We have just submitted a new paper — A flexible open-source modular framework for ecohydrological modeling: Application and validation of GEOSPACE-1D — by Concetta D'Amato, Paolo Benettin, Andrea Rinaldo, and Riccardo Rigon. It is the natural companion and follow-up to the GEOSPACE framework paper published in Geoscientific Model Development earlier this year, which described the design principles and modular architecture of GEOSPACE. That paper introduced the framework; this one puts it to work.
The core of the paper addresses three questions: Can GEOSPACE reproduce observed ecohydrological dynamics across such diverse conditions? What are the practical advantages of its modular structure? And does it enable novel analyses — the kind that open new scientific doors rather than merely close validation loops?
On performance: GEOSPACE reproduces soil water pressure dynamics, depth-resolved water content, bottom drainage, and evapotranspiration fluxes across all four lysimeters with R² values of 0.87, 0.81, 0.83, and 0.73 for L2, L1, L4, and L3 respectively. Mean residual biases are negligible throughout. The slightly lower performance over bare soil reflects a known structural limitation of the Penman–Monteith formulation for soil evaporation under conditions where thermal inertia matters — an honest diagnosis rather than a defect to be papered over.
On modularity: the willow lysimeter was simulated with three alternative evapotranspiration formulations — GEOET-Prospero-PM, GEOET-Priestley-Taylor, and GEOET-Penman-Monteith FAO — keeping the soil component (WHETGEO) and the partitioning solver (BrokerGEO) identical across all three runs. All formulations close the cumulative water balance (~600 mm over the experiment), but the Prospero-PM formulation captures sub-daily peak dynamics with roughly half the residual spread of the other two. The calibrated Priestley-Taylor α = 4.16 and FAO Kc = 3.9 — both well above standard values — are informative precisely because they expose structural limitations of simplified formulations when applied to a high-transpiration system dominated by stomatal control.
On novel capabilities: the model computes root water uptake (RWU) for every control volume at every time step, yielding a full depth–time distribution of uptake intensity. The willow shifts its water sourcing dynamically in response to moisture depletion and atmospheric demand, with the mean uptake depth varying over time in a way that closely mirrors the measured root density profile. This kind of depth-resolved diagnostic is directly relevant to isotope-based ecohydrology, where xylem water provides only a bulk integrated signal — GEOSPACE's spatial resolution of the RWU can help interpret what that bulk signal actually means.
The paper grew out of Concetta D'Amato's PhD work at the Center Agriculture Food Environment (C3A) at the University of Trento, supported by the WATZON COST Action and the PRIN 2017 WATZON project. Readers interested in the longer history of GEOSPACE and its components can find much of the background documented here on AboutHydrology: see the posts on GEOSPACE and WHETGEO, and in particular the earlier post on Concetta's PhD thesis and the exploration of the SPAC.
The source code is on GitHub at https://github.com/geoframecomponents/GEOSPACE-1D, with a frozen version on Zenodo. All simulation data are openly available. GEOSPACE continues to grow.
Waiting for the official preprint, you can download it here. Here instead, find the supplemental material.
This post is not self-explanatory and requires digging into other posts and some papers.
Please review Section 2 of Concetta's paper (https://onlinelibrary.wiley.com/doi/10.1002/eco.70009?af=R) and verify the calculations presented there.
The model in D'Amato and Rigon (2025) uses a non-capacitive approach (it doesn't account for the thermal capacity of plants), and so will be if the same derivation is specialized for snow (or soil), which is a limitation. However, this approach is still more physically based than semi-empirical formulations or degree-day methods commonly used. In the literature, these are referred to as "stationary solutions" of the system. Despite the name, these solutions respond instantaneously to changing boundary conditions (radiation, latent and sensible heat fluxes), as evident in equation (10), which varies with radiation, wind velocity, and roughness.
Equation (10) and subsequent equations in Concetta's paper are essentially the Prospero solutions (though exact implementation should be verified in Concetta's code). The time interval of integration is, in principle instantaneous, but eventually you would like to integrate it over a finite time step (a hour, or a day, for instance).
A non negligible aspect is that snow can melt into water and for any temperature you get from the energy budget, you need to partition the water in liquid water and ice. For this reason you probably need a partitioning function, like the one used for partitioning precipitation in rainfall and snowfall or you can simply use a melting law like in simple models but now the temperature used should not be the air tempeature but the snow temperature. See melting in simple models in the links below
for further information.
An important term is missing from the formulation: heat exchange by conduction with the ground, which should be represented as:
G = C_s T_Δg := C_s (T_g - T_s)
Where:
Since this flux depends on the independent variable, it introduces additional terms that modify solution (10). Please derive these calculations independently.
Other terms that don't depend on the independent state variables can be included in the S_nk term. With these modifications, the Prospero code can effectively simulate the snow energy budget. Similar arguments apply to soil modeling.
A further consideration is the proper parameterization of the conductances C and C_E fluxes in equation (10), which differ from transpiration cases. For soil, according to Lehman-Or theory, evaporation should be modeled as potential until the water storage exceeds a threshold S_T, then decreasing proportionally with storage below this threshold when implementing an integrated model (Details ? I do not know).
I know that there are several missing aspects in this post. Who is interested, please ask.
