| 18 Sep 2025
Towards a semi-asynchronous method for hydrological modeling in climate change studies
Abstract. Hydrological impact assessments under climate change commonly rely on conventional modeling chains where climate projections are bias-corrected before being used in hydrological simulations. While this improves agreement with historical observations, it can introduce methodological uncertainties, reduce the diversity of climate ensembles, and smooth out extreme events. Asynchronous methods have been proposed as an alternative, allowing hydrological models to be calibrated directly with raw climate model outputs. However, fully asynchronous methods often fail to capture the timing of key hydrological processes, especially in snow-affected regions.
This study introduces and evaluates a semi-asynchronous calibration approach that incorporates a monthly temporal structure to address these limitations. Using the physically based WaSiM model, we compare the semi-asynchronous, fully asynchronous, and conventional methods across ten snow-influenced catchments in southern Quebec, Canada, under historical and future climate conditions.
The results show that while the fully asynchronous and semi-asynchronous methods perform well in preserving streamflow distributions and high-flow extremes, only the semi-asynchronous method succeeds in restoring the seasonal timing of key processes such as snowmelt and low flows. The semi-asynchronous method notably reduces intermodel variability in streamflow and snow water equivalent compared to the fully asynchronous approach. It also exhibits seasonal dynamics that closely align with observations and the conventional method, despite relying on uncorrected climate inputs. In contrast, the fully asynchronous method shows signs of desynchronization, with unrealistic snowmelt timing and elevated variability across projections. The conventional method, while more stable in the historical period, exhibits an increase in intermodel variability under future conditions, likely due to divergent magnitudes of projected change across climate models. The semi-asynchronous method presents a clear improvement over the fully asynchronous approach by restoring temporal coherence and improving the simulation of seasonal processes. It also reduces intermodel variability while maintaining the raw climate signal and preserving the distribution of streamflow.
Compared to the conventional method, which benefits from stable and consistent simulations but tends to dampen extremes through bias correction, the semi-asynchronous approach offers a compelling alternative. It strikes a different balance between realism, ensemble diversity, and the ability to represent extreme events, making it particularly valuable for future-oriented climate impact assessments.
This study highlights the potential of the semi-asynchronous method as an innovative and robust tool for hydrological modeling under climate change. As climate model simulations continue to improve and their biases are progressively reduced, the semi-asynchronous approach is poised to benefit significantly, enhancing its potential for future hydrological projections.
The paper of Talbot et al. addresses the topic of climate change hydrological projections, and specifically the issue of climate projections bias correction for hydrological modelling, by introducing a variation of the fully asynchronous calibration approach (in which hydrological models are calibrated directly with raw climate model outputs), whose main feature is constraining the calibration for each calendar month, rather than over the whole period.
The basic idea behind is conceptually simple, and the new approach is proposed as a “middle ground” between conventional methods based on climate output bias correction techniques and the fully asynchronous method. Results are interesting and deserve to contribute to the ongoing discussion on the addressed topic, but before publication, some major changes and/or clarifications are needed.
First of all, from a strictly mathematical point of view, a more detailed analysis is needed to examine the differences arising when using a single distribution rather than 12 distributions derived from the main distribution, highlighting their effects. This would strengthen the methodology significantly, along with other specific clarifications (e.g., why the calibration is constrained at a monthly scale rather than, e.g., seasonal or 15-day scales?).
Then, calibrations based on RMSE (Eqs. 2 and 3) are more strongly driven by higher streamflow values than those based on KGE (Eq. 1). Therefore, the comparison between the three approaches is compromised by the use of two different objective functions. I suggest, for at least one catchment, using the same objective function (RMSE) in all cases and checking the changes and their extent.
Another question concerns the extent to which global climate model output can be used in the calibration of a hydrological model without prior bias correction. I mean, some global models, in some cases, perform so poorly locally that they do not even reproduce seasonal patterns (e.g., wet winters and dry summers, or vice versa). Is it correct to use raw data in these cases? Some restrictions should be considered and proposed for practical applications.
Finally, two methodological choices should be better justified, even though I acknowledge they are not the main focus of the paper.
a) L135: the area covered by a single ERA5 cell is equal to 31x31=961 km2, which is bigger than most of the selected catchments. The choice of referring to ERA5 rather than ERA5-Land is weak and partly unclear. Please consider testing at least one catchment with the finer ERA5-Land dataset.
b) LL182-185: Using IDW to downscale from more than 1° to 1000 m resolution is a very rough approach! Given the paper's main objective, less recent climate downscaling experiments, but at a higher resolution, would have been preferable.
Below, I add some other minor comments. I hope my review can help improve the robustness of the research and the quality of the manuscript.
L90: The concept that the asynchronous method can hinder the temporal coherence in hydrological processes should be better framed and contextualised. Is it only a problem concerning snow melting?
L235: unclear. Why “raw data”? I understand the data were corrected using the MBC algorithm.
LL289-290: That’s true, but, on the other hand, the computational cost of the bias correction should be accounted for. Please elaborate on that (maybe the best place is Section 4.2).
LL352-353: Please add units to numbers.
LL399-426 and Tables D1-D3: Considering the absolute mean instead of the mean is incorrect. This way, the climate change signal cannot be properly understood. Furthermore, Figure 5 is not very clear in highlighting the different performances of the three methods, especially regarding projected changes, which, in my view, is the most important feature to assess (in other words, how much do the different methods influence the projected climate change signal?).
Fig. 6 is not clear. The caption should explain the meanings of the terms in the legend.
I suggest reversing the order of Appendices E and F, because Appendix F is mentioned first in the text (therefore, Appendix F should become Appendix E and vice versa). Anyway, the hydrographic network should also be shown alongside the DTM.
Fig. 9 (and related text). Looking at Absolute Differences, it seems that the behaviours of Fully-Asynchronous and Conventional methods are much closer than Semi-Asynchronous and Fully-Asynchronous. Please provide more details about that.
LL628-635: not completely clear to me. Does the conventional method really produce an increased intermodal variability in the future? Is this a weakness of the conventional method? Why? And is this a problem for maintaining the ensemble’s diversity?
LL691-699: This paragraph does not provide particularly novel information. I suggest removing it (or shortening it substantially) for conciseness.
LL732-737: This paragraph is a kind of repetition.
Finally, I could not find some articles cited in the text in the reference section (Senatore et al., 2022; Chae and Chung, 2024). An overall check would be useful.