Advanced Topics in Snow Hydrology: Measurements, Modeling, & Remote Sensing

In Partnership with the Fulbright Scholars Fellowship, University of Trento Italy, Portland State University USA, and EURAC Italy

 

18 – 21 February, 2025 – In-person at University of Trento, Italy, and remotely via Zoom.

 

Instruction Format: Collaborative teaching with structured lectures (morning) and hands-on exercises or fieldwork (afternoon). Lectures will be recorded and posted for future access.

Instructors: Kelly E. Gleason¹, John Mohd Wani², Giacomo Bertoldi⁴, Michele Bozzoli^2,4, Valentina Premier⁴, and Riccardo Rigon^2,3

 

The School will be held in person at the Department of Civil, Environmental, and Mechanical Engineering in Trento

• All sessions will be recorded and made available online after the course.
• No registration fee is required.
• Participants are responsible for their own travel and accommodation expenses.

✉️ To register, please contact: johnmohd.wani [at] unitn.it

1. Department of Environmental Science and Management, Portland State University, Portland, Oregon, USA

2. Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy

3. C3A - Center Agriculture Food Environment, University of Trento, San Michele all‘Adige, Trento, Italy

4. Institute for Alpine Environment, EURAC Research Bolzano, Italy

Day 1: Introduction to Snow Hydrology and Snow Processes

 

Morning (9:00-12:30) 

Lecture Session: Intro to Snow Hydrology

Instructor: Kelly Gleason (9:00-10:00)

• General snow hydrology: Overview of snow hydrology. 
• Snow energy and mass balance: Energy and mass dynamics in snowpacks.

Break: 10:00-10:15

Lecture Session: Spatial and Temporal Distribution of Snow, and how is it changing?

Instructor: Michele Bozzoli (10:30-11:30)

• Snowfall trends in the Alps: Historical and current trends; climate change implications.

Break: 11:30-11:45

Instructor: Kelly Gleason (11:30-12:00)

• Snow-climate-forest interactions: Influence of forests and climate on snow hydrology.

Instructor: Riccardo Rigon (12:00-12:30)

• Permafrost and permafrost/snow relations: Overview of permafrost and its interactions with snow processes and modeling.

Afternoon (14:00-17:00) 

• Exercise 1: Snow mass balance calculations (Instructor: Kelly Gleason)
• Students calculate snow energy and mass balance using sample datasets. 
• Exercise 2: Snowfall trend analysis (Instructor: Michele Bozzoli)
• Students conduct analysis of real-world snowfall data using statistical tools.

Day 2: Snow Modeling and Data Integration into Snow Modeling

 

Morning (9:00-12:30)

Lecture Session: Intro to Snow Modeling

Instructor: Kelly Gleason (9:00-10:00)

• General snow modeling: Principles, methodologies, and applications of snow modeling.
• Parameterization of snow processes and integration of empirical data into snow models: Role of observational data in improving model accuracy.

Break 10:00-10:15

Lecture Session: Intro to Snow Modeling with GEOTop 

Instructor: John Mohd Wani (10:30-12:30)

• Snow Modeling with the GEOtop Model: Introduction and case study overview.

Lunch 12:30-14:00

Afternoon Lecture

Instructor: Giacomo Bertoldi (14:00-15:00)

o   GEOtop Modeling: EURAC research case study in snow modeling

Afternoon 15:00-17:00 (Exercises)

• Exercise 3: Snow model output analysis (Instructor: John Mohd Wani)
• Students use the GEOtop and GEOFrame model output data to evaluate model performance under different conditions relative to snow station data.

Day 3: Remote Sensing and Advanced Applications

 

Morning 9:00-12:30

Lecture Session: Remote Sensing of Snow (Video)

Instructor: Kelly Gleason (9:00-10:00)

·      Introduction of Remote Sensing of Snow across Scales: Discussing the principles of detecting snow from field based, drone, airborne, and satellite-based methods. 

