the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the Impact of Earth Observation Data-Driven Calibration of the Melting Coefficient on the LISFLOOD Snow Module
Abstract. LISFLOOD is a comprehensive hydrological model widely used in Europe. Among various hydrological processes, it simulates snowmelt using a degree-day-based snow module. Traditionally, the snowmelt coefficient is calibrated using discharge data. This study evaluates LISFLOOD's current snow module and explores an alternative calibration approach based on Earth Observation (EO) derived snow cover fraction (SCF) observations across nine European basins with varying degrees of snow cover influence. We utilize a novel integration of Sentinel-2 and MODIS data to address issues related to data gaps and missed snow cover detection in complex topography. Using EO SCF, we estimate a spatially distributed snowmelt coefficient, which contrasts with the uniform coefficients currently used in LISFLOOD. The new calibration approach, involving an optimization routine to match modeled and observed SCF, outperforms a previous method that did not deal with fractional snow as well as discontinuous snow cover periods. When compared with EO SCF, the traditional calibration shows bias values ranging from -0.56 % to 22.50 %, with root mean squared error (RMSE) values varying from 20.43 % to 54.64 %. We obtained improvements up to 8 % both in bias and RMSE when the optimization approach is used. While the optimized coefficients did not significantly alter simulated discharge, the improved SCA representation led in some cases to shifts in the timing and magnitude of snowmelt and total runoff. These findings highlight the potential of integrating EO data to enhance snowmelt simulations and improve water balance predictions, with important implications for hydrological modeling and water resource management.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2157', Anonymous Referee #1, 26 May 2025
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AC1: 'Reply on RC1', Valentina Premier, 08 Jul 2025
We thank the Anonymous Reviewer for providing valuable feedback. We believe that the manuscript can be improved by considering his suggestions and by clarifying several critical aspects that were not previously well explained. Please, see the attached document where we provide our point-by-point responses, highlighted in red.
Best regards,
Valentina Premier on behalf of all Co-Authors
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AC1: 'Reply on RC1', Valentina Premier, 08 Jul 2025
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RC2: 'Comment on egusphere-2025-2157', Francesco Avanzi, 29 Jun 2025
Premier and colleagues have presented an improved calibration approach for the LISFLOOD snow module that is based on leveraging high resolution satellite data. This approach minimizes the error between observed and simulated snow cover fraction, and directly impacts the simulation of SWE. Authors test this method across a variety of study catchments in Europe and provide a detailed analysis of the improvements in snow simulation, as well as an interesting water balance perspective.
This paper is technically sound, and the effort of using high resolution satellite data into a large scale hydrological model is relevant and interesting for HESS. At the same time, there remain some aspects that could be expanded and improved. I still see value in this manuscript, and thus I am recommending a major revision.
My main point is that using snow data in addition to streamflow data in calibrating a hydrologic model has been widely explored (see for example https://www.tandfonline.com/doi/abs/10.1080/01431161.2010.483493, https://www.sciencedirect.com/science/article/abs/pii/S0022169419312132?via%3Dihub, https://www.sciencedirect.com/science/article/abs/pii/S002216941300320X, https://hess.copernicus.org/articles/26/5627/2022/hess-26-5627-2022.html, https://www.sciencedirect.com/science/article/abs/pii/S0022169424013167). As a result of significant research in the hydrologic community over the last 15 years, a multi-objective calibration that involves at least snow and streamflow data is now considered state of the art. Meanwhile, even doing so does not necessarily imply an improvement in model performance.
This manuscript fits in this state of the art and confirms most of the conclusions above. To me, the most interesting points here are the use of high resolution satellite data and the inclusion of a variety of catchments, with different snow climatologies and various hydrologic characteristics. In the revised manuscript, I would invest more effort in leveraging this variety of cathments as a way to draw process-based conclusions from this study that could allow for generalization: how are these catchments representative of specific snow climates? What do differences in results across these catchments tell us in terms of hydrological processes and the applicability of this approach in ungauged regions?
Authors also consider a fairly long period of data, with several snow drought episodes. Maybe commenting results across extremes and average years could be another way of bringing about more novelty.
The Introduction and the Discussion section could be revised to (1) make the scope of the manuscript broader and (2) discuss how this methodology compares to previous attempts in this realm (see above).
I agree with authors that a calibration in terms of snow cover fraction is currently the only feasible approach at these scales, even though this requires the additional complication of a SCF parametrization to convert modelled SWE into modelled SCF. This is well discussed in the manuscript, with the only recommendation of providing some additional results on the calibration of the k constants.
In general, the manuscript is well written.
Citation: https://doi.org/10.5194/egusphere-2025-2157-RC2 -
AC2: 'Reply on RC2', Valentina Premier, 08 Aug 2025
We thank Dr. Avanzi for providing valuable feedback. We believe that the manuscript can be improved by considering his suggestions and by clarifying several critical aspects that were not previously well explained. Please, see the attached document where we provide our point-by-point responses, highlighted in red.
