the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 May 2026
Synergizing grassland and soil system model expertise by coupling GRASSMIND (v2.0) and BODIUM (v1.2)
Abstract. Ecological models often have a specific focus, simplifying other system components. In the context of landscapes under climate change, it is increasingly important to include all relevant components and their interactions in detail in the models. Grassland models, advising management strategies for this important vegetation type of European landscapes, often lack detailed and reliable hydrological and soil resource dynamics that influence plant growth in grasslands. This study investigates the potential to overcome this issue by coupling an existing grassland with a soil system model, making use of their expertise in a specialized area. Here, the individual- and process-based grassland model GRASSMIND is coupled to the systemic soil model BODIUM using the coupling framework FINAM. The influence of soil water on grassland dynamics is shown to be more reliable with the coupled models than with GRASSMIND alone. In addition, the coupling offers the potential to tackle shortcomings in the representation of other plant processes such as root growth. However, the most urgent challenge is to overcome the ambiguity in the parametrization of GRASSMIND itself. Our experience suggests that maintaining the native models as independent components provides flexibility for future improvements but also complicates updating parametrizations in the combined system as the individual models evolve.
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Status: open (until 29 Jun 2026)
- RC1: 'Comment on egusphere-2026-1713', Anonymous Referee #1, 19 May 2026 reply
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RC2: 'Comment on egusphere-2026-1713', Anonymous Referee #2, 28 May 2026
reply
The authors try to replace GRASSMIND's simplified hydrology with BODIUM's detailed soil-water physics while keeping both models independently maintainable by coupling them using the FINAM framework. They introduce a "self-coupling test" to identify and verify interface variables, and they evaluate the coupled system against multi-year biomass, yield, soil-moisture and mineral-nitrogen data. The coupled model reproduces soil moisture far better than GRASSMIND alone and gives a more credible balance of water- and nitrogen-limitation on growth. The coupling is carefully executed, the self-coupling test is a genuinely transferable idea, and the topic is core GMD territory. That said, several issues need to be resolved before the quantitative conclusions can be trusted and my substantive concerns are below, followed by minor points:
Major:
The Code and Data Availability section states that GRASSMIND v2.0 is "only available on request," and the v3.0 fork is described as not yet tested or documented. GMD's policy does not permit this as the model code described in the paper must be deposited in a persistent public archive under an open-source-compatible license at the time of submission, and "on request" plus a registration-gated, non-redistributable license does not meet that bar. The input data are archived exemplarily, and BODIUM and the coupling scripts are available, so the gap is concentrated on the one model that matters most.The weather preparation uses a Thornthwaite PET, which is well documented to underestimate evapotranspiration in temperate lowlands relative to Penman-Monteith. The discussion's recurring puzzle is that the deeper soil layers stay too wet and that soil evaporation appears too low, which is exactly the symptom a low-biased PET would produce. Before attributing this to BODIUM's evaporation barrier being "too strong," the authors should rule out the forcing. Radiation and temperature are already available, so a Penman-Monteith comparison run is feasible and would clarify whether the residual is a model problem or an input problem.
The 2013 to 2020 measurements come from a Kern DBS60-3, which reports gravimetric water content (the figure axis confirms "% per fresh weight"), while the 2020 to 2025 SMT100 data and the model output are volumetric ("% per fresh volume"). No bulk-density conversion is described, yet soil moisture enters the calibration objective and underpins the main claim that the coupled model matches observations. Need further explanation.
In Tables 2 and 3 the overlap factor pins to its upper bound in essentially every run, the rooting parameters and the height-width ratio vary substantially between the two repetitions of the same setup, and several other parameters sit on their bounds. The authors note the rooting parameters are "ambiguous," but running each setup only twice samples the response surface too sparsely to characterize that. A global sensitivity screen to identify the influential parameters, followed by a Bayesian or ensemble calibration returning posterior distributions, would either resolve the ambiguity or document it properly. It would also give the ensemble spread in Figures 3 to 9 a real basis rather than one driven solely by mortality stochasticity.
The objective function is under-specified, and equal weighting is questionable for me. The cost is "the sum of the absolute differences divided by the mean of the measurement values," but with biomass, yield and soil moisture combined, and very different observation counts and measurement uncertainties per type (the sensors carry a stated ±3% accuracy), the implicit weighting can dominate the optimum. Please further explain.
