PROMISE Project: A PROcess-based MountaIn permafrost Sensitivity indEx for the Euregio

The recent Blatten event (2025) and the cascade of slope failures of the last few summers in the Alps have made a question unavoidable for our community: where will the next cryospheric mass movement occur, and how likely is it at a regional scale? Comprehensive monitoring of the entire Alpine arc is, and will remain, infeasible. What we need are tools that focus attention — that tell hazard specialists, infrastructure managers and policy makers where to look.

PROMISE — PROcess-based MountaIn permafrost Sensitivity indEx — is our proposal to build one such tool for the Euregio Tyrol–South Tyrol–Trentino, led by Jan Beutel (UIBK), Andrea Andreoli (UNIBZ) and myself (UNITN), with Dominik Amschwand (UIBK) coordinating the scientific deliverables and John Mohd Wani (UNITN) leading the modelling effort. Stephan Gruber (Carleton) and colleagues from the Chinese Academy of Sciences accompany us as international partners.

Photo by B Edmaier: https://www.facebook.com/B.Edmaier/posts/thawing-permafrost-due-to-global-warming-can-trigger-huge-landslides-like-yester/835248057961452/

The idea in one paragraph

Permafrost in mountains is not a uniform sheet of frozen ground that retreats upward as it warms. It is a mosaic — rock walls, talus cones, rock glaciers, ice-cored moraines, scree slopes — and each of these landforms transmits atmospheric warming to the deeper subsurface very differently. Some are buffers (an ice-rich rock glacier with a thick coarse-blocky active layer can stay cold for decades after the climate signal arrives); others are amplifiers (a steep, snow-poor rock wall couples almost directly to the air). PROMISE will produce, for the first time at regional scale in the European Alps, a thermal sensitivity index (thSI) that classifies the Euregio terrain along this buffer–amplifier axis, in the present and into the near future.

The thSI is not a permafrost distribution map. Distribution maps answer: where is permafrost likely to be present? The thSI answers a different and, today, more pressing question: where is the ground thermal regime most vulnerable to ongoing and future change?

Why now, and why this approach

There are precedents — but none for the Alps. Smith & Burgess (2004) built a semi-quantitative sensitivity index for the Canadian Arctic using the buffer-layer model of Luthin & Guymon. The Qinghai–Tibet community has produced beautiful regional assessments (Du et al. 2024 mapped the 1982–2020 trend in soil thermal conductivity, revealing a self-reinforcing degradation: as ground warms toward 0 °C, wetting raises STC, which raises heat flux, which accelerates thaw). But the QTP is a low-relief plateau with fine-grained soils. The Euregio is rugged terrain dominated by coarse-blocky debris, fractured bedrock and snow-driven microclimates. We need a landform-resolved approach.

Two findings from the recent literature anchor our framing:

1. Preconditioning (Hauck & Hilbich 2024). A single hot-dry summer can permanently flip an ice-rich landform from buffered to sensitive: once meltwater drains, the latent-heat reservoir is gone and cannot be refilled. Climate sensitivity is not a static material property.
2. Snow melt-out timing dominates over winter snow thickness for many high-elevation landforms. Once snow exceeds the ~0.6 m decoupling threshold, mid-winter variations matter little; what matters is the snow melt-out date (SMOD), after which the dark debris is exposed to the June sun (Amschwand et al. 2025b).

Both effects make a process-based, time-varying index essential. An empirical regression on today's climatology won't capture them.

The framework: thermal resistance of the snow–active-layer system

The thSI rests on a simple but physically grounded backbone. The heat transfer between atmosphere and permafrost is $Q = \Delta T / R_{tot}$, where the total thermal resistance is additive across the seasonal snow cover and the active layer:

$$ R_{tot} = R_{snow} + R_{AL} = \frac{h_{snow}}{k_{snow}} + \frac{h_{AL}}{\alpha_{eff} C_v} $$

A high (or rising) $R_{tot}$ means climate-robust terrain; a low (or falling) $R_{tot}$ means climate-sensitive terrain. The thSI is then a convex combination of $R_{tot}$ and its relative change with respect to a reference time. Continuous values are binned into four ordinal classes — low, moderate, high, very high sensitivity — following the spirit of Smith & Burgess (2004).

The trick is that none of the terms in $R_{tot}$ is a constant. $h_{snow}$ and $k_{snow}$ evolve through the season; $\alpha_{eff}$ in coarse blocks includes non-conductive transfer (subsurface airflow) and depends non-linearly on water/ice content. This is where physically based modelling enters.