Break 10:00-10:15

Lecture Session: Remote Sensing of Snow and Snow Modeling Case Study (Video)

Instructor: Valentina Premier (10:15-11:15)

·      Remote Sensing of Snow: Introduction and research in remote sensing of snow

Break 11:15-11:30

Instructor: Michele Bozzoli (11:30-12:30)

• Integration of Remote Sensing and Snow Modeling in the GEOFrame Model: Applications through the lens of PhD dissertation work. (Video)

Lunch 12:30-14:00

Afternoon Lecture

Instructor: Kelly Gleason (14:00-14:30)

o   Forest/Snow Case Study: Snow and forest interactions and uncertainty in remote sensing observations

Afternoon 14:30-17:00 (Exercises)

·      Exercise 4: Remote sensing data uncertainty across scales (Instructor: Kelly Gleason)

o   Students use remote sensing observations to estimate snow properties over space and time, and evaluate uncertainty of remote sensing across scales.

Day 4: Fieldwork and Integration of Snow Hydrology Concepts

 

All day field trip (9:00-17:00) to measure snow properties including snow depth, SWE, density, and grain size.

Morning (9:00-12:30)

Instructors: Giacomo Bertoldi and Kelly Gleason

• Field Trip Introduction: Overview of field methods and objectives.
• Field Data Collection:
• Snowpack measurement techniques (density, SWE, snow depth, grain size).
• Observations of forest-snow and permafrost-snow interactions.

Lunch (12:30-14:00)

Afternoon (14:00-17:00)

Field Data Analysis and Wrap-Up

• Exercise 5: Field snow data analysis (Instructors: Kelly Gleason)
• Students analyze collected snow hydrology data and apply principles learned throughout the course.
• Wrap-Up Discussion:
• Synthesis of course concepts, student reflections, and Q&A.

Graduate Students will conduct a final project conducting mini snow hydrology research project using existing data and analyzing it to answer a simple research question or learning to apply these concepts into the GEOtop model. This mini research project will be documented in a 4-page short form manuscript for submission to instructors for evaluation in the style of the Geophysical Research Letters journal format.

______________________________________________________________________________

Materials and Resources

• Daily readings will be provided to prepare for the next day’s coursework.
• Lecture slides and notes (provided by instructors).
• Datasets for exercises and fieldwork preparation.
• Access to GEOtop and GEOframe modeling tools.

Assessment

• Participation in lectures, exercises, and fieldwork (40%).
• Daily exercises will primarily be completed in class during the afternoons, but any unfinished work will be finished by students as homework (30%) 
• Graduate student final project conducting mini snow hydrology research project using existing data and analyzing it to answer a simple research question or learning to apply these concepts into the GEOtop model (30%).

This structure ensures a balance of theoretical learning and practical application, allowing students to immediately apply knowledge from lectures to real-world and simulated contexts.

Summarizing my cryospheric work (made with a good company)

The hydrological cycle is significantly influenced by the presence of water in its condensed states in middle and extreme latitudes. Various hydrological parameters change below 0  Celsius, such as water viscosity, thermal capacity, and hydraulic conductivity. Consequently, mainstream hydrology treatments that neglect freezing provide incorrect results in winter, high elevations, and the far north and south for most of the year. In the current state of global warming that threatens the cryosphere which is progressively disappearing, it is even more crucial to address its dynamics. 

A little of-of-date itinerary can be found in a previous post here. To understand our progress, three milestone theses summarize the work done

• Matteo's  Together, we worked out the Thermodynamics of non equilibrium for ice-systems and the theory of freezing soils. Matteo implemented also an integrator in GEOtop, not the perfect one, but acceptable. Matteo's 2011 paper is a benchmark paper in the topic. 
• Stefano's brought GEOtop to some maturity and especially fine tuned the various tools related to snow and ice. Stefano's 2014 paper remains a landmark in our work. 
• Niccolò's  pushes forward the previous work. Especially remarkable is his work on re-implementing the informatics according to new (for us) concepts in OO programming and using (finally) safe algorithms for the integration of the equations. His WHETGEO and FreeThaw papers are a must read for completeness and clarity.

Our work's focus was primarily on the critical zone, where we modified the Darcy-Buckingham law to account for freezing and thawing and their related hydrological and mechanical effects. We primarily focused on the hydrological effects neglecting the mechanical ones but not neglecting the energy budget, a common practice in hydrology, which is obviously not possible. Consequently, we faced the necessity to simultaneously solve both the mass budget and the energy budget.

