Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1483-2023
© 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/nhess-23-1483-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2023
Research article |
| 21 Apr 2023
| 21 Apr 2023
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Mateo Moreno
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Alice Crespi
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Peter James Zellner
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Stefano Luigi Gariano
CNR IRPI, Perugia, Italy
Maria Teresa Brunetti
CNR IRPI, Perugia, Italy
Massimo Melillo
CNR IRPI, Perugia, Italy
Silvia Peruccacci
CNR IRPI, Perugia, Italy
Francesco Marra
Department of Geosciences, University of Padova, Padua, Italy
Institute of Atmospheric Sciences and Climate, National Research Council (CNR-ISAC), Bologna, Italy
Robin Kohrs
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Jason Goetz
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Volkmar Mair
Office for Geology and Building Materials Testing, Autonomous Province of Bolzano – South Tyrol, Cardano, Italy
Massimiliano Pittore
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Related authors
No articles found.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
Hydrol. Earth Syst. Sci., 29, 4929–4949, https://doi.org/10.5194/hess-29-4929-2025, https://doi.org/10.5194/hess-29-4929-2025, 2025
Short summary
Short summary
This study compared global satellite and reanalysis precipitation datasets to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.
Alice Crespi, Katharina Enigl, Sebastian Lehner, Klaus Haslinger, and Massimiliano Pittore
EGUsphere, https://doi.org/10.5194/egusphere-2025-3686, https://doi.org/10.5194/egusphere-2025-3686, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Extreme precipitation poses a risk for the Alpine region as it can trigger floods, debris flows, landslides and rockfalls, which can have severe consequences for people and their activities. This study investigates how past precipitation extremes in a transboundary Alpine area can be detected, especially those corresponding with hazard occurrences. The findings can inform risk managers and support the methodological choices for the development of effective early warming systems.
Gabriella Tocchi, Massimiliano Pittore, and Maria Polese
Nat. Hazards Earth Syst. Sci., 25, 3665–3692, https://doi.org/10.5194/nhess-25-3665-2025, https://doi.org/10.5194/nhess-25-3665-2025, 2025
Short summary
Short summary
This study identifies different types of urban areas in Italy based on population, location, and economic conditions to understand their vulnerability to risks. Using public data and clustering methods, it defines 18 urban archetypes. These archetypes provide a structured understanding of urban vulnerability, helping policymakers assess disaster risk, allocate adaptation funding, and design targeted resilience strategies for urban settlements at regional and national scales.
Chiara Crippa, Stefan Steger, Giovanni Cuozzo, Francesca Bearzot, Volkmar Mair, and Claudia Notarnicola
The Cryosphere, 19, 3493–3515, https://doi.org/10.5194/tc-19-3493-2025, https://doi.org/10.5194/tc-19-3493-2025, 2025
Short summary
Short summary
Our study, focused on South Tyrol (NE Italy), develops an updated and comprehensive activity classification system for all rock glaciers in the current regional inventory. Using multisource products, we integrate climatic, morphological, and differential interferometric synthetic aperture radar (DInSAR) data in replicable routines and multivariate statistical methods, producing a comprehensive classification based on the updated Rock Glacier Inventories and Kinematic (RGIK) 2023 guidelines. Results leave only 3.5 % of the features non-classified, as opposed to 13–18.5 % in previous studies.
Francesco Marra, Nadav Peleg, Elena Cristiano, Efthymios I. Nikolopoulos, Federica Remondi, and Paolo Tarolli
Nat. Hazards Earth Syst. Sci., 25, 2565–2570, https://doi.org/10.5194/nhess-25-2565-2025, https://doi.org/10.5194/nhess-25-2565-2025, 2025
Short summary
Short summary
Climate change is escalating the risks related to hydro-meteorological extremes. This preface introduces a special issue originating from a European Geosciences Union (EGU) session. It highlights the challenges posed by these extremes, ranging from hazard assessment to mitigation strategies, and covers both water excess events like floods, landslides, and coastal hazards and water deficit events such as droughts and fire weather. The collection aims to advance understanding, improve resilience, and inform policy-making.
Francesco Marra, Eleonora Dallan, Marco Borga, Roberto Greco, and Thom Bogaard
EGUsphere, https://doi.org/10.5194/egusphere-2025-3378, https://doi.org/10.5194/egusphere-2025-3378, 2025
Short summary
Short summary
We highlight an important conceptual difference between the duration used in intensity-duration thresholds and the duration used in the intensity-duration-frequency curves that has been overlooked by the landslide literature so far.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Giorgia Fosser, Eleonora Dallan, Marco Borga, and Francesco Marra
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-111, https://doi.org/10.5194/wes-2025-111, 2025
Revised manuscript accepted for WES
Short summary
Short summary
We examined the power spectra of wind speed in three convection-permitting models in central Europe and found these models have a better representation of wind variability characteristics than standard wind datasets like the New European Wind Atlas, due to different simulation approaches, providing more reliable extreme wind predictions.
