Research in Arctic Permafrost. A new start

In the rapidly changing Arctic, understanding permafrost behavior is critical for infrastructure, ecosystems, and climate science. Marianna Tavonatti's master's thesis at the University of Trento has delivered some achievements that points towards the advance our understanding of Canadian Arctic permafrost dynamics and provide essential tools for climate adaptation.

Permafrost—permanently frozen ground—covers 24% of the Northern Hemisphere and is rapidly thawing due to climate change. This thesis focused on the Canadian Arctic near the Inuvik-Tuktoyaktuk Highway, using advanced computer modeling to understand how permafrost responds to changing temperatures over time scales from decades to seasons. 

Permafrost - https://www.maggiebaylor.com/permafrost

Marianna Thesis covers: 

1. GEOtop Model Implementation: First comprehensive application of the GEOtop model for Canadian Arctic permafrost, successfully validated against real ground temperature data.

2. Historical Analysis: 73-year simulation (1950-2023) revealing clear evidence of accelerating permafrost warming and active layer deepening.

3. Future Projections: Advanced climate scenarios showing significant future changes in permafrost stability with direct implications for infrastructure planning.

4. Advanced Theoretical Framework: Enhanced understanding of frozen soil physics, improving how we model phase changes in complex soil systems.

5. Methodological Innovation: Created an integrated GlobSim-GEOtop modeling chain applicable to Arctic regions worldwide.

6. Practical Applications: Provided quantitative data essential for Arctic infrastructure design and climate adaptation strategies.

7. Scientific Contributions: Advanced climate change understanding with implications for carbon cycling and global climate feedbacks.

However, the best things is to read the thesis that you can get by clicking on the figure. 

Marianna's work establishes a foundation for future advancement in Arctic permafrost science. The research identifies specific opportunities for enhanced spatial modeling, improved climate projections, and expanded ecosystem coupling—providing a clear roadmap for continued innovation in this critical field.

As the Arctic continues to experience rapid environmental change, research like Marianna's becomes increasingly vital for understanding system responses and supporting sustainable development in one of Earth's most climate-sensitive regions. 

About erosion and hillslope evolution: a lost master thesis rediscovered

 This 2003/2004 Master thesis by Martina Brotto deserves some more visibility. It presents an exploration into how slopes evolve over time through erosion processes and tackles one of the fundamental challenges in geomorphology: understanding and predicting how hillsides change their shape through the complex interplay of soil production and erosion.

What makes this research particularly interesting is its departure from traditional landscape evolution models. While most existing models assume that erosion is limited only by the transport capacity of flowing water or wind (what scientists call "transport-limited" processes), Brotto introduces a more realistic approach. Her model considers that erosion can also be limited by how quickly rock breaks down into soil and how much material is actually available to be moved ("detachment-limited" processes). This distinction might seem technical, but it's crucial for understanding real-world erosion, especially in areas where bedrock is close to the surface or where soil production rates are slow.

The heart of the research lies in developing two complementary mathematical models. The first focuses on diffusive erosion processes – the slow, continuous movement of soil particles down slopes through countless small disturbances like frost action, animal activity, and the impact of raindrops. Using sophisticated numerical techniques including the conjugate gradient method, Brotto shows how these processes gradually smooth out irregularities in the landscape, creating the gentle, rounded hills we often see in nature. Her simulations, some extending over 20,000 years of landscape evolution, reveal how factors like the initial slope angle and the rate of soil creep influence the final landform shape.

Beyond this introduction is the good thing to do is to read the thesis. You can find it by clicking on the Figure above (one beautiful painting by Vincent Van Gogh). 

Giulia Merler using GEOSPACE for simulations on a wineyard

Giulia Merler's Bachelor thesis investigates the effects of climate change on Trentino vineyards using GEOSPACE simulation software. While this research is preliminary and requires further validation, it reveals compelling insights into how global warming affects vine health.

For a high-resolution view of the poster, simply click on the image below. All supporting materials and data are accessible via the QR code provided.

