Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
27 Jun 2022
Model description paper |
| 27 Jun 2022
| 27 Jun 2022
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
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
Edoardo Cremonese
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Umberto Morra di Cella
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Sara Ratto
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Hervé Stevenin
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Related authors
Martina Leone, Francesco Avanzi, Umberto Morra di Cella, Simone Gabellani, Edoardo Cremonese, Michel Isabellon, Paolo Pogliotti, Riccardo Scotti, Andrea Monti, Luca Ferraris, and Roberto Colombo
Hydrol. Earth Syst. Sci., 30, 4649–4666, https://doi.org/10.5194/hess-30-4649-2026, https://doi.org/10.5194/hess-30-4649-2026, 2026
Short summary
Short summary
We investigated how glaciers helped sustain summer river flow during the severe snow shortages of 2022 and 2023 in the Italian Alps. Using high-resolution modeling and long-term data, we found that glacier melt contributed up to three times more than in average years. This enhanced melt partly compensated for low snow and precipitation, showing the critical role of glaciers in supporting water resources during droughts.
Francesco Avanzi, Hans Lievens, Michael Matiu, Paolo Filippucci, Oscar M. Baez Villanueva, Simone Gabellani, Fabio Delogu, Lorenzo Alfieri, Andrea Libertino, Pere Quintana-Seguì, Diego G. Miralles, Luca Brocca, Christian Massari, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2851, https://doi.org/10.5194/egusphere-2026-2851, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
Gökhan Sarigil, Mattia Neri, Francesco Avanzi, and Elena Toth
EGUsphere, https://doi.org/10.5194/egusphere-2026-2484, https://doi.org/10.5194/egusphere-2026-2484, 2026
Short summary
Short summary
The study evaluates Snow Water Equivalent estimates from three large-scale land-surface products against a national reference dataset in Italy. Highlighting the performances of the products over the different regions and relating them to the biases in the meteorological forcing, the results provide insights for using SWE estimates in long-term hydro-climatological assessments in Italy and for data selection when evaluating snow dynamics in mountainous regions lacking high-quality benchmark data.
Sofia Ortenzi, Lucio Di Matteo, Daniela Valigi, Marco Donnini, Marco Dionigi, Davide Fronzi, Josie Geris, Fabio Guadagnano, Ivan Marchesini, Paolo Filippucci, Francesco Avanzi, Daniele Penna, and Christian Massari
Hydrol. Earth Syst. Sci., 30, 1755–1778, https://doi.org/10.5194/hess-30-1755-2026, https://doi.org/10.5194/hess-30-1755-2026, 2026
Short summary
Short summary
The study presents an integrated approach to analyze groundwater–surface water interactions in a Central Italy catchment, combining hydrological, hydrochemical–isotopic, thermal drone, and satellite data. Results indicate that fractured limestone aquifers sustain streamflow, with snowmelt accounting for about 18 % of recharge. The workflow is transferable and suitable for similar data-scarce Mediterranean basins.
Francesco Avanzi, Stefano Terzi, Mariapina Castelli, Francesca Munerol, Margherita Andreaggi, Marta Galvagno, Andrea Galletti, Tessa Maurer, Christian Massari, Grace Carlson, Manuela Girotto, Giacomo Bertoldi, Edoardo Cremonese, Simone Gabellani, Umberto Morra di Cella, Marco Altamura, Lauro Rossi, and Luca Ferraris
EGUsphere, https://doi.org/10.22541/au.176055865.50800695/v2, https://doi.org/10.22541/au.176055865.50800695/v2, 2026
Short summary
Short summary
Snow droughts are periods with below-average snow accumulation and are becoming more frequent in a warming climate, yet their ecosystem and societal impacts remain poorly known. Using 13 years of data from 38 Italian catchments, we show that snow droughts reduced snow duration by ~50 %, doubled winter melt-out events, and cut summer runoff by ~50 %. Photosynthesis increased by up to 10 % due to earlier meltout. These events also caused widespread water-supply reductions, especially in foothills.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
Nat. Hazards Earth Syst. Sci., 26, 1075–1104, https://doi.org/10.5194/nhess-26-1075-2026, https://doi.org/10.5194/nhess-26-1075-2026, 2026
Short summary
Short summary
A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
The Cryosphere, 19, 4759–4783, https://doi.org/10.5194/tc-19-4759-2025, https://doi.org/10.5194/tc-19-4759-2025, 2025
Short summary
Short summary
Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, https://doi.org/10.5194/essd-17-5221-2025, 2025
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 2022. 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.
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.
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.
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.
