My reflections and notes about hydrology and being a hydrologist in academia. The daily evolution of my work. Especially for my students, but also for anyone with the patience to read them.
Tuesday, September 10, 2024
30-years (1991-2021) Snow Water Equivalent Dataset in the Po River District, Italy
Thursday, July 4, 2024
A Ph.D. position on snow modelling and the related runoff production
APPLY TODAY ! There is an opening until July 10 (<---Here it is the link) for a Ph.D. position on the SpaceItUP and SUPER projects (Snow and glacier rUnoff Production in alpine RivER basins 1990-2050)
Thursday, June 27, 2024
How much snow is in the mountains and what is its fate? by Manuela Girotto
Water resources such as snow or groundwater can be estimated using satellite remote sensing observations and numerical models. Both models and observations have inherent uncertainties and limitations related to observation errors, model parameterization, and input uncertainties. A promising method to alleviate shortcomings in models and observations is data assimilation because it combines existing and emerging observations with model estimates, thus bridging scale and limitation gaps between observations and models.
Using these tools, we can address the following science questions: How much water is stored as seasonal snow? How much is in the groundwater aquifers? Can we quantify hydrological changes due to human induced processes (e.g., irrigation)? This presentation will focus on the estimation of snow seasonal amounts in mountainous regions, the water towers of the world. They supply a substantial part of both natural and anthropogenic water demands and they are also highly sensitive and prone to climate change. Slides of the talk can be found by clicking on the above figure.
Saturday, October 28, 2023
CARITRO Project: Snow droughts e green water: how climate change modifies the hydrological cycle in the Alpine Region.
Friday, October 20, 2023
Identifying Snowfall Elevation Patterns by Assimilating Satellite- Based Snow Depth Retrievals
The analysis of the snowfall elevation patterns' spatial characteristics indicates that the proposed assimilation scheme results in more accurate spatial patterns in the snowfall distribution across the entire basin. The derived snowfall orographic patterns contribute to a comprehensive improvement of mountain hydrologic variables such as snow depth, snow cover area, and streamflow. The most significant enhancements in streamflow are observed during the spring and summer months when peak flow observations align more accurately with the posterior cases than the prior ones. These results primarily stem from the fact that the assimilation of Sentinel-1 assigns less snowfall to the lower-elevation regions of the basin, while higher rates are assigned to the higher elevation. As summer approaches, water is released more slowly from the higher elevation via snow-melt than in the prior case, which aligns better with observations. The assimilation of Sentinel-1 effectively downscales coarser-resolution precipitation products. While the prior snowfall cumulative elevation pattern has a small gradient across elevation bands, these patterns are consistent across elevations and precipitation products after the assimilation of snow depth retrievals. In conclusion, this study provides a framework for correcting snowfall orographic patterns across other seasonally-snow dominated mountain areas of the world, especially where in-situ data are scarce. The full paper can be found by clicking on the Figure above.
Reference
Girotto, Manuela, Giuseppe Formetta, Shima Azimi, Claire Bachand, Marianne Cowherd, Gabrielle De Lannoy, Hans Lievens, et al. 2023. “Identifying Snowfall Elevation Patterns by Assimilating Satellite-Based Snow Depth Retrievals.” The Science of the Total Environment, September, 167312. https://doi.org/10.1016/j.scitotenv.2023.167312.
Tuesday, July 12, 2022
Modelling the Thermodynamics of Glaciers
Is it possible to predict the ice temperature and its thermodynamic properties? In principle it would not seem difficult. The heat propagation equation has long been known. It therefore seems that it is enough to know the incoming solar radiation, have a simple model of heat propagation, a digital model of elevation of the glacier and the terrain, and that's it. There are some difficulties though. Assuming the physics works as described, we can understand fairly quickly that the heat capacity of the ice is enormous and if the mass of the glacier is large, this requires that the heat balance model must be run for hundreds of years to obtain reliable results. This is impractical because we would need to know the (forcing) meteorological conditions starting from a distant past (everyone knows that the future is unknown, fewer are those who reflect on the fact that not even the past and the present are perfectly known). In fact, as any mathematician knows, glacier modeling requires you to set the temperature and heat flow conditions around the glacier at any time (the boundary conditions). We do not have them but we can invent plausible ones, making use of a little art and a lot of artisan’s experience. This, to be honest, introduces some uncertainty into what is being built, but it would be enough to be clear about this and people could perhaps understand.