The three pillars

PROMISE rests on three tightly coupled work packages.

WP1 — Ground truth (UNIBZ, Andrea Andreoli, Raul-David Șerban). Continuing the PERMAWAT line, the consortium extends the existing 32-site ground-surface-temperature network in Matsch and Schnals (with the iconic Lazaun rock glacier) by adding paired soil-moisture/temperature loggers, and instruments two new permanent permafrost boreholes at Hochebenkar / "Auf dem Grat" in the Rofental — the first permanent permafrost boreholes in Tyrol, filling a long-standing observational gap. From these data, the team derives the full battery of thermal indices (MAGST, FDD/TDD, frost number, zero-curtain, SOD/SDD, SCD, frost-cracking window, ALT, MAGT, dza), and uses the semi-empirical FROSTNUM model (Nelson & Outcalt 1987, updated by Cao et al. 2022) as a first-pass hot-spot screening.

WP2 — Numerical modelling (UNITN, with John Mohd Wani). The role of the Trento group is to convert atmospheric forcing into atmosphere–ground transfer functions using GEOtop 3.0, the process-based, three-dimensional land-surface model that solves the coupled mass and energy balance, the multi-layer snowpack, Richards-equation flow with freeze–thaw, and (where relevant) non-conductive subsurface heat transfer in coarse-blocky terrain (Zegers et al. 2025). Two complementary tracks:

• A point-scale 1D ensemble at the cluster centroids defined by the TopoSUB landscape clustering, designed to provide statistically independent samples across the predictor space and to feed both the per-terrain transfer functions and the ML upscaling.
• Distributed catchment simulations (one in Tyrol, one in South Tyrol, one in Trentino, at 50–100 m resolution) that serve as full-physics process diagnostics — quantifying how much lateral water and heat fluxes, slope-aspect contrasts and terrain heterogeneity matter for the transfer functions.

The 1D ensemble is calibrated against the WP1 thermal indices; the distributed runs use the WP3 snow-cover products as boundary conditions and provide the sanity check on what 1D necessarily misses.

WP3 — Upscaling (UIBK, with the new PhD student). Two preparatory products — terrain classification from sub-3-m DEMs and rock glacier inventories, and a pixel-wise SMOD trend analysis over the Euregio building on the 30-m Landsat product of Bayle et al. (2025) — feed into the final thSI map. We will explicitly test the hypothesis that SMOD trends dominate over $R_{snow}$ variability for most high-elevation landforms, by comparing alternative snow-cover descriptors in the GEOtop ensemble. The upscaling itself follows the TopoSUB hybrid statistical–process approach: cluster the landscape, simulate at centroids, map back to the full Euregio.

What's genuinely new

Three things, I think:

• Forward-looking, not descriptive. The thSI estimates rates of change, not present-day occurrence. It is meant to be read together with conventional permafrost distribution maps (Boeckli et al. 2012), not against them.
• Physics-grounded, not regression-fitted. Every grid cell of the final map is anchored, via the cluster centroid it belongs to, to a GEOtop simulation that explicitly resolves snow dynamics, freeze–thaw, and (where relevant) convective heat transfer in coarse blocks.
• A bridge between communities. Mountain permafrost research is, today, fragmented between detailed point-scale process studies (the Murtèl and Matterhorn traditions) and large-scale Arctic / QTP statistical assessments. PROMISE deliberately operates at the intermediate scale where the Alps actually live.

Caveats — what the thSI is not

This deserves emphasis. The thSI quantifies the thermal-conditioning component of permafrost vulnerability. It does not predict slope failures, rockfall release, or rock-glacier surges. Mechanical instability also depends on lithology, fracture geometry, glacial debuttressing, and meteorological triggers we do not model here. The thSI is meant to be added to — not to replace — the topographic, geological and geomorphological information already available to hazard assessors.

Wider context on this blog

For readers who want to follow the technical thread, the cryospheric work of the group is documented here in some detail:

• Summarizing my cryospheric work (made with good company) — the most recent overview, with all the relevant references.
• Permafrost is not ice — soil-freezing thermodynamics — slides and video on why frozen soil is a multiphase system and why proper models must carry three thermodynamic potentials.
• Freezing soil requires new algorithms — on the Newton–Casulli–Zanolli (NCZ) nested-Newton method that underpins our energy-conserving freezing-soil solver.
• Advances in permafrost modelling: application of the Nested Newton algorithm for solving the heat equation — the follow-up on Tubini, Gruber & Rigon (2021, The Cryosphere).
• New Insights in Permafrost modelling (EGU Wien 2017) — the earlier conceptual setup.
• Snow, Ice and Permafrost — the broader cryosphere framing with Alberto Bellin.
• Advanced Topics in Snow Hydrology — measurements, modelling, remote sensing — context on snow–permafrost interactions.
• All posts tagged Permafrost and Snow.

