the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Aug 2025
Improving Snow Simulations through Improved Representations of Vegetation Conditions: Insights from High Resolution Simulations over California
Abstract. Most hydrological models underestimate snow water equivalent (SWE) in the mountainous western US. Key limitations may be due to coarse resolution precipitation input and inadequate representations of snow-vegetation interactions. Vegetation affects snow dynamics through snow interception, throughfall/unloading, and energy transfer through the canopy, yet basin-scale studies on the vegetation effects are limited. To address this issue, we applied the Noah-MP version 5.0 with a dynamic vegetation module to the Sacramento and San Joaquin River Basins in California at 1 km resolution driven by forcing from Analysis of Record for Calibration (AORC) and NLDAS-2, downscaled to 1 km using the WRF-Hydro Meteorological Forcing Engine. We carried out eight model experiments driven by the two forcing datasets and two vegetation schemes over the two Basins. Compared to 4 km PRISM and 1 km AORC, the downscaled 1 km NLDAS-2 forcing data shows higher precipitation and lower temperatures over the mountains, resulting in more snowfall and SWE, which is more consistent with in-situ SWE observations. Using the 1-km NLDAS-2 forcing, the default vegetation scheme with prescribed leaf area index (LAI) and vegetation cover fraction produces too much SWE on the ground due mainly to the strong canopy shading effect despite more snow intercepted by the canopy. The dynamic vegetation module produces smaller LAI than the prescribed (more consistent with MODIS data) and thus leads to more shortwave radiation reaching the snow surface, thereby enhancing episodic melting during the accumulation season. Validation against in-situ snow data suggests that the use of dynamic vegetation model and the downscaled NLDAS-2 data performs the best. These findings highlight the importance of vegetation effects and downscaling of atmospheric forcing to increase the accuracy of snow modeling over the Southwest US.
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Status: open (until 13 Dec 2025)
- RC1: 'Comment on egusphere-2025-3884', Anonymous Referee #1, 20 Oct 2025 reply
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RC2: 'Comment on egusphere-2025-3884', Anonymous Referee #2, 05 Dec 2025
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The authors collect a large number of meteorological and snow data sets for the Sacramento and San Joaquin River Basins in California and conduct model simulations using Noah-MP with different forcing data and vegetation representations. I am impressed by the amount of different data sets included into the analysis, however, I am not sure whether the study delivers on what was promised in the title. I did not see results that actually showed that the snow modelling below the canopy improved. The two LAI representations (prescribed monthly values and the dynamically modeled values) seem to be quiet off compared to the satellite-based observations and only compensations / enhancements of errors originating from the meteorological input data seem to be discussed. While the general approach of a dynamical canopy interacting with snow processes is very attractive, the authors need to sharpen the focus of their study and use model configurations and evaluation data sets that are relevant to back up their pre-defined research questions. Below detailed comments / questions:
Line 35: Why are shading effects more 'important'? Important for what?
Line 50: Please define SNOWTEL here. There might be readers that are not familiar with the area and do not know.
Line 65: I do not understand how canopy shading is reducing surface albedo. Isn't the albedo a material characteristic and should not change depending on shading?
Line 73: According to my information, remote sensing data are not (yet) very valuable for assessing snow-canopy interactions. Satellites are very good in seeing snow in open areas (and also on the top of the canopy), but inside the forest it is difficult and very uncertain. Why do you not use satellite-based snow data to evaluate the snow modelling (at least in the open areas)?
Line 96: Please define high resolution and why it is considered high resolution for your type of application.
Line 102: What are HUC-4 basins? Please try to avoid abbreviations that are not know to people from other countries our continents.
Line 107: How much area do the different vegetation types cover in different elevation? Please consider to include another figures illustrating this.
Figure 1: Please consider different color schemes. The overlap of land cover and DEM with same colors and also the ocean with blue might confuse readers.
Line 118: Please define CONUS regions.
Line 128: What makes PRISM the evaluation data set? Why do you consider PRISM better than the other models? Why this the reference?
Line 138: What is NWM v3.0? If you consider this an important data set that is valuable for the evaluation, please explain in more detail.