Marc Lemus-Canovas, Alice Crespi, Elena Maines, Stefano Terzi, and Massimiliano Pittore
EGUsphere, https://doi.org/10.5194/egusphere-2025-1347, https://doi.org/10.5194/egusphere-2025-1347, 2025
Short summary
Short summary
We studied a severe compound drought and heatwave event in the Adige River basin in May 2022 and found that similar events are now hotter and drier due to current warming. These changes worsen water stress and river drying. We show that timing matters: events in June are now more critical than in April, as the snowmelt contribution to streamflow in June has become much lower than in the past. However, many climate models still fail to capture these changes.
Giulio Bongiovanni, Michael Matiu, Alice Crespi, Anna Napoli, Bruno Majone, and Dino Zardi
Earth Syst. Sci. Data, 17, 1367–1391, https://doi.org/10.5194/essd-17-1367-2025, https://doi.org/10.5194/essd-17-1367-2025, 2025
Short summary
Short summary
EEAR-Clim is a new and unprecedented observational dataset gathering in situ daily measurements of air temperature and precipitation from a network of about 9000 weather stations covering the European Alps. Data collected, including time series from recordings up to 2020 and time series significantly enhancing data coverage at high elevations, were tested for quality and homogeneity. The dataset aims to serve as a powerful tool for better understanding climate change over the European Alpine region.
Jess Delves, Kathrin Renner, Piero Campalani, Jesica Piñón, Stefan Schneiderbauer, Stefan Steger, Mateo Moreno, Maria Belen Benito Oterino, Eduardo Perez, and Massimiliano Pittore
EGUsphere, https://doi.org/10.5194/egusphere-2024-3445, https://doi.org/10.5194/egusphere-2024-3445, 2025
Short summary
Short summary
This scientific paper presents a multi-hazard risk assessment for Burundi, focusing on flooding, torrential rains, landslides, earthquakes, and strong winds. The study identifies key risk hotspots with estimated economic losses of 92 million USD (2.5 % of GDP). Climate change projections indicate increased precipitation. The paper highlights data limitations and stresses the need for improved hazard models and the consideration of compounding risks in future assessments.
Kevin Kenfack, Francesco Marra, Zéphirin Yepdo Djomou, Lucie Angennes Djiotang Tchotchou, Alain Tchio Tamoffo, and Derbetini Appolinaire Vondou
Weather Clim. Dynam., 5, 1457–1472, https://doi.org/10.5194/wcd-5-1457-2024, https://doi.org/10.5194/wcd-5-1457-2024, 2024
Short summary
Short summary
The results of this study show that moisture advection induced by horizontal wind anomalies and vertical moisture advection induced by vertical velocity anomalies were crucial mechanisms behind the anomalous October 2019 exceptional rainfall increase over western central Africa. The information we derive can be used to support risk assessment and management in the region and to improve our resilience to ongoing climate change.
Talia Rosin, Francesco Marra, and Efrat Morin
Hydrol. Earth Syst. Sci., 28, 3549–3566, https://doi.org/10.5194/hess-28-3549-2024, https://doi.org/10.5194/hess-28-3549-2024, 2024
Short summary
Short summary
Knowledge of extreme precipitation probability at various spatial–temporal scales is crucial. We estimate extreme precipitation return levels at multiple scales (10 min–24 h, 0.25–500 km2) in the eastern Mediterranean using radar data. We show our estimates are comparable to those derived from averaged daily rain gauges. We then explore multi-scale extreme precipitation across coastal, mountainous, and desert regions.
Peter James Zellner, Michele Claus, Tyna Dolezalova, Rufai Omowunmi Balogun, Jonas Eberle, Henryk Hodam, Robert Eckardt, Stephan Meißl, Alexander Jacob, and Anca Anghelea
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 157–162, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-157-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-157-2024, 2024
Aldo Bertone, Nina Jones, Volkmar Mair, Riccardo Scotti, Tazio Strozzi, and Francesco Brardinoni
The Cryosphere, 18, 2335–2356, https://doi.org/10.5194/tc-18-2335-2024, https://doi.org/10.5194/tc-18-2335-2024, 2024
Short summary
Short summary
Traditional inventories display high uncertainty in discriminating between intact (permafrost-bearing) and relict (devoid) rock glaciers (RGs). Integration of InSAR-based kinematics in South Tyrol affords uncertainty reduction and depicts a broad elevation belt of relict–intact coexistence. RG velocity and moving area (MA) cover increase linearly with elevation up to an inflection at 2600–2800 m a.s.l., which we regard as a signature of sporadic-to-discontinuous permafrost transition.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
Short summary
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Luca Carturan, Fabrizio De Blasi, Roberto Dinale, Gianfranco Dragà, Paolo Gabrielli, Volkmar Mair, Roberto Seppi, David Tonidandel, Thomas Zanoner, Tiziana Lazzarina Zendrini, and Giancarlo Dalla Fontana
Earth Syst. Sci. Data, 15, 4661–4688, https://doi.org/10.5194/essd-15-4661-2023, https://doi.org/10.5194/essd-15-4661-2023, 2023
Short summary
Short summary
This paper presents a new dataset of air, englacial, soil surface and rock wall temperatures collected between 2010 and 2016 on Mt Ortles, which is the highest summit of South Tyrol, Italy. Details are provided on instrument type and characteristics, field methods, and data quality control and assessment. The obtained data series are available through an open data repository. This is a rare dataset from a summit area lacking observations on permafrost and glaciers and their climatic response.
