Articles | Volume 10, issue 2
https://doi.org/10.5194/tc-10-511-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-511-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Mar 2016
Research article |
| 04 Mar 2016
Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
Carlo De Michele
CORRESPONDING AUTHOR
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Francesco Avanzi
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Daniele Passoni
University of Genova, Department of Civil, Chemical and Environmental Engineering, Via Montallegro 1, 16145 Genoa, Italy
Riccardo Barzaghi
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Livio Pinto
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Paolo Dosso
Studio di Ingegneria Terradat, Paderno Dugnano, Italy
Antonio Ghezzi
Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Roberto Gianatti
a2a Group, Grosio, Italy
Giacomo Della Vedova
a2a Group, Grosio, Italy
Related authors
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
Short summary
Short summary
Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
F. Ioli, E. Bruno, D. Calzolari, M. Galbiati, A. Mannocchi, P. Manzoni, M. Martini, A. Bianchi, A. Cina, C. De Michele, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, 2023
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
Short summary
Short summary
Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, https://doi.org/10.5194/tc-16-1031-2022, 2022
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modelling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between April and September since higher temperatures protect snow from erosion. We also compared modelled and observed firn density, obtaining a satisfying agreement.
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
Short summary
Short summary
Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
Short summary
Short summary
The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357, https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modeling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between May and September, since higher temperatures protect snow from erosion. We also compared modeled and observed firn density obtaining a satisfying agreement.
Katia Cugerone, Carlo De Michele, Antonio Ghezzi, Vorne Gianelle, and Stefania Gilardoni
Atmos. Chem. Phys., 18, 4831–4842, https://doi.org/10.5194/acp-18-4831-2018, https://doi.org/10.5194/acp-18-4831-2018, 2018
Short summary
Short summary
The aerosol particle number size distributions (PNSDs) measured in one urban background site (Milan) and in one rural mountainous site (Oga San Colombano) have been studied and compared. Detailed statistical analyses have shown that a common empirical PNSD pattern exists, except for the urban winter data. In order to explain this phenomenon, we analysed the aerosol dynamics by considering the influence of primary aerosol components and the interaction with precipitation and high wind speed.
Francesco Avanzi, Alberto Bianchi, Alberto Cina, Carlo De Michele, Paolo Maschio, Diana Pagliari, Daniele Passoni, Livio Pinto, Marco Piras, and Lorenzo Rossi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-57, https://doi.org/10.5194/tc-2017-57, 2017
Revised manuscript not accepted
Short summary
Short summary
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
Francesco Avanzi, Hiroyuki Hirashima, Satoru Yamaguchi, Takafumi Katsushima, and Carlo De Michele
The Cryosphere, 10, 2013–2026, https://doi.org/10.5194/tc-10-2013-2016, https://doi.org/10.5194/tc-10-2013-2016, 2016
Short summary
Short summary
We investigate capillary barriers and preferential flow in layered snow during nine cold laboratory experiments. The dynamics of each sample were replicated solving Richards equation within the 1-D multi-layer physically based SNOWPACK model. Results show that both processes affect the speed of water infiltration in stratified snow and are marked by a high degree of spatial variability at cm scale and complex 3-D patterns.
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
P. Da Ronco and C. De Michele
Hydrol. Earth Syst. Sci., 18, 4579–4600, https://doi.org/10.5194/hess-18-4579-2014, https://doi.org/10.5194/hess-18-4579-2014, 2014
Short summary
Short summary
The negative impacts of cloud obstruction in snow mapping from MODIS and a new reliable cloud removal procedure for the Italian Alps.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-156, https://doi.org/10.5194/essd-2025-156, 2025
Preprint under review for ESSD
Short summary
Short summary
Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2023. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
EGUsphere, https://doi.org/10.5194/egusphere-2025-423, https://doi.org/10.5194/egusphere-2025-423, 2025
Short summary
Short summary
Reliable SWE and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we suggest a Long Short-Term Memory neural network for operational data assimilation in snow hydrology. Once trained, it cuts computation by 70 % versus an EnKF, with a slight RMSE increase (+6 mm SWE, +6 cm snow depth). This work advances deep learning in snow hydrology, offering an efficient, scalable, and low-cost modeling framework.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Federica Gaspari, Francesco Ioli, Federico Barbieri, Samuele Bonora, Rebecca Fascia, Livio Pinto, and Federica Migliaccio
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 59–66, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-59-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-59-2024, 2024
R. Fascia, F. Barbieri, F. Gaspari, F. Ioli, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W4-2024, 205–212, https://doi.org/10.5194/isprs-archives-XLVIII-2-W4-2024-205-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W4-2024-205-2024, 2024
Francesca Munerol, Francesco Avanzi, Eleonora Panizza, Marco Altamura, Simone Gabellani, Lara Polo, Marina Mantini, Barbara Alessandri, and Luca Ferraris
Geosci. Commun., 7, 1–15, https://doi.org/10.5194/gc-7-1-2024, https://doi.org/10.5194/gc-7-1-2024, 2024
Short summary
Short summary
To contribute to advancing education in a warming climate and prepare the next generations to play their role in future societies, we designed “Water and Us”, a three-module initiative focusing on the natural and anthropogenic water cycle, climate change, and conflicts. This study aims to introduce the initiative's educational objectives, methods, and early results.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
Short summary
Short summary
Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervé Stevenin, Sara Ratto, Luca Ferraris, and Alberto Viglione
The Cryosphere, 17, 5317–5333, https://doi.org/10.5194/tc-17-5317-2023, https://doi.org/10.5194/tc-17-5317-2023, 2023
Short summary
Short summary
Automatic snow depth data are a valuable source of information for hydrologists, but they also tend to be noisy. To maximize the value of these measurements for real-world applications, we developed an automatic procedure to differentiate snow cover from grass or bare ground data, as well as to detect random errors. This procedure can enhance snow data quality, thus providing more reliable data for snow models.