Timeline and acknowledgements

If funded (decision in March 2027), PROMISE will run May 2027 – April 2030. Field installations and the first GEOtop terrain-specific runs are scheduled for the first year; the snow-cover trend product and the first thSI metrics for the second; the final Euregio thSI map and the dissemination workshops for the third. Data and code will go out FAIR and open-source, through PANGAEA, the partners' geoportals, and the existing GEOtop/GEOframe GitHub repositories.

A warm thank you to Dominik Amschwand and Jan Beutel (UIBK), Andrea Andreoli and Raul-David Șerban (UNIBZ), John Mohd Wani (UNITN), and Stephan Gruber (Carleton), whose combined expertise on rock-glacier thermodynamics, wireless sensor networks, periglacial geomorphology, cryo-hydrological modelling and topographic downscaling makes this proposal possible.

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Riccardo Rigon, with the PROMISE consortium.

TACOS Project: Redefining How We Map and Model Catchments Response

A few minutes before midnight on May 31, we submitted TACOS — Transferability of soil water process understanding Across sCales for flOod and shallow landSlide prediction — to the PRIN 2026 call (Prot. 2026P2HL9S). Five Research Units, 36 months, €1.49 M total budget, €1.19 M requested from MUR.

The question

How much does knowing the soil actually help us predict floods in small ungauged catchments and rainfall-induced shallow landslides — and at which spatial scales does that knowledge pay off?

The question sounds simple but is curiously unresolved. Operational frameworks for ungauged basins lean on climate, topography, and geology; soil tends to be either smoothed away or absorbed into calibration. Yet soil is the first interface water meets in the critical zone — its depth, layering, pore structure, macropore connectivity, hydraulic properties, and antecedent moisture decide whether intense rainfall infiltrates or runs off, and whether a slope holds or fails.

The idea: a data degradation experiment

The methodological heart of TACOS is straightforward to state, and (we believe) unusually clean. Hold the model fixed and progressively coarsen the soil inputs — high-resolution DSM from full field profiles (Scenario A) → national-scale soil databases (Scenario B) → global coarse products like SoilGrids (Scenario C) — then test the predictions against independent flood events and landslide inventories. Pre-declared metrics with a priori tolerances identify the soil-information ceiling: the coarsest scenario for which predictions remain reliable.

The point is not to demonstrate that better soil information is better — that's almost trivially true — but to find where the curve flattens. Where it does, expensive high-resolution soil mapping is not justified by predictive gains. Where it doesn't, it is. This is a decision rule public authorities can actually use to prioritise soil-monitoring investments.

Four knowledge gaps, four work packages

The project addresses four interlocking gaps:

KG1 — Hydrology-oriented soil mapping (WP2, led by RU_UniNA, F. Terribile). Existing maps were built for agronomy; their relevance for hydrological response has rarely been evaluated systematically. We re-derive uncertainty-aware soil classifications explicitly oriented toward hydrological behaviour, combining Sentinel-1/Sentinel-2/PRISMA, machine learning (Random Forest, Gradient Boosting, spatiotemporal Transformers), and targeted field campaigns.

KG2 — The scaling problem and the breakdown of continuum assumptions (WP3, led by RU_CNR, M. Rossi). Richards' equation works — until it doesn't. We investigate, experimentally and theoretically, the conditions under which preferential flow, macropore activation, and non-equilibrium effects overtake equilibrium-based formulations. The framework links flow-regime classification to a-priori-evaluable thresholds (soil type, antecedent moisture, rainfall intensity) that determine which governing equation enters the Scale-2 model.

KG3 — The soil-information ceiling (WP4, led by RU_UniPD, M. Borga). The data degradation experiment itself, run inside both hydrological (GEOframe, GEOtop, WHETGEO) and landslide-triggering (GEOtop-LFS, TRIGRS, SlideforMAP, LANDPLANER) frameworks.

KG4 — Regionalisation lacking pedologically meaningful storage (WP5, led by RU_PoliTO, P. Claps). Index-flood regionalisation across ~100 small catchments (FOCA / FOCA2), runoff-coefficient analysis via SEASONEX and SIREN, leave-one-region-out cross-validation with and without soil-derived covariates. If soil information does not reduce quantile error, we say so.