Martina Leone, Francesco Avanzi, Umberto Morra di Cella, Simone Gabellani, Edoardo Cremonese, Michel Isabellon, Paolo Pogliotti, Riccardo Scotti, Andrea Monti, Luca Ferraris, and Roberto Colombo
Hydrol. Earth Syst. Sci., 30, 4649–4666, https://doi.org/10.5194/hess-30-4649-2026, https://doi.org/10.5194/hess-30-4649-2026, 2026
Short summary
Short summary
We investigated how glaciers helped sustain summer river flow during the severe snow shortages of 2022 and 2023 in the Italian Alps. Using high-resolution modeling and long-term data, we found that glacier melt contributed up to three times more than in average years. This enhanced melt partly compensated for low snow and precipitation, showing the critical role of glaciers in supporting water resources during droughts.
Francesco Avanzi, Hans Lievens, Michael Matiu, Paolo Filippucci, Oscar M. Baez Villanueva, Simone Gabellani, Fabio Delogu, Lorenzo Alfieri, Andrea Libertino, Pere Quintana-Seguì, Diego G. Miralles, Luca Brocca, Christian Massari, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2851, https://doi.org/10.5194/egusphere-2026-2851, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
Gökhan Sarigil, Mattia Neri, Francesco Avanzi, and Elena Toth
EGUsphere, https://doi.org/10.5194/egusphere-2026-2484, https://doi.org/10.5194/egusphere-2026-2484, 2026
Short summary
Short summary
The study evaluates Snow Water Equivalent estimates from three large-scale land-surface products against a national reference dataset in Italy. Highlighting the performances of the products over the different regions and relating them to the biases in the meteorological forcing, the results provide insights for using SWE estimates in long-term hydro-climatological assessments in Italy and for data selection when evaluating snow dynamics in mountainous regions lacking high-quality benchmark data.
Sofia Ortenzi, Lucio Di Matteo, Daniela Valigi, Marco Donnini, Marco Dionigi, Davide Fronzi, Josie Geris, Fabio Guadagnano, Ivan Marchesini, Paolo Filippucci, Francesco Avanzi, Daniele Penna, and Christian Massari
Hydrol. Earth Syst. Sci., 30, 1755–1778, https://doi.org/10.5194/hess-30-1755-2026, https://doi.org/10.5194/hess-30-1755-2026, 2026
Short summary
Short summary
The study presents an integrated approach to analyze groundwater–surface water interactions in a Central Italy catchment, combining hydrological, hydrochemical–isotopic, thermal drone, and satellite data. Results indicate that fractured limestone aquifers sustain streamflow, with snowmelt accounting for about 18 % of recharge. The workflow is transferable and suitable for similar data-scarce Mediterranean basins.
Francesco Avanzi, Stefano Terzi, Mariapina Castelli, Francesca Munerol, Margherita Andreaggi, Marta Galvagno, Andrea Galletti, Tessa Maurer, Christian Massari, Grace Carlson, Manuela Girotto, Giacomo Bertoldi, Edoardo Cremonese, Simone Gabellani, Umberto Morra di Cella, Marco Altamura, Lauro Rossi, and Luca Ferraris
EGUsphere, https://doi.org/10.22541/au.176055865.50800695/v2, https://doi.org/10.22541/au.176055865.50800695/v2, 2026
Short summary
Short summary
Snow droughts are periods with below-average snow accumulation and are becoming more frequent in a warming climate, yet their ecosystem and societal impacts remain poorly known. Using 13 years of data from 38 Italian catchments, we show that snow droughts reduced snow duration by ~50 %, doubled winter melt-out events, and cut summer runoff by ~50 %. Photosynthesis increased by up to 10 % due to earlier meltout. These events also caused widespread water-supply reductions, especially in foothills.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
Nat. Hazards Earth Syst. Sci., 26, 1075–1104, https://doi.org/10.5194/nhess-26-1075-2026, https://doi.org/10.5194/nhess-26-1075-2026, 2026
Short summary
Short summary
A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
The Cryosphere, 19, 4759–4783, https://doi.org/10.5194/tc-19-4759-2025, https://doi.org/10.5194/tc-19-4759-2025, 2025
Short summary
Short summary
Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, https://doi.org/10.5194/essd-17-5221-2025, 2025
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 2022. 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.
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.
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.
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.
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.
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.
