This manuscript addresses an important question in catchment-scale dissolved organic carbon (DOC) modelling: whether acceptable outlet DOC simulations are achieved for the right reasons—namely, through physically plausible internal DOC dynamics. By comparing lumped and landscape-explicit model structures across four headwater catchments, and by contrasting baseline and constrained calibration approaches, the study makes a valuable contribution to the field. The central conclusion is compelling: satisfactory stream DOC performance alone does not guarantee realistic internal behavior, and a landscape-explicit structure paired with internal constraints offers a more robust framework for interpretation and scenario analysis.

Overall, the manuscript is promising; however, several substantial issues must be addressed before it is suitable for publication.

Major Comments:

1. The constrained calibration framework requires greater transparency. While the manuscript explains that 100,000 parameter sets were generated, screened against internal constraints, and then filtered for the highest-KGE solution, it remains unclear how restrictive these constraints are and the extent to which they reduce the feasible solution space. I encourage the authors to explicitly report the number of parameter sets that satisfy each individual constraint, as well as all constraints jointly. Furthermore, it would be highly beneficial to better illustrate how the constrained calibration alters the distributions of both parameters and internal states.

2. The delineation of riparian areas necessitates stronger justification. Because the core argument of the paper is build on the explicit separation of upland and riparian units, the conclusions are highly likely to be sensitive to the definition of the riparian extent. Incorporating a brief sensitivity analysis, or at a minimum, a more comprehensive discussion regarding the uncertainties associated with the chosen delineation approach, would significantly strengthen the manuscript.

3. The scenario analysis should be interpreted with greater caution. Although the manuscript notes that the experiment focuses solely on increased upland carbon inputs without representing the hydrological alterations associated with forest dieback, this limitation must be stated more prominently. The exercise should be explicitly framed as a structural stress test rather than a comprehensive prediction of forest-dieback impacts.

4. The authors should more explicitly acknowledge that while the landscape-explicit structure represents a significant advancement, it is not yet a complete solution. The study demonstrates that baseline outlet calibration can yield unrealistic internal DOC dynamics, and that the landscape-explicit model performs better under constrained calibration and scenario testing. Nevertheless, the discussion also indicates that riparian DOC concentrations may still be underestimated relative to observations, underscoring the need for further process refinement.

Minor Comments:

1. Several formatting and technical errors must be addressed. Notably, the heading “3 Methodology” mistakenly appears at the beginning of the Results section. Additionally, the export equations should be carefully verified for consistency with the conceptual model description.

2. The reliance on Kling-Gupta Efficiency (KGE) as the primary evaluation criterion should be explicitly justified, particularly given the authors' observation that model ranking is sensitive to the choice of performance metric.

3. The manuscript would benefit from a clearer presentation of how many behavioral models satisfy the imposed internal constraints. For instance, the finding that only 53% of behavioral landscape-explicit models meet all constraints under baseline calibration is highly significant and warrants greater emphasis.

4. Several figures are visually dense and should be streamlined. Shifting some of the highly detailed material to the Supplementary Information would enhance overall readability.

5. Given that the study's novelty stems largely from its model implementation and calibration design, making the code and computational workflow openly accessible would substantially elevate the paper's impact and reproducibility.

The present manuscript (MS) compares the performance of a lumped and a landscape-explicit version of a conceptual DOC catchment model. While both versions could satisfactorily simulate streamflow DOC, low groundwater DOC and increased C-input could only be simulated by the more complex, landscape-explicit model version. The paper is nicely written and gets to the point that a more complex model structure and additional information, i.e. low groundwater DOC, adds to the realism of model simulations.

However, these results seem expectable at first sight and so far the contributions to general knowledge which go beyond this particular model and beyond this particular application to four specific German basins seem rather limited. Eventually, a more detailed representation of DOC formation, but for sure a more detailed data analysis are warranted to justify the model structure: is the presented model really a valid compromise between required complexity and computational efficiency and therefore might be regarded as a straight-forward tool for practitioners? As follows three major concerns are listed.

1. Simplified representation of DOC origin

The conceptualization of carbon transformation follows the well-known INCA-C model by (Futter et al., 2007) but is even simplified by single net transformation between the soil organic carbon pool and DOC. While the authors state that this reduces equifinality, this very simple assumption combines various individual multicomponent reactions. An existing example in HESS shows that more physical basis is possible with reasonable computational effort (Wen et al. 2020, https://doi.org/10.5194/hess-24-945-2020). Those authors incorporated coupled elemental cycling, stoichiometry, thermodynamics and kinetics in their DOC catchment model already 6 years ago. This means that a more sophisticated representation of DOC formation is possible and might be warranted also in the present model. If the authors decide to maintain their simplified approach, this needs strong justification and more detailed checks.

2. Missing carbon mass balance

In the introduction, the authors criticize the model by Birkel et al. 2014 for not closing carbon mass balances. But in their own model application, they only study runoff and DOC concentrations separately, but do not calculate DOC loads or carbon mass fluxes. This would also be valuable for their “scenario” of forest dieback: Is their landscape-explicit model version really superior when it comes to a realistic carbon mass balance? By the way, also for drinking water reservoirs, realistic DOC loads are more important than reproducing DOC concentration patterns.  