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, and Maria Teresa Brunetti
Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, https://doi.org/10.5194/essd-15-2863-2023, 2023
Short summary
Short summary
ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) is the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. ITALICA currently lists 6312 landslides that occurred between January 1996 and December 2021. The information was collected using strict objective and homogeneous criteria. The high spatial and temporal accuracy makes the catalogue suitable for reliably defining the rainfall conditions capable of triggering future landslides.
Juan Camilo Gómez Zapata, Massimiliano Pittore, Nils Brinckmann, Juan Lizarazo-Marriaga, Sergio Medina, Nicola Tarque, and Fabrice Cotton
Nat. Hazards Earth Syst. Sci., 23, 2203–2228, https://doi.org/10.5194/nhess-23-2203-2023, https://doi.org/10.5194/nhess-23-2203-2023, 2023
Short summary
Short summary
To investigate cumulative damage on extended building portfolios, we propose an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are being constantly developed by experts from various research fields to be used within a multi-risk context. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios.
Chiara Montemagni, Stefano Zanchetta, Martina Rocca, Igor M. Villa, Corrado Morelli, Volkmar Mair, and Andrea Zanchi
Solid Earth, 14, 551–570, https://doi.org/10.5194/se-14-551-2023, https://doi.org/10.5194/se-14-551-2023, 2023
Short summary
Short summary
The Vinschgau Shear Zone (VSZ) is one of the largest and most significant shear zones developed within the Late Cretaceous thrust stack in the Austroalpine domain of the eastern Alps. 40Ar / 39Ar geochronology constrains the activity of the VSZ between 97 and 80 Ma. The decreasing vorticity towards the core of the shear zone, coupled with the younging of mylonites, points to a shear thinning behavior. The deepest units of the Eo-Alpine orogenic wedge were exhumed along the VSZ.
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
Short summary
Short summary
Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the impacts of these events are expected to increase. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
Short summary
Short summary
Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
Short summary
Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Aldo Bertone, Chloé Barboux, Xavier Bodin, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Hanne H. Christiansen, Margaret M. Darrow, Reynald Delaloye, Bernd Etzelmüller, Ole Humlum, Christophe Lambiel, Karianne S. Lilleøren, Volkmar Mair, Gabriel Pellegrinon, Line Rouyet, Lucas Ruiz, and Tazio Strozzi
The Cryosphere, 16, 2769–2792, https://doi.org/10.5194/tc-16-2769-2022, https://doi.org/10.5194/tc-16-2769-2022, 2022
Short summary
Short summary
We present the guidelines developed by the IPA Action Group and within the ESA Permafrost CCI project to include InSAR-based kinematic information in rock glacier inventories. Nine operators applied these guidelines to 11 regions worldwide; more than 3600 rock glaciers are classified according to their kinematics. We test and demonstrate the feasibility of applying common rules to produce homogeneous kinematic inventories at global scale, useful for hydrological and climate change purposes.
Assaf Hochman, Francesco Marra, Gabriele Messori, Joaquim G. Pinto, Shira Raveh-Rubin, Yizhak Yosef, and Georgios Zittis
Earth Syst. Dynam., 13, 749–777, https://doi.org/10.5194/esd-13-749-2022, https://doi.org/10.5194/esd-13-749-2022, 2022
Short summary
Short summary
Gaining a complete understanding of extreme weather, from its physical drivers to its impacts on society, is important in supporting future risk reduction and adaptation measures. Here, we provide a review of the available scientific literature, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean region.
Francesco Marra, Moshe Armon, and Efrat Morin
Hydrol. Earth Syst. Sci., 26, 1439–1458, https://doi.org/10.5194/hess-26-1439-2022, https://doi.org/10.5194/hess-26-1439-2022, 2022
Short summary
Short summary
We present a new method for quantifying the probability of occurrence of extreme rainfall using radar data, and we use it to examine coastal and orographic effects on extremes. We identify three regimes, directly related to precipitation physical processes, which respond differently to these forcings. The methods and results are of interest for researchers and practitioners using radar for the analysis of extremes, risk managers, water resources managers, and climate change impact studies.
Yoav Ben Dor, Francesco Marra, Moshe Armon, Yehouda Enzel, Achim Brauer, Markus Julius Schwab, and Efrat Morin
Clim. Past, 17, 2653–2677, https://doi.org/10.5194/cp-17-2653-2021, https://doi.org/10.5194/cp-17-2653-2021, 2021
Short summary
Short summary
Laminated sediments from the deepest part of the Dead Sea unravel the hydrological response of the eastern Mediterranean to past climate changes. This study demonstrates the importance of geological archives in complementing modern hydrological measurements that do not fully capture natural hydroclimatic variability, which is crucial to configure for understanding the impact of climate change on the hydrological cycle in subtropical regions.
Juan Camilo Gomez-Zapata, Nils Brinckmann, Sven Harig, Raquel Zafrir, Massimiliano Pittore, Fabrice Cotton, and Andrey Babeyko
Nat. Hazards Earth Syst. Sci., 21, 3599–3628, https://doi.org/10.5194/nhess-21-3599-2021, https://doi.org/10.5194/nhess-21-3599-2021, 2021
Short summary
Short summary
We present variable-resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models and physical vulnerability assessment. Their geo-cell sizes are inversely proportional to underlying distributions that account for the combination between hazard intensities and exposure proxies. We explore their efficiency and associated uncertainties in risk–loss estimations and mapping from decoupled scenario-based earthquakes and tsunamis in Lima, Peru.