P.S. - These components have then to be carefully coupled to the other components. With respect to this, please consider the following:
First review the presentation materials I've shared:
Getting new features to the linear systems (Vimeo2025) |
| Please find the paper by clicking on the Figure |
This thesis aims to address the complex issue of SPA interactions by developing a comprehensive set of models capable of representing the intricate dynamics of this system. At the core of this research lies the integration of sophisticated descriptions of hydrological and plant biochemical processes into a novel ecohydrological model, GEOSPACE-1D (Soil Plant Atmosphere Continuum Estimator model in GEOframe).
In conclusion, this thesis provides valuable insights into SPA interactions and lays the groundwork for future research and applications. By embracing the challenge of under- standing and modeling the SPA continuum, this work contributes to the ongoing efforts to address environmental challenges and promote sustainable practices. The thesis draft can be dowloaded by clicking on the figure.
In our collaborative work with Concetta D'Amato for the WATERSTEM project, we encountered the initial constraint of transpiration imposed by the hydraulic conductance of the stem-root system. Through our research, inspired by Manzoni et al. [2013], we discovered that the sigmoidal form of conductivity leads to an optimum for transpiration. We attempted to reproduce this phenomenon using the data provided by Kroeber et al. [2-13]. After considerable effort, we successfully generated the gray curve in the Figure, which exhibits a peak just before -4 MPa and enables too high transpiration.
However, we realized that the soil resistance was missing from our analysis. To address this, we incorporated the conductivity of a Silt Loam soil using the van Genuchten Mualem parameterization. The resulting brownish curves serve as evidence that the soil plays a crucial role, as anticipated by Carminati and Javaux [2020]. It is important to note that these curves depict the limits imposed by the soil and stem, which determine the potential sapflow rates, but do not reflect the constraints imposed by plant physiology. To account for plant physiology, we introduced the stomatal resistance, represented by the three dashed curves under different working hypotheses whose parameterization was taken from Daly et al. [2004]. The red points in the Figure represent the plant's working points (although the coupling with the atmospheric boundary layer is not depicted). One notable aspect of the Figure is that at typical soil suctions, the sapflow curves appear relatively flat, and the working points result in relatively constant sapflow despite variations in xylem/leaves pressure. The complete story will soon be available in Concetta's Ph.D. thesis, and the detailed process of creating the Figure can be found in its supplemental material notebooks.
References
Carminati, Andrea, and Mathieu Javaux. 2020. “Soil Rather Than Xylem Vulnerability Controls Stomatal Response to Drought.” Trends in Plant Science 25 (9): 868–80. https://doi.org/10.1016/j.tplants.2020.04.003.
Daly, Edoardo, Amilcare Porporato, and Ignacio Rodriguez-Iturbe. 2004. “Coupled Dynamics of Photosynthesis, Transpiration, and Soil Water Balance. Part I: Upscaling from Hourly to Daily Level.” Journal of Hydrometeorology 5 (3): 546–58. https://doi.org/10.1175/1525-7541(2004)005<0546:cdopta>2.0.co;2.
Kröber, Wenzel, Shouren Zhang, Merten Ehmig, and Helge Bruelheide. 2014. “Linking Xylem Hydraulic Conductivity and Vulnerability to the Leaf Economics Spectrum—A Cross-Species Study of 39 Evergreen and Deciduous Broadleaved Subtropical Tree Species.” PloS One 9 (11): e109211. https://doi.org/10.1371/journal.pone.0109211.
Manzoni, Stefano, Giulia Vico, Gabriel Katul, Sari Palmroth, Robert B. Jackson, and Amilcare Porporato. 2013. “Hydraulic Limits on Maximum Plant Transpiration and the Emergence of the Safety-Efficiency Trade-Off.” The New Phytologist 198 (1): 169–78. https://doi.org/10.1111/nph.12126.
As a conclusion of what done for the DICAM excellence project we organized half a day of seminars. As keynote speaker we invited Andrea Rinaldo (GS), one of our mentors, who gave a keynote talk entitled "Reflected In Water".
The contribution by Andrea was followed by the talk given by Giuseppe Formetta (GS) who was hired upon financial support of the project and presented his research activities. His talk was: Modeling multiple natural hazards with the GEOframe system in the Trentino Alto-Adige region (Video).
Finally we had contributions from the three doctoral students also hired on the project,
@ The Italian Hydrological Society Hydrology days, Concetta D'Amato presented her work on the LysGEO model. As some knows LysGEO put together the WHETGEO 1D component with the (revised) Prospero component. The first estimates infiltration, the second performs evaporation and transpiration. Together they constitute a soil-water-atmosphere model, that it is what LysGEO is. Or if you prefer, it is a tool to investigate the critical zone.
LysGEO was already described elsewhere in the blog. However, in this case there is a relevant addition, derived from the work done utilizing the funding support of the WATSON cost action in Lausanne with Andrea Rinaldo e Paolo Benettin. They built a lysimeter whose seems to be the right experiment to test LysGEO. The presentation shows the first results (with almost no calibration). Clicking on the image above, you get the slides (in English). Here you can appreciate the presentation in Italian given by Concetta. LysGEO is a product of the WATZON PRIN project.
The work of Concetta D’Amato Ph.D. started with joining together a reasonable evapotranspiration model with a solid infiltration model building a virtual lysimeter.
