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.
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