F. Gaspari, F. Barbieri, J. P. Duque, R. Fascia, F. Ioli, G. Zani, D. Carrion, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 299–306, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-299-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-299-2023, 2023
F. Ioli, F. Barbieri, F. Gaspari, F. Nex, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1037–1044, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1037-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1037-2023, 2023
F. Gaspari, F. Ioli, F. Barbieri, C. Rivieri, M. Dondi, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 661–668, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-661-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-661-2023, 2023
F. Gaspari, F. Barbieri, I. Demnati, F. Ioli, L. Pinto, and V. Toscani
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W7-2023, 61–66, https://doi.org/10.5194/isprs-archives-XLVIII-4-W7-2023-61-2023, https://doi.org/10.5194/isprs-archives-XLVIII-4-W7-2023-61-2023, 2023
F. Ioli, E. Bruno, D. Calzolari, M. Galbiati, A. Mannocchi, P. Manzoni, M. Martini, A. Bianchi, A. Cina, C. De Michele, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, 2023
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
Short summary
Short summary
Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Giulia Bruno, Doris Duethmann, Francesco Avanzi, Lorenzo Alfieri, Andrea Libertino, and Simone Gabellani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-416, https://doi.org/10.5194/hess-2022-416, 2022
Manuscript not accepted for further review
Short summary
Short summary
Hydrological models often have issues during droughts. We used the distributed Continuum model over the Po river basin and independent datasets of streamflow (Q), evapotranspiration (ET), and storage. Continuum simulated Q well during wet years and moderate droughts. Performances declined for a severe drought and we explained this drop with an increased uncertainty in ET anomalies in human-affected croplands. These findings provide guidelines for assessments of model robustness during droughts.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
Short summary
Short summary
This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
Short summary
Short summary
Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
F. Gaspari, F. Ioli, F. Barbieri, E. Belcore, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 995–1002, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-995-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-995-2022, 2022
F. Ioli, A. Pinto, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1025–1032, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1025-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1025-2022, 2022
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
Short summary
Short summary
Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Christian Massari, Francesco Avanzi, Giulia Bruno, Simone Gabellani, Daniele Penna, and Stefania Camici
Hydrol. Earth Syst. Sci., 26, 1527–1543, https://doi.org/10.5194/hess-26-1527-2022, https://doi.org/10.5194/hess-26-1527-2022, 2022
Short summary
Short summary
Droughts are a creeping disaster, meaning that their onset, duration and recovery are challenging to monitor and forecast. Here, we provide further evidence of an additional challenge of droughts, i.e. the fact that the deficit in water supply during droughts is generally much more than expected based on the observed decline in precipitation. At a European scale we explain this with enhanced evapotranspiration, sustained by higher atmospheric demand for moisture during such dry periods.
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, https://doi.org/10.5194/tc-16-1031-2022, 2022
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modelling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between April and September since higher temperatures protect snow from erosion. We also compared modelled and observed firn density, obtaining a satisfying agreement.
Tessa Maurer, Francesco Avanzi, Steven D. Glaser, and Roger C. Bales
Hydrol. Earth Syst. Sci., 26, 589–607, https://doi.org/10.5194/hess-26-589-2022, https://doi.org/10.5194/hess-26-589-2022, 2022
Short summary
Short summary
Predicting how much water will end up in rivers is more difficult during droughts because the relationship between precipitation and streamflow can change in unexpected ways. We differentiate between changes that are predictable based on the weather patterns and those harder to predict because they depend on the land and vegetation of a particular region. This work helps clarify why models are less accurate during droughts and helps predict how much water will be available for human use.
Piera Belotti, Fabio Conzi, Chiara Dell’Orto, Maurizio Federici, Luigi Fregonese, Gabriele Garnero, Emilio Guastamacchia, Franco Guzzetti, Livio Pinto, and Marco Scaioni
Proc. Int. Cartogr. Assoc., 4, 12, https://doi.org/10.5194/ica-proc-4-12-2021, https://doi.org/10.5194/ica-proc-4-12-2021, 2021
F. Ioli, L. Pinto, and F. Ferrario
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 25–32, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-25-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-25-2021, 2021
L. Rossi, F. Ioli, E. Capizzi, L. Pinto, and M. Reguzzoni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 61–68, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-61-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-61-2021, 2021
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
Short summary
Short summary
Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Mirko Reguzzoni, Daniela Carrion, Carlo Iapige De Gaetani, Alberta Albertella, Lorenzo Rossi, Giovanna Sona, Khulan Batsukh, Juan Fernando Toro Herrera, Kirsten Elger, Riccardo Barzaghi, and Fernando Sansó
Earth Syst. Sci. Data, 13, 1653–1666, https://doi.org/10.5194/essd-13-1653-2021, https://doi.org/10.5194/essd-13-1653-2021, 2021
Short summary
Short summary
The International Service for the Geoid provides free access to a repository of geoid models. The most important ones are freely available to perform analyses on the evolution of the geoid computation research field. Furthermore, the ISG performs research taking advantage of its archive and organizes specific training courses on geoid determination. This paper aims at describing the service and showing the added value of the archive of geoid models for the scientific community and technicians.
Francesco Avanzi, Giulia Ercolani, Simone Gabellani, Edoardo Cremonese, Paolo Pogliotti, Gianluca Filippa, Umberto Morra di Cella, Sara Ratto, Hervè Stevenin, Marco Cauduro, and Stefano Juglair
Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, https://doi.org/10.5194/hess-25-2109-2021, 2021
Short summary
Short summary
Precipitation tends to increase with elevation, but the magnitude and distribution of this enhancement remain poorly understood. By leveraging over 11 000 spatially distributed, manual measurements of snow depth (snow courses) upstream of two reservoirs in the western European Alps, we show that these courses bear a characteristic signature of orographic precipitation. This opens a window of opportunity for improved modeling accuracy and, ultimately, our understanding of the water budget.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
Short summary
Short summary
The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357, https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modeling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between May and September, since higher temperatures protect snow from erosion. We also compared modeled and observed firn density obtaining a satisfying agreement.
Francesco Avanzi, Joseph Rungee, Tessa Maurer, Roger Bales, Qin Ma, Steven Glaser, and Martha Conklin
Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, https://doi.org/10.5194/hess-24-4317-2020, 2020
Short summary
Short summary
Multi-year droughts in Mediterranean climates often see a lower fraction of precipitation allocated to runoff compared to non-drought years. By comparing observed water-balance components with simulations by a hydrologic model (PRMS), we reinterpret these shifts as a hysteretic response of the water budget to climate elasticity of evapotranspiration. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including this mechanism.
