Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Feb 2023
Data description paper |
| 08 Feb 2023
| 08 Feb 2023
IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)
Francesco Avanzi
CORRESPONDING AUTHOR
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Simone Gabellani
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Fabio Delogu
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Francesco Silvestro
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Flavio Pignone
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Giulia Bruno
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16145 Genoa, Italy
Luca Pulvirenti
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Giuseppe Squicciarino
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Elisabetta Fiori
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Lauro Rossi
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Silvia Puca
Italian Civil Protection Department, Rome, Italy
Alexander Toniazzo
Italian Civil Protection Department, Rome, Italy
Pietro Giordano
Italian Civil Protection Department, Rome, Italy
Marco Falzacappa
Italian Civil Protection Department, Rome, Italy
Sara Ratto
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Hervè Stevenin
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Antonio Cardillo
Civil Protection, Regional Functional Center, Molise Region, Campochiaro, CB, Italy
Matteo Fioletti
Environmental Protection Agency of Lombardy, Milan, Italy
Orietta Cazzuli
Environmental Protection Agency of Lombardy, Milan, Italy
Edoardo Cremonese
Aosta Valley Regional Environmental Protection Agency, loc. La Maladière 48, 11020 Saint-Christophe, Italy
Umberto Morra di Cella
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Aosta Valley Regional Environmental Protection Agency, loc. La Maladière 48, 11020 Saint-Christophe, Italy
Luca Ferraris
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16145 Genoa, Italy
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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.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
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Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
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
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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.
Jan Pisek, Angela Erb, Lauri Korhonen, Tobias Biermann, Arnaud Carrara, Edoardo Cremonese, Matthias Cuntz, Silvano Fares, Giacomo Gerosa, Thomas Grünwald, Niklas Hase, Michal Heliasz, Andreas Ibrom, Alexander Knohl, Johannes Kobler, Bart Kruijt, Holger Lange, Leena Leppänen, Jean-Marc Limousin, Francisco Ramon Lopez Serrano, Denis Loustau, Petr Lukeš, Lars Lundin, Riccardo Marzuoli, Meelis Mölder, Leonardo Montagnani, Johan Neirynck, Matthias Peichl, Corinna Rebmann, Eva Rubio, Margarida Santos-Reis, Crystal Schaaf, Marius Schmidt, Guillaume Simioni, Kamel Soudani, and Caroline Vincke
Biogeosciences, 18, 621–635, https://doi.org/10.5194/bg-18-621-2021, https://doi.org/10.5194/bg-18-621-2021, 2021
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Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
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
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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.
Silvia Terzago, Valentina Andreoli, Gabriele Arduini, Gianpaolo Balsamo, Lorenzo Campo, Claudio Cassardo, Edoardo Cremonese, Daniele Dolia, Simone Gabellani, Jost von Hardenberg, Umberto Morra di Cella, Elisa Palazzi, Gaia Piazzi, Paolo Pogliotti, and Antonello Provenzale
Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, https://doi.org/10.5194/hess-24-4061-2020, 2020
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In mountain areas high-quality meteorological data to drive snow models are rarely available, so coarse-resolution data from spatial interpolation of the available in situ measurements or reanalyses are typically employed. We perform 12 experiments using six snow models with different degrees of complexity to show the impact of the accuracy of the forcing on snow depth and snow water equivalent simulations at the Alpine site of Torgnon, discussing the results in relation to the model complexity.
Maria Laura Poletti, Francesco Silvestro, Silvio Davolio, Flavio Pignone, and Nicola Rebora
Hydrol. Earth Syst. Sci., 23, 3823–3841, https://doi.org/10.5194/hess-23-3823-2019, https://doi.org/10.5194/hess-23-3823-2019, 2019
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In this work a probabilistic rainfall nowcasting model, a non-hydrostatic high-resolution numerical weather prediction (NWP) model corrected with data assimilation, and a distributed hydrological model are used together with radar observations to implement a hydrological nowcasting chain. This chain is used to obtain a useful discharge prediction in small catchments with a time horizon of 2–8 h.