Best regards,
Valentina Premier on behalf of all Co-Authors
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AC2: 'Reply on RC2', Valentina Premier, 08 Aug 2025
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RC3: 'Comment on egusphere-2025-2157', Anonymous Referee #3, 09 Jul 2025
Premier et al. present a calibration method for simulating snow melt using the LISFLOOD hydrological model. The authors tested the method on a sufficient number of basins in Europe and demonstrated the benefits for SCF, SWE, and to a lesser extent, for water balance components.
In my view, the paper's novelty lies in the use of improved high-resolution snow cover data, combined with an SWE to SCF approach and an optimization algorithm. It seems to me that the topic is relevant and of interest to the journal, but it requires a major revision.
The paper is missing some points:
It appears that the authors have introduced the SCF calibration as an alternative to the discharge calibration. Stepwise calibration or data assimilation using snow data, soil moisture, and evapotranspiration has been performed quite often in the last decade. Even with the Lisflood model attempts have been made by Thirel et al. (https://www.mdpi.com/2072-4292/5/11/5825, https://www.sciencedirect.com/science/article/pii/S0034425712003604).
The paper does not employ a multistep approach (first snow, second discharge) to improve overall calibration. As mentioned, Lisflood is the driving hydrological model of EFAS and GloFAS, which focus on discharge forecasting. An improvement in SCF is fine, but it cannot be the final goal. An improvement in SCF can even lead to a worse objective criterion of discharge, but might still be an improvement because it reduces the error of overfitting.
Here, the focus is solely on the snow ablation process, using the snow melt coefficient (SMC) as the parameter. The process of snow accumulation, with parameters such as snow factor or temperature threshold that determine whether precipitation falls as rain or snow, is overlooked.
The Lisflood snow modul is not explained fully. It is mentioned that Lisflood uses three different elevation zones (line 134), but it is not explained how the SCF calculation from SWE, the calculation of SMC (e.g., equation 12), or the optimization is performed with these three zones.
Especially with higher resolution (here 1 arcmin) the day-degree approach can accumulate too much snow at high altitudes, as temperatures will not too often drop below 1° C. Lisflood uses a workaround to melt additional snow in Summer (IceMeltS). The paper does not mention this approach, nor does it take it into account.
The effect on the water balance is calculated on a monthly basis, even when the model is run on 6-hour timesteps. Here, it is really necessary to go on daily basis. With monthly evaluations, you miss the main advantage of your SCF calibration: having a better estimate of the timing of the main snow ablation, and therefore a more accurate estimate of the timing and magnitude of spring floods.
The 2nd step of calibration for discharge is missing, as is an explanation of how to derive a better KGE with a change in SMC. In L_Cm version of Lisflood, SMC is calibrated to improve the KGE (SMC is optimized for discharge KGE). In the EO-Cm version, you changed only the SMC, and you keep all the other calibration parameters? The improvement in KGE (even the tiny one) can only be explained by a bigger range of SMC and/or by the single cell values. However, the calibration was performed on daily discharge; therefore, a comparison with daily values would be appropriate.
A 2nd discharge calibration is necessary to see the improvement vs the original calibration, using the new SMC as predifined values. I am not asking for all 9 basins but for those where you have only one subbasin (Arve, Laborec, Morrumsan, Umealven)
Specific comments:
- L2: I would not call it traditionally. It is not made because of tradition, but it has a reason. If you call it later traditional calibration, it is ok
- L21: This is unclear. It cannot be globally between 40-90% snow contribution from mountains. Please check Viviroli again
- L25: “LISFLOOD is one of the most comprehensive operational models used in Europe to simulate hydrological processes”. This is very general sentence. Maybe a unique selling point: Lisflood is one of few operational models calibrated for Europe to simulate hydrological processes.
- L34: I think the equation which takes rain into account is from: Speers, D.D., Versteeg, J.D. (1979) Runoff forecasting for reservoir operations - the past and the future. In: Proceedings 52nd Western Snow Conference, 149-156
- L65: for a “novel” method you explain not much in L183-184
- L103: “The current model setup operates …”. Maybe put this after line 106, because the first part explains Lisflood, the second a special application of Lisflood for the EFAS setting
- L131 it is rainfall per day not rainfall intensity. Somewhere else it should be hydrological year instead hydrological season
- L136: The 3 zones can be explained in more detail and has to be included in 2.3 and 2.4.
- Also the IceMelt part in https://github.com/ec-jrc/lisflood-code/ is not explained at all and not taken into account.
- L146ff: This part can be done in a nicer way. E.g. https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1214/egusphere-2025-1214.pdf has a better way to structure this.
- L152: How is kaccum calculated?
- L155: What is the reference of this equation?