L. Pinto, F. Bianchini, V. Nova, and D. Passoni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1145–1150, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1145-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1145-2020, 2020
F. Ioli, L. Pinto, D. Passoni, V. Nova, and M. Detert
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 597–604, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-597-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-597-2020, 2020
R. Barzaghi, M. Reguzzoni, C. I. De Gaetani, S. Caldera, and L. Rossi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 209–216, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, 2019
C. Cappelletti, M. Boniardi, A. Casaroli, C. I. De Gaetani, D. Passoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W9, 227–234, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, 2019
D. Pagliari and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 813–820, https://doi.org/10.5194/isprs-archives-XLII-2-813-2018, https://doi.org/10.5194/isprs-archives-XLII-2-813-2018, 2018
L. Rossi, C. I. De Gaetani, D. Pagliari, E. Realini, M. Reguzzoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 991–998, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, 2018
Katia Cugerone, Carlo De Michele, Antonio Ghezzi, Vorne Gianelle, and Stefania Gilardoni
Atmos. Chem. Phys., 18, 4831–4842, https://doi.org/10.5194/acp-18-4831-2018, https://doi.org/10.5194/acp-18-4831-2018, 2018
Short summary
Short summary
The aerosol particle number size distributions (PNSDs) measured in one urban background site (Milan) and in one rural mountainous site (Oga San Colombano) have been studied and compared. Detailed statistical analyses have shown that a common empirical PNSD pattern exists, except for the urban winter data. In order to explain this phenomenon, we analysed the aerosol dynamics by considering the influence of primary aerosol components and the interaction with precipitation and high wind speed.
Hiroyuki Hirashima, Francesco Avanzi, and Satoru Yamaguchi
Hydrol. Earth Syst. Sci., 21, 5503–5515, https://doi.org/10.5194/hess-21-5503-2017, https://doi.org/10.5194/hess-21-5503-2017, 2017
Short summary
Short summary
We reproduced the formation of capillary barriers and the development of preferential flow through snow using a multi-dimensional water transport model, which was then validated using laboratory experiments of liquid water infiltration into layered, initially dry snow. Simulation results showed that the model reconstructs some relevant features of capillary barriers and the timing of liquid water arrival at the snow base.
S. Gagliolo, R. Fagandini, B. Federici, I. Ferrando, D. Passoni, D. Pagliari, L. Pinto, and D. Sguerso
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 81–88, https://doi.org/10.5194/isprs-archives-XLII-5-W1-81-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-81-2017, 2017
Francesco Avanzi, Alberto Bianchi, Alberto Cina, Carlo De Michele, Paolo Maschio, Diana Pagliari, Daniele Passoni, Livio Pinto, Marco Piras, and Lorenzo Rossi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-57, https://doi.org/10.5194/tc-2017-57, 2017
Revised manuscript not accepted
Short summary
Short summary
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
Francesco Avanzi, Hiroyuki Hirashima, Satoru Yamaguchi, Takafumi Katsushima, and Carlo De Michele
The Cryosphere, 10, 2013–2026, https://doi.org/10.5194/tc-10-2013-2016, https://doi.org/10.5194/tc-10-2013-2016, 2016
Short summary
Short summary
We investigate capillary barriers and preferential flow in layered snow during nine cold laboratory experiments. The dynamics of each sample were replicated solving Richards equation within the 1-D multi-layer physically based SNOWPACK model. Results show that both processes affect the speed of water infiltration in stratified snow and are marked by a high degree of spatial variability at cm scale and complex 3-D patterns.
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
P. Da Ronco and C. De Michele
Hydrol. Earth Syst. Sci., 18, 4579–4600, https://doi.org/10.5194/hess-18-4579-2014, https://doi.org/10.5194/hess-18-4579-2014, 2014
Short summary
Short summary
The negative impacts of cloud obstruction in snow mapping from MODIS and a new reliable cloud removal procedure for the Italian Alps.
Related subject area
Seasonal Snow
Sublimation measurements of tundra and taiga snowpack in Alaska
Insights into microphysical and optical properties of typical mineral dust within industrial-polluted snowpack via wet/dry deposition in Changchun, Northeastern China
An examination of changes in autumn Eurasian snow cover and its relationship with the winter Arctic Oscillation using 20th Century Reanalysis version 3
Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend
Influence of Snow Spatial Variability on Cosmic Ray Neutron SWE
Benchmarking of snow water equivalent (SWE) products based on outcomes of the SnowPEx+ Intercomparison Project
A random forest derived 35-year snow phenology record reveals climate trends in the Yukon River Basin
Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps
Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America
Leveraging Citizen Science, LiDAR, and Machine Learning for Snow Depth Estimation in Complex Terrain Environments
Dynamic identification of snow phenology in the Northern Hemisphere
Trends in the annual snow melt-out day over the French Alps and the Pyrenees from 38 years of high resolution satellite data (1986–2023)
Characterization of non-Gaussianity in the snow distributions of various landscapes
A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes
Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera
Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018
Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets
Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas
Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia
The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain
Impact of measured and simulated tundra snowpack properties on heat transfer
Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Past changes in natural and managed snow reliability of French Alps ski resorts from 1961 to 2019
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset
Recent changes in pan-Arctic sea ice, lake ice, and snow-on/off timing
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Observed snow depth trends in the European Alps: 1971 to 2019
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)
Local-scale variability of snow density on Arctic sea ice
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Evaluation of long-term Northern Hemisphere snow water equivalent products
Towards a webcam-based snow cover monitoring network: methodology and evaluation
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers
Converting snow depth to snow water equivalent using climatological variables
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
Kelsey A. Stockert, Eugénie S. Euskirchen, and Svetlana L. Stuefer
The Cryosphere, 19, 1739–1755, https://doi.org/10.5194/tc-19-1739-2025, https://doi.org/10.5194/tc-19-1739-2025, 2025
Short summary
Short summary
Sublimation is the hidden portion of the water cycle where snow changes phase directly to water vapor, skipping the liquid state. Though sublimation is difficult to measure, especially in remote regions such as Arctic and subarctic Alaska where this study took place, our measurements confirm that sublimation is a substantial component of the annual water cycle. Results from this research contribute to knowledge of how site conditions affect sublimation rates and the winter hydrologic cycle.
