| 17 Apr 2026
Same Streamflow, Different Water Stories: The Hidden Impacts of Streamflow-Only Calibration in Distributed Hydrological Modeling
Abstract. Distributed hydrological models enable the characterization of spatial heterogeneities in states and fluxes, including streamflow at inner points of a basin. Despite the growing number of remotely sensed observations, calibrating the model parameters using only streamflow observed at the catchment outlet remains a popular practice. In this paper, we examine how streamflow-only calibration impacts the average seasonality and spatial patterns of simulated evapotranspiration (ET), soil moisture (SM), land surface temperature (LST), and fractional snow-covered area (fSCA). To this end, we conduct calibration experiments with the Variable Infiltration Capacity (VIC) model in six basins located in Chile, using (i) different streamflow-based objective functions, and (ii) regularizing parameters associated with different physical processes. For the latter step, we develop and test a novel spatial regularization strategy based on principal component analysis of physiographic attributes associated with the modeling units contained within each basin. Our results suggest that these decisions may have large effects on the spatial representation of ET, SM1 (i.e., SM from the first soil layer in VIC), LST, and fSCA, without degrading the performance of streamflow simulations. The average streamflow seasonality can be simulated reasonably well, with large biases in ET, fSCA, SM1, and LST (in that order). In particular, different calibration configurations can yield the same annual cycle of streamflow through very different ET seasonalities, affecting the catchment-scale seasonal water balance. Additional calibration experiments incorporating ET and SM1 besides streamflow reaffirm tradeoffs in the fidelity of different simulated variables. Overall, the results presented here reinforce the benefits of including spatial patterns of hydrological variables in the calibration of distributed hydrological models and highlight the need to verify the seasonality of other simulated variables besides streamflow.
This manuscript addresses a highly relevant and persistent problem in distributed hydrological modeling: the extent to which streamflow-only calibration compromises the fidelity of simulated internal states and fluxes. By systematically exploring combinations of objective functions and spatial regularization strategies across six diverse Chilean catchments, the authors provide compelling evidence that similar streamflow performance can emerge from substantially different internal hydrological dynamics. The manuscript is generally well written, the hypotheses are clearly stated. However, several technical and presentation issues need to be addressed before publication.
Specific comments:
1. The manuscript proposes a PCA-based spatial regularization strategy using physiographic variables. While this approach is interesting, the novelty relative to existing regionalization and transfer-function approaches (e.g., MPR) remains somewhat unclear. In addition, the approach appears somewhat empirical, the motivation for using only PC1 is insufficiently explained. Could the use of additional PCs, or a weighted combination of PCs, improve the transferability of the approach? The physical meaning of the PCA-derived spatial fields is insufficiently discussed.
2. The manuscript repeatedly emphasizes equifinality and compensation among fluxes and states. However, the study does not provide a formal parameter uncertainty or identifiability analysis. I’m wondering how different objective functions influence parameter constraints, whether the inclusion of ET or SM observations reduces parameter equifinality, and to what extent the proposed PCA-based regularization framework effectively improves parameter identifiability and mitigates equifinality in distributed hydrological modeling.
3. One of the key findings is that ET seasonalities differ substantially despite similar streamflow simulations. This is an interesting and important result. The analysis relating these ET shifts to moisture availability in deeper soil layers (Figure 9) is insightful. However, the discussion stops short of a full diagnostic. It would be beneficial to explicitly state which process parameterizations are primarily impactful in the configurations that produce the most erroneous ET seasonalities. For instance, are large ET biases consistently linked to unrealistic soil water storage dynamics in layer 3? Whether snow accumulation/melt timing contributes to ET discrepancies? A more mechanistic interpretation would elevate the paper's impact.
4. The additional two multi-objective calibration experiments are valuable but appear somewhat preliminary relative to the broader conclusions of the paper. Why did the authors not consider combining multivariate calibration with the proposed spatial regularization framework?
5. Although the manuscript explicitly states that one of the research questions is how to overcome the tradeoffs between accurately replicating streamflow annual cycles and effectively simulating the seasonal patterns of other hydrological variables, the study does not appear to provide a clear or systematic answer to this question beyond demonstrating the existence of such tradeoffs.
6. The study only used a single calibration period (2005–2018) without independent validation. Given the strong conclusions regarding model realism and process fidelity, an independent validation period is important to demonstrate robustness and avoid overfitting.