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
Related authors
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
Revised manuscript accepted 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.
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.
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.
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.
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.
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.
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.
Giulia Bruno, Laurent Strohmenger, and Doris Duethmann
Hydrol. Earth Syst. Sci., 29, 4473–4489, https://doi.org/10.5194/hess-29-4473-2025, https://doi.org/10.5194/hess-29-4473-2025, 2025
Short summary
Short summary
Decreases in streamflow during dry periods threaten ecosystems and society, and increases in evapotranspiration may contribute to them. From data for small catchments in Germany, summer low flows decreased over 1970–2019, and evapotranspiration increases significantly contributed to that. Lower-than-expected annual streamflow occurred during the 1989–1993 drought in catchments with increases in evapotranspiration. Changes in evapotranspiration need full consideration for streamflow prediction.
Jaime Gaona, Davide Bavera, Guido Fioravanti, Sebastian Hahn, Pietro Stradiotti, Paolo Filippucci, Stefania Camici, Luca Ciabatta, Hamidreza Mosaffa, Silvia Puca, Nicoletta Roberto, and Luca Brocca
Hydrol. Earth Syst. Sci., 29, 3865–3888, https://doi.org/10.5194/hess-29-3865-2025, https://doi.org/10.5194/hess-29-3865-2025, 2025
Short summary
Short summary
Soil moisture is crucial for the water cycle since it is at the front line of drought. Satellite, model and in situ data help identify soil moisture stress but are challenged by data uncertainties. This study evaluates trends and data coherence of common active/passive microwave sensors and model-based soil moisture data against in situ stations across Europe from 2007 to 2022. Data reliability is increasing, but combining data types improves soil moisture monitoring capabilities.
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
Revised manuscript accepted 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.
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024, https://doi.org/10.5194/nhess-24-199-2024, 2024
Short summary
Short summary
This work describes Flood-PROOFS East Africa, an impact-based flood forecasting system for the Greater Horn of Africa. It is based on hydrological simulations, inundation mapping, and estimation of population and assets exposed to upcoming river floods. The system supports duty officers in African institutions in the daily monitoring of hydro-meteorological disasters. A first evaluation shows the system performance for the catastrophic floods in the Nile River basin in summer 2020.
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.
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.
Rosa Claudia Torcasio, Alessandra Mascitelli, Eugenio Realini, Stefano Barindelli, Giulio Tagliaferro, Silvia Puca, Stefano Dietrich, and Stefano Federico
Nat. Hazards Earth Syst. Sci., 23, 3319–3336, https://doi.org/10.5194/nhess-23-3319-2023, https://doi.org/10.5194/nhess-23-3319-2023, 2023
Short summary
Short summary
This work shows how local observations can improve precipitation forecasting for severe weather events. The improvement lasts for at least 6 h of forecast.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
Short summary
Short summary
The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Arthur Bayle, Bradley Z. Carlson, Anaïs Zimmer, Sophie Vallée, Antoine Rabatel, Edoardo Cremonese, Gianluca Filippa, Cédric Dentant, Christophe Randin, Andrea Mainetti, Erwan Roussel, Simon Gascoin, Dov Corenblit, and Philippe Choler
Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, https://doi.org/10.5194/bg-20-1649-2023, 2023
Short summary
Short summary
Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. We used remote sensing approaches to study early succession dynamics as it allows to analyze the deglaciation, colonization, and vegetation growth within a single framework. We found that the heterogeneity of early succession dynamics is deterministic and can be explained well by local environmental context. This work has been done by an international consortium.
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.
Andrea Taramelli, Margherita Righini, Emiliana Valentini, Lorenzo Alfieri, Ignacio Gatti, and Simone Gabellani
Nat. Hazards Earth Syst. Sci., 22, 3543–3569, https://doi.org/10.5194/nhess-22-3543-2022, https://doi.org/10.5194/nhess-22-3543-2022, 2022
Short summary
Short summary
This work aims to support decision-making processes to prioritize effective interventions for flood risk reduction and mitigation for the implementation of flood risk management concepts in urban areas. Our findings provide new insights into vulnerability spatialization of urban flood events for the residential sector, demonstrating that the nature of flood pathways varies spatially and is influenced by landscape characteristics, as well as building features.