Tenglong Shi, Jiayao Wang, Daizhou Zhang, Jiecan Cui, Zihang Wang, Yue Zhou, Wei Pu, Yang Bai, Zhigang Han, Meng Liu, Yanbiao Liu, Hongbin Xie, Minghui Yang, Ying Li, Meng Gao, and Xin Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-124, https://doi.org/10.5194/egusphere-2025-124, 2025
Short summary
Short summary
This study examines the properties of dust in snow in Changchun, China, using advanced technology to analyze its size, shape, and light absorption. We found that dust composition affects how much heat is absorbed by snow, with certain minerals, like hematite, making snow melt faster. Our research highlights the importance of creating clear standards for classifying dust, which could improve climate models and field observations. This work helps better understand dust's role in climate change.
Gareth J. Marshall
The Cryosphere, 19, 663–683, https://doi.org/10.5194/tc-19-663-2025, https://doi.org/10.5194/tc-19-663-2025, 2025
Short summary
Short summary
Eurasian autumn snow cover (SC) can influence Northern Hemisphere weather in the following winter by affecting the Arctic Oscillation (AO) mode of atmospheric variability. Using data back to 1836, we show that there have been significant decreases in October and November SC. For the first time, we describe a robust relationship between September SC in northeastern Eurasia and the AO. In addition, the longer dataset reveals the temporal variability in previously identified SC–AO relationships.
Vincenzo Capozzi, Francesco Serrapica, Armando Rocco, Clizia Annella, and Giorgio Budillon
The Cryosphere, 19, 565–595, https://doi.org/10.5194/tc-19-565-2025, https://doi.org/10.5194/tc-19-565-2025, 2025
Short summary
Short summary
This “journey through time” discovers historical information about snow precipitation in the Italian Apennines. In this area, in the second half of the past century, a gradual decline in snow persistence on the ground, as well as in the frequency of occurrence of snowfall events, has been observed, especially in sites located above 1000 m above sea level. The old data rescued in this study strongly enhance our knowledge about past snowfall variability and climate in the Mediterranean area.
Haejo Kim, Eric Sproles, and Samuel E. Tuttle
EGUsphere, https://doi.org/10.5194/egusphere-2025-31, https://doi.org/10.5194/egusphere-2025-31, 2025
Short summary
Short summary
Monitoring of a shallow and highly variable snowpack’s water content has been shown to be reliable with Cosmic Ray Neutron Sensing (CRNS). After hundreds of simulations, we show a CRNS instrument is best placed near areas of low snow that are nearby regions of high for an accurate estimate of the prairie snowpack’s water content. The snow water equivalent from a CRNS was 2 to 5 times more likely to be representative of the prairie snow, compared to traditional snow monitoring methods.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, https://doi.org/10.5194/tc-19-201-2025, 2025
Short summary
Short summary
We evaluate and rank 23 different datasets on their ability to accurately estimate historical snow amounts. The evaluation uses new a set of surface snow measurements with improved spatial coverage, enabling evaluation across both mountainous and nonmountainous regions. Performance measures vary tremendously across the products: while most perform reasonably in nonmountainous regions, accurate representation of snow amounts in mountainous regions and of historical trends is much more variable.
Caleb G. Pan, Kristofer Lasko, John S. Kimball, Jinyang Du, Tate G. Meehan, Peter B. Kirchner, and Sean P. Griffin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3608, https://doi.org/10.5194/egusphere-2024-3608, 2025
Short summary
Short summary
This study examines 35 years of snow cover changes in Alaska’s Yukon River Basin using machine learning to track snowmelt timing and disappearance. Results show snow is melting earlier and disappearing faster due to rising temperatures, highlighting the effects of climate change on water resources, ecosystems, and communities. The findings improve understanding of snow dynamics and provide critical insights for addressing climate-driven challenges in the region.
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
The Cryosphere, 18, 6005–6026, https://doi.org/10.5194/tc-18-6005-2024, https://doi.org/10.5194/tc-18-6005-2024, 2024
Short summary
Short summary
Snow depth plays an important role in water resources, mountain tourism, and hazard management across the European Alps. Our study uses station-based historical observations to quantify how changes in temperature and precipitation affect average seasonal snow depth. We find that the relationship between these variables has been surprisingly robust over the last 120 years. This allows us to more accurately estimate how future climate will affect seasonal snow depth in different elevation zones.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Dane Liljestrand, Ryan Johnson, Bethany Neilson, Patrick Strong, and Elizabeth Cotter
EGUsphere, https://doi.org/10.5194/egusphere-2024-3545, https://doi.org/10.5194/egusphere-2024-3545, 2024
Short summary
Short summary
This work introduces a model specifically designed for high-resolution snow depth estimation, leveraging citizen-science snow observations and snow-off LiDAR terrain features to provide an accessible and cost-effective method for snowpack modeling in regions lacking high-quality data products or collection networks. This work demonstrates that reliable basin-scale snow depth estimates can be achieved in difficult environments with very few observations and low institutional costs.
Le Wang, Xin Miao, Xinyun Hu, Yizhuo Li, Bo Qiu, Jun Ge, and Weidong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3431, https://doi.org/10.5194/egusphere-2024-3431, 2024
Short summary
Short summary
Snow phenology is a crucial indicator for assessing seasonal changes in snow. In this work, we find that snow phenology is significantly impacted by datasets and methods used, and current methods often overlook the spatial and temporal variability in snow across the Northern Hemisphere. To address this, we develop a dynamic threshold method, which contributes to better representing the seasonal changes of snow cover across the Northern Hemisphere, especially on the Tibetan Plateau.
