Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-3723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Dec 2023
Research article |
| 01 Dec 2023
| 01 Dec 2023
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Adriaan L. van Natijne
CORRESPONDING AUTHOR
Department of Geoscience & Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Thom A. Bogaard
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Thomas Zieher
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innrain 25, 6020 Innsbruck, Austria
Jan Pfeiffer
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innrain 25, 6020 Innsbruck, Austria
Roderik C. Lindenbergh
Department of Geoscience & Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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Revised manuscript not accepted
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Knowing the isotopic composition of water vapor in the air is a difficult task. The estimation of δ18O and δ2H has to be done carefully, because it is accompanied by a high risk of methodological errors (if it is sampled) or wrong assumptions that can lead to incorrect values (if it is modeled). The aim of this work was to compare available sampling methods for water vapor in the air and estimate their isotopic composition, comparing the results against direct measurements of the sampled air.
M. Bremer, V. Wichmann, M. Rutzinger, T. Zieher, and J. Pfeiffer
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R. Lindenbergh, S. van der Kleij, M. Kuschnerus, S. Vos, and S. de Vries
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L. Truong-Hong, D. F. Laefer, and R. C. Lindenbergh
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B. B. van der Horst, R. C. Lindenbergh, and S. W. J. Puister
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Y. Zang and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1169–1175, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, 2019
Y. Ao, J. Wang, M. Zhou, R. C. Lindenbergh, and M. Y. Yang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 13–20, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, 2019
K. Zhou, Y. Chen, I. Smal, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 155–161, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, 2019
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
J. Pfeiffer, T. Zieher, M. Rutzinger, M. Bremer, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 421–428, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, 2019
M. Soilán, R. Lindenbergh, B. Riveiro, and A. Sánchez-Rodríguez
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 445–452, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, 2019
T. Zieher, M. Bremer, M. Rutzinger, J. Pfeiffer, P. Fritzmann, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 461–467, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, 2019
César~Dionisio Jiménez-Rodríguez, Miriam Coenders-Gerrits, Thom Bogaard, Erika Vatiero, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-538, https://doi.org/10.5194/hess-2018-538, 2018
Manuscript not accepted for further review
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Petra Hulsman, Thom A. Bogaard, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 22, 5081–5095, https://doi.org/10.5194/hess-22-5081-2018, https://doi.org/10.5194/hess-22-5081-2018, 2018
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R. P. A. Bormans, R. C. Lindenbergh, and F. Karimi Nejadasl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 141–148, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, 2018
I. Puente, R. Lindenbergh, A. Van Natijne, R. Esposito, and R. Schipper
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 923–929, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, 2018
B. Riveiro, G. Cubreiro, B. Conde, M. Cabaleiro, R. Lindenbergh, M. Soilán, and J. C. Caamaño
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 969–974, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, 2018
A. L. van Natijne, R. C. Lindenbergh, and R. F. Hanssen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1137–1144, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, 2018
J. Wang and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163–1168, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, 2018
E. Widyaningrum, R. C. Lindenbergh, B. G. H. Gorte, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199–1205, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, 2018
K. Zhou, B. Gorte, R. Lindenbergh, and E. Widyaningrum
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, 2018
T. Zieher, I. Toschi, F. Remondino, M. Rutzinger, Ch. Kofler, A. Mejia-Aguilar, and R. Schlögel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1243–1250, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, 2018
David J. Peres, Antonino Cancelliere, Roberto Greco, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018, https://doi.org/10.5194/nhess-18-633-2018, 2018
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We investigate the influence of imprecise identification of triggering instants on landslide early warning thresholds by perturbing an error-free synthetic dataset. Combined impacts of uncertainty with respect to temporal discretization of data and criteria for singling out rainfall events are assessed as well. Results show that thresholds can be significantly affected by these uncertainty sources.
Thom Bogaard and Roberto Greco
Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, https://doi.org/10.5194/nhess-18-31-2018, 2018
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The vast majority of shallow landslides and debris flows are precipitation initiated and predicted using historical landslides plotted versus observed precipitation information. However, this approach has severe limitations. This is partly due to the fact that it is not precipitation that initiates a landslide or debris flow but rather the hydrological dynamics in the soil and slope. We propose to include hydrological information in the regional hydro-meteorological hazard assessment.
Y. Shen, R. Lindenbergh, B. Hofland, and R. Kramer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 139–147, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, 2017
A. Nurunnabi, Y. Sadahiro, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 63–70, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, 2017
J. Böhm, M. Bredif, T. Gierlinger, M. Krämer, R. Lindenberg, K. Liu, F. Michel, and B. Sirmacek
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 301–307, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, 2016
Marie K. M. Charrière and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 16, 1175–1188, https://doi.org/10.5194/nhess-16-1175-2016, https://doi.org/10.5194/nhess-16-1175-2016, 2016
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This paper present the results of interviews that were conducted with the developers of apps dedicated to avalanche risk communication. The study investigates the context of their development to determine how choices of content and visualization were made as well as how their effectiveness is evaluated. Results show that consensus is achieved in terms of message but not in terms of visualization. However, progress remains in terms of effectiveness evaluation.
W. Shao, T. A. Bogaard, M. Bakker, and R. Greco
Hydrol. Earth Syst. Sci., 19, 2197–2212, https://doi.org/10.5194/hess-19-2197-2015, https://doi.org/10.5194/hess-19-2197-2015, 2015
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The effect of preferential flow on the stability of landslides is studied through numerical simulation of two types of rainfall events on a hypothetical hillslope. A model is developed that consists of two parts. The first part is a model for combined saturated/unsaturated subsurface flow and is used to compute the spatial and temporal water pressure response to rainfall. Preferential flow is simulated with a dual-permeability continuum model consisting of a matrix/preferential flow domain.