- L161: How is this calculated with the 3 elevation zones
- L169f: The equation 11 is invalid for glaciers and cannot applied for areas with always snow and with several snow-cycles. Equa 12 is again without the snow elevation zones.You showed it anyway, that this equation is not leading somewhere. It is fine to keep this approach.
- L183f: You featured this as “novel” approach. It appears to be an optimization for a single-parameter part of the standard SciPy library. Does seem to be novel and not explained at all.
- Table1: the max elevation is not explained. In the original Merrit DEM it is much higher, so I assume you average that for 1 arcmin. But in the model the max elevation is the highest elevation zone. I think you should correct the elevation by the values from original Merrit DEM
- L297ff: Why using a monthly comparison. The original model is calibrated on daily values. The biggest effect of your improvents are the timing in days of the biggest drop in snow accumulation. The comparison to daily discharge is necessary to conclude if the models performance is improved (maybe the over discharge KGE is reduced, but some spring floods are better timed, the snow cover is better estimated).
- Fig 7: Gallego and Guadelfeo have some reservoirs included. It would be better to use subbasins without too much human interference. From the results, you cannot see if it is the snow or the reservoirs. You explained why Guadelfeo has a bad KGE. One solution could be to use only those years without reservoirs,
- Table 4: This is not suitable for comparison. 1) you keep the other parameter constant (I assume, it is missing in the paper) 2) you compare on monthly values 3) you did not recalibrated the other parameters after setting SMC to your values.
- I think it is necessary to re-calibrate for a number of basins (maybe only those where you do not have upstream-downstream calibration) and discuss the effect of your improved SMC e.g. worse KGE but better representation of snow, more exact timing of snow-induced flooding,
Overall, the topic is interesting, and the potential for a good paper is there, but it lacks structure, and fundamental key points are not included yet.
Citation: https://doi.org/10.5194/egusphere-2025-2157-RC3 -
AC3: 'Reply on RC3', Valentina Premier, 08 Aug 2025
We thank the Anonymous Reviewer for providing valuable feedback. We believe that the manuscript can be improved by considering his suggestions and by clarifying several critical aspects that were not previously well explained. Please, see the attached document where we provide our point-by-point responses, highlighted in red.
Best regards,
Valentina Premier on behalf of all Co-Authors
Data sets
Daily Snow Cover Fraction (SCF) for the nine river basins across Europe (1st October 2017 - 30th September 2023) V. Premier et al. https://zenodo.org/records/14961639
Model code and software
SCA4LISFLOOD: Snow Module Evaluation for LISFLOOD Valentina Premier https://github.com/vpremier/SCA4LISFLOOD
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Review paper egusphere-2025-2157
Assessing the Impact of Earth Observation Data-Driven Calibration of the Melting Coefficient on the LISFLOOD Snow Module
By Premier et al.
General Comments
This study investigates the calibration of the snowmelt coefficient in the LISFLOOD hydrological model using Earth Observation (EO)-derived snow cover data. The authors propose two EO-based calibration methods and assess their impact on snow cover fraction (SCF), snow water equivalent (SWE), and discharge simulations across nine European river basins. The manuscript contributes to ongoing efforts to integrate remotely sensed data into large-scale hydrological modeling.
However, several methodological ambiguities, design inconsistencies, and literature gaps limit the manuscript’s clarity, reproducibility, and broader relevance. The introduction focuses heavily on LISFLOOD while neglecting to situate the work within the substantial body of existing literature on EO-based snow calibration and assimilation techniques, many of which have long addressed multi-objective calibration using snow and streamflow data. The authors should better articulate the novelty of their approach beyond its application to LISFLOOD.
This is particularly important given that the improvements achieved with EO-calibrated snowmelt coefficients remain modest, or even questionable, with respect to discharge simulations. This raises broader concerns about the hydrological value and operational significance of the proposed methodology.
Moreover, several critical aspects of the methodology—such as the SWE–SCF parameterization, spatial resolution strategy, calibration procedure, and test basin selection—are poorly explained, inconsistently justified, or insufficiently analyzed. The comparison between models, methods, and performance metrics is often difficult to follow and underinterpreted. Key information for reproducibility (e.g., calibration configurations, data preprocessing protocols) is also lacking.
While the topic is relevant and the integration of EO data into hydrological modeling remains important, the manuscript in its current form suffers from fundamental methodological opacity, weak novelty positioning, and limited hydrological impact. Key sections are unclear or poorly justified, the experimental design is inconsistent, and the results do not support the claimed contributions. For these reasons, I recommend rejection. A revised version would require a substantial restructuring of both the methodology and the scientific framing to meet the standards expected for publication in HESS.
Specific Comments
The manuscript would benefit from careful revision for clarity, structure, and language. Sections are often dense and overly technical, with insufficient explanation of key decisions. A clearer narrative structure, consistent terminology, and simplified figures would greatly improve readability.