Line 148: Why do you not use the 800 m PRISM in your study?
Line 184: Please define USGS.
Line 199: This is existing model code, right? Did you extent and improve any of the snow or canopy representation in Noah-MP?
Line 232: What about other meteorological variables, such as temperature, humidity and wind speed?
Line 246: What is the 'mountain mapper algorithm'?
Line 250: What are those 'other variables'?
Line 274: Where do you see the precipitation intensity? It is long term averages values, right?
Figure 2: NLDAS2 was downscaled using PRISM data. Why are the two data sets so different then? Isn't it the conclusion that the downscaling didn't work?Line 303: You compare three products. How can you say that one is better than the other?
Line 323: I do not think you are able to say it is better. The result is that it is closer to SNODAS, another model.
Line 324: Yes, meteorological input matters! Later down the road also the selected snow process representation and parameters are relevant. Isn't this the main result of your study?
Figure 4: Please add to the figure caption what time period is presented here. Please also consider to show the years individually. 5 years is not much and there might be quite a lot of (interesting) interannual variability here. I think it would be valuable to the reader to see the LAIs and SWE dynamics not averaged, but for the entire period.
Figure 5: I assume that the measurement site is not below the canopy, but actually positioned in an open area and that there is some vegetation/forest in the surrounding. Isn't it the case that you compare point measurement from the open with a modelled snow value from a 1 km grid cell having canopy cover. For the cells you assume an average vegetation and also consider fractional cover. I do not see how the comparison of this point observations from the open can support the evaluation of modelled snow inside the canopy. Please explain.
Figure 6: Where are the prescribed values from? They seem quiet off... Also the dynamic values are very different compared to satellite-based data. If you consider the satellite-data to be the 'truth', so why don't you adjust the prescribed values to the satellite-based values?
Line 416-425: In my opinion, all the comparison and evaluation presented here does not relate to snow-canopy processes and their evaluation. You assess the uncertainty of meteorological input data (=biggest source of error).
Discussion: In general, a very short discussion. Please try to sharpen the result section and extent the discussion!
Line 451: It sounds like the snow-canopy interaction is used to compensate for an error originating from the input data. Please be careful to not compensate one error with another error and conclude that it is an improvement.
Citation: https://doi.org/10.5194/egusphere-2025-3884-RC2
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- 1
The paper by A. Gupta et al. addresses the representation of vegetation conditions and their effects on snow simulations based on high-resolution modeling over California. Several model experiments were conducted using different forcing datasets and vegetation schemes. While the paper contains several elements of interest to The Cryosphere readership, it currently does not sufficiently highlight its main contributions, and substantial revisions are required. Below, I outline my main concerns, followed by line-by-line comments.
Main comments
My main concerns are related to the evaluation data. The authors rely heavily on gridded products for model evaluation, which they treat as ground truth. For instance, a 4 km daily gridded climate product is insufficient to evaluate the downscaled NLDAS-2 data, because it cannot resolve topographic gradients and local effects and introduces its own biases. Station observations (e.g., GHCN-d) are available and should be used to validate the downscaling and provide many evaluation points within the study region.
A similar issue applies to the UA-SWE product: please include an error analysis against actual SNOTEL sites within the study region, rather than validating against a gridded or assimilated product. Section 3.2 should be revised for clarity-it is currently not obvious which datasets are used for which evaluation. I recommend adding a summary table listing, for each variable, the datasets used for validation.
Regarding the evaluation of SWE at the selected CDWR station in the main manuscript: why was this specific station chosen? While I acknowledge that results from other stations appear in the supplementary material, it would be valuable to present more than a single station in the main text.
For the first part of the results (meteorological downscaling): since the downscaling was not performed or developed by the authors, it should not be presented as a primary result. While such an evaluation is important context, it would be more appropriate in an appendix or supplementary section, serving as supporting material for the discussion. Moreover, the assessment itself is rather limited (mostly qualitative or daily means); please expand it to include more quantitative analysis.
The discussion is extremely brief and does not adequately engage with the caveats and implications of the study. It needs to be substantially expanded and rewritten to interpret results critically in light of known uncertainties.