V. J. Cortes Arevalo, M. Charrière, G. Bossi, S. Frigerio, L. Schenato, T. Bogaard, C. Bianchizza, A. Pasuto, and S. Sterlacchini
Nat. Hazards Earth Syst. Sci., 14, 2681–2698, https://doi.org/10.5194/nhess-14-2681-2014, https://doi.org/10.5194/nhess-14-2681-2014, 2014
V. H. Phan, R. C. Lindenbergh, and M. Menenti
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-2425-2014, https://doi.org/10.5194/tcd-8-2425-2014, 2014
Revised manuscript not accepted
D. M. Krzeminska, T. A. Bogaard, T.-H. Debieche, F. Cervi, V. Marc, and J.-P. Malet
Earth Surf. Dynam., 2, 181–195, https://doi.org/10.5194/esurf-2-181-2014, https://doi.org/10.5194/esurf-2-181-2014, 2014
U. Ehret, H. V. Gupta, M. Sivapalan, S. V. Weijs, S. J. Schymanski, G. Blöschl, A. N. Gelfan, C. Harman, A. Kleidon, T. A. Bogaard, D. Wang, T. Wagener, U. Scherer, E. Zehe, M. F. P. Bierkens, G. Di Baldassarre, J. Parajka, L. P. H. van Beek, A. van Griensven, M. C. Westhoff, and H. C. Winsemius
Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014, https://doi.org/10.5194/hess-18-649-2014, 2014
J. F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L. S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R. C. Lindenbergh, and M. Kleinherenbrink
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-5433-2013, https://doi.org/10.5194/tcd-7-5433-2013, 2013
Revised manuscript not accepted
V. H. Phan, R. C. Lindenbergh, and M. Menenti
Hydrol. Earth Syst. Sci., 17, 4061–4077, https://doi.org/10.5194/hess-17-4061-2013, https://doi.org/10.5194/hess-17-4061-2013, 2013
J. E. van der Spek, T. A. Bogaard, and M. Bakker
Hydrol. Earth Syst. Sci., 17, 2171–2183, https://doi.org/10.5194/hess-17-2171-2013, https://doi.org/10.5194/hess-17-2171-2013, 2013
D. M. Krzeminska, T. A. Bogaard, J.-P. Malet, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 17, 947–959, https://doi.org/10.5194/hess-17-947-2013, https://doi.org/10.5194/hess-17-947-2013, 2013
M. Hrachowitz, H. Savenije, T. A. Bogaard, D. Tetzlaff, and C. Soulsby
Hydrol. Earth Syst. Sci., 17, 533–564, https://doi.org/10.5194/hess-17-533-2013, https://doi.org/10.5194/hess-17-533-2013, 2013
Related subject area
Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
Review article: Physical vulnerability database for critical infrastructure hazard risk assessments – a systematic review and data collection
Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg
Dynamical changes in seismic properties prior to, during, and after the 2014–2015 Holuhraun eruption, Iceland
The World Wide Lightning Location Network (WWLLN) over Spain
AscDAMs: advanced SLAM-based channel detection and mapping system
Shoreline and land use–land cover changes along the 2004-tsunami-affected South Andaman coast: understanding changing hazard susceptibility
Satellite-based data for agricultural index insurance: a systematic quantitative literature review
A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal
Insights into the development of a landslide early warning system prototype in an informal settlement: the case of Bello Oriente in Medellín, Colombia
Tsunami hazard perception and knowledge of alert: early findings in five municipalities along the French Mediterranean coastlines
Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution
A neural network model for automated prediction of avalanche danger level
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine
Quantifying unequal urban resilience to rainfall across China from location-aware big data
Comparison of machine learning techniques for reservoir outflow forecasting
Development of black ice prediction model using GIS-based multi-sensor model validation
Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model
A dynamic hierarchical Bayesian approach for forecasting vegetation condition
Using a single remote-sensing image to calculate the height of a landslide dam and the maximum volume of a lake
Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador
Gridded flood depth estimates from satellite-derived inundations
ProbFire: a probabilistic fire early warning system for Indonesia
Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response
Brief communication: Monitoring a soft-rock coastal cliff using webcams and strain sensors
Multiscale analysis of surface roughness for the improvement of natural hazard modelling
EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds
Are sirens effective tools to alert the population in France?
UAV survey method to monitor and analyze geological hazards: the case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)
Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria
CHILDA – Czech Historical Landslide Database
Review article: Detection of actionable tweets in crisis events
Long-term magnetic anomalies and their possible relationship to the latest greater Chilean earthquakes in the context of the seismo-electromagnetic theory
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Opportunities and risks of disaster data from social media: a systematic review of incident information
Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text
Predicting power outages caused by extratropical storms
Near-real-time automated classification of seismic signals of slope failures with continuous random forests
Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin
Responses to severe weather warnings and affective decision-making
The object-specific flood damage database HOWAS 21
A spaceborne SAR-based procedure to support the detection of landslides
GIS-based DRASTIC and composite DRASTIC indices for assessing groundwater vulnerability in the Baghin aquifer, Kerman, Iran
Review article: The spatial dimension in the assessment of urban socio-economic vulnerability related to geohazards
Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy
CCAF-DB: the Caribbean and Central American active fault database
Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain
Sadhana Nirandjan, Elco E. Koks, Mengqi Ye, Raghav Pant, Kees C. H. Van Ginkel, Jeroen C. J. H. Aerts, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 24, 4341–4368, https://doi.org/10.5194/nhess-24-4341-2024, https://doi.org/10.5194/nhess-24-4341-2024, 2024
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Critical infrastructures (CIs) are exposed to natural hazards, which may result in significant damage and burden society. Vulnerability is a key determinant for reducing these risks, yet crucial information is scattered in the literature. Our study reviews over 1510 fragility and vulnerability curves for CI assets, creating a unique publicly available physical vulnerability database that can be directly used for hazard risk assessments, including floods, earthquakes, windstorms, and landslides.
Fabio Brill, Pedro Henrique Lima Alencar, Huihui Zhang, Friedrich Boeing, Silke Hüttel, and Tobia Lakes
Nat. Hazards Earth Syst. Sci., 24, 4237–4265, https://doi.org/10.5194/nhess-24-4237-2024, https://doi.org/10.5194/nhess-24-4237-2024, 2024
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Droughts are a threat to agricultural crops, but different factors influence how much damage occurs. This is important to know to create meaningful risk maps and to evaluate adaptation options. We investigate the years 2013–2022 in Brandenburg, Germany, and find in particular the soil quality and meteorological drought in June to be statistically related to the observed damage. Measurement of crop health from satellites is also related to soil quality and not necessarily to anomalous yields.