Minor:
The sentence "it is increasingly important to include all relevant components and their interactions in detail in the models" overstates the case and contradicts the paper's own sensible simplifications (a single grass functional type, deactivated components, FINAM judged unnecessary). No model includes everything, as "All models are wrong, but some are useful". I would recommend rewording to emphasize selective process detail where it matters for the question.
The text calls setup 1a "calibrated with soil moisture" and 1b "calibrated without," which reverses the definitions in Section 3.4.
All hydraulic and texture values derive from a single 2005 survey, while the run extends to 2025. This sits awkwardly against the discussion of drought-driven changes in porosity and conductivity.
Citation: https://doi.org/10.5194/egusphere-2026-1713-RC2 -
RC3: 'Comment on egusphere-2026-1713', Anonymous Referee #3, 31 May 2026
reply
This manuscript presents the coupling of the grassland model GRASSMIND and the soil system model BODIUM through the FINAM framework, aiming to combine the strengths of both modelling approaches while maintaining their independent development. The study addresses a relevant challenge in grassland modelling, namely the representation of interactions between vegetation dynamics and soil water processes under changing environmental conditions. The coupling strategy is well motivated, and the concept of integrating the species- and process-oriented representation of GRASSMIND with the more detailed soil hydrology of BODIUM is scientifically sound. The manuscript demonstrates the potential of this approach to improve the representation of soil moisture dynamics and to provide additional insights into plant-soil interactions. My main comments are provided below, followed by a number of minor remarks.
Minor comments:
- The notation of nitrogen species should be checked throughout the manuscript and Table 1 to ensure correct chemical formatting (e.g., NH₄⁺ and NO₃⁻ subscripts/superscripts are not consistently displayed).
- The study objective is introduced relatively late in the introduction (line 58), which weakens the narrative flow. As aspects of the model coupling are already introduced earlier (lines 29-30), the later statement appears somewhat repetitive. We recommend presenting the research gap and study objective earlier in the introduction and then introducing the modelling approach. A clearer progression from gap to aim to methodology would improve readability and reduce redundancy.
- In Figure 6, the caption states that mineral nitrogen was only measured in layer D1; however, model results are also shown for layers D2-D3. This may lead to ambiguity regarding which outputs are supported by observations and which are model-only results. We recommend clarifying this distinction in either the figure caption or the figure itself.
- For Figure 7, the different model setups are difficult to distinguish visually. Consider using a more contrasting colour palette and/or different line styles to improve readability.
- The statement "The case of BODIUM and GRASSMIND is somehow unusual..." (line 65) would benefit from further clarification. As many process-based ecosystem and agroecosystem models explicitly represent plant and soil processes while using weather and management as external inputs, it is not immediately clear what aspect of the BODIUM-GRASSMIND combination is considered unusual. We recommend clarifying the specific novelty or distinguishing characteristic being referred to.
Major:
1. The coupling strategy introduces a temporal lag in the exchange of soil water and nitrogen information between BODIUM and GRASSMIND. As described in lines 197-202 and illustrated in Figure 2, GRASSMIND uses soil water potential and nitrogen concentrations originating from the previous coupling step, while updated soil states become available only after BODIUM has completed its calculations. This effectively alters the original process sequence of GRASSMIND. While such sequential coupling approaches are common, additional discussion would be helpful regarding the implications of this one-step delay. In particular, could the authors elaborate on the expected effects on plant water and nitrogen uptake during periods of rapid environmental change (e.g., drought onset, rewetting events, or fertilization)? Was the potential influence of the lag assessed or considered negligible relative to the daily time step?
2. The weather input data originate from multiple sources and processing steps, including site measurements, gap-filled observations, Copernicus-derived radiation data, and derived estimates of potential evapotranspiration. Additional information on the implications of these preprocessing choices would be helpful. In particular, temperature gaps exceeding 30 days were filled using linear interpolation. Given that the study focuses on drought-related processes, it would be useful to discuss the potential impact of this approach on the representation of climatic variability and extreme events. Furthermore, a brief justification of the selected radiation data source and the overall consistency of the assembled weather dataset would strengthen the methodological transparency of the study.