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3505, https://doi.org/10.5194/egusphere-2024-3505, 2024
Short summary
Short summary
We generated annual maps of snow melt-out day at 20 m resolution over a period of 38 years from ten different satellites. This study fills a knowledge gap on the evolution of mountain snow in Europe by covering a much longer period and by characterizing trends at much higher resolution than previous studies. We found a trend for earlier melt-out with an average reduction of 5.51 days per decade over the French Alps and of 4.04 day per decade over the Pyrenees over the period 1986–2023.
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
The Cryosphere, 18, 5139–5152, https://doi.org/10.5194/tc-18-5139-2024, https://doi.org/10.5194/tc-18-5139-2024, 2024
Short summary
Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Short summary
We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Shirui Yan, Yang Chen, Yaliang Hou, Kexin Liu, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, Wei Pu, and Xin Wang
The Cryosphere, 18, 4089–4109, https://doi.org/10.5194/tc-18-4089-2024, https://doi.org/10.5194/tc-18-4089-2024, 2024
Short summary
Short summary
The snow cover over the Tibetan Plateau (TP) plays a role in climate and hydrological systems, yet there are uncertainties in snow cover fraction (SCF) estimations within reanalysis datasets. This study utilized the Snow Property Inversion from Remote Sensing (SPIReS) SCF data to assess the accuracy of eight widely used reanalysis SCF datasets over the TP. Factors contributing to uncertainties were analyzed, and a combined averaging method was employed to provide optimized SCF simulations.
Stefanie Arndt, Nina Maaß, Leonard Rossmann, and Marcel Nicolaus
The Cryosphere, 18, 2001–2015, https://doi.org/10.5194/tc-18-2001-2024, https://doi.org/10.5194/tc-18-2001-2024, 2024
Short summary
Short summary
Antarctic sea ice maintains year-round snow cover, crucial for its energy and mass budgets. Despite its significance, snow depth remains poorly understood. Over the last decades, Snow Buoys have been deployed extensively on the sea ice to measure snow accumulation but not actual depth due to snow transformation into meteoric ice. Therefore, in this study we utilize sea ice and snow models to estimate meteoric ice fractions in order to calculate actual snow depth in the Weddell Sea.
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024, https://doi.org/10.5194/tc-18-1959-2024, 2024
Short summary
Short summary
Information about snow depth is important within climate research but also many other sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a coarser-resolution state-of-the-art product. Also, daily snow depth extremes are well reproduced by two models.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Vilna Tyystjärvi, Pekka Niittynen, Julia Kemppinen, Miska Luoto, Tuuli Rissanen, and Juha Aalto
The Cryosphere, 18, 403–423, https://doi.org/10.5194/tc-18-403-2024, https://doi.org/10.5194/tc-18-403-2024, 2024
Short summary
Short summary
At high latitudes, winter ground surface temperatures are strongly controlled by seasonal snow cover and its spatial variation. Here, we measured surface temperatures and snow cover duration in 441 study sites in tundra and boreal regions. Our results show large variations in how much surface temperatures in winter vary depending on the landscape and its impact on snow cover. These results emphasise the importance of understanding microclimates and their drivers under changing winter conditions.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
Short summary
Short summary
Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
Short summary
Short summary
Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
Short summary
Short summary
Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Xuemei Li, Xinyu Liu, Kaixin Zhao, Xu Zhang, and Lanhai Li
The Cryosphere, 17, 2437–2453, https://doi.org/10.5194/tc-17-2437-2023, https://doi.org/10.5194/tc-17-2437-2023, 2023
Short summary
Short summary
Quantifying change in the potential snowfall phenology (PSP) is an important area of research for understanding regional climate change past, present, and future. However, few studies have focused on the PSP and its change in alpine mountainous regions. We proposed three innovative indicators to characterize the PSP and its spatial–temporal variation. Our study provides a novel approach to understanding PSP in alpine mountainous regions and can be easily extended to other snow-dominated regions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Short summary
Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Zachary S. Miller, Erich H. Peitzsch, Eric A. Sproles, Karl W. Birkeland, and Ross T. Palomaki
The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, https://doi.org/10.5194/tc-16-4907-2022, 2022
Short summary
Short summary
Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic natural processes. Our study of a winter time series of high-resolution snow depth maps found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability at a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
Short summary
Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
Short summary
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Lucas Berard-Chenu, Hugues François, Emmanuelle George, and Samuel Morin
The Cryosphere, 16, 863–881, https://doi.org/10.5194/tc-16-863-2022, https://doi.org/10.5194/tc-16-863-2022, 2022
Short summary
Short summary
This study investigates the past snow reliability (1961–2019) of 16 ski resorts in the French Alps using state-of-the-art snowpack modelling. We used snowmaking investment figures to infer the evolution of snowmaking coverage at the individual ski resort level. Snowmaking improved snow reliability for the core of the winter season for the highest-elevation ski resorts. However it did not counterbalance the decreasing trend in snow cover reliability for lower-elevation ski resorts and in spring.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
Short summary
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
Short summary
Short summary
We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Alicia A. Dauginis and Laura C. Brown
The Cryosphere, 15, 4781–4805, https://doi.org/10.5194/tc-15-4781-2021, https://doi.org/10.5194/tc-15-4781-2021, 2021
Short summary
Short summary
This work examines changes in the timing (on/off dates) of Arctic snow, lake ice, and sea ice to investigate how they have responded to recent climate change and determine if they are responding similarly. We looked at pan-Arctic trends since 1997 and regional trends since 2004 using (mainly) satellite data. Strong regional variability was shown in the snow and ice trends, which highlights the need for a detailed understanding of the regional response to ongoing changes in the Arctic climate.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
Short summary
Short summary
We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
Short summary
Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
Short summary
Short summary
Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
Short summary
Short summary
There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
Short summary
Short summary
Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Céline Portenier, Fabia Hüsler, Stefan Härer, and Stefan Wunderle
The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, https://doi.org/10.5194/tc-14-1409-2020, 2020
Short summary
Short summary
We present a method to derive snow cover maps from freely available webcam images in the Swiss Alps. With marginal manual user input, we can transform a webcam image into a georeferenced map and therewith perform snow cover analyses with a high spatiotemporal resolution over a large area. Our evaluation has shown that webcams could not only serve as a reference for improved validation of satellite-based approaches, but also complement satellite-based snow cover retrieval.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
Short summary
Short summary
Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
Stefanie Arndt and Christian Haas
The Cryosphere, 13, 1943–1958, https://doi.org/10.5194/tc-13-1943-2019, https://doi.org/10.5194/tc-13-1943-2019, 2019
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
Short summary
Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
Short summary
Short summary
The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
Short summary
Short summary
We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019, https://doi.org/10.5194/tc-13-21-2019, 2019
Short summary
Short summary
We examine the timing of early season snowpack relevant to oversnow vehicle (OSV) recreation over the past 3 decades in the Lake Tahoe region (USA). Data from two independent data sources suggest that the timing of achieving sufficient snowpack has shifted later by 2 weeks. Increasing rainfall and more dry days play a role in the later onset. Adaptation strategies are provided for winter travel management planning to address negative impacts of loss of early season snowpack for OSV usage.