Cited articles
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow
observations into a macroscale hydrology model, Adv. Water Resour.,
29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 2006. a
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and
Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir
operations for water supply in snow-dominated river catchments, Water
Resour. Res., 52, 4209–4225, https://doi.org/10.1002/2015WR017864, 2016. a
Avanzi, F. and Delogu, F.: c-hydro/s3m-dev: (v5.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4663899, 2021. a
Avanzi, F., Petrucci, G., Matzl, M., Schneebeli, M., and De Michele, C.: Early
formation of preferential flow in a homogeneous snowpack observed by
micro-CT, Water Resour. Res., 53, 3713–3729,
https://doi.org/10.1002/2016WR019502,
2017. a, b
Avanzi, F., Maurer, T., Malek, S., Glaser, S. D., Bales, R. C., and Conklin,
M. H.: Feather River Hydrologic Observatory: Improving Hydrological Snowpack
Forecasting for Hydropower Generation Using Intelligent Information Systems,
Tech. rep., California's Fourth Climate Change Assessment, California Energy
Commission, 2018. a
Avanzi, F., Johnson, R. C., Oroza, C. A., Hirashima, H., Maurer, T., and
Yamaguchi, S.: Insights Into Preferential Flow Snowpack Runoff Using Random
Forest, Water Resour. Res., 55, 10727–10746,
https://doi.org/10.1029/2019WR024828,
2019. a, b
Avanzi, F., Rungee, J., Maurer, T., Bales, R., Ma, Q., Glaser, S., and Conklin, M.: Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts, Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, 2020. a
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, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
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
Bales, R. C., Hopmans, J. W., O'Geen, A. T., Meadows, M., Hartsough, P. C.,
Kirchner, P., Hunsaker, C. T., and Beaudette, D.: Soil moisture response to
snowmelt and rainfall in a Sierra Nevada mixed-conifer forest, Vadose Zone
J., 10, 786–799, 2011. a
Banfi, F. and De Michele, C.: A local model of snow–firn dynamics and application to the Colle Gnifetti site, The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, 2022. a
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, b
Barry, R. G.: The cryosphere – past, present, and future: a review of the
frozen water resources of the world, Polar Geography, 34, 219–227,
https://doi.org/10.1080/1088937X.2011.638146, 2011. 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, b
Bartolini, E., Allamano, P., Laio, F., and Claps, P.: Runoff regime estimation at high-elevation sites: a parsimonious water balance approach, Hydrol. Earth Syst. Sci., 15, 1661–1673, https://doi.org/10.5194/hess-15-1661-2011, 2011. a
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320,
18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Blanchet, J., Marty, C., and Lehning, M.: Extreme value statistics of snowfall
in the Swiss Alpine region, Water Resour. Res., 45, W05424,
https://doi.org/10.1029/2009WR007916, 2009. 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
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A
review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305,
1995. a
Bongio, M., Avanzi, F., and De Michele, C.: Hydroelectric power generation in
an Alpine basin: future water-energy scenarios in a run-of-the-river plant,
Adv. Water Resour., 94, 318–331,
https://doi.org/10.1016/j.advwatres.2016.05.017, 2016. a, b, c
Brock, B. W., Mihalcea, C., Kirkbride, M. P., Diolaiuti, G., Cutler, M. E. J.,
and Smiraglia, C.: Meteorology and surface energy fluxes in the 2005–2007
ablation seasons at the Miage debris-covered glacier, Mont Blanc Massif,
Italian Alps, J. Geophys. Res.-Atmos., 115, D09106,
https://doi.org/10.1029/2009JD013224, 2010. a
Calonne, N., Geindreau, C., Flin, F., Morin, S., Lesaffre, B., Rolland du Roscoat, S., and Charrier, P.: 3-D image-based numerical computations of snow permeability: links to specific surface area, density, and microstructural anisotropy, The Cryosphere, 6, 939–951, https://doi.org/10.5194/tc-6-939-2012, 2012. a, b
Carmagnola, C. M., Morin, S., Lafaysse, M., Domine, F., Lesaffre, B., Lejeune, Y., Picard, G., and Arnaud, L.: Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model, The Cryosphere, 8, 417–437, https://doi.org/10.5194/tc-8-417-2014, 2014. a
Cluzet, B., Revuelto, J., Lafaysse, M., Tuzet, F., Cosme, E., Picard, G.,
Arnaud, L., and Dumont, M.: Towards the assimilation of satellite reflectance
into semi-distributed ensemble snowpack simulations, Cold Reg. Sci.