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.
P. Garieri, F. Diotri, G. Forlani, U. Morra di Cella, and R. Roncella
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 665–672, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-665-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-665-2022, 2022
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.
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.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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.
Cited articles
Alfieri, L., Avanzi, F., Delogu, F., Gabellani, S., Bruno, G., Campo, L., Libertino, A., Massari, C., Tarpanelli, A., Rains, D., Miralles, D. G., Quast, R., Vreugdenhil, M., Wu, H., and Brocca, L.:
High-resolution satellite products improve hydrological modeling in northern Italy, Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, 2022. a
Apicella, L., Puca, S., Lagasio, M., Meroni, A., Milelli, M., Vela, N., Garbero, V., Ferraris, L., and Parodi, A.:
The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy, Bulletin of Atmospheric Science and Technology, 2, 1–14, 2021. a
Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari, D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.:
Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation, Remote Sens.-Basel, 10, 765, https://doi.org/10.3390/rs10050765, 2018. a
Avanzi, F., Maurer, T., Glaser, S. D., Bales, R. C., and Conklin, M. H.:
Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters, J. Hydrol., 582, 124478, https://doi.org/10.1016/j.jhydrol.2019.124478, 2020. a, b
Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.:
Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, 2021a. a, b, c, d, e, f, g
Avanzi, F., Gabellani, S., Delogu, F., and Silvestro, F.:
c-hydro/s3m-dev: (v5.1.0), Zenodo [software], https://doi.org/10.5281/zenodo.4663899, 2021b. a
Avanzi, F.: c-hydro/fp-s3m: (v1.0.1), Zenodo [software], https://doi.org/10.5281/zenodo.5040388, 2021c. a
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Cremonese, E., Morra di Cella, U., Ratto, S., and Stevenin, H.:
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt, Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, 2022a. a, b, c, d, e
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., Pulvirenti, L., Squicciarino, G., Fiori, E., Rossi, L., Puca, S., Toniazzo, A., Giordano, P., Falzacappa, M., Ratto, S., Stevenin, H., Cardillo, A., Fioletti, M., Cazzuli, O., Cremonese, E., Morra di Cella, U., and Ferraris, L.:
IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations, Zenodo [data set], https://doi.org/10.5281/zenodo.7034956, 2022b. a, b, c, d
Bales, R., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and Dozier, J.:
Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006. a, b
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.:
Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Bartelt, P. and Lehning, M.:
A physical SNOWPACK model for the Swiss avalanche warning Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a
Bisci, C., Fazzini, M., Beltrando, G., Cardillo, A., and Romeo, V.:
The February 2012 exceptional snowfall along the Adriatic side of Central Italy, Meteorol. Z., 21, 503–508, https://doi.org/10.1127/0941-2948/2012/0536, 2012. a
Blöschl, G.:
Scaling issues in snow hydrology, Hydrol. Process., 13, 2149–2175, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8, 1999. a
Bouamri, H., Boudhar, A., Gascoin, S., and Kinnard, C.:
Performance of temperature and radiation index models for point-scale snow water equivalent (SWE) simulations in the Moroccan High Atlas Mountains, Hydrolog. Sci. J., 63, 1844–1862, https://doi.org/10.1080/02626667.2018.1520391, 2018. a
Bruno, G., Pignone, F., Silvestro, F., Gabellani, S., Schiavi, F., Rebora, N., Giordano, P., and Falzacappa, M.:
Performing Hydrological Monitoring at a National Scale by Exploiting Rain-Gauge and Radar Networks: The Italian Case, Atmosphere, 12, 771, https://doi.org/10.3390/atmos12060771, 2021. a, b, c, d, e
Bruno, G., Avanzi, F., Gabellani, S., Ferraris, L., Cremonese, E., Galvagno, M., and Massari, C.:
Disentangling the role of subsurface storage in the propagation of drought through the hydrological cycle, Adv. Water Resour., 169, 104305, https://doi.org/10.1016/j.advwatres.2022.104305, 2022. a
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.:
Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016. a