Maria R. P. Sudibyo, Eva P. S. Eibl, Sebastian Hainzl, and Matthias Ohrnberger
Nat. Hazards Earth Syst. Sci., 24, 4075–4089, https://doi.org/10.5194/nhess-24-4075-2024, https://doi.org/10.5194/nhess-24-4075-2024, 2024
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We assessed the performance of permutation entropy (PE), phase permutation entropy (PPE), and instantaneous frequency (IF), which are estimated from a single seismic station, to detect changes before, during, and after the 2014–2015 Holuhraun eruption in Iceland. We show that these three parameters are sensitive to the pre-eruptive and eruptive processes. Finally, we discuss their potential and limitations in eruption monitoring.
Enrique A. Navarro, Jorge A. Portí, Alfonso Salinas, Sergio Toledo-Redondo, Jaume Segura-García, Aida Castilla, Víctor Montagud-Camps, and Inmaculada Albert
Nat. Hazards Earth Syst. Sci., 24, 3925–3943, https://doi.org/10.5194/nhess-24-3925-2024, https://doi.org/10.5194/nhess-24-3925-2024, 2024
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The World Wide Lightning Location Network (WWLLN) operates a globally distributed network of stations that detect lightning signals at a planetary scale. A detection efficiency of 29 % with a location accuracy of between 2 and 3 km is obtained for the area of Spain by comparing WWLLN data with those of the Spanish State Meteorological Agency. The network's capability to resolve convective-storm cells generated in a cutoff low-pressure system is also demonstrated in the west Mediterranean Sea.
Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, and Ping Shen
Nat. Hazards Earth Syst. Sci., 24, 3075–3094, https://doi.org/10.5194/nhess-24-3075-2024, https://doi.org/10.5194/nhess-24-3075-2024, 2024
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Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Vikas Ghadamode, Aruna Kumari Kondarathi, Anand K. Pandey, and Kirti Srivastava
Nat. Hazards Earth Syst. Sci., 24, 3013–3033, https://doi.org/10.5194/nhess-24-3013-2024, https://doi.org/10.5194/nhess-24-3013-2024, 2024
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In 2004-tsunami-affected South Andaman, tsunami wave propagation, arrival times, and run-up heights at 13 locations are computed to analyse pre- and post-tsunami shoreline and land use–land cover changes to understand the evolving hazard scenario. The LULC changes and dynamic shoreline changes are observed in zones 3, 4, and 5 owing to dynamic population changes, infrastructural growth, and gross state domestic product growth. Economic losses would increase 5-fold for a similar tsunami.
Thuy T. Nguyen, Shahbaz Mushtaq, Jarrod Kath, Thong Nguyen-Huy, and Louis Reymondin
EGUsphere, https://doi.org/10.5194/egusphere-2024-1527, https://doi.org/10.5194/egusphere-2024-1527, 2024
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We reviewed the use of satellite-based data in designing agricultural index-based insurance (IBI) products, an effective tool for managing climate risk and promoting sustainable development. Despite the increasing number of studies since 2010 to present, the review revealed a gap in applying the approach to perennial crops in developing countries. We also highlighted the growing importance of satellite data for index insurance, employing high-resolution datasets to reduce basis risk.
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-101, https://doi.org/10.5194/nhess-2024-101, 2024
Revised manuscript accepted for NHESS
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We describe a methodology to systematically gather evidence of the breadth of single natural hazards and their multi-hazard interrelationships in data-scarce urban settings. We apply this methodology to Kathmandu Valley, Nepal, where we find evidence of 21 single hazard types, and 83 multi-hazard interrelationships. This evidence is supplemented with multi-hazard scenarios developed by practitioner stakeholders engaged in disaster risk reduction research and practice in Kathmandu Valley.
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Tamara Breuninger, Moritz Gamperl, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 1843–1870, https://doi.org/10.5194/nhess-24-1843-2024, https://doi.org/10.5194/nhess-24-1843-2024, 2024
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Early warning systems (EWSs) promise to decrease the vulnerability of self-constructed (informal) settlements. A living lab developed a partially functional prototype of an EWS for landslides in a Medellín neighborhood. The first findings indicate that technical aspects can be manageable, unlike social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced-support scenario.
Johnny Douvinet, Noé Carles, Pierre Foulquier, and Matthieu Peroche
Nat. Hazards Earth Syst. Sci., 24, 715–735, https://doi.org/10.5194/nhess-24-715-2024, https://doi.org/10.5194/nhess-24-715-2024, 2024
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This study provided an opportunity to assess both the perception of the tsunami hazard and the knowledge of alerts in five municipalities located along the French Mediterranean coastlines. The age and location of the respondents explain several differences between the five municipalities surveyed – more so than gender or residence status. This study may help local authorities to develop future tsunami awareness actions and to identify more appropriate strategies to be applied in the short term.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
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Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
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We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023, https://doi.org/10.5194/nhess-23-2523-2023, 2023
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Snow avalanches are a natural hazard that can cause danger to human lives. This threat can be reduced by accurate prediction of the danger levels. The development of mathematical models based on past data and present conditions can help to improve the accuracy of prediction. This research aims to develop a neural-network-based model for correlating complex relationships between the meteorological variables and the profile variables.
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
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Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
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Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun
Nat. Hazards Earth Syst. Sci., 23, 789–807, https://doi.org/10.5194/nhess-23-789-2023, https://doi.org/10.5194/nhess-23-789-2023, 2023
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This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
Jan Freihardt and Othmar Frey
Nat. Hazards Earth Syst. Sci., 23, 751–770, https://doi.org/10.5194/nhess-23-751-2023, https://doi.org/10.5194/nhess-23-751-2023, 2023
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In Bangladesh, riverbank erosion occurs every year during the monsoon and affects thousands of households. Information on locations and extent of past erosion can help anticipate where erosion might occur in the upcoming monsoon season and to take preventive measures. In our study, we show how time series of radar satellite imagery can be used to retrieve information on past erosion events shortly after the monsoon season using a novel interactive online tool based on the Google Earth Engine.
Jiale Qian, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Ting Ma, and Tao Pei
Nat. Hazards Earth Syst. Sci., 23, 317–328, https://doi.org/10.5194/nhess-23-317-2023, https://doi.org/10.5194/nhess-23-317-2023, 2023
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Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874, https://doi.org/10.5194/nhess-22-3859-2022, https://doi.org/10.5194/nhess-22-3859-2022, 2022
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Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
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This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, https://doi.org/10.5194/nhess-22-2703-2022, 2022
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The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, https://doi.org/10.5194/nhess-22-2725-2022, 2022
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The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and
agro-ecological zones.