The findings may surprise those unfamiliar with viticulture: elevated temperatures place significant stress on grapevines, posing a serious threat to wine production. While experts in the field may find this unsurprising, seeing the impact quantified through sophisticated modeling tools brings the reality into sharper focus and provides valuable data for future planning. This work pairs with the one by Marco Feltrin that can be found here. 

Anna De Nardi works on EPNs

Our three years degrees on Environmental Sciences requires for graduation a work exercise that can be really interesting.  Among the last graduations, I followed the work by Anna De Nardi, on the Extended Petri Net which I think was particularly valuable.  Anna, in fact, elaborated  all the MARRMoTs hydrological models using the Extended Petri Nets graphs. This show in practice that the statement that all the integral distributed hydrological models  (a.k.a. lumped distributed models, the hydrological dynamical systems, semi-distributed hydrological models) can be represented by means of the EPN is actually true. 

The MARRMoT (Modular Assessment of Rainfall-Runoff Models Toolkit) is a collection of hydrological models designed to simulate rainfall-runoff processes across different scales and conditions. Developed in MATLAB, MARRMoT provides a modular framework that allows researchers to compare, assess, and implement various conceptual hydrological models consistently

Key Features and Purpose:

• Model Variety: MARRMoT includes 47 different rainfall-runoff models, ranging from simple lumped models to more complex, semi-distributed structures.
• Comparative Framework: Its standardized environment enables researchers to test models on the same data sets and conditions, making it easier to assess model performance, suitability, and sensitivity.
• Customization: Users can adapt model configurations or modify parameters within the toolkit to better fit specific research objectives or study basins.
• - Open-source and Accessible: MARRMoT is open-source, encouraging community contributions, modifications, and application across various hydrological research areas.

This toolkit is particularly useful for researchers looking to improve or validate hydrological models and for those who want a structured way to compare the effects of model structure on hydrological simulation outcomes.

Extended Petri Nets (EPNs) are an enhanced version of traditional Petri Nets, a mathematical modeling language originally designed to describe distributed systems with concurrent, asynchronous processes. EPNs incorporate additional features to increase modeling flexibility and allow for more complex system representations. In scientific modeling, they are used for representing and analyzing systems in which discrete and continuous interactions are critical, like biological networks, chemical reactions, ecological models, or engineering processes.

For getting the poster at higher resolution, please click on the above image. For all the material, including all the MARRMoT models, please look at the here:

• All the  MARRMoT models represented with EPNs
• All the  EPNs-MARRMoT models catalogued by the number of reservoirs
• All the fluxes variables names found in the MARRMoT  collections

Other Hydrological models represented with EPNs can be found in Bancheri et al., 2019 below. 

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.

Marialaura, Bancheri, Francesco Serafin, and Riccardo Rigon. 2019. “Supporting Material for: The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN).” Water Resources Research.

Knoben, W. J. M., J. Freer, K. J. A. Fowler, M. C. Peel, and R. A. Woods. 2019. “Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) v1.0: An Open Source, Extendable Framework Providing Implementations of 46 Conceptual Hydrologic Models as a Ontinuous Space-State Formulations.” GMDD, February, 1–26.

Knoben, W. J. M., J. Freer, K. J. A. Fowler, M. C. Peel, and R. A. Woods. 2019, “Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: An Open-Source, Extendable Framework Providing Implementations of 46 Conceptual Hydrologic Models as Continuous State-Space Formulations.” 2019. Copernicus GmbH. https://doi.org/10.5194/gmd-12-2463-2019-supplement.

Rigon, Riccardo, and Marialaura Bancheri. 2021. “On the Relations between the Hydrological Dynamical Systems of Water Budget, Travel Time, Response Time and Tracer Concentrations.” Hydrological Processes 35 (1). https://doi.org/10.1002/hyp.14007.

Trotter, L., W. Knoben, K. Fowler, M. Saft, and M. Peel. 2022. “Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: An Object-Oriented Implementation of 47 Established Hydrological Models for Improved Speed and Readability.” Geoscientific Model Development, August. https://doi.org/10.5194/gmd-15-6359-2022.

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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