The formulation of the equations can be found in the theses and papers cited above, and you will realize that establishing a correct relation between the Darcy scale energy content and the corresponding water (liquid or solid) is the main challenge. Proper physics requires the consideration of interfaces between the phases: air-water-soil-ice. While a complete understanding of this relation has not been yet achieved, some working approximations have been obtained. Looking at the two compartments, snow and ice in the soil, they differ in many aspects, with snow lacking soil and being affected by its aerial origin. Both snow and ice in the soil have their own complexities, which affect their evolution. They often interact and the fate of the soil with or without snow is quite different.

While determining the correct equations would be satisfactory goal for many, it remains unresolved how to numerically estimate these equations. It turns out that these mildly nonlinear equations pose problems when solved using the usual algorithms based on variations of the Newton method. Convergence of the numerical methods is not guaranteed, and many workarounds have been deployed to overcome these difficulties, often leading to issues with mass and energy conservation principles.

Fortunately, Casulli and Zanolli (2010) found a method to address these challenges. The fundamental paper can be found in bibliography, and a progressive approach to its formulation can be obtained by reading Casulli's lecture notes and completed by watching videos in this blog. Ideally, attending Casulli's annual school in Trento, held every second half of January after our GEOframe winter school, would provide the best understanding. Tubini's recent papers are the result of this approach, and FreeThaw and WHETGEO are concrete implementations of these algorithms in Java/OMS/GEOframe.

Another aspect to consider is the implementation of these algorithms in informatics. Concepts related to this can be found in parts of Tubini's thesis and the related papers, especially Tubini and Rigon, 2022.

Finally, as a source of information, all of these models' open-source codes can be found on GitHub, both for the older GEOtop and the more recent GEOframe model components.

References

Casulli, V. 2017. Advanced Numerical Methods for Free-Surface Hydrodynamics.

Casulli, Vincenzo, and ZANOLLI. 2010. “A Nested Newton-Type Algorithm for Finite Colume Methods Solving Richards’ Equation in Mixed Form.” SIAM Journal of Scientific Computing 32 (4): 2225–73.

Dall’Amico, Matteo. 2010. “Coupled Water and Heat Transfer in Permafrost Modeling.” Edited by Riccardo Rigon and Stephan Gruber. Phd, University of Trento. http://eprints-phd.biblio.unitn.it/335/.

Dall’Amico, M., S. Endrizzi, S. Gruber, and R. Rigon. 2011. “A Robust and Energy-Conserving Model of Freezing Variably-Saturated Soil.” The Cryosphere. https://tc.copernicus.org/articles/5/469/2011/.

Endrizzi, Stefano. 2007. “Snow Cover Modelling at a Local and Distributed Scale over Complex Terrain.” Ph.D. Thesis, January, 1–189.

Endrizzi, S., S. Gruber, M. Dall’Amico, and R. Rigon. 2014. “GEOtop 2.0: Simulating the Combined Energy and Water Balance at and below the Land Surface Accounting for Soil Freezing, Snow Cover and Terrain Effects.” Geoscientific Model Development 7 (6): 2831–57. https://doi.org/10.5194/gmd-7-2831-2014.

Tubini, N. 2021, June. “Theoretical and Numerical Tools for Studying the Critical Zone from Plots to Catchments.” Edited by R. Rigon and S. Gruber. Ph.D., Dipartimento di Ingegneria Civile, Ambientale e Meccanica, Università di Trento.

Tubini, Niccolò, Stephan Gruber, and Riccardo Rigon. 2021. “A Method for Solving Heat Transfer with Phase Change in Ice or Soil That Allows for Large Time Steps While Guaranteeing Energy Conservation.” The Cryosphere 15 (6): 2541–68. https://doi.org/10.5194/tc-15-2541-2021.

Tubini, Niccolò, and Riccardo Rigon. 2022. “Implementing the Water, HEat and Transport Model in GEOframe (WHETGEO-1D v.1.0): Algorithms, Informatics, Design Patterns, Open Science Features, and 1D Deployment.” Geoscientific Model Development 15 (1): 75–104. https://doi.org/10.5194/gmd-15-75-2022.

Modelling discharge in an Alpine basin with JGrass-NewAGE

This and a related post reports about the Master thesis by Niccolò Tubini and Stefano Tasin. It was a couple years ago that I graduate my last Master guy, and I am happy with these two graduations.
Stefano thesis is in Italian. So I am summarising it a little bit below.
JGrass-NewAGE has a a snow module that was developed by Giuseppe Formetta (GS). Giuseppe developed also a component called  Adige-Hymod for runoff estimation. The two were not tested conjointly (well, they were), and we would like to have a new case to understand more about the behaviour of the model and sharpen the methods we use with it.