Jason Goetz, Robin Kohrs, Eric Parra Hormazábal, Manuel Bustos Morales, María Belén Araneda Riquelme, Cristián Henríquez, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 21, 2543–2562, https://doi.org/10.5194/nhess-21-2543-2021, https://doi.org/10.5194/nhess-21-2543-2021, 2021
Short summary
Short summary
Debris flows are fast-moving landslides that can cause incredible destruction to lives and property. Using the Andes of Santiago as an example, we developed tools to finetune and validate models predicting likely runout paths over large regions. We anticipate that our automated approach that links the open-source R software with SAGA-GIS will make debris-flow runout simulation more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.
Alice Crespi, Michael Matiu, Giacomo Bertoldi, Marcello Petitta, and Marc Zebisch
Earth Syst. Sci. Data, 13, 2801–2818, https://doi.org/10.5194/essd-13-2801-2021, https://doi.org/10.5194/essd-13-2801-2021, 2021
Short summary
Short summary
A 250 m gridded dataset of 1980–2018 daily mean temperature and precipitation records for Trentino–South Tyrol (north-eastern Italian Alps) was derived from a quality-controlled and homogenized archive of station observations. The errors associated with the final interpolated fields were assessed and thoroughly discussed. The product will be regularly updated and is meant to support regional climate studies and local monitoring and applications in integration with other fine-resolution data.
Maria Teresa Brunetti, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci
Hydrol. Earth Syst. Sci., 25, 3267–3279, https://doi.org/10.5194/hess-25-3267-2021, https://doi.org/10.5194/hess-25-3267-2021, 2021
Short summary
Short summary
Satellite and rain gauge data are tested to predict landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For this purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Yair Rinat, Francesco Marra, Moshe Armon, Asher Metzger, Yoav Levi, Pavel Khain, Elyakom Vadislavsky, Marcelo Rosensaft, and Efrat Morin
Nat. Hazards Earth Syst. Sci., 21, 917–939, https://doi.org/10.5194/nhess-21-917-2021, https://doi.org/10.5194/nhess-21-917-2021, 2021
Short summary
Short summary
Flash floods are among the most devastating and lethal natural hazards worldwide. The study of such events is important as flash floods are poorly understood and documented processes, especially in deserts. A small portion of the studied basin (1 %–20 %) experienced extreme rainfall intensities resulting in local flash floods of high magnitudes. Flash floods started and reached their peak within tens of minutes. Forecasts poorly predicted the flash floods mostly due to location inaccuracy.
Cited articles
Adler, S., Chimani, B., Drechsel, S., Haslinger, K., Hiebl, J., Meyer, V.,
Resch, G., Rudolph, J., Vergeiner, J., Zingerle, C., Marigo, G., Fischer,
A., and Seiser, B.: Das Klima: Von Tirol-Südtirol-Belluno, ZAMG,
Autonome Provinz Bozen, ARPAV, http://www.3pclim.eu/images/Das_Klima_von_Tirol-Suedtirol-Belluno.pdf (last access: 5 October 2022), 2015.
Alvioli, M., Melillo, M., Guzzetti, F., Rossi, M., Palazzi, E., von
Hardenberg, J., Brunetti, M. T., and Peruccacci, S.: Implications of climate
change on landslide hazard in Central Italy, Sci. Total Environ., 630, 1528–1543, https://doi.org/10.1016/j.scitotenv.2018.02.315, 2018.
Basher, R.: Global early warning systems for natural hazards: systematic and
people-centred, Philos. T. Roy. Soc. A, 364, 2167–2182, https://doi.org/10.1098/rsta.2006.1819, 2006.
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018.
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R.,
Stevens, M. H. H., and White, J.-S. S.: Generalized linear mixed models: a
practical guide for ecology and evolution, Trends Ecol. Evol.,
24, 127–135, https://doi.org/10.1016/j.tree.2008.10.008, 2009.
Brenning, A.: Spatial cross-validation and bootstrap for the assessment of
prediction rules in remote sensing: The R package sperrorest, in: 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012, IEEE International, 5372–5375, https://doi.org/10.1109/IGARSS.2012.6352393, 2012.
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010.
Brunetti, M. T., Peruccacci, S., Antronico, L., Bartolini, D., Deganutti, A.
M., Gariano, S. L., Iovine, G., Luciani, S., Luino, F., Melillo, M.,
Palladino, M. R., Parise, M., Rossi, M., Turconi, L., Vennari, C., Vessia,
G., Viero, A., and Guzzetti, F.: Catalogue of Rainfall Events with Shallow
Landslides and New Rainfall Thresholds in Italy, in: Engineering Geology for
Society and Territory, Cham, 2, 1575–1579,
https://doi.org/10.1007/978-3-319-09057-3_280, 2015.
Caine, N.: The Rainfall Intensity - Duration Control of Shallow Landslides
and Debris Flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.1080/04353676.1980.11879996, 1980.