Biagio Di Mauro, Roberto Garzonio, Micol Rossini, Gianluca Filippa, Paolo Pogliotti, Marta Galvagno, Umberto Morra di Cella, Mirco Migliavacca, Giovanni Baccolo, Massimiliano Clemenza, Barbara Delmonte, Valter Maggi, Marie Dumont, François Tuzet, Matthieu Lafaysse, Samuel Morin, Edoardo Cremonese, and Roberto Colombo
The Cryosphere, 13, 1147–1165, https://doi.org/10.5194/tc-13-1147-2019, https://doi.org/10.5194/tc-13-1147-2019, 2019
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The snow albedo reduction due to dust from arid regions alters the melting dynamics of the snowpack, resulting in earlier snowmelt. We estimate up to 38 days of anticipated snow disappearance for a season that was characterized by a strong dust deposition event. This process has a series of further impacts. For example, earlier snowmelts may alter the hydrological cycle in the Alps, induce higher sensitivity to late summer drought, and finally impact vegetation and animal phenology.
Francesco Silvestro, Antonio Parodi, Lorenzo Campo, and Luca Ferraris
Hydrol. Earth Syst. Sci., 22, 5403–5426, https://doi.org/10.5194/hess-22-5403-2018, https://doi.org/10.5194/hess-22-5403-2018, 2018
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In this work we adopted a high-resolution meteorological reanalysis dataset together with a rainfall downscaling algorithm and a rainfall bias correction technique in order to produce input for a hydrological model; the resulting modeling chain allows the production of long time series of distributed hydrological variables in the Liguria region of Italy, located in the northern part of Italy. The aim is to evaluate how such a kind of modeling chain is able to reproduce the hydrology in an area.
Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani
The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, https://doi.org/10.5194/tc-12-2287-2018, 2018
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The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.
Teresa Salvatici, Veronica Tofani, Guglielmo Rossi, Michele D'Ambrosio, Carlo Tacconi Stefanelli, Elena Benedetta Masi, Ascanio Rosi, Veronica Pazzi, Pietro Vannocci, Miriana Petrolo, Filippo Catani, Sara Ratto, Hervè Stevenin, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, https://doi.org/10.5194/nhess-18-1919-2018, 2018
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In this paper, we present the application of the physically based HIRESSS model (High Resolution Stability Simulator) to forecast the occurrence of shallow landslides in a portion of the Aosta Valley region (Italy). An in-depth study of the geotechnical and hydrological properties of the hillslopes controlling shallow landslides formation was conducted, in order to generate an input map of parameters. The main aim of this study is to set up a regional landslide early warning system.
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
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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.
Luca Cenci, Luca Pulvirenti, Giorgio Boni, Marco Chini, Patrick Matgen, Simone Gabellani, Giuseppe Squicciarino, and Nazzareno Pierdicca
Adv. Geosci., 44, 89–100, https://doi.org/10.5194/adgeo-44-89-2017, https://doi.org/10.5194/adgeo-44-89-2017, 2017
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This research aims at improving hydrological modelling skills of flash flood prediction by exploiting earth observation data. To this aim, high spatial/moderate temporal resolution soil moisture maps, derived from Sentinel 1 acquisitions, were used in a data assimilation framework. Findings revealed the potential of Sentinel 1-based soil moisture data assimilation for flash flood risk reduction and improved our understanding of the capabilities of the aforementioned satellite-derived product.
Antonio Parodi, Luca Ferraris, William Gallus, Maurizio Maugeri, Luca Molini, Franco Siccardi, and Giorgio Boni
Clim. Past, 13, 455–472, https://doi.org/10.5194/cp-13-455-2017, https://doi.org/10.5194/cp-13-455-2017, 2017
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Initial and boundary condition data from the 20th Century Reanalysis Project in ensemble mode are used to address the feasibility of performing cloud-resolving simulations with 1 km horizontal grid spacing of a historic extreme event that occurred over Liguria: the 1915 San Fruttuoso case. The proposed approach focuses on the ensemble Weather Research and Forecasting model runs that show strong convergence over the Ligurian Sea, as these runs are the ones most likely to best simulate the event.
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
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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
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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.
Francesco Silvestro, Nicola Rebora, Lauro Rossi, Daniele Dolia, Simone Gabellani, Flavio Pignone, Eva Trasforini, Roberto Rudari, Silvia De Angeli, and Cristiano Masciulli
Nat. Hazards Earth Syst. Sci., 16, 1737–1753, https://doi.org/10.5194/nhess-16-1737-2016, https://doi.org/10.5194/nhess-16-1737-2016, 2016
Carlo De Michele, Francesco Avanzi, Daniele Passoni, Riccardo Barzaghi, Livio Pinto, Paolo Dosso, Antonio Ghezzi, Roberto Gianatti, and Giacomo Della Vedova
The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, https://doi.org/10.5194/tc-10-511-2016, 2016
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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.