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
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Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, María Antonieta Vásquez, Camilo Zapata, Santiago Bonilla-Bedoya, Paulina Cubillo, Diego Francisco Orbe, Marco Córdova, Jonathan Menoscal, and Elisa Sevilla
Nat. Hazards Earth Syst. Sci., 22, 1699–1721, https://doi.org/10.5194/nhess-22-1699-2022, https://doi.org/10.5194/nhess-22-1699-2022, 2022
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We assess how greenspaces could guide risk-informed planning and reduce disaster risk for the urbanising city of Quito, Ecuador, which experiences earthquake, volcano, landslide, and flood hazards. We use satellite data to evaluate the use of greenspaces as safe spaces following an earthquake. We find disparities regarding access to and availability of greenspaces. The availability of greenspaces that could contribute to community resilience is high; however, many require official designation.
Seth Bryant, Heather McGrath, and Mathieu Boudreault
Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, https://doi.org/10.5194/nhess-22-1437-2022, 2022
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The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here accomplishes this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
Tadas Nikonovas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin
Nat. Hazards Earth Syst. Sci., 22, 303–322, https://doi.org/10.5194/nhess-22-303-2022, https://doi.org/10.5194/nhess-22-303-2022, 2022
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Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.
Yahong Liu and Jin Zhang
Nat. Hazards Earth Syst. Sci., 22, 227–244, https://doi.org/10.5194/nhess-22-227-2022, https://doi.org/10.5194/nhess-22-227-2022, 2022
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Through a comprehensive analysis of the current remote sensing technology resources, this paper establishes the database to realize the unified management of heterogeneous sensor resources and proposes a capability evaluation method of remote sensing cooperative technology in geohazard emergencies, providing a decision-making basis for the establishment of remote sensing cooperative observations in geohazard emergencies.
Diego Guenzi, Danilo Godone, Paolo Allasia, Nunzio Luciano Fazio, Michele Perrotti, and Piernicola Lollino
Nat. Hazards Earth Syst. Sci., 22, 207–212, https://doi.org/10.5194/nhess-22-207-2022, https://doi.org/10.5194/nhess-22-207-2022, 2022
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In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open-source hardware and software, trying to correlate both meteorological information with measures obtained from crack meters and webcams, aiming to recognize potential precursor signals that could be triggered by instability phenomena.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
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Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
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The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Johnny Douvinet, Anna Serra-Llobet, Esteban Bopp, and G. Mathias Kondolf
Nat. Hazards Earth Syst. Sci., 21, 2899–2920, https://doi.org/10.5194/nhess-21-2899-2021, https://doi.org/10.5194/nhess-21-2899-2021, 2021
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This study proposes to combine results of research regarding the spatial inequalities due to the siren coverage, the political dilemma of siren activation, and the social problem of siren awareness and trust for people in France. Surveys were conducted using a range of complementary methods (GIS analysis, statistical analysis, questionnaires, interviews) through different scales. Results show that siren coverage in France is often determined by population density but not risks or disasters.
Fabio Brighenti, Francesco Carnemolla, Danilo Messina, and Giorgio De Guidi
Nat. Hazards Earth Syst. Sci., 21, 2881–2898, https://doi.org/10.5194/nhess-21-2881-2021, https://doi.org/10.5194/nhess-21-2881-2021, 2021
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In this paper we propose a methodology to mitigate hazard in a natural environment in an urbanized context. The deformation of the ground is a precursor of paroxysms in mud volcanoes. Therefore, through the analysis of the deformation supported by a statistical approach, this methodology was tested to reduce the hazard around the mud volcano. In the future, the goal is that this dangerous area will become both a naturalistic heritage and a source of development for the community of the area.
Doris Hermle, Markus Keuschnig, Ingo Hartmeyer, Robert Delleske, and Michael Krautblatter
Nat. Hazards Earth Syst. Sci., 21, 2753–2772, https://doi.org/10.5194/nhess-21-2753-2021, https://doi.org/10.5194/nhess-21-2753-2021, 2021
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Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
Michal Bíl, Pavel Raška, Lukáš Dolák, and Jan Kubeček
Nat. Hazards Earth Syst. Sci., 21, 2581–2596, https://doi.org/10.5194/nhess-21-2581-2021, https://doi.org/10.5194/nhess-21-2581-2021, 2021
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The online landslide database CHILDA (Czech Historical Landslide Database) summarises information about landslides which occurred in the area of Czechia (the Czech Republic). The database is freely accessible via the https://childa.cz/ website. It includes 699 records (spanning the period of 1132–1989). Overall, 55 % of all recorded landslide events occurred only within 15 years of the extreme landslide incidence.
Anna Kruspe, Jens Kersten, and Friederike Klan
Nat. Hazards Earth Syst. Sci., 21, 1825–1845, https://doi.org/10.5194/nhess-21-1825-2021, https://doi.org/10.5194/nhess-21-1825-2021, 2021
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Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Enrique Guillermo Cordaro, Patricio Venegas-Aravena, and David Laroze
Nat. Hazards Earth Syst. Sci., 21, 1785–1806, https://doi.org/10.5194/nhess-21-1785-2021, https://doi.org/10.5194/nhess-21-1785-2021, 2021
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We developed a methodology that generates free externally disturbed magnetic variations in ground magnetometers close to the Chilean convergent margin. Spectral analysis (~ mHz) and magnetic anomalies increased prior to large Chilean earthquakes (Maule 2010, Mw 8.8; Iquique 2014, Mw 8.2; Illapel 2015, Mw 8.3). These findings relate to microcracks within the lithosphere due to stress state changes. This physical evidence should be thought of as a last stage of the earthquake preparation process.
Corey M. Scheip and Karl W. Wegmann
Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, https://doi.org/10.5194/nhess-21-1495-2021, 2021
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For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
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In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong
Nat. Hazards Earth Syst. Sci., 21, 1179–1194, https://doi.org/10.5194/nhess-21-1179-2021, https://doi.org/10.5194/nhess-21-1179-2021, 2021
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We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä
Nat. Hazards Earth Syst. Sci., 21, 607–627, https://doi.org/10.5194/nhess-21-607-2021, https://doi.org/10.5194/nhess-21-607-2021, 2021
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Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
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Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Luiz Felipe Galizia, Thomas Curt, Renaud Barbero, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 21, 73–86, https://doi.org/10.5194/nhess-21-73-2021, https://doi.org/10.5194/nhess-21-73-2021, 2021
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This paper aims to provide a quantitative evaluation of three remotely sensed fire datasets which have recently emerged as an important resource to improve our understanding of fire regimes. Our findings suggest that remotely sensed fire datasets can be used to proxy variations in fire activity on monthly and annual timescales; however, caution is advised when drawing information from smaller fires (< 100 ha) across the Mediterranean region.