Chleborad, A. F.: Preliminary method for anticipating the occurrence of
precipitation-induced landslides in Seattle, Washington, US Department of
the Interior, US Geological Survey, Open-File Report 2000-469, https://doi.org/10.3133/ofr00469, 2000.
Chleborad, A. F., Baum, R. L., Godt, J. W., and Powers, P. S.: A prototype
system for forecasting landslides in the Seattle, Washington, area, Reviews
in Engineering Geology, 20, 103–120, https://doi.org/10.1130/2008.4020(06),2008.
Crespi, A., Matiu, M., Bertoldi, G., Petitta, M., and Zebisch, M.: High-resolution daily series (1980 - 2018) and monthly climatologies (1981 - 2010) of mean temperature and precipitation for Trentino - South Tyrol (north-eastern Italian Alps), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.924502, 2020.
Crespi, A., Matiu, M., Bertoldi, G., Petitta, M., and Zebisch, M.: A high-resolution gridded dataset of daily temperature and precipitation records (1980–2018) for Trentino-South Tyrol (north-eastern Italian Alps), Earth Syst. Sci. Data, 13, 2801–2818, https://doi.org/10.5194/essd-13-2801-2021, 2021.
Crozier, M. J.: Landslides: causes, consequences & environment,
Routledge, London; New York, ISBN 0415039673, 9780415039673, 1989.
Crozier, M. J.: Prediction of rainfall-triggered landslides: a test of the
Antecedent Water Status Model, Earth Surf. Proc. Land., 24, 825–833, https://doi.org/10.1002/(SICI)1096-9837(199908)24:9<825::AID-ESP14>3.0.CO;2-M, 1999.
Destro, E., Marra, F., Nikolopoulos, E. I., Zoccatelli, D., Creutin, J. D.,
and Borga, M.: Spatial estimation of debris flows-triggering rainfall and
its dependence on rainfall return period, Geomorphology, 278, 269–279,
https://doi.org/10.1016/j.geomorph.2016.11.019, 2017.
Distefano, P., Peres, D. J., Scandura, P., and Cancelliere, A.: Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks, Nat. Hazards Earth Syst. Sci., 22, 1151–1157, https://doi.org/10.5194/nhess-22-1151-2022, 2022.
Frattini, P., Crosta, G., and Sosio, R.: Approaches for defining thresholds
and return periods for rainfall-triggered shallow landslides, Hydrol. Process., 23, 1444–1460, https://doi.org/10.1002/hyp.7269, 2009.
Frei, C. and Schär, C.: Detection Probability of Trends in Rare Events:
Theory and Application to Heavy Precipitation in the Alpine Region, J.
Climate, 14, 1568–1584, https://doi.org/10.1175/1520-0442(2001)014<1568:DPOTIR>2.0.CO;2, 2001.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate,
Earth-Sci. Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S.,
Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of
rainfall thresholds for shallow landslide forecasting in Sicily, southern
Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019, 2015.
Geokatalog: Geodatabase of the Autonomous Province of South Tyrol, Autonomous Province of South Tyrol, http://geokatalog.buergernetz.bz.it/geokatalog/#! (last
access: 2 October 2022), 2021.
Giannecchini, R., Galanti, Y., D'Amato Avanzi, G., and Barsanti, M.:
Probabilistic rainfall thresholds for triggering debris flows in a
human-modified landscape, Geomorphology, 257, 94–107,
https://doi.org/10.1016/j.geomorph.2015.12.012, 2016.
Glade, T., Crozier, M., and Smith, P.: Applying Probability Determination to
Refine Landslide-triggering Rainfall Thresholds Using an Empirical
“Antecedent Daily Rainfall Model”, Pure Appl. Geophys., 157, 1059–1079,
https://doi.org/10.1007/s000240050017, 2000.
Glade, T., Anderson, M., and Crozier, M. J. (Eds.): Landslide hazard and
risk, J. Wiley, Chichester, West Sussex, England; Hoboken, NJ, 802 pp., ISBN: 0471486639, 2005.
Gonzalez-Ollauri, A. and Mickovski, S. B.: Hydrological effect of vegetation
against rainfall-induced landslides, J. Hydrol., 549, 374–387, https://doi.org/10.1016/j.jhydrol.2017.04.014, 2017.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall
intensity–duration control of shallow landslides and debris flows: an
update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini,
I., Rossi, M., and Melillo, M.: Geographical landslide early warning
systems, Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., and Melillo,
M.: Chapter 15 – Rainfall and landslide initiation, in: Rainfall, edited by:
Morbidelli, R., Elsevier, 427–450, https://doi.org/10.1016/B978-0-12-822544-8.00012-3, 2022.
Haque, U., da Silva, P. F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A.,
Wilopo, W., Andersen, P., Lu, P., Lee, J., Yamamoto, T., Keellings, D., Wu,
J.-H., and Glass, G. E.: The human cost of global warming: Deadly landslides
and their triggers (1995–2014), Sci. Total Environ., 682, 673–684, https://doi.org/10.1016/j.scitotenv.2019.03.415, 2019.