Technol., 170, 102918, https://doi.org/10.1016/j.coldregions.2019.102918, 2020. a
Colbeck, S. C.: One-dimensional water flow through snow, Tech. rep., Cold
Regions Research and Engineering Laboratory, Hanover, NH, USA, 1971. a
Colombero, C., Comina, C., De Toma, E., Franco, D., and Godio, A.: Ice
Thickness Estimation from Geophysical Investigations on the Terminal Lobes of
Belvedere Glacier (NW Italian Alps), Remote Sensing, 11, 805,
https://doi.org/10.3390/rs11070805, 2019. 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
Davaze, L., Rabatel, A., Arnaud, Y., Sirguey, P., Six, D., Letreguilly, A., and Dumont, M.: Monitoring glacier albedo as a proxy to derive summer and annual surface mass balances from optical remote-sensing data, The Cryosphere, 12, 271–286, https://doi.org/10.5194/tc-12-271-2018, 2018. a
DeWalle, D. R. and Rango, A.: Principles of Snow Hydrology, Cambridge
University Press, https://doi.org/10.1017/CBO9780511535673, 2011. a, b, c
Diolaiuti, G., D'Agata, C., and Smiraglia, C.: Belvedere Glacier, Monte Rosa,
Italian Alps: Tongue Thickness and Volume Variations in the Second Half of
the 20th Century, Arct. Antarct. Alp. Res., 35, 255–263,
https://doi.org/10.1657/1523-0430(2003)035[0255:BGMRIA]2.0.CO;2, 2003. a
Domine, F., Taillandier, A.-S., and Simpson, W. R.: A parameterization of the
specific surface area of seasonal snow for field use and for models of
snowpack evolution, J. Geophys. Res.-Earth Surf., 112,
F02031, https://doi.org/10.1029/2006JF000512, 2007. a, b
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial distribution
of snow water equivalent in the world's mountains, Wiley Interdisciplinary
Reviews: Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016. a, b
Dramsch, J. S.: Chapter One – 70 years of machine learning in geoscience in
review, in: Machine Learning in Geosciences, edited by: Moseley, B. and
Krischer, L., vol. 61 of Advances in Geophysics, Elsevier, 1–55,
https://doi.org/10.1016/bs.agph.2020.08.002,
2020. a
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M. A., Beljaars, A., Schär,
C., and Elder, K.: An Improved Snow Scheme for the ECMWF Land Surface Model:
Description and Offline Validation, J. Hydrometeorol., 11, 899–916, https://doi.org/10.1175/2010JHM1249.1,
2010. a
Eiriksson, D., Whitson, M., Luce, C. H., Marshall, H. P., Bradford, J., Benner,
S. G., Black, T., Hetrick, H., and McNamara, P.: An evaluation of the
hydrologic relevance of lateral flow in snow at hillslope and catchment
scales, Hydrol. Process., 27, 640–654, https://doi.org/10.1002/hyp.9666, 2013. a
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014. a
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015. a
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, b, c
Filippa, G., Maggioni, M., Zanini, E., and Freppaz, M.: Analysis of continuous
snow temperature profiles from automatic weather stations in Aosta Valley (NW
Italy): Uncertainties and applications, Cold Reg. Sci. Technol.,
104–105, 54–62, 2014. 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
Follum, M. L., Downer, C. W., Niemann, J. D., Roylance, S. M., and Vuyovich,
C. M.: A radiation-derived temperature-index snow routine for the GSSHA
hydrologic model, J. Hydrol., 529, 723–736,
https://doi.org/10.1016/j.jhydrol.2015.08.044,
2015. a
Forster, R. R., Box, J. E., van den Broeke, M. R., Miege, C., Burgess,
E. W., van Angelen, J. H., Lenaerts, J. T. M., Koenig, L. S., Paden, J.,
Lewis, C., Prasad Gogineni, S., Leuschen, C., and McConnell, J. R.:
Extensive liquid meltwater storage in firn within the Greenland ice sheet,
Nat. Geosci., 7, 95–98, https://doi.org/10.1038/ngeo2043, 2014. 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, b, c
Fyffe, C. L., Reid, T. D., Brock, B. W., Kirkbride, M. P., Diolaiuti, G.,
Smiraglia, C., and Diotri, F.: A distributed energy-balance melt model of an
alpine debris-covered glacier, J. Glaciol., 60, 587–602,
https://doi.org/10.3189/2014JoG13J148, 2014. a