Chiambretti, I., Dellavedova, P., Segor, V., Valt, M., and Cianfarra, P.: Winter 2013/2014 on the Italian Alps – Analysis and Lesson Learned About Avalanche Risk Treatment and Management Strategies, in: Proceedings of the ISSW 2014, International Snow Science Workshop 2014 Proceedings, Banff, Canada, http://arc.lib.montana.edu/snow-science/item/2186 (last access: 3 February 2023), 2014. a
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.:
The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011. a
Cox, L., Bartee, L., Crook, A., Farnes, P., and Smith, J.:
The care and feeding of snow pillows, in: Proceedings of the 46th Annual Western Snow Conference, 18–20 April 1978, Otter Rock, Oregon, 40–47, 1978. a
Cui, G., Bales, R., Rice, R., Anderson, M., Avanzi, F., Hartsough, P., and Conklin, M.:
Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks, J. Hydrometeorol., 21, 2061–2081, 2020. a
Da Ronco, P., Avanzi, F., De Michele, C., Notarnicola, C., and Schaefli, B.:
Comparing MODIS snow products Collection 5 with Collection 6 over Italian Central Apennines, Int. J. Remote Sens., 41, 4174–4205, https://doi.org/10.1080/01431161.2020.1714778, 2020. a, b
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.:
Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016. a, b
Demirtaş, M.:
The large-scale environment of the European 2012 high-impact cold wave: prolonged upstream and downstream atmospheric blocking, Weather, 72, 297–301, https://doi.org/10.1002/wea.3020, 2017. a
DeWalle, D. R. and Rango, A.:
Principles of Snow Hydrology, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511535673, 2011. a
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. a
Dozier, J.:
Mountain hydrology, snow color, and the fourth paradigm, Eos T. Am. Geophys. Un., 92, 373–374, https://doi.org/10.1029/2011EO430001, 2011. a
Dozier, J., Bair, E. H., and Davis, R. E.:
Estimating the spatial distribution of snow water equivalent in the world's mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016. a
Fiddes, J., Aalstad, K., and Westermann, S.:
Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering, Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, 2019. a, b
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 N 83, IACS Contribution N 1, UNESCO – IHP, Paris, 2009. a
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.:
Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155, https://doi.org/10.1038/ngeo1062, 2011. a
Froidurot, S., Zin, I., Hingray, B., and Gautheron, A.:
Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables, J. Hydrometeorol., 15, 685–696, https://doi.org/10.1175/JHM-D-13-073.1, 2014. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.:
Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019a. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.:
Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019b. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.:
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
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. a
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. a
Harder, P., Schirmer, M., Pomeroy, J., and Helgason, W.:
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle, The Cryosphere, 10, 2559–2571, https://doi.org/10.5194/tc-10-2559-2016, 2016. a
Harpold, A. A., Dettinger, M., and Rajagopal, S.:
Defining snow drought and why it matters, EOS, 98, https://doi.org/10.1029/2017EO068775, 2017. a
Harrison, B. and Bales, R.:
Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds, J. Hydrol. Eng., 21, 04016002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001327, 2016. a, b, c
Hatchett, B. J. and McEvoy, D. J.:
Exploring the Origins of Snow Drought in the Northern Sierra Nevada, California, Earth Interact., 22, 1–13, https://doi.org/10.1175/EI-D-17-0027.1, 2018. a
Hatchett, B. J., Daudert, B., Garner, C. B., Oakley, N. S., Putnam, A. E., and White, A. B.:
Winter Snow Level Rise in the Northern Sierra Nevada from 2008 to 2017, Water, 9, 899, https://doi.org/10.3390/w9110899, 2017. a
Huning, L. S. and AghaKouchak, A.:
Global snow drought hot spots and characteristics, P. Natl. Acad. Sci. USA, 117, 19753–19759, https://doi.org/10.1073/pnas.1915921117, 2020. a
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.:
Climate Change Will Affect the Asian Water Towers, Science, 328, 1382–1385, 2010. a
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, 2020. a
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.:
LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, 2014. a
Kling, H., Fuchs, M., and Paulin, M.:
Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Knoben, W. J. M., Freer, J. E., and Woods, R. A.:
Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Lagasio, M., Fagugli, G., Ferraris, L., Fiori, E., Gabellani, S., Masi, R., Mazzarella, V., Milelli, M., Parodi, A., Pignone, F., Puca, S., Pulvirenti, L., Silvestro, F., Squicciarino, G., and Parodi, A.:
A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case, Remote Sens.-Basel, 14, 14, 6348, https://doi.org/10.3390/rs14246348, 2022. a
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.:
How much runoff originates as snow in the western United States, and how will that change in the future?, Geophys. Res. Lett., 44, 6163–6172, https://doi.org/10.1002/2017GL073551, 2017. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L., Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J., and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nat. Commun., 10, 1–12, 2019. a, b, c, d, e
Liu, Y., Fang, Y., and Margulis, S. A.:
High Mountain Asia UCLA Daily Snow Reanalysis, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/HNAUGJQXSCVU, 2021. a
Lundquist, J. D., Hughes, M., Henn, B., Gutmann, E. D., Livneh, B., Dozier, J., and Neiman, P.:
High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California, J. Hydrometeorol., 16, 1773–1792, https://doi.org/10.1175/JHM-D-15-0019.1, 2015. a
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.:
Evaluating snow models with varying process representations for hydrological applications, Water Resour. Res., 51, 2707–2723, 2015. a
Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., and Gascon, F.:
Sen2Cor for Sentinel-2, in: Image and Signal Processing for Remote Sensing XXIII, edited by: Bruzzone, L., vol. 10427, p. 1042704, International Society for Optics and Photonics, SPIE, https://doi.org/10.1117/12.2278218, 2017. a
Malek, S. A., Avanzi, F., Brun-Laguna, K., Maurer, T., Oroza, C. A., Hartsough, P. C., Watteyne, T., and Glaser, S. D.:
Real-Time Alpine Measurement System Using Wireless Sensor Networks, Sensors, 17, 2583, https://doi.org/10.3390/s17112583, 2017. a
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.:
A Particle Batch Smoother Approach to Snow Water Equivalent Estimation, J. Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015. a, b
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.:
A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015), J. Hydrometeorol., 17, 1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a, b
Montoya, E. L., Dozier, J., and Meiring, W.:
Biases of April 1 snow water equivalent records in the Sierra Nevada and their associations with large-scale climate indices, Geophys. Ress Letts, 41, 1–7, 2014. a
Musselmann, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.:
Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219, https://doi.org/10.1038/nclimate3225, 2017. a
Niwano, M., Suya, M., Nagaya, K., Yamaguchi, S., Matoba, S., Harada, I., and Ohkawara, N.:
Estimation of Seasonal Snow Mass Balance all over Japan Using a High-Resolution Atmosphere-Snow Model Chain, SOLA, 18, 193–198, https://doi.org/10.2151/sola.2022-031, 2022. a
Pagano, T., Garen, D., and Sorooshian, S.:
Evaluation of Official Western U. S. Seasonal Water Supply Outlooks, 1922–2002, J. Hydrometeorol., 5, 896–909, https://doi.org/10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2, 2004. a
Pagano, T. C., Wood, A. W., Ramos, M.-H., Cloke, H. L., Pappenberger, F., Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., and Verkade, J. S.:
Challenges of Operational River Forecasting, J. Hydrometeorol., 15, 1692–1707, https://doi.org/10.1175/JHM-D-13-0188.1, 2014. a
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C., and Winstral, A.:
The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo, Remote Sens. Environ., 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016. a, b
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B. H., Rich, J., Sharp, M. J., and The Randolph Consortium: The Randolph Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014. a
Piazzi, G., Thirel, G., Campo, L., and Gabellani, S.:
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment, The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, 2018. a
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, 2020. a
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. a