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018, https://doi.org/10.5194/tc-12-1249-2018, 2018
Short summary
Short summary
This article addresses local changes of seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps, for the period 1960–2100. We use an ensemble of adjusted RCM outputs consistent with IPCC AR5 GCM outputs (RCPs 2.6, 4.5 and 8.5) and the snowpack model Crocus. Beyond scenario-based approach, global temperature levels on the order of 1.5 °C and 2 °C above preindustrial levels correspond to 25 and 32% reduction of mean snow depth.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
Short summary
Short summary
Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Cited articles
Agisoft: Agisoft PhotoScan User Manual Professional Edition, Version 1.1,
2014.
Anderton, S. P., White, S. M., and Alvera, B.: Evaluation of spatial
variability in snow water equivalent for a high mountain catchment, Hydrol.
Process., 18, 435–453, https://doi.org/10.1002/hyp.1319, 2004.
Avanzi, F., De Michele, C., Ghezzi, A., Jommi, C., and Pepe, M.: A processing
modeling routine to use SNOTEL hourly data in snowpack dynamic models, Adv.
Water Resour., 73, 16–29, 2014.
Bavay, M., Lehning, M., Jonas, T., and Löwe, H.: Simulations of future
snow cover and discharge in Alpine headwater catchments, Hydrol. Process.,
23, 95–108, https://doi.org/10.1002/hyp.7195, 2009.
Bavay, M., Grünewald, T., and Lehning, M.: Response of snow cover and
runoff to climate change in high Alpine catchments of Eastern Switzerland,
Adv. Water Resour., 55, 4–16, https://doi.org/10.1016/j.advwatres.2012.12.009, 2013.
Bavera, D., Bavay, M., Jonas, T., Lehning, M., and De Michele, C.: A
comparison between two statistical and a physically-based model in snow water
equivalent mapping, Adv. Water Resour., 63, 167–178,
https://doi.org/10.1016/j.advwatres.2013.11.011, 2014.
Blöschl, G. and Kirnbauer, R.: An Analysis of snow cover patterns in a
small alpine catchment, Hydrol. Process., 6, 99–109,
https://doi.org/10.1002/hyp.3360060109, 1992.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow
depth in alpine terrain with unmanned aerial systems (UAS): potential and
limitations, The Cryosphere Discuss., https://doi.org/10.5194/tc-2015-220, in review,
2016.
Carroll, S. S. and Cressie, N.: Spatial modeling of snow water equivalent
using covariances estimated from spatial and geomorphic attributes,
J. Hydrol., 190, 42–59, https://doi.org/10.1016/S0022-1694(96)03062-4, 1996.
Cline, D., Elder, K., and Bales, R.: Scale effects in a distributed snow
water equivalence and snowmelt model fir mountain basins, Hydrol. Process.,
12, 1527–1536, 1998.
Colomina, I. and Molina, P.: Unmanned Aerial Systems for Photogrammetry and
Remote Sensing: a review, ISPRS J. Photogramm., 92, 79–97,
https://doi.org/10.1016/j.isprsjprs.2014.02.013, 2014.
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence of snow
depth distribution and accumulation over glaciers, J. Geophys. Res., 115,
F01012, https://doi.org/10.1029/2009JF001261, 2010.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of
Snow Depth from Lidar Data, J. Hydrometeorol., 7, 285–297,
https://doi.org/10.1175/JHM487.1, 2006.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154,
2013.
De Michele, C., Avanzi, F., Ghezzi, A., and Jommi, C.: Investigating the
dynamics of bulk snow density in dry and wet conditions using a
one-dimensional model, The Cryosphere, 7, 433–444,
https://doi.org/10.5194/tc-7-433-2013, 2013.
Delacourt, C., Allemand, P., Jaud, M., Grandjean, P., Deschamps, A., Ammann,
J., Cuq, V., and Suanez, S.: DRELIO: An Unmanned Helicopter for Imaging
Coastal Areas, J. Coastal Res., Special Issue, 56, 1489–1493, 2009.
Di Mauro, B., Fava, F., Ferrero, L., Garzonio, R., Baccolo, G., Delmonte, B.,
and Colombo, R.: Mineral dust impact on snow radiative properties in the
European Alps combining ground, UAV and satellite observations, J. Geophys.
Res.-Atmos., 120, 6080–6097, https://doi.org/10.1002/2015JD023287, 2015.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow
– a review of available methods, Int. J. Remote Sens., 33, 4094–4134,
https://doi.org/10.1080/01431161.2011.640964, 2012.
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,
https://doi.org/10.1002/hyp.6130, 2006.
Dunford, R., Michel, K., Gagnage, M., Piegay, H., and Tremelo, M. L.:
Potential and constraints of Unmanned Aerial Vehicle technology for the
characterization of Mediterranean riparian forest, Int. J. Remote Sens., 30,
4915–4935, 2009.
Eisenbeiss, H.: UAV Photogrammetry, 194, Institute of Geodesy and
Photogrammetry, ETH Zürich, 2009.
Elder, K., Rosenthal, W., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalence in a montane watershed, Hydrol.
Process., 12, 1793–1808, 1998.
Erxleben, J., Elder, K., and Davis, R.: Comparison of spatial interpolation
methods for estimating snow distribution in the Colorado Rocky Mountains,
Hydrol. Process., 16, 3627–3649, https://doi.org/10.1002/hyp.1239, 2002.