The pilot sites

We work across three nested scales:

• Scale 1 (pore → pedon → hillslope): laboratory infiltration experiments, X-ray microtomography (SkyScan 1273, 3.5 μm/voxel), tracer transport tests, and seasonal/interannual in-situ parameter monitoring.
• Scale 2 (hillslope → small basin): three pilot catchments spanning a useful range — Ressi (0.02 km², Italian pre-Alps; the long-term ecohydrological catchment of the Padova group), Sangone at Trana (145 km², mixed forest–agricultural), Cordevole at La Vizza (7 km², high-elevation Dolomitic). The Collazzone pilot area (Umbria, 90 km²) anchors the landslide work; Langhe (NW Italy, 600 km²) provides the statistical-benchmarking testbed via the 1994 widespread event.
• Scale 3 (regional → territorial): ~100 small catchments nationwide.

The team

The consortium pulls together complementary competences:

• RU_UniTN — R. Rigon, G. Formetta. Coordination, process-based modelling (GEOframe/GEOtop), hydrological connectivity, kinetic theory of unsaturated flow.
• RU_PoliTO — P. Claps (substitute PI), S. Tamea, P. Mazzoglio. Regional hydrology, flood frequency analysis, the FOCA and I²-RED national databases, regionalisation methodology.
• RU_UniNA — F. Terribile, G. Langella, N. Mzid. Pedology, Digital Soil Mapping, EO covariates, LANDSUPPORT legacy.
• RU_UniPD — M. Borga. Mountain basin response, debris flows, hillslope-to-channel transfer indices, geo-hydrological modelling.
• RU_CNR (IRPI / ISAFOM) — M. Rossi, M. Bancheri, R. De Mascellis, S. L. Gariano. Physical infiltration experiments, X-ray CT, shallow-landslide thresholds, preferential flow.

The five RUs commit 38.6 person-months of permanent-staff effort plus 11 new temporary contracts (~228 PM total).

What's next

If funded, TACOS would start in 2027. Either way, the proposal is now part of how I think about soil hydrology: not as a parameter to calibrate, but as an empirically measurable structural control whose value for prediction is a question we can settle by experiment rather than assertion.

The intellectual threads reach back through several conversations on this blog — DARTHs and participatory digital twins; the kinetic theory of unsaturated flow that replaces Richards' equation with a pore-occupancy distribution; the percolation-based reading of field capacity and macropore connectivity; the GEOframe ecosystem. TACOS is where these strands meet operational prediction in ungauged basins, with shallow landslides as the natural companion problem — same soil–water dynamics, different observable.

Thanks to the four co-PIs and their teams for the intense final weeks, and to the RETURN PNRR and AdBPo communities for the questions that made this proposal, in a real sense, write itself.

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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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

4DHydro - Hyper-resolution Earth observations and land-surface modeling for a better understanding of the water cycle

The 4DHydro Project, a shortname for Hyper-resolution Earth observations and land-surface modeling for a better understanding of the water cycle is an ESA contract  . The overarching objective of the Hyper-resolution Earth observations and land-surface modelling for a better understanding of the water cycle project, hereafter 4DHydro , is to foster a wide and thriving collaboration between the EO water cycle community developing novel high-resolution EO data products, and the land surface and hydrological modelling community engaged in advancing hyper-resolution modelling of the hydrological cycle at regional and continental scales.

Participants include most of the groups working on these topics in Europe. The synthesis of the proposal can be found here. 

Snow droUghts predictioN in the Alps: a changing climate assessmEnT: SUNSET PRIN Project

Reduced mountain snowpack, i.e. a “snow drought”, due to rising temperatures or changes in precipitation amount has critical implications on water resources availability. Ongoing climate change may influence the frequency and severity of snow droughts. SUNSET aims to develop an integrated methodology to assess the impact of climatic variations and changes on snow droughts and on the seasonal low flows in the Italian Alps. To meet this objective, SUNSET develops based on two main scientific advances. The first advance are the Convection-Permitting Models (CPM) for climate simulations, with improved topographical features, physical representation of mountain‐precipitation interactions, and avoided errors from convective parameterizations. A second recent advance is the availability of improved multi-objective calibration strategies for hydrological-snowpack models which permit reliable simulations of both snow pack evolution and runoff variability. With SUNSET, a modelling chain linking bias-corrected Convective Precipitation Modelling (CPM) and Regional Climate Models (RCM) outputs as well as state-of-the art hydrological models will be developed and exploited to quantify changes in the frequency and severity of snow droughts through the end of the current century. The project focuses on two broad study areas in the Western and Eastern Italian Alps, where a set of catchments with detailed process observations are available.

