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.

Integrating Earth Observation Data in Hydrological Modeling: Challenges and Opportunities

Some time ago, I wrote about the need for remote sensing experts to collaborate more closely with hydrologists to improve both hydrological forecasting and remote sensing applications. While the communities have largely continued working separately (and who am I to challenge such a fruitful paradigm?), my involvement in several projects with ASI (the Italian Space Agency) and ESA (the European Space Agency) has made this integration an unavoidable priority for me. This means I must become an Earth Observation (EO) scientist myself to cover the gap.  First question actually would be:, is the EO data resolution enough for hydrologic simulations ? (The answer here is usually no, it is usually not, but we have, as usual,  to survive with this and understand how the coarser resolution can be useful. Paraphrasing a well know motto: EO are usually wrong but can be useful).

Two Categories of Earth Observation Data

Earth Observation (EO) data can be broadly divided into two categories: quantities we routinely observe on the ground and those we typically do not (or could but usually don't) measure directly.

Ground-Measured Quantities

For simplicity, let's assume proximal data are correct, subject to verification and cross-checking with EO and modeled data. The first category includes rainfall, air temperature, point discharge measurements, surface water levels, groundwater storage (though less frequently measured), solar radiation, soil moisture (rarely measured), wind speed and evapotranspiration if we fully trust eddy covariance towers, which I don't take for granted.

EO-Derived Quantities

The second category encompasses snow cover, depth, and water content, along with soil moisture, vegetation water content, and evapotranspiration. It also includes various ancillary data such as albedo, leaf area index, land cover and land use classifications, surface temperature (which is distinct from the air temperature we typically measure), groundwater storage variations, precipitable water content, cloud cover, atmospheric liquid water content, and other quantities I'm not recalling at the moment. (Yes evapotranspiration is also on this side)

Challenges with Ground-Measured Quantities

For ground-measured quantities, comparing data sources is essential, acknowledging that ground measurements are generally more accurate but come with important caveats. There exists a fundamental scale mismatch where ground measurements are point observations, while EO measurements represent areal averages derived from radiation or gravity observations. Following Rodriguez-Iturbe and Mejia (1974), connecting point values to areal values requires careful assumptions about spatial scaling. Additionally, any systematic differences between ground and EO measurements must be identified and eliminated through proper bias correction procedures. How EO data are biased if compared to ground stations ?

However, ground data are spatially sparse. Obtaining geographic information requires interpolation, typically using methods like Kriging that preserve spatial correlation structure. Yet hydrological processes may be spatially nonlinear, raising the question: Do EO data, even if biased, better reproduce the spatial structure of hydrological variables?

If so, merging ground and EO data could yield superior spatial fields—an approach we pursued in Azimi et al. (2025). These quantities share another important characteristic: they can be produced by hydrological models and serve as inputs, validation data, or assimilation targets to improve model performance (which raises questions about how we evaluate "improvement").

Challenges with EO-Derived Quantities

The second category presents different challenges. These are primarily ancillary quantities that help estimate hydrological variables—leaf area index being the classic example for estimating transpiration, since plants without leaves don't transpire. Each quantity must find its proper role in hydrological models to be truly useful, requiring them to become model parameters.

The key question becomes: How can we effectively use these data in models?

While leaf area index integration seems straightforward, the data become useless if transpiration is estimated using temperature-only formulas. Similarly, how do we use land use data in models? What model characteristics connect to "land use" if it's not an explicit parameter?

Dal Molin et al. (2020) provide an interesting answer: geological setting relates to model structure within the framework of Hydrological Dynamical Systems (reservoir-based hydrological models). This expands possibilities but complicates the connections.

Another crucial question: Are these data actually necessary for the modeler's purpose?

Perhaps not, if the goal is simply discharge forecasting, since calibration procedures have proven effective at compensating for missing data and model's structure (as shown in the 4DHydro project outcomes). However, if the objective is reproducing realistic water budgets, these data may be essential, making assessment of all intermediate steps necessary, as shown in the 4DHydro Science Case 5. 