Greve, R. and Blatter, H.: Comparison of thermodynamics solvers in the polythermal ice sheet model SICOPOLIS, Polar Science, 10, 11–23, 2016
Hewitt, I. and Schoof, C.: A model for polythermal ice incorporating gravity-driven moisture transport, Journal of fluid mechanics, 797, 2016
Monday, April 11, 2022
Snow droUghts predictioN in the Alps: a changing climate assessmEnT: SUNSET PRIN Project
SUNSET is based on two types of data: i) 20-to-30 years time series of validated catchment precipitation-temperature-discharge data from the network of twelve project study basins; ii) process data, collected in previous projects which will help improving the hydrological model validation. The past dataset includes observations from MODIS and Sentinel-1 that are functional to provide correct snow cover areas and snow water equivalent data.
Climate simulations will be based on 12-member CPM and RCM ensembles from the FPS-CORDEX project available for 3 time slices (historical, 1996-2005; near future, 2041-2050; far future, 2090-2099) for the extreme climate change scenario, i.e. RCP8.5. RCM outputs from CORDEX will also be considered to evaluate the added value of CPM compared to coarser resolution models and to investigate the climate change signal under different scenarios which are not available in CMP simulations, i.e. RCP4.5 (six-member RCM ensembles) versus RCP8.5 (six-member RCM ensembles. Hydrological models are part of an expandable system called GEOframe developed along the last 15 years and have already a setup for most of the study basin network.
SUNSET will communicate and disseminate the project results to a wide audience of residents in the two study areas and beyond through collaborations with Local Authorities. SUNSET will test, archive, document, and keep track of versions of the research code and software using open-source software and data protocols and accessible documentation.
Thursday, October 15, 2020
How snowy are the Alps ?
A recent preprint was submitted to The Cryosphere were a large group of colleagues scientists analyzed the snow precipitation in more than 2000 gauges all over the Alps. This research is not only important for assessing the effects of climate change but also will be a benchmark to other more local studies on snowfall.
I think it could be a good reading for many, therefore I am sharing its information here. By clicking on the Figure you download the paper.
Tuesday, July 16, 2019
Snow, Ice and Permafrost
There I briefly summarise three of the topics related on the cryosphere on which I and my colleague Alberto Bellin (GS) and our group did something. Snow, glaciers and permafrost, not only are hydrological topics, they are certainly among the most fascinating ones.
Thursday, April 4, 2019
EGU Wien 2019: Snow Water Equivalent modeling: comparing GEOtop physically based approach with temperature-index-based models in GEOframe-NewAge
Monday, March 18, 2019
Snow for GEOtop 4.0
Get the presentation by clicking on the Figure above. The title is in Italian (Elements for the development of a new snow model for GEOtop 4.0), but the contents in English.
On similar topic were also the seminar by
- Giacomo Bertoldi, Entitled: Snow, Forest and Climate Change: which feedbacks ?
Tuesday, November 13, 2018
Crocus and Snowpack in a nutshell
- Bartelt, P., & Lehning, M. (2002). A physical SNOWPACK model for the Swiss avalanche warning Part I. Numerical model. Cold Regions Science and Technology, 35(3), 123–145. https://doi.org/10.1016/S0165-232X(02)00073-3
- Lehning, M., Bartelt, P., Brown, B., Fierz, C., & Satyawali, P. (2002). A physical SNOWPACK model for the Swiss avalanche warning Part II. Snow microstructure. Cold Regions Science and Technology, 35(3), 147–167. Retrieved from www.elsevier.com/locate/coldregions
- Lehning, M., Bartelt, P., Brown, B., & Fierz, C. (2002). A physical SNOWPACK model for the Swiss avalanche warning Part III: meteorological forcing, thin layer formation and evaluation. Cold Regions Science and Technology, 35(3), 169–184. Retrieved from www.elsevier.com/locate/coldregions
CROCUS
- Brun, E., David, P., Sudul, M., & Brunot, G. (1992). A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. Journal of Glaciology (Vol. 38).