Philippe Weyrich, Anna Scolobig, Florian Walther, and Anthony Patt
Nat. Hazards Earth Syst. Sci., 20, 2811–2821, https://doi.org/10.5194/nhess-20-2811-2020, https://doi.org/10.5194/nhess-20-2811-2020, 2020
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 20, 2503–2519, https://doi.org/10.5194/nhess-20-2503-2020, https://doi.org/10.5194/nhess-20-2503-2020, 2020
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The flood damage database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. This paper presents HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
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In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Mohammad Malakootian and Majid Nozari
Nat. Hazards Earth Syst. Sci., 20, 2351–2363, https://doi.org/10.5194/nhess-20-2351-2020, https://doi.org/10.5194/nhess-20-2351-2020, 2020
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The present study estimated the Kerman–Baghin aquifer vulnerability using DRASTIC and composite DRASTIC (CDRASTIC) indices with the aid of geographic information system (GIS) techniques. The aquifer vulnerability maps indicated very similar results, identifying the north-west parts of the aquifer as areas with high to very high vulnerability. According to the results, parts of the studied aquifer have a high vulnerability and require protective measures.
Diana Contreras, Alondra Chamorro, and Sean Wilkinson
Nat. Hazards Earth Syst. Sci., 20, 1663–1687, https://doi.org/10.5194/nhess-20-1663-2020, https://doi.org/10.5194/nhess-20-1663-2020, 2020
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The socio-economic condition of the population determines their vulnerability to earthquakes, tsunamis, volcanic eruptions, landslides, soil erosion and land degradation. This condition is estimated mainly from population censuses. The lack to access to basic services, proximity to hazard zones, poverty and population density highly influence the vulnerability of communities. Mapping the location of this vulnerable population makes it possible to prevent and mitigate their risk.
Simona Colombelli, Francesco Carotenuto, Luca Elia, and Aldo Zollo
Nat. Hazards Earth Syst. Sci., 20, 921–931, https://doi.org/10.5194/nhess-20-921-2020, https://doi.org/10.5194/nhess-20-921-2020, 2020
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We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
Richard Styron, Julio García-Pelaez, and Marco Pagani
Nat. Hazards Earth Syst. Sci., 20, 831–857, https://doi.org/10.5194/nhess-20-831-2020, https://doi.org/10.5194/nhess-20-831-2020, 2020
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The Caribbean and Central American region is both tectonically active and densely populated, leading to a large population that is exposed to earthquake hazards. Until now, no comprehensive fault data covering the region have been available. We present a new public fault database for Central America and the Caribbean that synthesizes published studies with new mapping from remote sensing to provide fault sources for the CCARA seismic hazard and risk analysis project and to aid future research.
María del Pilar Jiménez-Donaire, Ana Tarquis, and Juan Vicente Giráldez
Nat. Hazards Earth Syst. Sci., 20, 21–33, https://doi.org/10.5194/nhess-20-21-2020, https://doi.org/10.5194/nhess-20-21-2020, 2020
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A new combined drought indicator (CDI) is proposed that integrates rainfall, soil moisture and vegetation dynamics. The performance of this indicator was evaluated against crop damage data from agricultural insurance schemes in five different areas in SW Spain. Results show that this indicator was able to predict important droughts in 2004–2005 and 2011–2012, marked by crop damage of between 70 % and 95 % of the total insured area. This opens important applications for improving insurance schemes.
Cited articles
Belward, A. S. and Skøien, J. O.: Who Launched What, When and Why; Trends in Global Land-Cover Observation Capacity from Civilian Earth Observation Satellites, ISPRS J. Photogramm., 103, 115–128, https://doi.org/10.1016/j.isprsjprs.2014.03.009, 2015. a
Bengio, Y., Simard, P., and Frasconi, P.: Learning Long-Term Dependencies with Gradient Descent Is Difficult, IEEE T. Neural Networ., 5, 157–166, https://doi.org/10.1109/72.279181, 1994. a
Bogaard, T. A. and Greco, R.: Landslide Hydrology: From Hydrology to Pore Pressure, WIRES Water, 3, 439–459, https://doi.org/10.1002/wat2.1126, 2015. a, b
Bossi, G. and Marcato, G.: Planning Landslide Countermeasure Works through Long Term Monitoring and Grey Box Modelling, Geosciences, 9, 185, https://doi.org/10.3390/geosciences9040185, 2019. a
Cai, Z., Xu, W., Meng, Y., Shi, C., and Wang, R.: Prediction of Landslide Displacement Based on GA-LSSVM with Multiple Factors, B. Eng. Geol. Environ., 75, 637–646, https://doi.org/10.1007/s10064-015-0804-z, 2016. a
Cao, Y., Yin, K., Alexander, D. E., and Zhou, C.: Using an Extreme Learning Machine to Predict the Displacement of Step-like Landslides in Relation to Controlling Factors, Landslides, 13, 725–736, https://doi.org/10.1007/s10346-015-0596-z, 2016. a
Carlà, T., Intrieri, E., Di Traglia, F., Nolesini, T., Gigli, G., and Casagli, N.: Guidelines on the Use of Inverse Velocity Method as a Tool for Setting Alarm Thresholds and Forecasting Landslides and Structure Collapses, Landslides, 14, 517–534, https://doi.org/10.1007/s10346-016-0731-5, 2017. a