L. Wingate, J. Ogée, E. Cremonese, G. Filippa, T. Mizunuma, M. Migliavacca, C. Moisy, M. Wilkinson, C. Moureaux, G. Wohlfahrt, A. Hammerle, L. Hörtnagl, C. Gimeno, A. Porcar-Castell, M. Galvagno, T. Nakaji, J. Morison, O. Kolle, A. Knohl, W. Kutsch, P. Kolari, E. Nikinmaa, A. Ibrom, B. Gielen, W. Eugster, M. Balzarolo, D. Papale, K. Klumpp, B. Köstner, T. Grünwald, R. Joffre, J.-M. Ourcival, M. Hellstrom, A. Lindroth, C. George, B. Longdoz, B. Genty, J. Levula, B. Heinesch, M. Sprintsin, D. Yakir, T. Manise, D. Guyon, H. Ahrends, A. Plaza-Aguilar, J. H. Guan, and J. Grace
Biogeosciences, 12, 5995–6015, https://doi.org/10.5194/bg-12-5995-2015, https://doi.org/10.5194/bg-12-5995-2015, 2015
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The timing of plant development stages and their response to climate and management were investigated using a network of digital cameras installed across different European ecosystems. Using the relative red, green and blue content of images we showed that the green signal could be used to estimate the length of the growing season in broadleaf forests. We also developed a model that predicted the seasonal variations of camera RGB signals and how they relate to leaf pigment content and area well.
F. Silvestro, S. Gabellani, R. Rudari, F. Delogu, P. Laiolo, and G. Boni
Hydrol. Earth Syst. Sci., 19, 1727–1751, https://doi.org/10.5194/hess-19-1727-2015, https://doi.org/10.5194/hess-19-1727-2015, 2015
P. Pogliotti, M. Guglielmin, E. Cremonese, U. Morra di Cella, G. Filippa, C. Pellet, and C. Hauck
The Cryosphere, 9, 647–661, https://doi.org/10.5194/tc-9-647-2015, https://doi.org/10.5194/tc-9-647-2015, 2015
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This study presents the thermal state and recent evolution of permafrost at Cime Bianche.
The analysis reveals that (i) spatial variability of MAGST is greater than its interannual variability and is controlled by snow duration and air temperature during the snow-free period, (ii) the ALT has a pronounced spatial variability caused by a different subsurface ice and water content, and (iii) permafrost is warming at significant rates below 8m of depth.
A. Hally, O. Caumont, L. Garrote, E. Richard, A. Weerts, F. Delogu, E. Fiori, N. Rebora, A. Parodi, A. Mihalović, M. Ivković, L. Dekić, W. van Verseveld, O. Nuissier, V. Ducrocq, D. D'Agostino, A. Galizia, E. Danovaro, and A. Clematis
Nat. Hazards Earth Syst. Sci., 15, 537–555, https://doi.org/10.5194/nhess-15-537-2015, https://doi.org/10.5194/nhess-15-537-2015, 2015
S. Puca, F. Porcu, A. Rinollo, G. Vulpiani, P. Baguis, S. Balabanova, E. Campione, A. Ertürk, S. Gabellani, R. Iwanski, M. Jurašek, J. Kaňák, J. Kerényi, G. Koshinchanov, G. Kozinarova, P. Krahe, B. Lapeta, E. Lábó, L. Milani, L'. Okon, A. Öztopal, P. Pagliara, F. Pignone, C. Rachimow, N. Rebora, E. Roulin, I. Sönmez, A. Toniazzo, D. Biron, D. Casella, E. Cattani, S. Dietrich, F. Di Paola, S. Laviola, V. Levizzani, D. Melfi, A. Mugnai, G. Panegrossi, M. Petracca, P. Sanò, F. Zauli, P. Rosci, L. De Leonibus, E. Agosta, and F. Gattari
Nat. Hazards Earth Syst. Sci., 14, 871–889, https://doi.org/10.5194/nhess-14-871-2014, https://doi.org/10.5194/nhess-14-871-2014, 2014
F. Silvestro, N. Rebora, and G. Cummings
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-1-7497-2013, https://doi.org/10.5194/nhessd-1-7497-2013, 2013
Revised manuscript not accepted
A. Rinollo, G. Vulpiani, S. Puca, P. Pagliara, J. Kaňák, E. Lábó, L'. Okon, E. Roulin, P. Baguis, E. Cattani, S. Laviola, and V. Levizzani
Nat. Hazards Earth Syst. Sci., 13, 2695–2705, https://doi.org/10.5194/nhess-13-2695-2013, https://doi.org/10.5194/nhess-13-2695-2013, 2013
A. Mugnai, D. Casella, E. Cattani, S. Dietrich, S. Laviola, V. Levizzani, G. Panegrossi, M. Petracca, P. Sanò, F. Di Paola, D. Biron, L. De Leonibus, D. Melfi, P. Rosci, A. Vocino, F. Zauli, P. Pagliara, S. Puca, A. Rinollo, L. Milani, F. Porcù, and F. Gattari
Nat. Hazards Earth Syst. Sci., 13, 1959–1981, https://doi.org/10.5194/nhess-13-1959-2013, https://doi.org/10.5194/nhess-13-1959-2013, 2013
F. Silvestro, S. Gabellani, F. Delogu, R. Rudari, and G. Boni
Hydrol. Earth Syst. Sci., 17, 39–62, https://doi.org/10.5194/hess-17-39-2013, https://doi.org/10.5194/hess-17-39-2013, 2013
Related subject area
Domain: ESSD – Ice | Subject: Snow and Sea Ice
A sea ice deformation and rotation rate dataset (2017–2023) from the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS)
An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery
Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging
Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023
SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea
A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)
Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle
Earth Syst. Sci. Data, 17, 423–434, https://doi.org/10.5194/essd-17-423-2025, https://doi.org/10.5194/essd-17-423-2025, 2025
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Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Hee-Sung Jung, Sang-Moo Lee, Joo-Hong Kim, and Kyungsoo Lee
Earth Syst. Sci. Data, 17, 233–258, https://doi.org/10.5194/essd-17-233-2025, https://doi.org/10.5194/essd-17-233-2025, 2025
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This dataset consists of reference sea ice concentration (SIC) data records over the Arctic Ocean, which were derived from the 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. Each SIC map is given in a 6.25 km polar stereographic grid and is catalogued into one of the 12 regions of the Arctic Ocean. This dataset was produced to be used as a reference in the validation of various SIC products.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
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High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Michael Warscher, Thomas Marke, Erwin Rottler, and Ulrich Strasser
Earth Syst. Sci. Data, 16, 3579–3599, https://doi.org/10.5194/essd-16-3579-2024, https://doi.org/10.5194/essd-16-3579-2024, 2024
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Continuous observations of snow and climate at high altitudes are still sparse. We present a unique collection of weather and snow cover data from three automatic weather stations at remote locations in the Ötztal Alps (Austria) that include continuous recordings of snow cover properties. The data are available over multiple winter seasons and enable new insights for snow hydrological research. The data are also used in operational applications, i.e., for avalanche warning and flood forecasting.
Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, and Robert Ricker
Earth Syst. Sci. Data, 16, 3149–3170, https://doi.org/10.5194/essd-16-3149-2024, https://doi.org/10.5194/essd-16-3149-2024, 2024
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We describe a sea ice thickness dataset based on SMOS satellite measurements, initially designed for the Arctic but adapted for Antarctica. We validated it using limited Antarctic measurements. Our findings show promising results, with a small difference in thickness estimation and a strong correlation with validation data within the valid thickness range. However, improvements and synergies with other sensors are needed, especially for sea ice thicker than 1 m.
Weixin Zhu, Siqi Liu, Shiming Xu, and Lu Zhou
Earth Syst. Sci. Data, 16, 2917–2940, https://doi.org/10.5194/essd-16-2917-2024, https://doi.org/10.5194/essd-16-2917-2024, 2024
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In the polar ocean, wind waves generate and propagate into the sea ice cover, forming marginal ice zones (MIZs). Using ESA's CryoSat-2, we construct a 12-year dataset of the MIZ in the Atlantic Arctic, a key region for climate change and human activities. The dataset is validated with high-resolution observations by ICESat2 and Sentinel-1. MIZs over 300 km wide are found under storms in the Barents Sea. The new dataset serves as the basis for research areas, including wave–ice interactions.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
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It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
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Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
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Sea ice in the Arctic and Antarctic can move several tens of kilometers per day due to wind and ocean currents. By analysing thousands of satellite images, we measured how sea ice has been moving every single day from 1991 through to 2020. We compare our data to how buoys attached to the ice moved and find good agreement. Other scientists will now use our data to better understand if climate change has modified the way sea ice moves and in what way.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
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We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
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Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Cited articles
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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.
Snow cover has profound implications for worldwide water supply and security, but knowledge of...
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