Georgakakos, K. P., Graham, N. E., Carpenter, M., and Yao, H.: Integrating
climate-hydrology forecasts and multi-objective reservoir management for
northern California, Eos, Transactions American Geophysical Union, 86,
122–127, https://doi.org/10.1029/2005EO120002, 2004. a
Ghanjkhanlo, H., Vafakhah, M., Zeinivand, H., and Fathzadeh, A.: Prediction of
snow water equivalent using artificial neural network and adaptive
neuro-fuzzy inference system with two sampling schemes in semi-arid region of
Iran, J. Mt. Sci., 17, 1712–1723, 2020. a
Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., and Seibert, J.: Assessing the degree of detail of temperature-based snow routines for runoff modelling in mountainous areas in central Europe, Hydrol. Earth Syst. Sci., 24, 4441–4461, https://doi.org/10.5194/hess-24-4441-2020, 2020. a
Grossi, F., Lahaye, E., Moulins, A., Borroni, A., Rosso, M., and Tepsich, P.:
Locating ship strike risk hotspots for fin whale (Balaenoptera physalus) and
sperm whale (Physeter macrocephalus) along main shipping lanes in the
North-Western Mediterranean Sea, Ocean Coast. Manag., 212, 105820,
https://doi.org/10.1016/j.ocecoaman.2021.105820,
2021. 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
Guyomarc'h, G., Bellot, H., Vionnet, V., Naaim-Bouvet, F., Déliot, Y., Fontaine, F., Puglièse, P., Nishimura, K., Durand, Y., and Naaim, M.: A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.), Earth Syst. Sci. Data, 11, 57–69, https://doi.org/10.5194/essd-11-57-2019, 2019. a
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in
Snowpack Simulations – Assessing the Impact of Model Structure, Parameter
Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model
Performance, Water Resour. Res., 55, 2779–2800,
https://doi.org/10.1029/2018WR023403,
2019. a
Hanzer, F., Carmagnola, C. M., Ebner, P. P., Koch, F., Monti, F., Bavay, M.,
Bernhardt, M., Lafaysse, M., Lehning, M., Strasser, U., François, H., and
Morin, S.: Simulation of snow management in Alpine ski resorts using three
different snow models, Cold Reg. Sci. Technol., 172, 102995,
https://doi.org/10.1016/j.coldregions.2020.102995,
2020. 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
Heilig, A., Mitterer, C., Schmid, L., Wever, N., Schweizer, J., Marshall,
H.-P., and Eisen, O.: Seasonal and diurnal cycles of liquid water in snow -
measurements and modeling, J. Geophys. Res.-Earth Surf.,
2015. a
Helbig, N., Moeser, D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J.-E., and Monnet, J.-M.: Snow processes in mountain forests: interception modeling for coarse-scale applications, Hydrol. Earth Syst. Sci., 24, 2545–2560, https://doi.org/10.5194/hess-24-2545-2020, 2020. a
Hirashima, H., Avanzi, F., and Wever, N.: Wet-Snow Metamorphism Drives the
Transition From Preferential to Matrix Flow in Snow, Geophys. Res.
Lett., 46, 14548–14557, https://doi.org/10.1029/2019GL084152,
2019. 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
Huss, M. and Fischer, M.: Sensitivity of Very Small Glaciers in the Swiss Alps
to Future Climate Change, Front. Earth Sci., 4, 34,
https://doi.org/10.3389/feart.2016.00034, 2016. a, b
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
IPCC: AR6 Climate Change 2021: The Physical Science Basis, 2021. a
Jennings, K. S., Kittel, T. G. F., and Molotch, N. P.: Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget, The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, 2018. a
Jouvet, G. and Huss, M.: Future retreat of Great Aletsch Glacier, J. Glaciol., 65, 869–872, https://doi.org/10.1017/jog.2019.52, 2019. a
Katsushima, T., Kumakura, T., and Takeuchi, Y.: A multiple snow layer model
including a parameterization of vertical water channel process in snowpack,
Cold Reg. Sci. Technol., 59, 143–151,
https://doi.org/10.1016/j.coldregions.2009.09.002, 2009. a
Katsushima, T., Yamaguchi, S., Kumakura, T., and Sato, A.: Experimental
analysis of preferential flow in dry snowpack, Cold Reg. Sci.