Cerqueira, V., Torgo, L., and Soares, C.: A case study comparing machine learning with statistical methods for time series forecasting: size matters, J. Intell. Inf. Syst., 59, 415–433, https://doi.org/10.1007/s10844-022-00713-9, 2022. a
Chen, H. and Zeng, Z.: Deformation Prediction of Landslide Based on Improved Back-Propagation Neural Network, Cogn. Comput., 5, 56–62, https://doi.org/10.1007/s12559-012-9148-1, 2013. a
Colesanti, C. and Wasowski, J.: Investigating Landslides with Space-Borne Synthetic Aperture Radar (SAR) Interferometry, Eng. Geol., 88, 173–199, https://doi.org/10.1016/j.enggeo.2006.09.013, 2006. a
Connor, J., Martin, R., and Atlas, L.: Recurrent Neural Networks and Robust Time Series Prediction, IEEE T. Neural Networ., 5, 240–254, https://doi.org/10.1109/72.279188, 1994. a
Corominas, J., Moya, J., Ledesma, A., Lloret, A., and Gili, J. A.: Prediction of Ground Displacements and Velocities from Groundwater Level Changes at the Vallcebre Landslide (Eastern Pyrenees, Spain), Landslides, 2, 83–96, https://doi.org/10.1007/s10346-005-0049-1, 2005. a
Crosetto, M., Solari, L., Mróz, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D. A., Bateson, L., Guerrieri, L., Comerci, V., and Andersen, H. S.: The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service, Remote Sens., 12, 2043, https://doi.org/10.3390/rs12122043, 2020. a
Deng, L., Smith, A., Dixon, N., and Yuan, H.: Machine Learning Prediction of Landslide Deformation Behaviour Using Acoustic Emission and Rainfall Measurements, Eng. Geol., 293, 106315, https://doi.org/10.1016/j.enggeo.2021.106315, 2021. a
Du, J., Yin, K., and Lacasse, S.: Displacement Prediction in Colluvial Landslides, Three Gorges Reservoir, China, Landslides, 10, 203–218, https://doi.org/10.1007/s10346-012-0326-8, 2013. a
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP) Mission, P. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010. a, b
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land-Use Planning, Eng. Geol., 102, 99–111, https://doi.org/10.1016/j.enggeo.2008.03.014, 2008. a, b
Gholamy, A., Kreinovich, V., and Kosheleva, O.: Why 70/30 or 80/20 Relation between Training and Testing Sets: A Pedagogical Explanation, Departmental Technical Reports (CS), https://scholarworks.utep.edu/cs_techrep/1209 (last access: 8 September 2022), 2018. a
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy, Geomorphology, 31, 181–216, https://doi.org/10.1016/S0169-555X(99)00078-1, 1999. a, b
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. a
Hanssen, R. F.: Radar Interferometry: Data Interpretation and Error Analysis, Remote Sensing and Digital Image Processing, vol. 2, Springer Netherlands, Dordrecht, https://doi.org/10.1007/0-306-47633-9, 2001. a
Hartke, S. H., Wright, D. B., Kirschbaum, D. B., Stanley, T. A., and Li, Z.: Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System, J. Hydrometeorol., 21, 1741–1759, https://doi.org/10.1175/JHM-D-19-0295.1, 2020. a
Heggen, R. J.: Normalized Antecedent Precipitation Index, J. Hydrol. Eng., 6, 377–381, https://doi.org/10.1061/(ASCE)1084-0699(2001)6:5(377), 2001. a
Herrera, G., Mateos, R. M., García-Davalillo, J. C., Grandjean, G., Poyiadji, E., Maftei, R., Filipciuc, T.-C., Jemec Auflič, M., Jež, J., Podolszki, L., Trigila, A., Iadanza, C., Raetzo, H., Kociu, A., Przyłucka, M., Kułak, M., Sheehy, M., Pellicer, X. M., McKeown, C., Ryan, G., Kopačková, V., Frei, M., Kuhn, D., Hermanns, R. L., Koulermou, N., Smith, C. A., Engdahl, M., Buxó, P., Gonzalez, M., Dashwood, C., Reeves, H., Cigna, F., Liščák, P., Pauditš, P., Mikulėnas, V., Demir, V., Raha, M., Quental, L., Sandić, C., Fusi, B., and Jensen, O. A.: Landslide Databases in the Geological Surveys of Europe, Landslides, 15, 359–379, https://doi.org/10.1007/s10346-017-0902-z, 2018. a
Hill, T., Marquez, L., O'Connor, M., and Remus, W.: Artificial Neural Network Models for Forecasting and Decision Making, Int. J. Forecasting, 10, 5–15, https://doi.org/10.1016/0169-2070(94)90045-0, 1994. a
Hilley, G. E., Bürgmann, R., Ferretti, A., Novali, F., and Rocca, F.: Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis, Science, 304, 1952–1955, https://doi.org/10.1126/science.1098821, 2004. a
Hochreiter, S.: The Vanishing Gradient Problem during Learning Recurrent Neural Nets and Problem Solutions, Int. J. Uncertain. Fuzz., 06, 107–116, https://doi.org/10.1142/S0218488598000094, 1998. a
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a, b, c
Hornik, K., Stinchcombe, M., and White, H.: Multilayer Feedforward Networks Are Universal Approximators, Neural Networks, 2, 359–366, https://doi.org/10.1016/0893-6080(89)90020-8, 1989. a
Huang, F., Huang, J., Jiang, S., and Zhou, C.: Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine, Eng. Geol., 218, 173–186, https://doi.org/10.1016/j.enggeo.2017.01.016, 2017. a
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Jackson, T.: GPM IMERG Early Precipitation L3 Half Hourly 0.1 Degree × 0.1 Degree V06, NASA [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06, 2019. a, b
Intrieri, E., Raspini, F., Fumagalli, A., Lu, P., Del Conte, S., Farina, P., Allievi, J., Ferretti, A., and Casagli, N.: The Maoxian Landslide as Seen from Space: Detecting Precursors of Failure with Sentinel-1 Data, Landslides, 15, 123–133, https://doi.org/10.1007/s10346-017-0915-7, 2018. a
Jain, A., Jianchang Mao, and Mohiuddin, K.: Artificial Neural Networks: A Tutorial, Computer, 29, 31–44, https://doi.org/10.1109/2.485891, 1996. a