Haslinger, K., Holawe, F., and Blöschl, G.: Spatial characteristics of
precipitation shortfalls in the Greater Alpine Region – a data-based
analysis from observations, Theor. Appl. Climatol., 136, 717–731,
https://doi.org/10.1007/s00704-018-2506-5, 2019.
Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X.: Applied logistic
regression, 3rd edn., Wiley, Hoboken, New Jersey, 500 pp., https://doi.org/10.1002/9781118548387, 2013.
Iadanza, C., Trigila, A., Starace, P., Dragoni, A., Biondo, T., and
Roccisano, M.: IdroGEO: A Collaborative Web Mapping Application Based on
REST API Services and Open Data on Landslides and Floods in Italy, ISPRS
Int. J. Geo-Inf., 10, 89, https://doi.org/10.3390/ijgi10020089, 2021 (data available at: https://idrogeo.isprambiente.it/app/page/open-data, last access: 22 October 2022).
Jakob, M. and Weatherly, H.: A hydroclimatic threshold for landslide
initiation on the North Shore Mountains of Vancouver, British Columbia,
Geomorphology, 54, 137–156, https://doi.org/10.1016/S0169-555X(02)00339-2, 2003.
Krøgli, I. K., Devoli, G., Colleuille, H., Boje, S., Sund, M., and Engen, I. K.: The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides, Nat. Hazards Earth Syst. Sci., 18, 1427–1450, https://doi.org/10.5194/nhess-18-1427-2018, 2018.
Leonarduzzi, E. and Molnar, P.: Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall, Nat. Hazards Earth Syst. Sci., 20, 2905–2919, https://doi.org/10.5194/nhess-20-2905-2020, 2020.
Lin, Q., Lima, P., Steger, S., Glade, T., Jiang, T., Zhang, J., Liu, T., and
Wang, Y.: National-scale data-driven rainfall induced landslide
susceptibility mapping for China by accounting for incomplete landslide
data, Geosci. Front., 12, 101248, https://doi.org/10.1016/j.gsf.2021.101248, 2021.
Lin, Q., Steger, S., Pittore, M., Zhang, J., Wang, L., Jiang, T., and Wang,
Y.: Evaluation of potential changes in landslide susceptibility and
landslide occurrence frequency in China under climate change, Sci. Total Environ., 850, 158049, https://doi.org/10.1016/j.scitotenv.2022.158049, 2022.
Loche, M., Alvioli, M., Marchesini, I., Bakka, H., and Lombardo, L.:
Landslide susceptibility maps of Italy: Lesson learnt from dealing with
multiple landslide types and the uneven spatial distribution of the national
inventory, Earth-Sci. Rev., 232, 104125,
https://doi.org/10.1016/j.earscirev.2022.104125, 2022.
Luna, L. V. and Korup, O.: Seasonal Landslide Activity Lags Annual
Precipitation Pattern in the Pacific Northwest, Geophys. Res. Lett., 49, e2022GL098506, https://doi.org/10.1029/2022GL098506, 2022.
Maraun, D., Knevels, R., Mishra, A. N., Truhetz, H., Bevacqua, E., Proske,
H., Zappa, G., Brenning, A., Petschko, H., Schaffer, A., Leopold, P., and
Puxley, B. L.: A severe landslide event in the Alpine foreland under
possible future climate and land-use changes, Communications Earth & Environment, 3, 1–11, https://doi.org/10.1038/s43247-022-00408-7, 2022.
Marra, F., Nikolopoulos, E. I., Creutin, J. D., and Borga, M.: Space–time
organization of debris flows-triggering rainfall and its effect on the
identification of the rainfall threshold relationship, J. Hydrol., 541, 246–255, https://doi.org/10.1016/j.jhydrol.2015.10.010, 2016.
Martinengo, M., Zugliani, D., and Rosatti, G.: Uncertainty analysis of a rainfall threshold estimate for stony debris flow based on the backward dynamical approach, Nat. Hazards Earth Syst. Sci., 21, 1769–1784, https://doi.org/10.5194/nhess-21-1769-2021, 2021.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., and Guzzetti,
F.: An algorithm for the objective reconstruction of rainfall events
responsible for landslides, Landslides, 12, 311–320,
https://doi.org/10.1007/s10346-014-0471-3, 2015.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A.,
and Guzzetti, F.: A tool for the automatic calculation of rainfall
thresholds for landslide occurrence, Environ. Modell. Softw., 105, 230–243, https://doi.org/10.1016/j.envsoft.2018.03.024, 2018.
Miglietta, M. M. and Davolio, S.: Dynamical forcings in heavy precipitation events over Italy: lessons from the HyMeX SOP1 campaign, Hydrol. Earth Syst. Sci., 26, 627–646, https://doi.org/10.5194/hess-26-627-2022, 2022.
Mirus, B. B., Becker, R. E., Baum, R. L., and Smith, J. B.: Integrating
real-time subsurface hydrologic monitoring with empirical rainfall
thresholds to improve landslide early warning, Landslides, 15, 1909–1919,
https://doi.org/10.1007/s10346-018-0995-z, 2018.
Molnar, P. and Burlando, P.: Variability in the scale properties of
high-resolution precipitation data in the Alpine climate of Switzerland,
Water Resour. Res., 44, W10404, https://doi.org/10.1029/2007WR006142, 2008.