Technol., 85, 206–216, https://doi.org/10.1016/j.coldregions.2012.09.012, 2013. a
Kelleners, T. J., Chandler, D. G., McNamara, J. P., Gribb, M. M., and Seyfried,
M. S.: Modeling the Water and Energy Balance of Vegetated Areas with Snow
Accumulation, Vadose Zone J., 8, 1013–1030,
https://doi.org/10.2136/vzj2008.0183, 2009. a
Kirchner, H. O. K., Michot, G., Narita, H., and Suzuki, T.: Snow as a foam of
ice: plasticity, fracture and the brittle-to-ductile transition,
Philosophical Magazine A, 81, 2161–2181, 2001. 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
Krol, Q. and Löwe, H.: Relating optical and microwave grain metrics of snow: the relevance of grain shape, The Cryosphere, 10, 2847–2863, https://doi.org/10.5194/tc-10-2847-2016, 2016. a
Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., and Morin, S.: A multiphysical ensemble system of numerical snow modelling, The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017. a
Laramie, R. L. and Schaake, J. C. J.: Simulation of the continuous snowmelt
process, Tech. rep., MIT, Cambridge, 1972. a
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model
for the Swiss avalanche warning Part III: meteorological forcing, thin layer
formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184,
2002. a
Li, H., Beldring, S., Xu, C.-Y., Huss, M., Melvold, K., and Jain, S. K.:
Integrating a glacier retreat model into a hydrological model – Case
studies of three glacierised catchments in Norway and Himalayan region,
J. Hydrol., 527, 656–667,
https://doi.org/10.1016/j.jhydrol.2015.05.017,
2015. a
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. a
Lundquist, J. D., Dickerson-Lange, S. E., Lutz, J. A., and Cristea, N. C.:
Lower forest density enhances snow retention in regions with warmer winters:
A global framework developed from plot-scale observations and modeling, Water Resour. Res., 49, 6356–6370, https://doi.org/10.1002/wrcr.20504, 2013. a
Machguth, H., MacFerrin, M., van As, D., Box, J. E., Charalampidis, C.,
Colgan, W., Fausto, R. S., Meijer, H. A. J., Mosley-Thompson, E., and van
de Wal, R. S. W.: Greenland meltwater storage in firn limited by
near-surface ice formation, Nat. Clim. Change, 6, 390–393,
https://doi.org/10.1038/nclimate2899, 2016. a
Martinec, J.: Snowmelt-runoff model for stream flow forecasts, Nordic
Hydrology, 6, 145–154, 1975. a
Masiokas, M. H., Rabatel, A., Rivera, A., Ruiz, L., Pitte, P., Ceballos, J. L.,
Barcaza, G., Soruco, A., Bown, F., Berthier, E., Dussaillant, I., and
MacDonell, S.: A Review of the Current State and Recent Changes of the Andean
Cryosphere, Front. Earth Sci., 8, 99,
https://doi.org/10.3389/feart.2020.00099, 2020. a
Maurer, T., Avanzi, F., Oroza, C. A., Glaser, S. D., Conklin, M., and Bales,
R. C.: Optimizing spatial distribution of watershed-scale hydrologic models
using Gaussian Mixture Models, Environ. Model. Softw., 142,
105076, https://doi.org/10.1016/j.envsoft.2021.105076,
2021. a
Mazzoleni, M., Noh, S. J., Lee, H., Liu, Y., Seo, D.-J., Amaranto, A., Alfonso,
L., and Solomatine, D. P.: Real-time assimilation of streamflow observations
into a hydrological routing model: effects of model structures and updating
methods, Hydrol. Sci. J., 63, 386–407,
https://doi.org/10.1080/02626667.2018.1430898, 2018. a
Mazzotti, G., Webster, C., Essery, R., and Jonas, T.: Increasing the Physical
Representation of Forest-Snow Processes in Coarse-Resolution Models: Lessons
Learned From Upscaling Hyper-Resolution Simulations, Water Resour. Res., 57, e2020WR029064, https://doi.org/10.1029/2020WR029064,
2021. a
Mitterer, C., Techel, F., Fierz, C., and Schweizer, J.: An operational
supporting tool for assessing wet-snow avalanche danger, in: International
Snow Science Workshop Grenoble – Chamonix Mont-Blanc – 2013, 7–11 October 2013, Grenoble/France, https://arc.lib.montana.edu/snow-science/item/1860 (last access: 23 June 2022), 2013. a, b
Mizukami, N. and Perica, S.: Spatiotemporal Characteristics of Snowpack
Density in the Mountainous Regions of the Western United States, J.