Rolland, C.:
Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions, J. Climate, 16, 1032–1046, https://doi.org/10.1175/1520-0442(2003)016<1032:SASVOA>2.0.CO;2, 2003. a
Rosenberg, E. A., Wood, A. W., and Steinemann, A. C.:
Statistical applications of physically based hydrologic models to seasonal streamflow forecasts, Water Resour. Res., 47, W00H14, https://doi.org/10.1029/2010WR010101, 2011. a
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. a, b, c
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.:
The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Sinclair, S. and Pegram, G.:
Combining radar and rain gauge rainfall estimates using conditional merging, Atmos. Sci. Lett., 6, 19–22, 2005. a
Slatyer, R. A., Umbers, K. D. L., and Arnold, P. A.:
Ecological responses to variation in seasonal snow cover, Conserv. Biol., 36, e13727, https://doi.org/10.1111/cobi.13727, 2022. a
Soruco, A., Vincent, C., Rabatel, A., Francou, B., Thibert, E., Sicart, J. E., and Condom, T.:
Contribution of glacier runoff to water resources of La Paz city, Bolivia (16∘ S), Ann. Glaciol., 56, 147–154, https://doi.org/10.3189/2015AoG70A001, 2015. a
Sturm, M. and Liston, G. E.:
Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications, J. Hydrometeorol., 22, 2917–2938, https://doi.org/10.1175/JHM-D-21-0070.1, 2021. a, b
Sturm, M., Holmgren, J., and Liston, G. E.:
A seasonal snow cover classification system for local to global applications, J. Climate, 8, 1261–1283, 1995. a
Sturm, M., Goldstein, M. A., and Parr, C.:
Water and life from snow: A trillion dollar science question, Water Resour. Res., 53, 3534–3544, https://doi.org/10.1002/2017WR020840, 2017. a, b
Tang, Q. and Lettenmaier, D. P.:
Use of satellite snow-cover data for streamflow prediction in the Feather River Basin, California, Int. J. Remote Sens., 31, 3745–3762, https://doi.org/10.1080/01431161.2010.483493, 2010. a
Terzago, S., Andreoli, V., Arduini, G., Balsamo, G., Campo, L., Cassardo, C., Cremonese, E., Dolia, D., Gabellani, S., von Hardenberg, J., Morra di Cella, U., Palazzi, E., Piazzi, G., Pogliotti, P., and Provenzale, A.:
Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments, Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, 2020. a
Toreti, A., Bavera, D., Avanzi, F., Cammalleri, C., De Felice, M., De Jager, A., Di Ciollo, C., Gabellani, S., Maetens, W., Magni, D., Manfron, G., Masante, D., Mazzeschi, M., Mccormick, N., Naumann, G., Niemeyer, S., Rossi, L., Seguini, L., Spinoni, J., and Van Den Berg, M.:
Drought in northern Italy – March 2022: GDO analytical report, European Commission, Joint Research Centre
Luxembourg, https://doi.org/10.2760/781876, 2022.
a
Valery, A., Andréassian, V., and Perrin, C.: Inverting the hydrological cycle: when streamflow measurements help assess altitudinal precipitation gradients in mountain areas, IAHS Publ., 333, 281–286, 2009. a
Valt, M., Guyennon, N., Salerno, F., Petrangeli, A. B., Salvatori, R., Cianfarra, P., and Romano, E.:
Predicting new snow density in the Italian Alps: A variability analysis based on 10 years of measurements, Hydrol. Process., 32, 3174–3187, https://doi.org/10.1002/hyp.13249, 2018. a
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.:
The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022. a, b
Viviroli, D., Messerli, H. H. D. B., Meybeck, M., and Weingartner, R.:
Mountains of the world, water towers for humanity: Typology, mapping, and global significance, Water Resour. Res., 43, W07447, https://doi.org/10.1029/2006WR005653, 2007. a, b, c
WMO:
Guide to Instruments and Methods of Observation, World Meteorological Organization (WMO), Geneva, 2018. a
Zanotti, F., Endrizzi, S., Bertoldi, G., and Rigon, R.:
The GEOTOP snow module, Hydrol. Process., 18, 3667–3679, https://doi.org/10.1002/hyp.5794, 2004. a
Zhang, Z., Glaser, S., Bales, R., Conklin, M., Rice, R., and Marks, D.:
Insights into mountain precipitation and snowpack from a basin-scale wireless-sensor network, Water Resour. Res., 53, 6626–6641, https://doi.org/10.1002/2016WR018825, 2017a. a, b
Zhang, Z., Glaser, S. D., Bales, R. C., Conklin, M., Rice, R., and Marks, D.:
Technical Report: The Design and Evaluation of a Basin-scale Wireless Sensor Network for Mountain Hydrology, Water Resour. Res., 53, 4487–4498, https://doi.org/10.1002/2016WR019619, 2017b. a
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...
Altmetrics
Final-revised paper
Preprint