SnowClim v1.0: High-resolution snow model and data for the western United States

Lute, Abby C.; Abatzoglou, John; Link, Timothy

doi:https://doi.org/10.5194/gmd-2021-407

Preprints

https://doi.org/10.5194/gmd-2021-407

Preprints

Submitted as: model description paper

16 Dec 2021

Submitted as: model description paper | 16 Dec 2021

Status: a revised version of this preprint is currently under review for the journal GMD.

SnowClim v1.0: High-resolution snow model and data for the western United States

Abby C. Lute^1,a, John Abatzoglou², and Timothy Link³ Abby C. Lute et al.

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¹Water Resources Program, University of Idaho, Moscow, ID, 83844, USA
²Management of Complex Systems, University of California, Merced, CA, 95343, USA
³Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, 83844, USA
^anow at: Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration/Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, 08540, USA

Received: 07 Dec 2021 – Discussion started: 16 Dec 2021

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems.

We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

Abby C. Lute et al.

Status: final response (author comments only)

CC1:
'Comment on gmd-2021-407', Shiheng Duan, 21 Dec 2021

This is a great model that produces a high temporal and spatial resolution of SWE dataset over western CONUS. I am not an expert in the physical-based snow models and might miss something, but I wonder besides the calibration period from 2000 to 2013, did the author compare the result from these optimal parameters with the SNOTEL observations after the year 2013? Thanks!

Citation: https://doi.org/10.5194/gmd-2021-407-CC1
- AC1: 'Reply on CC1', Abigail Lute, 22 Dec 2021
  
  Hi Shiheng Duan,
  Thank you for your interest in our paper and for your insightful comment. This would be an excellent evaluation of the model. Unfortunately, we do not have climate forcing data after 2013. The WRF dataset we derived our climate forcing data from only covers 2000-2013 in the historical period. That said, we could do a temporal cross-validation within the time period of available data, perhaps calibrating on cooler years and validating the model on warmer years.
  Thanks,
  Abby and coauthors
  
  Citation: https://doi.org/10.5194/gmd-2021-407-AC1
CEC1:
'Comment on gmd-2021-407', Juan Antonio Añel, 03 Jan 2022

Dear authors,
After checking your manuscript and acknowledging your efforts to store the data in an open repository, we would like to request you upload your data to one of the alternative repositories listed in our policy (https://www.geoscientific-model-development.net/policies/code_and_data_policy.html). We understand the commitment that HydroShare can have for long-term archival; however, as it states in its webpage, it is subject to funding cycles of five years. This is not the kind of repository that we can accept.

Also, in line 100, you state that the SnowClim model is written in Matlab. Matlab is not a language but a proprietary system to interpret the code, and the language of the model is 'M'. If you use Matlab to run the model and perform your calculations, please, make explicit the version used. Take into account that SnowClim could be run using the FLOSS alternative GNU Octave if all the functions used in the model are fully 'compatible' with this other interpreter. Therefore, your statement is wrong. Please, modify it.
Regards,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor

Citation: https://doi.org/10.5194/gmd-2021-407-CEC1
- AC2:
  'Reply on CEC1', Abigail Lute, 05 Jan 2022
  
  Dear Dr. Añel,
  Thank you for bringing these issues to our attention. We are disappointed to hear that HydroShare does not provide sufficient storage and we fully support the goal of longer-term data storage. Would Dryad be an acceptable alternative?
  I am surprised to learn that MATLAB is not a language, especially since it is referred to as such in other GMD articles (e.g. https://gmd.copernicus.org/articles/14/5999/2021/). Instead of stating that SnowClim is written in MATLAB, would it be acceptable to instead say 'The SnowClim model can be run in MATLAB (2020b) and requires the Statistics and Machine Learning Toolbox'?
  Thank you again for your helpful comments.
  Sincerely,
  Abby and coauthors
  
  Citation: https://doi.org/10.5194/gmd-2021-407-AC2
  - CEC2: 'Reply on AC2', Juan Antonio Añel, 05 Jan 2022
    
    Dear authors,
    Yes, we can accept Dryad. It is listed in our policy as one of the suitable repositories.
    