Hydrometeorol., 9, 1416–1426,
https://doi.org/10.1175/2008JHM981.1, 2008. a
Mosaffa, H., Sadeghi, M., Mallakpour, I., Naghdyzadegan Jahromi, M., and
Pourghasemi, H. R.: Chapter 43 – Application of machine learning algorithms
in hydrology, in: Computers in Earth and Environmental Sciences, edited by:
Pourghasemi, H. R., Elsevier, 585–591,
https://doi.org/10.1016/B978-0-323-89861-4.00027-0,
2022. a
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. a
Nicholson, L. and Benn, D. I.: Calculating ice melt beneath a debris layer
using meteorological data, J. Glaciol., 52, 463–470,
https://doi.org/10.3189/172756506781828584, 2006. a
Niwano, M., Aoki, T., Kuchiki, K., Hosaka, M., and Kodama, Y.: Snow
Metamorphism and Albedo Process (SMAP) model for climate studies: Model
validation using meteorological and snow impurity data measured at Sapporo,
Japan, J. Geophys. Res.-Earth Surf., 117, F03008,
https://doi.org/10.1029/2011JF002239, 2012. a
Ohara, N. and Kavvas, M. L.: Field observations and numerical model experiments
for the snowmelt process at a field site, Adv. Water Resour., 29,
194–211, https://doi.org/10.1016/j.advwatres.2005.03.016, 2006. 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, b, c
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and
Corripio, J.: An enhanced temperature-index glacier melt model including the
shortwave radiation balance: development and testing for Haut Glacier
d’Arolla, Switzerland, J. Glaciol., 51, 573–587,
https://doi.org/10.3189/172756505781829124, 2005. a, b, c, d, e, f, g, h, i
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, b
Pielmeier, C., Techel, F., Marty, C., and Stucki, T.: Wet snow avalanche
activity in the Swiss Alps–trend analysis for mid-winter season, in:
Proceedings of the International Snow Science Workshop, Grenoble and
Chamonix, 1240–1246, 2013. a
Pinzer, B. R.: Dynamics of temperature gradient snow metamorphism, PhD
Dissertation, ETH Zurich, 2009. a
Pomeroy, J. and Brun, E.: Physical properties of snow, in: Snow ecology: an interdisciplinary examination of snow-covered ecosystems, edited by: Jones, H. G., Pomeroy, J. W., Walker, D. A., and Hoham, R. W., Cambridge University Press, 45–126, ISBN 9780521584838, 2001. a
Rabatel, A., Sanchez, O., Vincent, C., and Six, D.: Estimation of Glacier
Thickness From Surface Mass Balance and Ice Flow Velocities: A Case Study on
Argentière Glacier, France, Front. Earth Sci., 6, 112,
https://doi.org/10.3389/feart.2018.00112, 2018. a
Rango, A. and Martinec, J.: Revisiting the degree-day method for snowmelt
computations, J. Am. Water Resour. As., 31,
657–669, 1995. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu,
R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow:
The NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am.
Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1, 2012. a
Razavi, S., Sheikholeslami, R., Gupta, H. V., and Haghnegahdar, A.: VARS-TOOL:
A toolbox for comprehensive, efficient, and robust sensitivity and
uncertainty analysis, Environ. Model. Softw., 112, 95–107,
https://doi.org/10.1016/j.envsoft.2018.10.005,
2019. a
Revuelto, J., Billecocq, P., Tuzet, F., Cluzet, B., Lamare, M., Larue, F., and
Dumont, M.: Random forests as a tool to understand the snow depth
distribution and its evolution in mountain areas, Hydrol. Process., 34,
5384–5401, https://doi.org/10.1002/hyp.13951,
2020. a
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: A Distributed Hydrological
Model with Coupled Water and Energy Budgets, J. Hydrometeorol., 7,
371–388, https://doi.org/10.1175/JHM497.1,
2006. a
Rössler, O., Froidevaux, P., Börst, U., Rickli, R., Martius, O., and Weingartner, R.: Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps – a matter of model limitations or unpredictable nature?, Hydrol. Earth Syst. Sci., 18, 2265–2285, https://doi.org/10.5194/hess-18-2265-2014, 2014. a
Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I.,
Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder,
K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson, D.,
Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V., Kuragina,
A., Lettenmaier, D., Li, W.-P., Luce, C., Martin, E., Nasonova, O., Pumpanen,
J., Pyles, R. D., Samuelsson, P., Sandells, M., Schädler, G., Shmakin, A.,
Smirnova, T. G., Stähli, M., Stöckli, R., Strasser, U., Su, H., Suzuki, K.,
Takata, K., Tanaka, K., Thompson, E., Vesala, T., Viterbo, P., Wiltshire, A.,
Xia, K., Xue, Y., and Yamazaki, T.: Evaluation of forest snow processes
models (SnowMIP2), J. Geophys. Res.-Atmos., 114, D06111,
https://doi.org/10.1029/2008JD011063, 2009. a, b, c
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