                    

SUNSET is based on two types of data: i) 20-to-30 years time series of validated catchment precipitation-temperature-discharge data from the network of twelve project study basins; ii) process data, collected in previous projects which will help improving the hydrological model validation. The past dataset includes observations from MODIS and Sentinel-1 that are functional to provide correct snow cover areas and snow water equivalent data.

Climate simulations will be based on 12-member CPM and RCM ensembles from the FPS-CORDEX project available for 3 time slices (historical, 1996-2005; near future, 2041-2050; far future, 2090-2099) for the extreme climate change scenario, i.e. RCP8.5. RCM outputs from CORDEX will also be considered to evaluate the added value of CPM compared to coarser resolution models and to investigate the climate change signal under different scenarios which are not available in CMP simulations, i.e. RCP4.5 (six-member RCM ensembles) versus RCP8.5 (six-member RCM ensembles. Hydrological models are part of an expandable system called GEOframe developed along the last 15 years and have already a setup for most of the study basin network.

SUNSET will communicate and disseminate the project results to a wide audience of residents in the two study areas and beyond through collaborations with Local Authorities. SUNSET will test, archive, document, and keep track of versions of the research code and software using open-source software and data protocols and accessible documentation.

By clicking on the above Figure you can access the full proposal. 
 

Finally we've got the project!
 

DARTH4MED - A Digital eARth Twin of Hydrology for the prediction of water scarcity in the Mediterranean area

The DARTH4MED, D4M for short, project aims to be a high resolution twin of the hydrology and carbon cycle of the Italian peninsula. It is based on Po, WATZON and WATERSTEM projects, making treasure of previous modelling efforts like GEOtop and the GEOframe system, and GIS tools implementations like Jgrass and the Horton Machine toolbox. It builds upon state-of-art hydrological modelling case studies of various catchment sizes, from hillslope to Po and Blue Nile. It also draws on experiences in IT applied to hydrology with developments of the object modelling system, OMS.

D4M gives substance, both technical and scientific, to the Digital Earth metaphor and exploits it to improve the work of scientists and professionals, and to support open science. It aims to provide a shared infrastructure usable by scientists and users to investigate the processes involved in the water, energy and carbon budgets, WB, EB and CB, at a very fine spatial and temporal scale, 1 km2, hourly.

The GEOframe system already contains a sophisticated and complete set of modelling components, constituting a solid basis of comparison for innovative developments. Open API and training will be offered to anyone to advance the mathematical, statistical and numerical descriptions of hydrological and eco-hydrological processes with little programming effort. From this perspective, the project will be an experiment in participatory science, since the tools developed could be improved and given back by collaborative researchers. The method of multiple hypothesis testing will be the rule of scientific endeavour.

The core of the system will manage the interactions of groundwater, vadose zone, surface water, snow, vegetation, atmosphere, usually analyzed separately, and join them seamlessly in the continuum containing the feedbacks among the parts. On these bases researchers will be able to evaluate climate, hydrologic, pedological, ecological droughts.

D4M has several primary objectives, listed below:

• To provide the core of a DE, defined as a Digital eARth Twin Hydrology system (a DARTH), to do hydrology by computer, with an infrastructure that allows partecipative hydrology and makes Earth system science practice easier for all the Italian Peninsula.
• To improve the modelling of the water budget, WB, energy budget, EB, Vegetation and Carbon Cycle.
• To provide forecasts for several variables, as detailed in the Synopsis.
• To resolve some research questions, as presented in the Synopsis.
• To give researchers sound tools on which to base their analysis of climate, hydrologic, pedological, ecological and agronomic droughts.
• To provide a high level of abstraction and encapsulation for modelling services, so to allow improvements to parts of the DARTHs by anyone without disrupting the whole.
• To give API and web services to final users, researchers, technical professionals, programmers, to connect their studies and products to the whole D4M, thus combatting the fragmentation of hydrological modelling through a participatory open platform.

Besides efficient algorithms, the effort will require the smart implementation of parallel computing infrastructures, which will remain mostly invisible to the users. All the infrastructure will be open source, built with open source tools and provided with open data.

The project was just submitted for the FIS call. Here below you find the proposal and the relevant annexes.