    About Matlab: I made a mistake in my previous comment. I confused Matlab and Mathematica, apologies. Despite it, your new sentence is better; please, use it instead of the current one in future reviewed versions of your manuscript.
    Regards,
    Juan A. Añel
    Geosc. Mod. Dev. Executive Editor
    
    Citation: https://doi.org/10.5194/gmd-2021-407-CEC2
    
    AC3: 'Reply on CEC2', Abigail Lute, 05 Jan 2022
    
    Dear Dr. Añel,
    Thank you for the quick reply. We will proceed to upload our datasets to Dryad and make appropriate modifications to the manuscript in revision. We will use the new language regarding MATLAB as suggested.
    Thank you,
    Abby and coauthors
    
    Citation: https://doi.org/10.5194/gmd-2021-407-AC3
RC1:
'Comment on gmd-2021-407', Anonymous Referee #1, 12 Jan 2022

Lute et al. present simulations of snow over a large domain for reasonably long periods and at high spatial resolution. To achieve this, they have made some quite severe approximations to limit computational expense. Neglecting forest effects seems like a limitation for water resource applications in the western US. Simple canopy models, and snow models with a few layers to allow a more physical representation, need not be very expensive. Has code optimization to offset the cost of improved model physics been considered? MATLAB is convenient for parallel computation, but otherwise might not be the best language for this. Having said that, the authors have adopted a particular development strategy and reporting the results is worthwhile.

I was able to access the code and run the MATLAB Online example easily (a quickstart guide might have saved me a couple of minutes).

I suggest some corrections and clarifications, identified by line number:

24

There are too many to list, but the abstract should give some idea of what kind of “snow metrics” are offered.

52

Should note that this is earlier but slower snowmelt

106

Equation (1) is not the surface energy balance because G is not a surface flux. I would describe it as the energy balance of the snowpack.

127

Note that separate visible and near-infrared radiation fluxes are not supplied as inputs; the albedo is simply averaged between these bands. The illumination angle dependence should only be applied to the direct-beam component of incoming radiation.

133

Equation (3) is incorrect. If the emissivity is not 1, the upward longwave radiation includes a reflected fraction of the downward radiation (Kirchoff’s law).

179

Constant G is not a very common (or realistic) feature of modern models. Etchevers et al. (2002) included an experiment with fixed G as a sensitivity study, but did not report the results.

189

Iterative solution of the surface energy balance to find snow surface temperature is not the only possibility. Best et al. (2011, https://gmd.copernicus.org/articles/4/677/2011/), for example, linearizes the surface energy balance equation to find a surface temperature solution without the expense of iteration. In either an iterative or linearised solution, the aim is to find a surface temperature that is consistent with the surface energy balance. I don’t see how this can be achieved with the pragmatic but ad hoc approach in SnowClim.

281

How were the 210 m and 1050 m resolutions selected? How many points are there in this domain?

330

SNOTEL sites may not be representative of larger areas. Could this calibration skew the model performance? shttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2005WR004229

362

Any ideas why model performance would improve from 12 to 24 hour timesteps?

369

There is no evaluation of the large-scale snow simulations for the western US. MODIS snow cover extent products would have a convenient resolution for this.

Figure 2

There are a lot of sites to balance in the calibration, but errors up to +/- 50 days in duration and +/- 50% in maximum SWE even after calibration seem large for practical applications. Other datasets used to demonstrate poorer agreement in the discussion were not calibrated to SNOTEL sites.

Table 1

Given temperature, pressure and one of dewpoint temperature, relative humidity and specific humidity, the other two can be calculated – they are not all required forcing data.

Table 2

As it is abbreviated as Q, I guess that the WRF mixing ratio output is taken as the SnowClim specific humidity input (they are not the same thing, but will have nearly identical values).

Table 5

The monthly SWE and snow depth are minimum, mean and maximum.

Citation: https://doi.org/10.5194/gmd-2021-407-RC1
- AC4: 'Reply on RC1', Abigail Lute, 28 Mar 2022
  
  We thank the referee for their thoughtful comments. We have attached our detailed responses in a supplementary file.
  