- Brun, E., Martin, E., Simon, V., Gendre, C., & Coleou, C. (1989). AN ENERGY AND MASS MODEL OF SNOW COVER SUITABLE FOR OPERATIONAL AVALANCHE FORECASTING. Journal of Glaciology (Vol. 35)
- Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., … Willemet, J. M. (2012). The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2. Geoscientific Model Development. https://doi.org/10.5194/gmd-5-773-2012
Sunday, February 28, 2016
Nevi/Snows
Nevi/Sneea
Alzando lo sguardo verso nord vedevi un tenue grigiore che dalle cime raggiungeva i boschi e che si abbassava verso il paese. E la punta del campanile e le campane erano già dentro il grigiore lattiginoso e poi anche la chiesa, i tetti delle case più alte. Sulle strade polverose, sulle cataste di legna, sui cortili e sopra le nostre teste arruffate cadevano le prime stille. Aprivamo la bocca verso il cielo per sentirle sciogliersi sulla lingua.
Le voci, i rumori del paese, i richiami dei passeri e degli scriccioli si facevano lievi, e a questo punto la brüskalan diventava vera sneea: neve abbondante e leggera giù dal molino del cielo.
E noi si andava trepidanti in soffitta a prendere gli sci e le lame, i nostri slittini monoposto: oggetto e nome che ho trovato identici in Scandinavia e che non hanno nulla a che fare con l’italiano lama.
Thursday, May 29, 2014
Snow (Neve)
- Precipitazioni nevose (chi volesse approfondire la termodinamica della formazione dei cristalli di neve veda qui). Audio 2014 (7.9 Mb). Audio 2015 (32.5 Mb)
- Metamorfismi e bilancio energetico (eventually to be split in two parts). Audio 2014 (26.9 Mb). Audio 2015: Metamorfsmi (6.4 Mb). Bilancio Energetico (14.7 Mb)
- Bilancio di radiazione e di Energia. Audio (24.5 Mb)
- La produzione del deflusso
- (to be added) Modelli semplificati di Bilancio della Neve
- Misure di neve
Alcuni webinar (seminari via web) in inglese, sulla neve sono indicizzati qui.
Wednesday, March 19, 2014
Ubiquitous Diffusion
Incidentally two of these equations present discontinuities due to phase transitions and the three of them require special numerical methods to be integrated. Here I suggest that a good method could be the Nested Newton one, introduced recently by Casulli and Zanolli (for integrating Richards), and before by Brugnano and Casulli (for integrating Boussinesq equation).
Below you can find also the audio (in Italian) of the lecture: Richards equation (21.9 Mb); Frozen Soil (18.4 Mb); Snow (7.1 Mb). I gave longer presentations on Richards equation, in Todini Symposium (here), and at the summer School on Landslide Modelling in Praia a Mare (here).
An update. A new treatment of part of this matter is given by Niccolo Tubini in his Master Thesis. The slides he used in the 2017 lecture are here.
Essential References
L. Brugnano and V. Casulli, Iterative solution of piecewise linear systems and applications
to flows in porous media, SIAM J. Sci. Comput., 31 (2009), pp. 1858–1873.
Casulli, V., and Zanolli, P., A Nested Newton-Type Algorithm for Finite Volume Methods Solving Richards' Equation in Mixed Form, SIAM J. Sci. Comput., 32(4), 2255–2273, Volume 32, Issue 4, 2010.
Cordano E., and Rigon R., A mass-conservative method for the integration of the two-dimensional groundwater (Boussinesq) equation, Water Resour. Res., 49, doi:10.1002/wrcr.20072, 2013.