• My 3600 characters CV
• The proposal document
• The challenges, methods and objectives
• Go to the previous post to know what DARTHs are

Compressing all the ideas in such a few words was quite difficult and the platform on which we had to upload the material with some issues (non accepting, for instance "()[]-/" and other characters. Some requirements quite stupid. The selection will be great. I obviously think that the gain for the country with such a project really great. Finger crossed and, if there are better projects, hope they'll win. 

WATERSTEM project

 It was a pleasure to work with the young guys who setup the PRIN proposal WATERSTEM. Please find below the abstract of the proposal and cross the fingers for the project having success.

Mediterranean mountainous basins provide critical water supply and ecosystem services, yet these environments are increasingly at risk due to anthropogenic stressors and competition for water across urban, agricultural and environmental demands. On the top of this, future climate projections suggest a drier and warmer Mediterranean with large increases in the frequency, duration, and severity of hydrological droughts (i.e., runoff and groundwater levels below than normal) with serious consequences for the management of water resources and natural ecosystems. In spite of the recent progress in land surface monitoring, current drought estimation in widely used operational products still largely relies on poorly parameterized potential evapotranspiration, in combination with simple hydrological bucket models (e.g., drought indices) which have shown to lead to questionable results. As hydrological systems are intrinsically intertwined with climatological and ecological systems, the propagation of meteorological droughts (i.e., precipitation below than normal and higher temperatures) through them is modulated by a variety of mechanisms which are linked to carbon and water cycle interactions and specifically to how different plant species i) access subsurface water storages and ii) respond to water stress, high CO2 and high evaporative demand. Ignoring the parameterization of these mechanisms is often the norm in state-of-the-art land surface and hydrological models and impacts water balance closure via incorrect representation of transpiration leading to uncertainties in hydrological drought prediction. The ultimate goal of WATERSTEM is to unravel the interactions between carbon and water cycles as to understand the modulating effect of the vegetation on water-supply deficit (as opposed to the more frequently addressed meteorological drought) and its impact on water resources and natural ecosystems in Mediterranean climates. The work plan will focus on six Mediterranean mountainous basins and will employ a novel combination of field monitoring (water stable isotopes, tree ring analysis and geophysical measurements), remote sensing, data assimilation and ecohydrological models. WATERSTEM will develop a multidisciplinary and novel conceptual framework that will be used to translate the acquired scientific knowledge into practices to support water resources and silvopasture management across a variety of Mediterranean climates and physiographic settings. This is the core research of WATERSTEM, which is intimately coupled to the concept of Critical Zone (CZ, the Earth's permeable near-surface layer from the top of the trees to the bottom of the groundwater, where rock, soil, water, air, and vegetation interact and sustain life) that is fundamental for understanding and predict: i) hydrological droughts, ii) episodes of vegetation mortality (e.g., forest dieback) and iii) whether natural ecosystems may become a source or sink of carbon during drought.

SnowSunMed Project

The overall objective of SNOWSUNMED is to enhance parsimonious water use and balance water allocation in mountain catchments of the Mediterranean area by demonstrating the integrated potential of hydrological, hydro- ecological and erosion simulation models together with novel, climate-smart irrigation technologies, novel materials and adapted forest landscape restoration (FLR) approaches. Such innovations will be implemented through multi-stakeholder processes in three demonstration sites (DS), drawing conclusions that can be replicated at Mediterranean-scale.

will start from characterizing water availability, demand and decision making in the three DS in Morocco (Laou River basin), Lebanon (Shouf Biosphere Reserve) and Italy (Adige River basin). It will progressively and iteratively integrate results from the field application of: (i) a new hydrological modelling approach, mixing process-based and machine-learning modelling solutions with ground and satellite data to quantify all water cycle components, including surface water availability, evapotranspiration and snowmelt; (ii) novel methods for quantifying ecosystem water needs and soil erosion, which have feedback effects on societal needs; (iii) Internet of Things- and Artificial Intelligence- based precision irrigation solutions and new polymers for water absorption; (iv) locally adapted Forest Landscape Restoration (FLR) practices for increased water collection especially during snowmelt. Results of such demonstrations will then be streamlined into local water and land use policies and IWRM practices to improve water allocation and to upscale water use efficiency at the catchment level. This iterative process will lead to improved water governance in the three DS, with focus on preventing socio-economic conflicts and environmental stress, and will finally be described for learning and replication in other Mediterranean mountain areas.

The project preproposal can be found here.