Monsieurs E., Dewitte O., Depicker A., and Demoulin A.: Towards a
Transferable Antecedent Rainfall–Susceptibility Threshold Approach for
Landsliding, Water, 11, 2202, https://doi.org/10.3390/w11112202, 2019.
Nikolopoulos, E. I., Borga, M., Creutin, J. D., and Marra, F.: Estimation of
debris flow triggering rainfall: Influence of rain gauge density and
interpolation methods, Geomorphology, 243, 40–50,
https://doi.org/10.1016/j.geomorph.2015.04.028, 2015.
Nikolopoulos, E. I., Destro, E., Maggioni, V., Marra, F., and Borga, M.:
Satellite Rainfall Estimates for Debris Flow Prediction: An Evaluation Based
on Rainfall Accumulation–Duration Thresholds, J. Hydrometeorol., 18, 2207–2214, 2017.
Norris, J. E., Stokes, A., Mickovski, S. B., Cammeraat, E., van Beek, R., Nicoll, B. C., and Achim, A.: Slope Stability and Erosion Control: Ecotechnological Solutions, 1st edn. 2008, Springer, 296 pp., ISBN 978-1-4020-6675-7, 2010.
Osman, N. and Barakbah, S. S.: Parameters to predict slope stability – Soil
water and root profiles, Ecol. Eng., 28, 90–95,
https://doi.org/10.1016/j.ecoleng.2006.04.004, 2006.
Ozturk, U., Bozzolan, E., Holcombe, E. A., Shukla, R., Pianosi, F., and
Wagener, T.: How climate change and unplanned urban sprawl bring more
landslides, Nature, 608, 262–265,
https://doi.org/10.1038/d41586-022-02141-9, 2022.
Pedersen, E. J., Miller, D. L., Simpson, G. L., and Ross, N.: Hierarchical
generalized additive models in ecology: an introduction with mgcv, PeerJ, 7,
e6876, https://doi.org/10.7717/peerj.6876, 2019.
Peres, D. J. and Cancelliere, A.: Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach, Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, 2014.
Peres, D. J. and Cancelliere, A.: Comparing methods for determining
landslide early warning thresholds: potential use of non-triggering rainfall
for locations with scarce landslide data availability, Landslides, 18,
3135–3147, https://doi.org/10.1007/s10346-021-01704-7, 2021.
Peruccacci, S., Brunetti, M. T., Luciani, S., Vennari, C., and Guzzetti, F.:
Lithological and seasonal control on rainfall thresholds for the possible
initiation of landslides in central Italy, Geomorphology, 139–140, 79–90,
https://doi.org/10.1016/j.geomorph.2011.10.005, 2012.
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., and
Guzzetti, F.: Rainfall thresholds for possible landslide occurrence in
Italy, Geomorphology, 290, 39–57, https://doi.org/10.1016/j.geomorph.2017.03.031, 2017.
Petley, D.: Global patterns of loss of life from landslides, Geology, 40,
927–930, https://doi.org/10.1130/G33217.1, 2012.
Piacentini, D., Troiani, F., Soldati, M., Notarnicola, C., Savelli, D.,
Schneiderbauer, S., and Strada, C.: Statistical analysis for assessing
shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy),
Geomorphology, 151–152, 196–206,
https://doi.org/10.1016/j.geomorph.2012.02.003, 2012.
Piciullo, L., Gariano, S. L., Melillo, M., Brunetti, M. T., Peruccacci, S.,
Guzzetti, F., and Calvello, M.: Definition and performance of a
threshold-based regional early warning model for rainfall-induced
landslides, Landslides, 14, 995–1008,
https://doi.org/10.1007/s10346-016-0750-2, 2017.
Piciullo, L., Calvello, M., and Cepeda, J. M.: Territorial early warning
systems for rainfall-induced landslides, Earth-Sci. Rev., 179, 228–247, https://doi.org/10.1016/j.earscirev.2018.02.013, 2018.
Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., and
Moramarco, T.: Assessment of rainfall thresholds and soil moisture modeling
for operational hydrogeological risk prevention in the Umbria region
(central Italy), Landslides, 9, 229–237, https://doi.org/10.1007/s10346-011-0287-3, 2012.
Postance, B., Hillier, J., Dijkstra, T., and Dixon, N.: Comparing threshold
definition techniques for rainfall-induced landslides: A national assessment
using radar rainfall: Comparing threshold techniques for rainfall induced
landslides, Earth Surf. Proc. Land., 43, 553–560, https://doi.org/10.1002/esp.4202, 2018.
Rosi, A., Segoni, S., Canavesi, V., Monni, A., Gallucci, A., and Casagli,
N.: Definition of 3D rainfall thresholds to increase operative landslide
early warning system performances, Landslides, 18, 1045–1057,
https://doi.org/10.1007/s10346-020-01523-2, 2021.
Scheevel, C. R., Baum, R. L., Mirus, B. B., and Smith, J. B.: Precipitation
thresholds for landslide occurrence near Seattle, Mukilteo, and Everett,
Washington, US Geological Survey, Open-File Report 2017-1039, https://doi.org/10.3133/ofr20171039, 2017.