 
Stefano did it, making leverage on the NewAGE database of river Adige and using, side by side with NewAGE, GEOtop as the true to reproduce in matter of snow. Other directions could have taken, but Stefano chose this one with excellent results. He had in mind a relatively small basin in the Norther part of Italy that was known to be dominated by snow (and glacier melt) and he wanted to investigate how much of discharge depends upon snow melting. The figure above is one of his results, which shows an excellent discharge fitting and quite impressive demonstration of how snowmelt counts in this case. Thinking that snow on the Alps is going to almost disappear cause the climate change, the basin will go to a quite large change in the discharge regime. It is foreseeable that winter discharge will grow in place of the summer ones, with possible modifications of the discharges distributions.
The thesis and the simulations files used are here.

Theoretical progress in freezing-thawing processes studies

This and a related post reports about the Master thesis by Niccolò Tubini and Stefano Tasin. It was a couple years ago that I graduate my last Master guy, and I am happy with these two graduations.
Niccolò thesis is about modelling permafrost.

I already worked on it during the Ph.D. thesis by Matteo Dall'Amico, obtaining interesting results, which were published in this 2011 paper. From it we built. Initially the idea was that the work by Matteo dall’Amico was clear enough to go directly to a full three-dimensional implementation of 3D algorithms on a unstructured grid. That was actually not the case an we had to rework all the theory. I do not want to waste its reading. So, if you want to know the story, please click here. 

The presentation Niccolò gave for his Master (Laurea) degree is here.

Studies on glaciers (snow, permafrost) in Trentino

I was invited to give a talk tonight at MUSE for the general public about the activity of our group in the cryosphere areas.
Please find the presentation (sorry in Italian) clicking on the Figure.
During the last ten years our group of hydrologists started a nice series of tasks, sometimes with higher and sometimes with lower intensity.

As a result, we have now, models, measures, expertise for doing much more. Personally I should go more on field trips! Enjoy

My Past Research on Cryopheric Hydrology

In [J22] it was demonstrated that a single-layer snowpack model can be sufficiently accurate in describing the evolution of the water equivalent of the snow, as long as the incident radiation is calculated accurately taking care of shadows and the complexity of mountain topography.

Subsequently, the single-layer model was replaced with a multilayer model in order to forecast the evolution of density and of metamorphism of the snow as well as the percolation phenomena within the snowpack, during the thesis of Stefano Endrizzi. Among the various studies carried out, one validates the snow model satellite data derived from MODIS [A41].  Furthermore, the same model was used to study the hydrological evolution of glaciers in Trentino (Alpine) and South America (Equatorial) [A39, A47].  Eventually, the modeling of the cryosphere moved towards considering evolutive processes of permafrost [thesis of Matteo Dall'Amico, and J30], that is the layer of soil subject to temperatures below zero centigrades for more than two consecutive years.  All of these research projects, as well as allowing the aforementioned studies, are necessary to modeling the entire yearly hydrological cycle in mountain environments such as Trentino.

[J30], drawing from an accurate work of reanalysis of process thermodynamics, implements a robust method for the integration of the freezing-soil equation.  The numeric algorithm used is globally convergent Newtonian method that is appropriate for the equations under study.  [J36] is a geomorphological survey of rock glaciers in Trentino, to be subsequently modelled with GEOtop.

References in English


[ J22] - Zanotti, F., Endrizzi, S, Bertoldi, G. e R. Rigon, The GEOTOP snow module, Hydrol. Proc., 18, 3667-3679 (2004), DOI 10.1002/hyp.5794

[j30]- M. Dall’Amico, S. Endrizzi, S. Gruber, and R. Rigon, An energy-conserving model of freezing variably-saturated soil, The Cryosphere, 5, 469-484, 2011, doi:10.5194/tc-5-469-2011

[J36] - R. Seppi, A. Carton, M. Zumiani, M. Dall’Amico, G. Zampedri, R. Rigon, "Inventory, distribution and topographic features of rock glaciers in the southern region of the Eastern Italian Alps (Trentino)" in Geografia Fisica e Dinamica Quaternaria, v. 2012, n. 35(2) (In press)