3. Tables 2 and 3 suggest potential parameter identifiability issues. Several parameters are estimated at or near their calibration bounds (e.g., overlap factor), while others, particularly the rooting parameters and height-width ratio, vary substantially between repeated calibrations of the same setup. We recommend discussing the implications of this parameter ambiguity and its potential impact on the robustness and interpretation of the model results.4. While the study demonstrates improved simulation of soil moisture through the coupling with BODIUM, the analysis of plant water availability appears to rely primarily on soil moisture dynamics within the simulated profile. Since rooting depth and drought response are central themes of the manuscript, additional information on groundwater conditions and potential groundwater contributions to plant water uptake would be valuable. It remains unclear whether groundwater interactions, capillary rise, or fluctuating water tables are represented in the modelling framework or are assumed to be negligible. This aspect should be clarified, as it may influence the interpretation of both rooting behaviour and drought-related water limitation.
Citation: https://doi.org/10.5194/egusphere-2026-1713-RC3
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- 1
The integration of GRASSMIND and BODIUM is technically robust, and the improvements in soil moisture modeling are acknowledged as valuable. Nevertheless, the reviewer identifies three principal areas requiring attention.
First, a major concern is the restricted accessibility of GRASSMIND v2.0. As noted in the manuscript (p. 28), the code is currently "only available on request," hindering transparency and reproducibility.
Second, the calibration process reveals parameter ambiguity. For example, the "overlap factor" (p. 10) consistently reaches its maximum limit of 1.0 across all simulations (Table 2), and the rooting parameters (r1–r2) exhibit considerable variability between calibration attempts (Tables 2–3).
Third, the post-2020 validation is inadequately analysed. Specific issues include a ~7–9 percentage point overestimation of deep-layer soil moisture (p. 17) and an unsatisfactory representation of early reseeding yields, particularly in the absence of sensitivity analyses (p. 16).
Beyond these primary concerns, this reviewer identifies several interpretative ambiguities. These include circular reasoning in water-limitation claims, as the coupled model's water limitation is judged "more reliable" primarily because it inherits BODIUM's moisture calculations (p. 22). There is also no detailed breakdown of drought-year yield losses, meaning the individual contributions of water, nitrogen and temperature to the actual yield reduction (not just limitation factor) are never quantified (Figure 7). Moreover, the significance of the self-coupling test is overstated: while technically valid, its scope is limited to verifying data-transfer mechanisms rather than confirming ecological equivalence (Appendix D).
Several minor points also warrant attention. These include figure captions lacking full clarity (e.g. specifying that GRASSMIND executes first in each timestep), unweighted objective functions despite variations in measurement uncertainty (e.g. early-year biomass and sensor-based soil moisture with ±3% accuracy are given equal weight; p. 11), an inconsistent treatment of the 2020 plowing/tillage event (GRASSMIND is restarted while BODIUM continues running and retains soil memory; p. 9, p. 24), the ambiguous presentation of model‑only nitrogen outputs (Figure 6 shows D2–D3 without measurements, though the caption states only D1 was measured) and inadequate discussion of plot‑to‑plot variability (the measured variance across the five GCEF parcels is never compared to the model's narrow ensemble envelopes; p. 26). This reviewer also suggests that Table 1 could be split into two tables (differentiating variables passed from GRASSMIND to BODIUM and vice versa) and recommends clarifying why "rain seeping into soil" is marked as "additional", implying BODIUM typically processes precipitation directly.
In addition, this reviewer notes that: (i) modifications to the shading scheme (Appendix B2, p. 31–33) might influence seedling competition dynamics, as switching from own leaf area index to overall leaf area index for shading calculations could exacerbate the poor early growth after reseeding; (ii) a spin-up phase with diverse initial soil profiles could better encompass observed plot-to-plot heterogeneity (p. 26), as the authors acknowledge that initial soil conditions vary across parcels but are not modelled; and (iii) the significance attributed to FINAM appears disproportionate to its actual contribution, as the authors state that FINAM "offered no additional value" and "complicated the workflow" (p. 27), yet it appears prominently in the abstract.