the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: model description paper 08 Apr 2021
Submitted as: model description paper | 08 Apr 2021
S3M 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
- 1CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
- 2Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
- 3Regione Autonoma Valle d’Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
- 1CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
- 2Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
- 3Regione Autonoma Valle d’Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Abstract. By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this shifting mechanism to climate, as well as the role played by the cryosphere in the Earth energy budget, has motivated the development of a broad spectrum of predictive models. Such models rarely combine a high degree of physical realism in both the seasonal snow and glaciers, and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present S3M v5.1, a spatially explicit and hydrology-oriented cryospheric model that successfully reconstructs seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol. Comparatively novel aspects of S3M with respect to the existing literature are an explicit representation of the spatial patterns of snow liquid-water content, an hybrid approach to snowmelt that decouples the radiation- and temperature-driven contributions, the implementation of the ∆h parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the Italian north-western Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises an user manual as well as resources to prepare input data and set up computational environments and libraries.
Francesco Avanzi et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2021-92', Anonymous Referee #1, 10 May 2021
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-92/gmd-2021-92-RC1-supplement.pdf
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AC1: 'Reply on RC1', Francesco Avanzi, 15 May 2021
We thanks Reviewer #1 for their constructive comments. All requested revisions are feasible and we will work in this direction as soon as the interactive discussion will be finalized. Please find a point-by-point reply to all questions in the attached pdf.
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AC1: 'Reply on RC1', Francesco Avanzi, 15 May 2021
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RC2: 'Comment on gmd-2021-92', Anonymous Referee #2, 25 Oct 2021
In “S3M 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt” the authors present a distributed cryosphere model to aid in flood forecasting. This is well written and generally easy to follow with a few portions unclear, somewhat due to the manuscript length.
My main criticism is regarding how the model’s process representations and how they are presented. In the abstract the authors note that “Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol.” This led me to believe I would be reading a paper about an energy balance model, however this is not at all the case. Indeed the model is a basic temperature-index snow model with a radiation component. There are no internal snowpack energetics, no longwave losses, no turbulent heat fluxes, and no sublimation. Nor is there a vegetation canopy parameterization. Large portions of the study area (c.f. Figure 8) appears to have vegetation cover, so I am at a loss as to how this interaction can be ignored. These are critical components that have been identified by snow hydrologists for many years.
Certainly this criticism could be waved away as a differing in model philosophy, however the results have issues that suggest there is something quite wrong with these simplifications as applied. In Figure 6 there are multiple years (2013, 2014, 2015, 2016) and sometimes multiple occurrences per year, where 1m to 2m snowpacks are almost instantly ablated. I don’t understand the physical process by which this could occur. Rain on snow run a-muck? A warm, sunny day “melting” a deep snowpack that has no cold content tracked? I understood these results to be with the data assimilation turned on (this was a bit unclear to me). Assuming this is true, then without the data assimilation system these results would have been even more wrong. The DA is then massively compensating for broken parameterizations. If this was without DA, then how is it ablating a 1m snow pack, then immediately reestablishing a 1m snowpack? A similar situation occurs on the glacier, where 2x melt is predicted or the case of Petit Graphillon where multiple meters of ablation are observed with no reaction of the model. Considering this model is described as a flood forecasting model, I am concerned with the non-physical behaviour that is being exhibited here.
I understand that the authors address some of these issues as ‘future work’, such as the canopy. However, these processes are so critical for basin hydrology that, to present a model to tie into a hydrology model without key cold-region processes, seems unfinished and incomplete. I also don’t agree with the framing that t-index paramterizations are a high degree of physical realism, especially when so many other critical processes are omitted. Lastly, I am surprised by the authors stating that coupling radiation to a temperature index model is novel. Hock (2003) details approaches that include this idea and more recent examples exist, e.g., Follum et al (2015).
Hock, R. Temperature index melt modelling in mountain areas. Water Resources Research 282, 104 115 (2003).
Follum, M. L., Downer, C. W., Niemann, J. D., Roylance, S. M. & Vuyovich, C. M. A radiation-derived temperature-index snow routine for the GSSHA hydrologic model. J Hydrol 529, 723–736 (2015).
Specific comment in format of
L<ine> [0-9]+ "quoted text"
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Page 1
L4 a high degree of physical realism
Do you mean they are mostly empirical? Certainly many models have good physical representations. Indeed I’d expect most models that simulate physical processes to have physical realism!
L7 reconstructs
Simulates?
L16 the paper comprises an user manual
I would like to see an actual science question. In my opinion, even in GMD, there should be some hypothesis testing and scientific questions answered that support model development.
Page 2
L22 during the warm, summer season
And spring
L22 when demand
By whom?
L25 while 1.4+ billion people in Asia rely on discharge from high-mountain
Citation?
L33 large portfolio
W/c
L36 avalanche forecasting
perhaps “Avalanche hazard forecast”
L36 so weather
?