Jiang, P. and Chen, J.: Displacement Prediction of Landslide Based on Generalized Regression Neural Networks with K-Fold Cross-Validation, Neurocomputing, 198, 40–47, https://doi.org/10.1016/j.neucom.2015.08.118, 2016. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, in: 3rd International Conference for Learning Representations, arXiv, https://doi.org/10.48550/arXiv.1412.6980, 22 December 2014. a
Kirschbaum, D. and Stanley, T.: Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness, Earth's Future, 6, 505–523, https://doi.org/10.1002/2017EF000715, 2018. a
Kohler, M. A. and Linsley, R. K.: Predicting the Runoff from Storm Rainfall, vol. 30, US Department of Commerce, Weather Bureau, https://books.google.nl/books?id=XMtaTBhT5p4C&printsec=frontcover (last access: 17 August 2022), 1951. a
Koppa, A., and Rains, D.: Global Land Evaporation Amsterdam Model (GLEAM) v3.5 [data set], https://www.gleam.eu (last access: 13 April 2021), 2021. a
Krkač, M., Špoljarić, D., Bernat, S., and Arbanas, S. M.: Method for Prediction of Landslide Movements Based on Random Forests, Landslides, 14, 947–960, https://doi.org/10.1007/s10346-016-0761-z, 2017. a, b
Krkač, M., Bernat Gazibara, S., Arbanas, Z., Sečanj, M., and Mihalić Arbanas, S.: A Comparative Study of Random Forests and Multiple Linear Regression in the Prediction of Landslide Velocity, Landslides, 17, 2515–2531, https://doi.org/10.1007/s10346-020-01476-6, 2020. a, b, c, d
Land Tirol, Department of Geoinformation: Vögelsberg deformation time series, Land Tirol [data set], https://www.tirol.gv.at/sicherheit/geoinformation/vermessung-monitoring/monitoring/ (last access: 20 October 2023), 2021. a
Li, C., Criss, R. E., Fu, Z., Long, J., and Tan, Q.: Evolution Characteristics and Displacement Forecasting Model of Landslides with Stair-Step Sliding Surface along the Xiangxi River, Three Gorges Reservoir Region, China, Eng. Geol., 283, 105961, https://doi.org/10.1016/j.enggeo.2020.105961, 2021. a
Li, H., Xu, Q., He, Y., and Deng, J.: Prediction of Landslide Displacement with an Ensemble-Based Extreme Learning Machine and Copula Models, Landslides, 15, 2047–2059, https://doi.org/10.1007/s10346-018-1020-2, 2018. a
Li, H., Xu, Q., He, Y., Fan, X., and Li, S.: Modeling and Predicting Reservoir Landslide Displacement with Deep Belief Network and EWMA Control Charts: A Case Study in Three Gorges Reservoir, Landslides, 17, 693–707, https://doi.org/10.1007/s10346-019-01312-6, 2020. a
Lian, C., Zeng, Z., Yao, W., and Tang, H.: Multiple Neural Networks Switched Prediction for Landslide Displacement, Eng. Geol., 186, 91–99, https://doi.org/10.1016/j.enggeo.2014.11.014, 2015. a
Lins, H. F.: USGS Hydro-Climatic Data Network 2009 (HCDN-2009), Fact Sheet 2012-3047, USGS, 2012. a
Liu, Y., Qiu, H., Yang, D., Liu, Z., Ma, S., Pei, Y., Zhang, J., and Tang, B.: Deformation Responses of Landslides to Seasonal Rainfall Based on InSAR and Wavelet Analysis, Landslides, 19, 199–210, https://doi.org/10.1007/s10346-021-01785-4, 2021. a
Liu, Z., Shao, J., Xu, W., Chen, H., and Shi, C.: Comparison on Landslide Nonlinear Displacement Analysis and Prediction with Computational Intelligence Approaches, Landslides, 11, 889–896, https://doi.org/10.1007/s10346-013-0443-z, 2014. a
Liu, Z.-Q., Guo, D., Lacasse, S., Li, J.-h., Yang, B.-b., and Choi, J.-c.: Algorithms for Intelligent Prediction of Landslide Displacements, J. Zhejiang Univ. Sci. A, 21, 412–429, https://doi.org/10.1631/jzus.A2000005, 2020. a
Logar, J., Turk, G., Marsden, P., and Ambrožič, T.: Prediction of rainfall induced landslide movements by artificial neural networks, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2017-253, 2017. a
Ma, J., Tang, H., Liu, X., Hu, X., Sun, M., and Song, Y.: Establishment of a Deformation Forecasting Model for a Step-like Landslide Based on Decision Tree C5.0 and Two-Step Cluster Algorithms: A Case Study in the Three Gorges Reservoir Area, China, Landslides, 14, 1275–1281, https://doi.org/10.1007/s10346-017-0804-0, 2017. a
Makridakis, S., Spiliotis, E., and Assimakopoulos, V.: Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward, PLOS One, 13, e0194889, https://doi.org/10.1371/journal.pone.0194889, 2018. a
Mansour, M. F., Morgenstern, N. R., and Martin, C. D.: Expected Damage from Displacement of Slow-Moving Slides, Landslides, 8, 117–131, https://doi.org/10.1007/s10346-010-0227-7, 2011. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b
Miao, F., Wu, Y., Xie, Y., and Li, Y.: Prediction of Landslide Displacement with Step-like Behavior Based on Multialgorithm Optimization and a Support Vector Regression Model, Landslides, 15, 475–488, https://doi.org/10.1007/s10346-017-0883-y, 2018. a
Miao, F., Xie, X., Wu, Y., and Zhao, F.: Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides, Sensors, 22, 481, https://doi.org/10.3390/s22020481, 2022. a, b, c
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a, b
Muñoz Sabater, J.: ERA5-Land Hourly Data from 2001 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/CDS.E2161BAC, 2019. a, b, c, d
Neaupane, K. and Achet, S.: Use of Backpropagation Neural Network for Landslide Monitoring: A Case Study in the Higher Himalaya, Eng. Geol., 74, 213–226, https://doi.org/10.1016/j.enggeo.2004.03.010, 2004. a
Nie, W., Krautblatter, M., Leith, K., Thuro, K., and Festl, J.: A modified tank model including snowmelt and infiltration time lags for deep-seated landslides in alpine environments (Aggenalm, Germany), Nat. Hazards Earth Syst. Sci., 17, 1595–1610, https://doi.org/10.5194/nhess-17-1595-2017, 2017. a