[A41] Endrizzi S., Bertoldi G., Neteler M., and Rigon R., Snow Cover Patterns and Evolution at Basin Scale: GEOtop Model Simulations and Remote Sensing Observations, Proceedings of the 63th Eastern Snow Conference,


References in Italian

[A47] Noldin I., Endrizzi S., Rigon R., Dall’Amico M, Sistema di drenaggio di un ghiacciaio alpino, Neve e Valanghe, n. 69, 48-52, 2010

Frost Heave

Frost heave is a phenomenon of accumulation of ice due  to peculiar thermodynamics circumstances. It is quite an important phenomenon that I encounter during the thesis of Matteo Dall'Amico, and we did not treat (but I  have some ideas about). Causally I found this youtube lecture that illustrate it, and can be used to understand a little more about.

References

1. Taber, S., Frost heaving, J. Geology 37 (1929) 429-461
2. Taber, S. The mechanics of frost heaving, J. Geology 38 (1930) 303-317
3. Dash, J.G., Fu, H. and Wettlaufer, J.S., The premelting of ice and its environmental consequences, Rep.Prog.Phys. 58 (1995) 115-167
4. Rempel, A.W., Wettlaufer, J.S. and Worster, M.G. Premelting dynamics in a continuum model of frost heave, J. Fluid Mech. 298 (2004) 227-244
5. Wettlaufer, J.S. and Worster, M.G. Premelting dynamics, Annu.Rev.FluidMech. 38 (2006) 427-452
6. Wettlaufer, J.S. and Worster, M.G. , Dynamics of premelted films: Frost heave in a capillary, Phys. Rev E., 51(5), 4679-4689, 1995

Remote Sensing of Snow

Preparing the slides for my hydrological class, and aiming to overview (just a little indeed) measurements methods, I discovered prof Hongjie Xie page, from which I withdraw some information. This was for me the excuse to copy and download some reference on passive microwaves and optical sensor dedicated to snow. Below it is a report of the page (with the bibliography a little edited to cope with available papers).

"Since the middle of the 1960’s, a number of satellite-derived snow products have been available, with a few available in near-real time through Internet (Bitner et al, 2002).
Space-board passive microwave radiometer, such as SMMR (Scanning Multichannel Microwave Radiometer), SSM/I (Special Sensor Microwave/Imager), and AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System), can penetrate clouds to detect microwave energy emitted by snow and ice and provide information on SWE or snow depth and thus estimating runoff (Pulliainen, 2006; Wulder et al., 2007). Since the 1970s, SWE retrieval from space-borne passive microwave has been investigated.  Space-borne passive microwave data are well suited to snow cover monitoring because of characteristics such as all weather imaging, a wide swath width with frequent overpass times, and a long available time series (Derksen et al., 2004). But the coarse spatial resolution (25 km of AMSR-E is the best available now) hinders their application in operational hydrological modeling and snow-caused disasters monitoring (Foster et al., 2003; Dressler, et al. 2006; Pulliainen，2006). Optical sensors such as AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectraradiometer), SPOT and Landsat have been well developed to produce snow cover maps with high spatial resolution (Salonmonson & Appel, 2004; Brown et al., 2007; Dozier&Painter, 2004). But due to the inherent limitation, optical sensors cannot see the earth surface when cloud is present. High cloud blockage becomes the biggest problem in applying snow products from optical sensor (Klein & Barnett, 2003; Zhou et al., 2005; Tekeli et al., 2005; Ault et al., 2006; Liang et al. 2008 a, b; Wang et al., 2008a, b; Wang and Xie 2009)"

Bibliography

Ault T.W.,⁎, Czajkowski K.P., Benko T., Coss J., Struble J., Spongberg A., Templin M.,  Gross C., Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region, Remote Sensing of Environment 105 (2006) 341–353

Bitner D., T. Carroll, D. Cline and P. Romanov, 2002: An assessment of the differences between 
three satellite snow cover mapping techniques, Hydrological Processes 16:3723–3733.