The abstract should also be updated to better reflect the findings actually supported by the results. For example, the statement: ‘Using the 1-km NLDAS-2 forcing, the default vegetation scheme with prescribed leaf area index (LAI) and vegetation cover fraction produces too much SWE on the ground due mainly to the strong canopy shading effect despite more snow intercepted by the canopy.’ is not properly supported by any quantitative analysis in the manuscript. Please ensure that claims in the abstract are clearly supported by the results.
In general, the language of this manuscript requires improvement. There are numerous incomplete or unclear sentences. Acronyms are inconsistently introduced or redefined (e.g., CONUS is never introduced, LSM several times, ..). Some hyperlinks are broken, and several cited papers are missing from the bibliography. The figures also need attention: improve overall quality, adjust color maps for clarity and accessibility, and ensure captions are complete and precise.
Line by line comments:
L50: SNOTEL needs to be introduced and defined.
L50: NCAR CLM v4.0 is an outdated version; please contextualize results or comparisons using more recent model developments.
L53–58: Add appropriate citations.
L66–68: Add citations.
L88: The acronym LSM was already defined in line 45.
L92: Clarify: “best by what metric?” Specify performance criteria.
L93: Cho et al., 2022 is missing from the references.
L96: Clarify the aim of the study. Define what “high resolution” means in this context and specify the dynamic vegetation model used.
L103: Revise - “significant snow cover” is vague; use a more quantitative or physically meaningful term.
L114: Since one forcing is considered superior, briefly justify why.
L116: CONUS acronym not introduced.
L126: The section structure is difficult to follow. Consider reorganizing by variable evaluated (e.g., temperature, precipitation, SWE) rather than by dataset name/acronym.
L127: Why are gridded products used for validation? Comparison outside of measurement sites lacks meaning; please justify or reconsider.
L135: “Averaged to 4 km resolution” — elaborate on the method used for upscaling.
L138: Why are actual SWE measurements not used here?
L144 / L152: Additional gridded products are introduced—justify their relevance or quality.
L223 ff: Add more detail on the radiation transfer model, including its assumptions and parameterizations.
L234 ff: Was there a model spin-up? If so, describe procedure and duration.
L247: Add a reference and brief explanation for the mountain mapper algorithm.
L248: Elaborate on how the correction factor is applied and justified.
Tables & Figures
Table 1: Do not reuse LAI both as an acronym for Leaf Area Index and as a simulation label - rename simulation identifiers.
Tables 2 & 3: Consider moving to the appendix or expand explanations in-text.
Figure 2: Improve colormap - differences in row 2 are hard to see. Since comparisons depend on elevation, include elevation contours.
Figure 3: Update colormap for clarity.
Figure 4: Clarify: what does “independent SWE estimates, SNODAS” mean? What are the spatial units?
Figure 5: The location of the label “GOL 2057 m” is confusing. Improve colormap and rewrite caption to make the number of stations and variables shown clear.
Figure 6: Revise colormap for readability.
Figure 7: Clarify whether values are averaged across the basin.
L337: Why was this single station chosen? Provide justification or include multiple stations for robustness.
L343: “AORC simulations are underestimated by ~500 mm” — be more specific (relative to what? annual mean? which period?).
L345: Explain why the GRM station performs poorly.
L388–389: The described “substantial modifications” are not visually evident in Figure 8. Consider including quantitative comparisons to support this claim.
Discussion
L410: Sentence incomplete (“more than” missing?).
L416: “This is favourable when compared to…” — rephrase in a more objective scientific tone.
L421: Revise for clarity.
L424: Omit “apparently”; use formal language.
Summary / Conclusions
L437: Specify the exact model version (e.g., CLM vX.Y) used.
L437: Toure et al., 2016 missing from references.
L442 ff: “The Noah-MP predicted SWE is most sensitive to the high-resolution…” — support this with a robust statistical test.
L453: MODIS LAI may not be an ideal reference; justify this choice.
L459–460: Revise sentence for clarity and conciseness.
L470: The link currently directs to the generic ArcGIS StoryMap site - please provide the actual map or dataset link. If possible, also provide SWE data for reproducibility.