L37 aridity
AR6 suggests location dependent, consider citing the newest IPCC report
L42 Regarding seasonal snow
Suggest adding FSM (Essery, 2015) to this list.
Page 3
L60 The evidence that simplified and complex models often yield comparable predictive
Certainly many models exist that show better SWE and sd when including full physics e.g Lafaysse (2017) and Vionnet (2021)
A multiphysical ensemble system of numerical snow modelling, Lafaysse, et al (2017) The Cryosphere
Multi-scale snowdrift-permitting modelling of mountain snowpack, Vionnet et al, The Cryosphere 2021
Even when considering just SWE and SD, the inclusion of multi-layer snowpack models is important for deep mountain snow covers, or rain on snow events.
L 63 low complexity when it comes to internal layering and micro-scale properties
Ok but what about the other processes that impact?
L65 real-world
L67 these four factors trace back
As written this suggests obs are related to empiricism, and I don’t understand how Obs location biases are due to empiricism.
L71 all the four factors
Seems like factors is used as “requirement” which is not true.
L72 parsimonious as for
Suggest this isn’t a requirement
L75 and so
?
L75 avalanche forecasting
Without the microstructure is this really true?
L81 Section 3 presents an example of results for an inner alpine valley
Ok great! I would like to see a science question. Even GMD should have-science questions and hypothesis testing
Page 4
L86 being
L86 no spatial interdependency
I read this to mean no lateral mass or energy transfer? Perhaps be explicit
L90 using a forward-Euler method.
This is a basic solver, why this method versus the RK, BE, etc methods? What tolerances were used? I assume a constant step size?
L106 The density of glacier ice is assumed equal to ρi.
So no further compression?
Page 5
L123 state variable
Some of these look like fluxes e.g M_G. The SWE etc looks to be a diagnostic variable and the ice lattice is the actual state variable. Later in the manuscript SWE is noted as a diagnostic variable. I would suggest tidying this up and making the diagram clear.
L123 inputs
Unclear what is an input: is an albedo input?
L128 where Sˆ
What do the hats denote? I don’t think it is noted explicitly in the text
L129 and Oˆ is the outflow mass flux
Above you call this a state in figure 1
Page 6
Figure 1: Main definitions,
Add units, see above note on fluxes/state
Page 7
L145 S W ED and S W EW
Are these not diagnostic?
Page 8
L169 is standard in degree-day model
From the intro I was not expecting this to be yet another temperature index model
L176 seems yielding satisfactory results
Grammar
L176 especially in suppressing mid-winter melt episodes that do not appear in validation data.
Mid winter melt will increase with climate warming though. Further, your results show non-physical mid winter ablation events. Lastly this appears to be an ad hoc calibration, is that true?
L177 decoupling radiative forcing
I mean, this is the point of a full energy balance model
L181 where Sr is incoming shortwave
This section needs units. I see the below section has it, please put these into the above text
L184 sets Sr to 0 between 7PM and 7AM according to forcing timestamps
This seems arbitrary. Why not just compute load sun rise and set?
Page 9
L195 otherwise.
Move to start of statement
L197 (timestamp time)
Whit does this mean?
L200 sensitivity of S3M to both is rather low.
Based on?
L201 closer to physics
What does this mean? Do you mean closer to a fully first-principals energy balance model?
L202 this hybrid approach
There are tons of temp+rad formulations. Either this isn’t new or the contribution is not clear me to me
L 206 Which is an isothermal, very efficient condition for shortwave radiation to convert into actual melt.
Awkward, suggest clarify
L208 regardless of the actual cold content.
Is the case the authors making that their contribution is a cold content temp index? Cold content is /required/ to correctly track energetics in deep mountain snowcovers
L 210 To mimic this transition
This is what the cold content tracking should do, sn’t it? It is not clear to me how this approach works with deep mountain snowcovers.
Page 10
L229 Refreezing is computed
Does this refreezing latent heat flux decrease the cold content ie warm snowpack?
L230 eq 19, R
Remind the reader what “R” is please
Page 12
L269 Report instabilities of Equation 20
Could this be due to using FE? And to be clear, this is the use of 20 that is a problem and not the solution to 20?
L 269 high saturation value
So numerics are likely a problem?
L269 very shallow snowpack
This is classically a tough problem and requires a good eb model + good numerical scheme
L 276 a representative element at 66% depth:
Where does 66% come from? This seems arbitrary
Page 13
L290 While it is set to 0°C otherwise.
So a 2m mountain snow cover is set to instantly isothermal?
Perhaps I am misreading this, but it seems to me that the authors are suggesting that as soon as Tair >0, they set the ground temperature to be = Tair? If that’s what is happening, then that is completel wrong, especially for deep mountain snow covers. If that isn’t what is happening, then please clarify this, as despite reading it a few times I am still uncertain on what, exactly, is being done.
