data.” https://doi.org/10.5194/hess-2022-155.