Conclusions

This blogpost does not aim to be comprehensive but just an aid to start the discussion. Two clear conclusions emerge from this analysis. First, EO ancillary data are required only if models have mechanisms to utilize them effectively. Second, they may not be necessary for all modeling purposes, particularly when the primary objective is discharge forecasting rather than comprehensive water budget reproduction.

Ultimately, new data should either increase the precision and accuracy of predictions or enhance our understanding of hydrological process mechanisms. The challenge lies in determining when and how to integrate these diverse data streams effectively.  The final goal is what stated in the last paragraph of this post, which I invite you to read again. 

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.

Cited References (an absolutely insufficient sample of the possible ones)

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.

Molin, Marco Dal, Mario Schirmer, Massimiliano Zappa, and Fabrizio Fenicia. 2020. “Understanding Dominant Controls on Streamflow Spatial Variability to Set up a Semi-Distributed Hydrological Model: The Case Study of the Thur Catchment.” Hydrology and Earth System Sciences 24 (3): 1319–45. https://doi.org/10.5194/hess-24-1319-2020.

Rodriguez‐Iturbe, I., and J. M. Mejía. 1974. “On the Transformation of Point Rainfall to Areal Rainfall.” Water Resources Research. https://doi.org/10.1029/WR010i004p00729.

Methodology and tools for analyzing the hydrology of catchments: four papers and a set of slides and videos.

Recently I recommended 5 papers of mine, which I consider  representative of my recent work. However, they are on the side of the theory/numerics/informatics work. Not less important are those that could be erroneously classified as applications. The four papers presented here, in fact, represent more than a straightforward run of models to individual catchments. They deploy a comprehensive methodology that integrates traditional surface water systems with new features and methods. Their approach combines mixed-resolution spatial discretization through strategic Hydrological Response Unit (HRU) refinement with accurate pre-analysis of the input multi-source validation using neutron probes (as representative of local field measurements), satellite data, and conventional discharge observations.

The modular GEOframe implementation provides flexible model configuration while preserving physical consistency and enabling validation of individual modeling components. A key finding across these papers is that careful analysis of input data can guide model organization and improve  predictions. Each paper targets complete water budget estimation, identifying inconsistencies and providing more robust assessments of catchment hydrology than traditional modeling approaches than the traditional simulation based on discharge alone.

These studies introduce  analytical tools that should become standard practice for catchment hydrology modelers and gently use Earth Observations in their specific contexts. The collective work establishes a framework where data-driven model organization, multi-source validation, and comprehensive water budget analysis modelling try to converge to advance our understanding of hydrological processes at the catchment scale.  Part of the lesson learned from these papers has been also summarized in the set of "Seven Steps in Modeling a catchment", a series of slides and videos that can be considered complementary to reading the papers.  

Much more can be done using the flexibility of the GEOframe system which remains undisclosed.

References

Abera, Wuletawu, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon. 2017. “Modeling the Water Budget of the Upper Blue Nile Basin Using the JGrass-NewAge Model System and Satellite Data.” Hydrology and Earth System Sciences 21 (6): 3145–65. https://doi.org/10.5194/hess-21-3145-2017.

Andreis, D; Formetta, G.;Bancheri, M. and Rigon R., Multiple Resolution Analysis of an Alpine Basin. submitted to Water Resources Research, 2025. Preprint

Abera, Wuletawu, Giuseppe Formetta, Marco Borga, and Riccardo Rigon. 2017. “Estimating the Water Budget Components and Their Variability in a Pre-Alpine Basin with JGrass-NewAGE.” Advances in Water Resources 104 (June): 37–54. https://doi.org/10.1016/j.advwatres.2017.03.010.

Azimi, Shima, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon. 2023. “On Understanding Mountainous Carbonate Basins of the Mediterranean Using Parsimonious Modeling Solutions.” Hydrology and Earth System Sciences 27 (24): 4485–4503. https://doi.org/10.5194/hess-27-4485-2023.