L293 thus implying that refreezing has no impact on snow structure
So, what is the point then? Latent heat and Cold Content tracking? I am picking on this specifically due to the noting of avalanche hazard forecasting in the introduction
Section 3.5 Data assimilation
This is direct insertion, correct? I understand the authors not wanting to cut and paste verbatim from existing papers, and I appreciate them keeping the length of this manuscript down. However I did struggle through this section to know, exactly, how this was done. Specifically that Swe and Sd are diagnostic variables, but seem to be assimilated as a state variable
Page 14
L 307 of Updating
Fix cap
Page 15
L347 total SWE in S3M v5.1 is only a diagnostic variable
As noted above this needs to be fixed in figure 1 and the text throughout for consistency
L347 S3M also supports assimilating only positive differences in Equation 36, that is, only correcting modeled SWE if observations are larger than simulations
I am a bit surprised by this tactic. Certainly it helps recover from the otherwise catastrophic mid winter melts, but doesn’t help with over estimates in SWE/SD. I’d like to see a bit more elaboration as to why it is done this way
Page 16
L376 equivalent to G1
Maybe section 2.4.1 would benefit from text that notes what g1 is as I got here and was confused. G1 is only used in the heading. Would be maybe nice to remind the reader what this is.
Page 17
L 391 any residual SWE at the end of each water year is added to hG
Ok so this ignores firn then. In multi-year firn processes I’m skeptical this can be ignored, but I understand that in some Alps glaciers this approximation can be valid. I would like to see supporting literature for this assumption.
L 398 parametrizing
Spelling
Page 18
L 446 spatialize and downscale weather-input data,
Aren’t these data already spatial datasets? So is this just a downscale to the numerical model grid? Please note the methods used to do so (eg what spatial interpolant/regridder is being used) [ ah, I see there is a note in the appendix on this, perhaps either reduce the text and move it into the main body or explicitly note the appendix section].
L 448 using the Continuum model
What is this?
Page 20
L470 the notions that
This is unclear to me what you mean by notions here. Do you mean ‘notion’? 100% seems very high
L470 of transmitted shortwave radiation
Transmitted through what? No reflectance?
L476 mainly for computational-resource constraints.
Less brute force methods can be quite efficient. E.g.,
Saman Razavi, Razi Sheikholeslami, Hoshin V. Gupta, Amin Haghnegahdar, VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis, Environmental Modelling & Software, Volume 112, 2019,
L486 , in line with expectations
L489 that the sensitivity of S3M to m′rad and mr is surprisingly low
Doesn’t this go against what was previously stated? If this is the case though, then what is causing the huge mid winter ablation events?
Page 21
3.2 Evaluation: point snow depth
Should note this has assimilation in it is my thinking was that this was sans assimilation like the previous section.
L 498 Sections
Page 23
3.3 Evaluation: the Torgnon study plot
This is with assimilation right? I’m not totally sure why but I’m struggling to remember which runs have assimilation and which do not.
L519 (Figure 6)
What is going on with the mid winter ablation events? It seems assimilation is heavily compensating for these impacts?
L536 not reported for brevity
L536 qualitatively
Wouldn’t this be quantitatively?
Page 27
L562 provides renovated
Not sure what this means. W/c
L566 S3M is among the first parsimonious snow models to provide such information.
I don’t accept that temp index models can work when applied to climate change forecasts. The loss of stationarity eg Milly(2008) leads to huge challenges in applying hindcast calibrated models to future conditions. For example, the increase in mid winter ablation makes calibrated models that assume a spring melt (and implicitly calibrate for the deep snowpacks) will likely fail when applied to very different types of winters.
Milly 2008, Stationarity Is Dead: Whither Water Management?
L 576 solid results
Word choice
L580 and the Petit Grapillon,
These results seem really poor if observations show 2m of ablation but 0m is predicted!
Page 29
L600 at a comparatively high standard in physical realism.
A temperature index snowmelt model with no: canopy interactions, sublimation, blowing snow, energy balance, etc. I am not convinced this is a high amount of physical realism.
Page 30
L610 to produce future scenarios of
See my previous comment on applying calibrated tindex models to future climates.
L613 for an ordinary laptop
What does this mean? Please give a sense of CPU arch, speeds, etc. As a model design philiosophy, I am not so sure that constraining the physics and conceptualization so-as to run on a laptop is optimal.
L616 the scarcity of open- source suites reconnecting them with the hydrologic cycl
I don’t I understand this comment
Page 32
L658 energy and mass balances,
Temperature index models are not an energy balance.
L679 I’ve never seen a license agreement in a code availability section. Not saying it is wrong, but I personally think it should be removed, indeed the code itself should have the license agreement in it.
L715I see that this is where the input data requirements are. I think this should be referred to explicitly from the main text
L751 I was not able to get this to compile on Macos due to a unlimit being specified in the .sh
Francesco Avanzi et al.
Model code and software
S3M-model source code Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro https://doi.org/10.5281/zenodo.4663899
Francesco Avanzi et al.
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