Farinotti, D., Magnusson, J., Huss, M., and Bauder, A.: Snow accumulation
distribution inferred from time-lapse photography and simple modelling,
Hydrol. Process., 24, 2087–2097, https://doi.org/10.1002/hyp.7629, 2010.
Fassnacht, S. R., Dressler, K. A., and Bales, R. C.: Snow water equivalent
interpolation for the Colorado River Basin from snow telemetry (SNOTEL)
data, Water Resour. Res., 39, 1208, https://doi.org/10.1029/2002WR001512, 2003.
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., Greene, E., McClung, D.,
Nishimura, K., Satyawali, P., and Sokratov, S.: The International
Classification for Seasonal Snow on the Ground, Tech. rep., IHP-VII,
Technical Documents in Hydrology No. 83, IACS Contribution No. 1, UNESCO –
IHP, Paris, 2009.
Fugazza, D., Senese, A., Azzoni, R. S., Smiraglia, C., Cernuschi, M., Severi,
D., and Diolaiuti, G. A.: High resolution mapping of glacier surface
features. The UAV survey of the Forni Glacier (Stelvio National Park, Italy),
Geogr. Fis. Din. Quat., 38, 25–33, 2015.
Funaki, M., Hirasawa, N., and the Ant Plane Group: Outline of a small
unmanned aerial vehicle (Ant-Plane) designed for Antarctic research, Polar
Sci., 2, 129–142, 2008.
Grünewald, T. and Lehning, M.: Are flat-field snow depth measurements
representative? A comparison of selected index sites with areal snow depth
measurements at the small catchment scale, Hydrol. Process., 29, 1717–1728,
https://doi.org/10.1002/hyp.10295, 2015.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and
temporal variability of snow depth and ablation rates in a small mountain
catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno
Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and
Lehning, M.: Statistical modelling of the snow depth distribution in open
alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021,
https://doi.org/10.5194/hess-17-3005-2013, 2013.
Hedrick, A., Marshall, H.-P., Winstral, A., Elder, K., Yueh, S., and Cline,
D.: Independent evaluation of the SNODAS snow depth product using
regional-scale lidar-derived measurements, The Cryosphere, 9, 13–23,
https://doi.org/10.5194/tc-9-13-2015, 2015.
Hood, J. L. and Hayashi, M.: Assessing the application of a laser rangefinder
for determining snow depth in inaccessible alpine terrain, Hydrol. Earth
Syst. Sci., 14, 901–910, https://doi.org/10.5194/hess-14-901-2010, 2010.
Hopkinson, C., Sitar, M., Chasmer, L., and Treitz, P.: Mapping snowpack depth
beneath forest canopies using airborne lidar, Photogramm. Eng. Rem., 70,
323–330, 2004.
Hopkinson, C., Collins, T., Anderson, A., Pomeroy, J., and Spooner, I.:
Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS
Data Layers in the Elbow River Watershed, Alberta, Can. Water Resour. J., 37,
69–87, https://doi.org/10.4296/cwrj3702893, 2012.
Hugenholtz, C. H., Whitehead, K., Brown, O. W., Barchyn, T. E., Moorman,
B. J., LeClair, A., Riddell, K., and Hamilton, T.: Geomorphological mapping
with a small unmanned aircraft system (sUAS): Feature detection and accuracy
assessment of a photogrammetrically-derived digital terrain model,
Geomorphology, 194, 16–24, https://doi.org/10.1016/j.geomorph.2013.03.023, 2013.
Koh, L. P. and Wich, S. A.: Dawn of drone ecology: low-cost autonomous aerial
vehicles for conservation, Trop. Conserv. Sci., 5, 121–132, 2012.
König, M. and Sturm, M.: Mapping snow distribution in the Alaskan
Arctic using aerial photography and topographic relationships, Water Resour.
Res., 34, 3471–3483, https://doi.org/10.1029/98WR02514, 1998.
Koutsoudis, A., Vidmar, B., Ioannakis, G., Arnaoutoglou, F., Pavlidis, G.,
and Chazmas, C.: Multi-image 3D reconstruction data evaluation, J. Cult.
Herit., 15, 73–79, 2014.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli,
M., and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes
and its application to snow hydrology, Hydrol. Process., 20, 2111–2128,
https://doi.org/10.1002/hyp.6204, 2006.
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous
precipitation distribution and snow transport in steep terrain, Water Resour.
Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008.
Lehning, M., Grünewald, T., and Schirmer, M.: Mountain snow distribution
governed by an altitudinal gradient and terrain roughness, Geophys. Res.
Lett., 38, L19504, https://doi.org/10.1029/2011GL048927, 2011.
Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo,
M. L., and Allemand, P.: Very high spatial resolution imagery for channel
bathymetry and topography from an unmanned mapping controlled platform, Earth
Surf. Proc. Land., 32, 1705–1725, 2007.
López-Moreno, J. I. and Nogués-Bravo, D.: Interpolating local snow
depth data: an evaluation of methods, Hydrol. Process., 20, 2217–2232,
https://doi.org/10.1002/hyp.6199, 2006.
López-Moreno, J. I., Fassnacht, S. R., Beguería, S., and Latron,
J. B. P.: Variability of snow depth at the plot scale: implications for mean
depth estimation and sampling strategies, The Cryosphere, 5, 617–629,
https://doi.org/10.5194/tc-5-617-2011, 2011.
López-Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K. N.,
Revuelto, J., Latron, J., Móran-Tejeda, E., and Jonas, T.: Small scale
spatial variability of snow density and depth over complex alpine terrain:
Implications for estimating snow water equivalent, Adv. Water Resour., 55,
40–52, 2013.
López-Moreno, J. I., Revuelto, J., Fassnacht, S. R., Azorín-Molina, C.,
Vicente-Serrano, S. M., Morán-Tejeda, E., and Sexstone, G. A.: Snowpack
variability across various spatio-temporal resolutions, Hydrol. Process., 29,
1213–1224, 2015.
Lucieer, A., Turner, D., King, D. H., and Robinson, S. A.: Using an Unmanned
Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds, Int.