3. DOC-export during different flow conditions

The authors only test their models by overall performance during a multi-year period. They do not study DOC simulations during different flow conditions. Again the study of Wen et al., 2020 might be relevant here. Their study showed that DOC-stocks are formed during periods of high temperature, while the main DOC export occurs during following wet conditions or during high magnitude runoff events. They also showed that streamflow DOC equals groundwater DOC during dry conditions, riparian zone DOC during intermediate conditions, and hillslope DOC during real wet conditions. These results could easily be checked or challenged by the present application. This might also prove the physical soundness and robustness of the present approach. Potentially, a shortcut of riparian buffer zones during high magnitude events might also play a role which is not included in the present model so far. Especially in the “Warme Bode”, the landscape-explicit model fails to predict single DOC peaks. Those are particularly evident following the 2015 European drought which was characterized by high temperatures and below average rainfall. The different behavior between of the four basins and how an improved model adequately represents this could be another test here.

This manuscript evaluates if accurate DOC simulations at catchment outlets reliably indicate realistic internal DOC dynamics. By comparing lumped and landscape-explicit models across diverse headwater catchments, the study assesses how model structures and calibration strategies (baseline and constrained) perform under varying conditions. The results demonstrate that good performance at the catchment outlet does not necessarily imply realistic internal process representation, as stream-level agreement can mask substantial internal discrepancies. The analysis further shows that landscape-explicit models incorporating physically motivated internal constraints provide enhanced interpretability and are better suited for scenario analyses. Overall, the study highlights that robust DOC modelling requires an adequate representation of internal dynamics and supports the use of landscape-explicit, internally constrained model structures for catchment-scale applications.

In summary, the manuscript shows clear potential; however, the outcome is somehow expectable. Several issues related to clarity, the linkage between hydrology and DOC dynamics, data uncertainty, and the interpretation of performance metrics need to be addressed:

1. Although the DOC model description is detailed, a short, high-level overview would benefit readers less familiar with the framework—particularly regarding how hydrological simulation outputs are incorporated into the DOC simulations (e.g. whether and how grid-based data are aggregated).
2. The introduction refers to a carbon mass balance. Please clarify whether the model applies a closed carbon mass balance and discuss any implications this assumption may have for the results.
3. Hydrology plays a key role in the modelling experiments.
   1. The 30 selected simulations exhibit similarly strong hydrological performance, including comparable representations of individual flow components (Figure S3). Expanding the analysis to include simulations with contrasting hydrological behavior—such as improved high-flow performance—could provide additional insight and strengthen the discussion and conclusions, even if overall performance is slightly reduced but still acceptable.
   2. Please discuss how the underestimation of high flows affects the simulated DOC dynamics.
4. Figure 4 indicates an uneven distribution of calibration and validation observations. In particular, Kalte Bode and Warme Bode have comparatively few validation observations at different times. The number of observations per period should be explicitly reported, as this imbalance may influence flow-condition representation and the interpretation of model performance.
5. Please address the uncertainty associated with the DOC measurements. Could higher observation frequency or event-based (e.g. daily) DOC observations potentially improve model performance.
6. The Kling–Gupta Efficiency (KGE) is used as the primary performance metric, integrating correlation, variability, and bias between simulated and observed DOC concentrations. Given that these components can contribute differently to the overall KGE—as suggested by NSE, R² and bias in the supplementary material—further discussion of their respective roles, as well as the strengths and limitations of KGE in this context, would be valuable. In particular, it would be informative to assess whether one component dominates KGE and how this relates to different flow conditions and associated DOC dynamics.

Minor Comments:

• Figures S4–S6: Please clarify in the captions whether the results refer to the calibration period, the validation period, or both.

General comments

The following manuscript compares two modeling approaches – one that combines all landscape units within a single catchment (i.e., lumped) and one that treats landscape units independently (i.e., landscape-explicit) – for predicting dissolved organic carbon (DOC) transport from upland areas into streams. Previous such modeling efforts have performed model calibration using only in-stream DOC concentrations; here, the authors additionally perform model calibration using interior (i.e., groundwater) DOC concentrations. Overall, this manuscript does contribute to future modeling efforts, but in order to be most useful, the authors would do well to provide (1) additional rationale for their model validation approaches and (2) explicit recommendations for those interested in applying this approach in other catchments, since they note the challenges inherent in using this model in different systems.

Specific comments

• The authors should provide more clarity in the methods to address how they reconciled using weekly to biweekly observations of DOC alongside daily estimated values. Was there any interpolation or other methods used to fill in the original dataset? How might this have affected the variability of their daily model estimates?
• As the authors found that results (under the baseline calibration) varied by evaluation metric, there should be additional justification added to the methods to provide rationale for their focus on using Kling-Gupta efficiency (KGE) values in the main text and primarily including NSE, BIAS, and R2 results only in the supplement.
• Additional text should be added to the discussion to further examine why simulated DOC concentrations were unrealistic in the groundwater and stream pools under the different calibrations. What might be some additional mechanisms/processes that the model is unable to account for or capture the variability of? And which might be masked or biased under baseline and constrained calibrations?
• Broadly, the discussion would also benefit from more analysis of results in the context of other models/results from other systems. Currently, it reads very narrowly focused on this particular study and location.
• Per my earlier comment, additional rationale for the authors’ primary reliance on KGE values would help to provide added confidence in their findings comparing baseline and constrained calibration values. Currently, it is hard to reconcile the “slight” decrease in landscape-explicit model performance with no change in NSE, BIAS, and R2 values between calibrations as well as the “significant” decrease in the lumped model performance with no change in R2 values. If the authors are using all four evaluation metrics jointly to describe model performance, this should be clearly stated in the methods; if instead, they weigh KGE values most in determining model performance, this instead should be clarified.

Technical Corrections

Line 40 – It is unclear why the authors focus specifically on temperate/boreal systems in this sentence. If the manuscript plans to do the same, this should be introduced prior.

Line 170 – Please review the subscripts in Equations 11 and 12; they do not appear to match the compartments displayed in Figure 1.

Line 293 – I believe this heading should read as “3 Results”.