Dall’Amico, M.; Endrizzi, S., Gruber, S; and Rigon, R. (2011), An energy-conserving model of freezing variably-saturated soil, The Cryosphere.
Endrizzi S., Gruber S., Dall’Amico M., Rigon R., 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, Geosci. Model Dev., 2015
Monday, March 10, 2014
Snow cyberseminars at CUASHI
Presenter: Dr. Jeff Dozier - University of California, Santa Barbara, Bren School of Environmental Science & Management
in conjunction with Dr. Anne Nolin - Oregon State University, Department of Geosciences
Sunday, December 29, 2013
Snowflakes (is Christmas time after all, and I live in a boreal place)
Some of the references could look strange (e.g. Fu et al., 2006). In that case, I was looking for literature in the area of fractal surfaces. Something else should be available. In particular, I was looking for a papers by J. Nittman and Eugene Stanley (a "star" among the "fractalists"). In any case, growth of this type of forms is pretty general and ubiquitous (as shown in many papers, and in particular, in Ben Jacob's ones).
I also indulged in adding some papers about snow crystal methamorphism. This is not the topic of the post, but for the moment I keep trace of them in here. Other papers were just added for getting some general reference to crystal growth (e.g. the Krug's one, and those looking to micro-meteorological aspects of the growth).
Interesting readings are also addressed in this older post.
References
Ben Jacob, E. (1993). From snowflake formation to growth of bacterial colonies: Part I. Diffusive patterning in azoic systems. Contemporary Physics, 34(5), 247–273. doi:10.1080/00107519308222085
Ben-Jacob, E. (1997). From snowflake formation to growth of bacterial colonies II: Cooperative formation of complex colonial patterns. Contemporary Physics, 38(3), 205–241. doi:10.1080/001075197182405
Nittmann, J., & Stanley, H. E. (1987). Non-deterministic approach to anisotropic growth patterns with continuously tunable morphology: the fractal properties of some real snowflakes. Journal of Physics a: Mathematical Genereral
Rango, A., P, W. W., & Erbe, E. F. (1996b), Snow crystal imaging using scanning electron microscopy: II. Metamorphosed snow. Hydrological Sciences–Journal–Des Sciences Hydrologiques, 41(2), 235–250.
Friday, May 24, 2013
The Snow Water Equivalent (NewAGE-SWE) model component in JGrass-NewAGE
The paper has been finally submitted to GMD, and the Discussion Manuscript is therefore visible to everybody on GMDD. The revised version of the paper can be seen instead here.
NewAge-JGrass modeling system and use many of its com- ponents such as those for radiation balance (SWRB), kriging (KRIGING), automatic calibration algorithms (particle swarm optimization), and tests of goodness of fit (NewAge-V), to build suitable modelling solutions (MS). Similarly to all the NewAge-JGrass components, the models can be exe- cuted both in raster and in vector mode. The simulation time step can be daily, hourly or sub-hourly, depending on user needs and availability of input data. The MS are applied on the Cache la Poudre river basin (CO, USA) using three test applications. First, daily snow water equivalent is simulated for three different measurement stations for two snow model formulations. Second, hourly snow water equivalent is sim- ulated using all the three different snow model formulations. Finally a raster mode application is performed to compute snow water equivalent maps for the whole Cache la Poudre basin. In all the applications the model performance is satis- factory in terms of goodness of fit relative to measured snow water equivalent time series and the results, and the differences in performances of the different modelling solutions are discussed."
The model, the data, and the simulations' scripts used in the paper will be made available through the appropriate links from this page in order to let people reproduce our results. In fact, we call it Reproducible Research. It is certainly a challenging strategy for any researcher, and sometimes data cannot be disclosed. However, I said what I said, and this is a couple of old posts which fully explain my position (About Scientific Software, No code - No paper).
Saturday, September 29, 2012
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
[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
Friday, May 25, 2012
Remote Sensing of Snow
"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