J. Appl. Earth Obs., 27, 53–62, 2014.
Luzi, G., Noferini, L., Mecatti, D., Macaluso, G., Pieraccini, M., Atzeni,
C., Schaffhauser, A., Fromm, R., and Nagler, T.: Using a Ground-Based SAR
Interferometer and a Terrestrial Laser Scanner to Monitor a Snow-Covered
Slope: Results From an Experimental Data Collection in Tyrol (Austria), IEEE
T. Geosci. Remote, 47, 382–393, https://doi.org/10.1109/TGRS.2008.2009994, 2009.
Marsh, C. B., Pomeroy, J. W., and Spiteri, R. J.: Implications of mountain
shading on calculating energy for snowmelt using unstructured triangular
meshes, Hydrol. Process., 26, 1767–1778, https://doi.org/10.1002/hyp.9329, 2012.
Ménard, C. B., Essery, R., and Pomeroy, J.: Modelled sensitivity of the
snow regime to topography, shrub fraction and shrub height, Hydrol. Earth
Syst. Sci., 18, 2375–2392, https://doi.org/10.5194/hess-18-2375-2014, 2014.
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R.:
Subgrid variability of snow water equivalent at operational snow stations in
the western USA, Hydrol. Process., 27, 2383–2400, 2013.
Molotch, N. P., Fassnacht, S. R., Bales, R. C., and Helfrich, S. R.:
Estimating the distribution of snow water equivalent and snow extent beneath
cloud cover in the Salt – Verde River basin, Arizona, Hydrol. Process., 18,
1595–1611, https://doi.org/10.1002/hyp.1408, 2004.
Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P.,
and Sudul, M.: An 18-yr long (1993–2011) snow and meteorological dataset
from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for
driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13–21,
https://doi.org/10.5194/essd-4-13-2012, 2012.
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., and Lehning, M.:
Orographic effects on snow deposition patterns in mountainous terrain,
J. Geophys. Res., 119, 1419–1439, https://doi.org/10.1002/2013JD019880, 2014.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft
on landscape scales at centimeter resolution using structure-from-motion
photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015,
2015.
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over
Austria, Hydrol. Earth Syst. Sci., 10, 679–689,
https://doi.org/10.5194/hess-10-679-2006, 2006.
Pollefeys, M., Koch, R., and Van Gool, L.: Self-calibration and metric
reconstruction in spite of varying and unknown internal camera parameters,
in: IJCV, Sixth International Conference on Computer Vision, Bombay, India,
90–95, https://doi.org/10.1109/ICCV.1998.710705, 1999.
Prokop, A., Schirmer., M., Rub, M., Lehning, M., and Stocker, M.:
A comparison of measurement methods: terrestrial laser scanning, tachymetry
and snow probing for the determination of the spatial snow-depth distribution
on slopes, Ann. Glaciol., 49, 210–216, https://doi.org/10.3189/172756408787814726,
2008.
Remondino, F.: Detectors and descriptors for photogrammetric applications,
ISPRS Archives, 36, 49–54, 2006.
Remondino, F. and El-Hakim, S.: Image-based 3D modelling: a review, The
Photogrametric Record, 21, 269–291, 2006.
Rice, R. and Bales, R. C.: Embedded-sensor network design for snow cover
measurements around snow pillow and snow course sites in the Sierra Nevada of
California, Water Resour. Res., 46, W03537, https://doi.org/10.1029/2008WR007318, 2010.
Ryan, J. C., Hubbard, A. L., Box, J. E., Todd, J., Christoffersen, P., Carr,
J. R., Holt, T.`O., and Snooke, N.: UAV photogrammetry and structure from
motion to assess calving dynamics at Store Glacier, a large outlet draining
the Greenland ice sheet, The Cryosphere, 9, 1–11, https://doi.org/10.5194/tc-9-1-2015,
2015.
Ryan, W. A., Doesken, N. J., and Fassnacht, S. R.: Evaluation of Ultrasonic
Snow Depth Sensors for U.S. Snow Measurements, J. Atmos. Ocean. Tech., 25,
667–684, https://doi.org/10.1175/2007JTECHA947.1, 2008.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and
Pulwarty, R. S.: Characteristics of the western United States snowpack from
snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999.
Skaugen, T.: Modelling the spatial variability of snow water equivalent at
the catchment scale, Hydrol. Earth Syst. Sci., 11, 1543–1550,
https://doi.org/10.5194/hess-11-1543-2007, 2007.
Sona, G., Pinto, L., Pagliari, D., Passoni, D., and Gini, R.: Sperimental
analysis of different software packages for orientation and digital surface
modelling from UAV images, Earth Sci. Inform., 7, 97–107,
https://doi.org/10.1007/s12145-013-0142-2, 2014.
Stretcha, C., Pylyänäinen, T., and Fua, P.: Dynamic and scalable
large scale image reconstruction, in: IEEE Conference on Computer Vision and
Pattern Recognition, San Francisco, USA, 406–413,
https://doi.org/10.1109/CVPR.2010.5540184, 2010.
Vander Jagt, B., Lucieer, A., Wallace, L., Turner, D., and Durand, M.: Snow
Depth Retrieval with UAS Using Photogrammetric Techniques, Geosciences, 5,
264–285, 2015.
Verhoeven, G.: Software Review – Taking Computer Vision Aloft –
Archaeological Three-dimensional Reconstructions from Aerial Photographs with
PhotoScan, Archaeol. Prospect., 18, 67–73, 2011.
Watts, A. C., Ambrosia, V. G., and Hinkley, E. A.: Unmanned Aircraft Systems
in Remote Sensing and Scientific Research: Classification and Considerations
of Use, Remote Sensing, 4, 1671–1692, https://doi.org/10.3390/rs4061671, 2012.
Worby, A. P., Markus, T., Steer, A. D., Lytle, V. I., and Massom, R. A.:
Evaluation of AMSR-E snow depth product over East Antarctic sea ice using in
situ measurements and aerial photography, J. Geophys. Res., 113, C05S94,
https://doi.org/10.1029/2007JC004181, 2008.
Short summary
We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
We investigate snow depth distribution at peak accumulation over a small Alpine area using...