Savenije, H. H. G.: HESS Opinions “The art of hydrology”, Hydrol. Earth Syst. Sci., 13, 157–161, https://doi.org/10.5194/hess-13-157-2009, 2009. a
Schaefli, B. and Huss, M.: Integrating point glacier mass balance observations into hydrologic model identification, Hydrol. Earth Syst. Sci., 15, 1227–1241, https://doi.org/10.5194/hess-15-1227-2011, 2011. a, b
Schaefli, B., Hingray, B., Niggli, M., and Musy, A.: A conceptual glacio-hydrological model for high mountainous catchments, Hydrol. Earth Syst. Sci., 9, 95–109, https://doi.org/10.5194/hess-9-95-2005, 2005. a
Schaefli, B., Hingray, B., and Musy, A.: Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties, Hydrol. Earth Syst. Sci., 11, 1191–1205, https://doi.org/10.5194/hess-11-1191-2007, 2007. a
Schaefli, B., Nicótina, L., Imfeld, C., Da Ronco, P., Bertuzzo, E., and Rinaldo, A.: SEHR-ECHO v1.0: a Spatially Explicit Hydrologic Response model for ecohydrologic applications, Geosci. Model Dev., 7, 2733–2746, https://doi.org/10.5194/gmd-7-2733-2014, 2014. a
Seibert, J., Vis, M. J. P., Kohn, I., Weiler, M., and Stahl, K.: Technical note: Representing glacier geometry changes in a semi-distributed hydrological model, Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018, 2018. a
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. a
Shen, C., Chen, X., and Laloy, E.: Editorial: Broadening the Use of Machine
Learning in Hydrology, Front. Water, 3, 38,
https://doi.org/10.3389/frwa.2021.681023,
2021. a
Silvestro, F., Gabellani, S., Delogu, F., Rudari, R., and Boni, G.: Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model, Hydrol. Earth Syst. Sci., 17, 39–62, https://doi.org/10.5194/hess-17-39-2013, 2013. a, b
Skiles, S. M., Mallia, D. V., Hallar, A. G., Lin, J. C., Lambert, A., Petersen,
R., and Clark, S.: Implications of a shrinking Great Salt Lake for dust on
snow deposition in the Wasatch Mountains, UT, as informed by a source to
sink case study from the 13–14 April 2017 dust event,
Environ. Res. Lett., 13, 124031, https://doi.org/10.1088/1748-9326/aaefd8, 2018. 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., 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
Tarboton, D. G. and Luce, C. H. (Eds.): Utah Energy Balance snow accumulation and
melt model (UEB), Computer model technical description and users guide,
Tech. rep., Utah Water Research Laboratory Utah State University and USDA
Forest Service, 1996. a
Techel, F. and Pielmeier, C.: Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects, The Cryosphere, 5, 405–418, https://doi.org/10.5194/tc-5-405-2011, 2011. a, b, c
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, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021. a
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
Wang, L., Zhou, J., Qi, J., Sun, L., Yang, K., Tian, L., Lin, Y., Liu, W.,
Shrestha, M., Xue, Y., Koike, T., Ma, Y., Li, X., Chen, Y., Chen, D., Piao,
S., and Lu, H.: Development of a land surface model with coupled snow and
frozen soil physics, Water Resour. Res., 53, 5085–5103,
https://doi.org/10.1002/2017WR020451, 2017. a
Webb, R. W., Jennings, K. S., Fend, M., and Molotch, N. P.: Combining
Ground-Penetrating Radar With Terrestrial LiDAR Scanning to Estimate the
Spatial Distribution of Liquid Water Content in Seasonal Snowpacks, Water Resour. Res., 54, 10339–10349, https://doi.org/10.1029/2018WR022680,
2018. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a, b, c, d
Wever, N., Vera Valero, C., and Fierz, C.: Assessing wet snow avalanche
activity using detailed physics based snowpack simulations, Geophys.
Res. Lett., 43, 5732–5740, https://doi.org/10.1002/2016GL068428, 2016. a, b
Würzer, S., Jonas, T., Wever, N., and Lehning, M.: Influence of initial
snowpack properties on runoff formation during rain-on-snow events, J. Hydrometeorol., 17, 1801–1815, https://doi.org/10.1175/JHM-D-15-0181.1, 2016. a
Würzer, S., Wever, N., Juras, R., Lehning, M., and Jonas, T.: Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow, Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, 2017. a, b
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
Zaramella, M., Borga, M., Zoccatelli, D., and Carturan, L.: TOPMELT 1.0: a topography-based distribution function approach to snowmelt simulation for hydrological modelling at basin scale, Geosci. Model Dev., 12, 5251–5265, https://doi.org/10.5194/gmd-12-5251-2019, 2019. a
Zheng, Z., Kirchner, P. B., and Bales, R. C.: Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data, The Cryosphere, 10, 257–269, https://doi.org/10.5194/tc-10-257-2016, 2016. a
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.
Knowing in real time how much snow and glacier ice has accumulated across the landscape has...