Brown R., Derksen C., Wang L, Assessment of spring snow cover duration variability over northern Canada from satellite datasets,  Remote Sensing of Environment 111 (2007) 367–381

C. Derksen C.,Brown, R., Walker A., Merging Conventional (1915–92) and Passive Microwave (1978–2002) Estimates of Snow Extent and Water Equivalent over Central North America, Journal of Hydromet, 5,  2004, 850-861

Dozier J,  Painter T.H, Multispectral and hyperspectral remote sensing of alpine snow, Annu. Rev. Earth Planet. Sci. 2004. 32:465–94 doi: 10.1146/annurev.earth.32.101802.120404

Dressler,K. A., Leavesley,G. H.,  Bales R. C. and Fassnacht S. R., Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model, Hydrol. Process. 20, 673–688 (2006)

Foster, J.L.,  Sunb C., Walkerd J.P.,  Kelly R., Changa A., Dong J.,  Powell U, Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sensing of Environment 94 (2005) 187–203

Klein A, Barnett A.C., Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000–2001 snow year, Remote Sensing of Environment 86 (2003) 162–176

Liang T., Zhang X., Xie X, Wu C., Feng Q, Huang X, Chen Q., Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements, Remote Sensing of Environment xxx (2008) xxx-xxx

Pulliainen J., Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations, Remote Sensing of Environment, Volume 101, Issue 2, 30 March 2006, Pages 257-269, ISSN 0034-4257, 10.1016/j.rse.2006.01.002.

Salomonson V.V, Appel, I., Estimating fractional snow cover from MODIS using the normalized difference snow index, Remote Sensing of Environment 89 (2004) 351 – 360

Tekelia A.E., Akyurek Z., Sorman A., Sensoy A, Sorman U., Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey, Remote Sensing of Environment 97 (2005) 216 – 230

Wang X., Xie H., Liang T., and  Huang X., Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China, Hydrol. Process. 23, 419–429 (2009) DOI: 10.1002/hyp.7151

Wulder, M.A., T. A. Nelson,  Derksen C, Seemann D,  Snow cover variability across central Canada (1978–2002) derived from satellite passive microwave data,  Climatic Change (2007) 82:113–130 DOI 10.1007/s10584-006-9148-9

Zhou X, Xieb H.,  Hendrickx J.M.H., Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements, Remote Sensing of Environment 94 (2005) 214–231

The microscopic^1 thermodynamics of snow

Listening to Michi Lehning talk, my curiosity was raised by a couple of passages. One of these was the use of mean field theories for describing the snow metamorphism.  Therefore I asked him for a little of literature on the subject. Here below his suggested readings:

I.M. Lifshitz and V.V. Slyozov. The kinetics of precipitation from supersaturated solid solutions. 

J. Phys. Chem. Solids, 19(1-2):35–50, 1961. 

[C. Wagner. Theorie der Alterung von Niederschlagen durch umlÅNosen (Ostwald-Reifung). Z. 

Elektrochem., 65(7-8):581–591, 1961. 

Probably easier in language and style: 

C.W.J. Beenakker and J. Ross. Theory of Ostwald ripening for open systems. J. Chem. Phys., 

83:4710–4714, 1985. 

L. Ratke and C. Beckermann. Concurrent growth and coarsening of spheres. Acta mater, 

49:4041–4054, 2001. 

S.P. Marsh and M.E. Glicksman. Kinetics of phase coarsening in dense systems. Acta Materialia, 

44(9):3761–3771, 1996. 

and with respect to snow: 

L. Legagneux and F. Domine. A mean field model of the decrease of the specific surface area of dry snow during isothermal metamorphism. J. Geophys. Res. Earth, 110(F4), NOV 18 2005.

Looking at the citations chain one can more or less recover most of the literature on the subject. You can also give a look to this more recent post.

^1 microscopic thermodynamics is, obviously, a sort of oxymoron. However, these thermodynamics work at a scale which is much smaller than what we want usually to treat (at field or catchment scale).

Michi Lehning: Snow hot and cold, dangerous and beautiful, fragile and persistent

Michi Lehning is one of the leading scientists in  snow and avalanches, and related boundary layer meteorology, research.

He is co-author of  three very good pieces of software:

• Alpine3D
• Snowpack
• MeteoIO

and several papers on the topics he is expert. Recently he worked on the uneven distribution of snow during storms that is very enlightening.  The last March 15, besides being hed of a group at WLSF (here his many projects),  he also became professor at EPFL-ENAC School.
I am writing this to advertise that  his opening lecture at EPFL can be watched and dowloaded here.