Articles | Volume 15, issue 1
https://doi.org/10.5194/tc-15-369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies
Marco Bongio
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Politecnico di
Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
Finnish Meteorological Institute, Erik Palménin aukio 1, P.O. Box 503, 00101 Helsinki, Finland
Cemal Melih Tanis
Finnish Meteorological Institute, Erik Palménin aukio 1, P.O. Box 503, 00101 Helsinki, Finland
Carlo De Michele
Department of Civil and Environmental Engineering, Politecnico di
Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
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Ali Hosingholizade, Yousef Erfanifard, Seyed Kazem Alavipanah, Virginia García Millan, Saied Pirasteh, and Ali Nadir Arslan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 611–617, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-611-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-611-2025, 2025
Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-225, https://doi.org/10.5194/essd-2025-225, 2025
Preprint under review for ESSD
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This article describes a dataset of annual snow depth maximum across Europe, from 1961 to 2015, based on a regional reanalysis. It evaluates the performance of the dataset, against in-situ snow depth observations. This dataset is found to perform well in most environments, with challenges at high elevation and some coastal areas. Assessing the quality of this dataset is necessary in order to use it as a baseline to infer future changes of extreme snow loads under climate change.
Ali Nadir Arslan and Cemal Melih Tanis
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-6-2025, 53–59, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-53-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-53-2025, 2025
Jung Jun Lin and Ali Nadir Arslan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-6-2025, 207–212, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-207-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-207-2025, 2025
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
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Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
F. Ioli, E. Bruno, D. Calzolari, M. Galbiati, A. Mannocchi, P. Manzoni, M. Martini, A. Bianchi, A. Cina, C. De Michele, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, 2023
Maiju Linkosalmi, Juha-Pekka Tuovinen, Olli Nevalainen, Mikko Peltoniemi, Cemal M. Taniş, Ali N. Arslan, Juuso Rainne, Annalea Lohila, Tuomas Laurila, and Mika Aurela
Biogeosciences, 19, 4747–4765, https://doi.org/10.5194/bg-19-4747-2022, https://doi.org/10.5194/bg-19-4747-2022, 2022
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Vegetation greenness was monitored with digital cameras in three northern peatlands during five growing seasons. The greenness index derived from the images was highest at the most nutrient-rich site. Greenness indicated the main phases of phenology and correlated with CO2 uptake, though this was mainly related to the common seasonal cycle. The cameras and Sentinel-2 satellite showed consistent results, but more frequent satellite data are needed for reliable detection of phenological phases.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
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Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, https://doi.org/10.5194/tc-16-1031-2022, 2022
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Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modelling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between April and September since higher temperatures protect snow from erosion. We also compared modelled and observed firn density, obtaining a satisfying agreement.
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
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Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357, https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
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Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modeling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between May and September, since higher temperatures protect snow from erosion. We also compared modeled and observed firn density obtaining a satisfying agreement.
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Short summary
The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
The capability of time-lapse photography to retrieve snow depth time series was tested. We...