  Citation: https://doi.org/10.5194/gmd-2021-407-AC4
RC2:
'Comment on gmd-2021-407', Anonymous Referee #2, 28 Jan 2022

This looks like it might be a useful addition to the suite of datasets of snow simulations available in the western United States. The combination of a reasonable physical basis, validation against observations in diverse climates, and an underlying high resolution provides, at least conceptually, a reasonably satisfying sense that the estimates provided near the end in Figures 5 and 6 are sound. It seems appropriate to publish this presentation of the details of the methods used in producing these data sets. At the same time, there are improvements that could be made in the writing to provide a more constructive contribution.

The paper gives a distinct sense of critique of the existing set of snow models available. Admittedly some are related to questions about run times rather than performance, but sometimes the criticisms come across more like aspersions than measured or tested issues. In this context, I look at all the plots from SNOWMIP2 (Rutter et al. 2009), and I’m left wondering where this model would fit into the cloud of lines. The bottom line is that this is another snow model on top of an already long list, with several new approximations, assertions, and assumptions, and we are left wondering what exactly we are learning from this particular modeling exercise. The focus was on improving model speed so that larger areas could be evaluated at finer resolutions in a reasonable time, but the net gain in understanding at these larger scales is not really highlighted so much as alluded to. This is a problem because there are several products out there and in use for larger scales, and while this model conceptually critiques these products, some uncited, it only has a somewhat philosophical basis for that critique. At a basic level, the claim for superiority of this modeling approach relates to a reductionist philosophy that if we can just do our simulations at finer and finer scales, we can resolve everything. The paper ultimately bumps into the fact that as we get into finer scale simulations, we find processes that depend on adjacent areas, and the upshot of this hazard appears in line 416 – near the end of the paper – where they note that the model is “most realistic in minimally vegetated areas with relatively little snow redistribution”, a rare piece of ground in the western US.

So, the question remains about what we learn from this modeling exercise. The central claim seems to be that when we have more resolution, we can resolve effects of things like aspect and solar radiation, so we have a better representation than we have had in the various existing undisclosed products. This may be true, but at the same time the paper’s central argument is fundamentally recommending further reduction in element scale with addition of vegetation and redistribution effects. Is there simply an argument for just doing the grind of getting complete lidar coverage over the western US, including data that can tell us about accumulation enhancement/loss patterns, and running a hyper-resolution snow model everywhere? Is this the only way that we can ultimately get a satisfying snow data set? At what scale does a reductionist argument end? Is there any utility in considering subgrid parameterizations of the effects of solar radiation, redistribution, and forests to a grid scale that is useful … or is that just a side-track that gets swallowed up by research in computing, storage, and remote sensing? My point is that this paper – though currently unstated – takes a stand with respect to these philosophical discussions in the literature. Being unstated, it is a strongly one sided stand being taken, and it’s possible the authors actually have a more nuanced perspective and this paper is simply an exploration in one dimension. In that case they might want to say something.

None of this detracts from the likelihood that this model product probably provides greater insights for some purposes about changes that are occurring than some existing products, but it is important to examine and state in the introduction what the central argument is and its philosophical history. At the very least, this makes it easier for others to follow up on the conceptual advance of the paper. In this case, the argument seems to be that some aspects of model physics can be sacrificed in order to better incorporate the effects of spatial heterogeneity in solar radiation, elevation, and temperature on snowpack over the same area, and potentially be able to display some of the heterogeneity in snowpack at finer scales for some purposes. This is a good question, and not entirely certain in its outcome, particularly when other subgrid processes are set aside. I don’t know that this tradeoff has been assessed in this paper so much as asserted, and it leaves on the table the question of whether a subgrid paramaterization coupled with earlier products could potentially generate greater benefits in time and accuracy … at least for purposes not needing to directly display fine-scale heterogeneity. It also leaves open questions about which particular process for heterogeneity are most critical to incorporate if following a reductionist approach.

It is worth noting here that we have seen images very similar to Figures 5 and 6 (which display fairly coarse features) in multiple recent publications that are not necessarily mentioned or cited (including by the first author). At some level, though, these earlier coarser resolution papers must be doing something somewhat correctly to get essentially the same images. The abstract and introduction begin by pointing out that there are no existing data sets that are “based on physical principles, simulated at high spatial resolution, and cover large geographic domains”, but they do not reflect on applications of these existing somewhat similar data sets, and clearly define “physical principles” or “high resolution” and explain for what purposes they are valuable. These definitions probably relate to the purposes of the simulation and the scale/resolution of the display. It is very likely that some of these existing products are adequate for many tasks, and rather than suggesting a position that this new data set makes all preceding simulations obsolete, perhaps discussing new applications the higher resolution data allow or note specific improvements they can demonstrate. This is partially addressed in Figure 7 and references, but only for one specific application where details of heterogeneity are important, although arguably could be focused on very small areas where redistribution may be important. On the net, if this data set is intended as replacement for the other data sets, it would be more polite to acknowledge them and their utility in the conversation so far rather than to ignore them. If in no other way, they could be acknowledged for their corroboration of images like Figures 5 and 6.