Schlögel, R., Kofler, C., Gariano, S. L., Van Campenhout, J., and
Plummer, S.: Changes in climate patterns and their association to natural
hazard distribution in South Tyrol (Eastern Italian Alps), Sci. Rep.-UK, 10,
5022, https://doi.org/10.1038/s41598-020-61615-w, 2020.
Schmaltz, E. M., Van Beek, L. P. H., Bogaard, T. A., Kraushaar, S., Steger,
S., and Glade, T.: Strategies to improve the explanatory power of a dynamic
slope stability model by enhancing land cover parameterisation and model
complexity, Earth Surf. Proc. Land., 44, 1259–1273,
https://doi.org/10.1002/esp.4570, 2019.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent
literature on rainfall thresholds for landslide occurrence, Landslides, 15,
1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018a.
Segoni, S., Piciullo, L., and Gariano, S. L.: Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception, Nat. Hazards Earth Syst. Sci., 18, 3179–3186, https://doi.org/10.5194/nhess-18-3179-2018, 2018b.
Sevruk, B., Ondrás, M., and Chvíla, B.: The WMO precipitation
measurement intercomparisons, Atmos. Res., 92, 376–380,
https://doi.org/10.1016/j.atmosres.2009.01.016, 2009.
Sidle, R. C. and Ochiai, H.: Landslides: processes, prediction, and land use, American Geophysical Union, Water Resources Monograph 18, https://doi.org/10.1029/WM018, 2006.
Slaymaker, O., Spencer, T., and Embleton-Hamann, C. (Eds.): Geomorphology
and global environmental change, Cambridge University Press, Cambridge; New
York, 434 pp., https://doi.org/10.1017/CBO9780511627057, 2009.
Stanley, T. A., Kirschbaum, D. B., Benz, G., Emberson, R. A., Amatya, P. M.,
Medwedeff, W., and Clark, M. K.: Data-Driven Landslide Nowcasting at the
Global Scale, Frontiers in Earth Science, 9, 640043,
https://doi.org/10.3389/feart.2021.640043, 2021.
Steger, S. and Glade, T.: The Challenge of “Trivial Areas” in Statistical
Landslide Susceptibility Modelling, in: Advancing Culture of Living with
Landslides, edited by: Mikos, M., Tiwari, B., Yin, Y., and Sassa, K.,
Springer International Publishing, Cham, 803–808,
https://doi.org/10.1007/978-3-319-53498-5_92, 2017.
Steger, S., Brenning, A., Bell, R., Petschko, H., and Glade, T.: Exploring
discrepancies between quantitative validation results and the geomorphic
plausibility of statistical landslide susceptibility maps, Geomorphology,
262, 8–23, https://doi.org/10.1016/j.geomorph.2016.03.015, 2016.
Steger, S., Brenning, A., Bell, R., and Glade, T.: The influence of
systematically incomplete shallow landslide inventories on statistical
susceptibility models and suggestions for improvements, Landslides, 14,
1767–1781, https://doi.org/10.1007/s10346-017-0820-0, 2017.
Steger, S., Mair, V., Kofler, C., Pittore, M., Zebisch, M., and
Schneiderbauer, S.: Correlation does not imply geomorphic causation in
data-driven landslide susceptibility modelling – Benefits of exploring
landslide data collection effects, Sci. Total Environ., 776,
145935, https://doi.org/10.1016/j.scitotenv.2021.145935, 2021.
Stingl, V. and Mair, V.: Einführung in die Geologie Südtirols:[aus
Anlass des 32. Internationalen Geologischen Kongresses im Sommer 2004 in
Florenz], Autonome Provinz Bozen-Südtirol, Amt f. Geologie u.
Baustoffprüfung, ISBN: 8870734080, 9788870734089, 2005.
Tasser, E., Mader, M., and Tappeiner, U.: Effects of land use in alpine
grasslands on the probability of landslides, Basic Appl. Ecol., 4, 271–280, https://doi.org/10.1078/1439-1791-00153, 2003.
Tehrani, F. S., Calvello, M., Liu, Z., Zhang, L., and Lacasse, S.: Machine
learning and landslide studies: recent advances and applications, Nat.
Hazards, 114, 1197–1245, https://doi.org/10.1007/s11069-022-05423-7, 2022.
Trigila, A., Iadanza, C., and Spizzichino, D.: Quality assessment of the
Italian Landslide Inventory using GIS processing, Landslides, 7, 455–470,
https://doi.org/10.1007/s10346-010-0213-0, 2010.
Wood, S. N.: Stable and efficient multiple smoothing parameter estimation
for generalized additive models, J. Am. Stat. Assoc., 99, 673–686, 2004.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal
likelihood estimation of semiparametric generalized linear models, J. Roy. Stat. Soc. B, 73, 3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
Wood, S. N.: Generalized Additive Models: An Introduction with R, 2nd edn.,
Chapman and Hall/CRC, New York, 496 pp., https://doi.org/10.1201/9781315370279, 2017.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., and Smith, G. M.: Mixed
Effects Models and Extensions in Ecology with R, 2009th edn., Springer, New
York, NY, 574 pp., https://doi.org/10.1007/978-0-387-87458-6, 2009.
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
We present a novel data-driven modelling approach to determine season-specific critical...
Altmetrics
Final-revised paper
Preprint