Novellino, A., Cesarano, M., Cappelletti, P., Di Martire, D., Di Napoli, M., Ramondini, M., Sowter, A., and Calcaterra, D.: Slow-Moving Landslide Risk Assessment Combining Machine Learning and InSAR Techniques, CATENA, 203, 105317, https://doi.org/10.1016/j.catena.2021.105317, 2021. a
Parajka, J., Kohnová, S., Merz, R., Szolgay, J., Hlavčová, K., and Blöschl, G.: Comparative Analysis of the Seasonality of Hydrological Characteristics in Slovakia and Austria, Hydrolog. Sci. J., 54, 456–473, https://doi.org/10.1623/hysj.54.3.456, 2009. a
Pfeiffer, J., Zieher, T., Schmieder, J., Rutzinger, M., and Strasser, U.: Spatio-temporal Assessment of the Hydrological Drivers of an Active Deep-seated Gravitational Slope Deformation: The Vögelsberg Landslide in Tyrol (Austria), Earth Surf. Proc. Land., 46, 1865–1881, https://doi.org/10.1002/esp.5129, 2021. a, b, c, d, e, f, g, h, i, j, k
Reichle, R., De Lannoy, G., Koster, R., Crow, W., Kimball, J., and Liu, Q.: SMAP L4 global 3-hourly 9 km EASE-grid surface and root zone soil moisture, version 6, NSIDC [data set], https://doi.org/10.5067/08S1A6811J0U, 2022. a, b
Ren, F., Wu, X., Zhang, K., and Niu, R.: Application of Wavelet Analysis and a Particle Swarm-Optimized Support Vector Machine to Predict the Displacement of the Shuping Landslide in the Three Gorges, China, Environ. Earth Sci., 73, 4791–4804, https://doi.org/10.1007/s12665-014-3764-x, 2015. a
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, Front. Earth Sci., 9, 640043, https://doi.org/10.3389/feart.2021.640043, 2021. a
TensorFlow Developers: TensorFlow, Zenodo [code], https://doi.org/10.5281/zenodo.4724125, 2022. a, b
Thomas, M. A., Collins, B. D., and Mirus, B. B.: Assessing the Feasibility of Satellite-based Thresholds for Hydrologically Driven Landsliding, Water Resour. Res., 55, 9006–9023, https://doi.org/10.1029/2019WR025577, 2019. a
van Asch, T. W. J., van Beek, L., and Bogaard, T.: Problems in Predicting the Mobility of Slow-Moving Landslides, Eng. Geol., 91, 46–55, https://doi.org/10.1016/j.enggeo.2006.12.012, 2007. a
van Natijne, A., Bogaard, T., van Leijen, F., Hanssen, R., and Lindenbergh, R.: World-Wide InSAR Sensitivity Index for Landslide Deformation Tracking, Int. J. Appl. Earth Obs., 111, 102829, https://doi.org/10.1016/j.jag.2022.102829, 2022. a
van Natijne, A. L., Lindenbergh, R. C., and Bogaard, T. A.: Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting, Sensors, 20, 1425, https://doi.org/10.3390/s20051425, 2020. a, b, c
Wang, Y., Tang, H., Wen, T., Ma, J., Zou, Z., and Xiong, C.: Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China, Geofluids, 2019, 8985325, https://doi.org/10.1155/2019/8985325, 2019. a
Wen, T., Tang, H., Wang, Y., Lin, C., and Xiong, C.: Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China, Nat. Hazards Earth Syst. Sci., 17, 2181–2198, https://doi.org/10.5194/nhess-17-2181-2017, 2017. a
World Meteorological Organization (WMO): WMO Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019) (WMO-no. 1267), Tech. Rep. 1267, WMO, Geneva, https://library.wmo.int/idurl/4/57564 (last access: 8 September 2022), 2021. a
Xie, P., Zhou, A., and Chai, B.: The Application of Long Short-Term Memory (LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides, IEEE Access, 7, 54305–54311, https://doi.org/10.1109/ACCESS.2019.2912419, 2019. a
Yang, B., Yin, K., Lacasse, S., and Liu, Z.: Time Series Analysis and Long Short-Term Memory Neural Network to Predict Landslide Displacement, Landslides, 16, 677–694, https://doi.org/10.1007/s10346-018-01127-x, 2019. a, b
Yatheendradas, S., Kirschbaum, D., Nearing, G., Vrugt, J. A., Baum, R. L., Wooten, R., Lu, N., and Godt, J. W.: Bayesian Analysis of the Impact of Rainfall Data Product on Simulated Slope Failure for North Carolina Locations, Computat. Geosci., https://doi.org/10.1007/s10596-018-9804-y, 2019. a
Zhang, X., Zhu, C., He, M., Dong, M., Zhang, G., and Zhang, F.: Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction, Remote Sens., 14, 166, https://doi.org/10.3390/rs14010166, 2021. a
Zhou, C., Yin, K., Cao, Y., and Ahmed, B.: Application of Time Series Analysis and PSO–SVM Model in Predicting the Bazimen Landslide in the Three Gorges Reservoir, China, Eng. Geol., 204, 108–120, https://doi.org/10.1016/j.enggeo.2016.02.009, 2016. a
Zhu, X., Xu, Q., Tang, M., Nie, W., Ma, S., and Xu, Z.: Comparison of Two Optimized Machine Learning Models for Predicting Displacement of Rainfall-Induced Landslide: A Case Study in Sichuan Province, China, Eng. Geol., 218, 213–222, https://doi.org/10.1016/j.enggeo.2017.01.022, 2017. a
Zhu, Z., Wulder, M. A., Roy, D. P., Woodcock, C. E., Hansen, M. C., Radeloff, V. C., Healey, S. P., Schaaf, C., Hostert, P., Strobl, P., Pekel, J.-F., Lymburner, L., Pahlevan, N., and Scambos, T. A.: Benefits of the Free and Open Landsat Data Policy, Remote SENS. Environ., 224, 382–385, https://doi.org/10.1016/j.rse.2019.02.016, 2019. a
Zieher, T., Pfeiffer, J., van Natijne, A., and Lindenbergh, R.: Integrated Monitoring of a Slowly Moving Landslide Based on Total Station Measurements, Multi-Temporal Terrestrial Laser Scanning and Space-Borne Interferometric Synthetic Aperture Radar, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, Brussels, Belgium, 11–16 July 2021, 942–945, https://doi.org/10.1109/IGARSS47720.2021.9553324, 2021. a
Short summary
Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Landslides are one of the major weather-related geohazards. To assess their potential impact and...
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