There is a similar set of discussions about philosophical subjects with respect to the framing set up in lines 55-65. There is a significant discussion in the hydrology literature at the moment, and spilling into geomorphology and snow as well, about the utility of machine learning approaches. Some argue that these are not “physically based”, but others note that the calibration needs of some models renders them effectively as non-physically based as some of the machine learning approaches. Quite a bit of work has been done in snow with ML and it is currently an area of active research. I’d recommend the authors take a look at a brief paper by Fleming et al 2021 to see how aspects of that framing might be unhelpful to furthering the goals of their work. I’m certainly in agreement that we all need to understand 1) the physics of the real world and 2) how our models represent that physics, but there are sound arguments that a priori assertions about the details of the physics in model formulation that could detract from our learning about the physics of the real world. There are others who deliver that argument better than I, and hopefully after looking at some of those you might find a better way to describe the meaning of “physics-based” in the paper.

The above points are for general framing and are important to address so that the contributions of this specific modeling exercise can be better placed among much that has occurred before and much going on concurrently.

A more specific point begins at line 180 in the characterization of “single” layer snowpack models and shortcomings, particular with respect to the UEB model. The UEB model deals with the issue in two ways. During non-melt periods, it simply uses a conduction estimate based on a high frequency and a low frequency component – essentially using a Fourier decomposition approach to calculating the conduction rather than a finite difference approach – which would use “layers”. UEB also makes further computations for heat transfer when the surface is melting. Insofar as you would like to emphasize the physical basis of snowclim, you may want to reconcile the “tax” approach and framing with the physical basis of conduction in the frequency domain. As it stands, when the surface is cooling, it looks like only a portion (as low as 10%) of the net flux at the surface is being removed from the pack – the remainder isn’t accounted for at all. It is not clear what errors this accounts for, and you may want to add some content (text and appendix figures?) to the paper to explain how equations 16 work. There are at least 4 parameters involved, all of which are calibration parameters. This seems potentially important, but without some validation against temperature data, its difficult to tell.

Note that the snow surface temperature calculation in UEB takes into account this conduction heat flux from the surface along with the several other fluxes at the surface (e.g. solar, longwave, latent, sensible) to find the T at which all fluxes balance. This was not a particularly computationally burdensome iteration and the uncertainty cited by Raleigh (2013) was in parameter estimates for conduction, which Snoclim just does not consider. In the broad scheme of uncertainties for energy balance, I don’t know that these are big numbers, but the criticisms in line 189-190, which are drawn mostly from Raleigh (2013) are not really warranted and don’t bear repeating in the context of this paper. There remains some chance that the UEB calculations might be nearly as parsimonious and generate less uncertainty. No assessment has been done in this paper to evaluate these issues.

Reference (other than already cited in the paper):

Fleming, S. W., Watson, J. R., Ellenson, A., Cannon, A. J., & Vesselinov, V. C. (2021). Machine learning in Earth and environmental science requires education and research policy reforms. , (12), 878-880.

Citation: https://doi.org/10.5194/gmd-2021-407-RC2
- AC5: 'Reply on RC2', Abigail Lute, 28 Mar 2022
  
  We thank the referee for their thoughtful comments. We have attached our responses in the supplementary file.
  
  Citation: https://doi.org/10.5194/gmd-2021-407-AC5

Abby C. Lute et al.

Data sets

SnowClim Data A. C. Lute, John Abatzoglou, Timothy Link https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/

Model code and software

SnowClim Model A. C. Lute, John Abatzoglou, Timothy Link https://www.hydroshare.org/resource/dc3a40e067bf416d82d87c664d2edcc7/

Abby C. Lute et al.

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Short summary

We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.


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