Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2021
Research article |
| 11 Feb 2021
| 11 Feb 2021
Predicting power outages caused by extratropical storms
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Ilona Láng
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Alexander Jung
Aalto University, Department of Computer Science, B.O. 11000, 00076 Aalto, Finland
Antti Mäkelä
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Related authors
No articles found.
Ilona Láng-Ritter, Terhi Kristiina Laurila, Antti Mäkelä, Hilppa Gregow, and VIctoria Anne SInclair
EGUsphere, https://doi.org/10.5194/egusphere-2024-3019, https://doi.org/10.5194/egusphere-2024-3019, 2024
Short summary
Short summary
We present a classification method for extratropical cyclones and windstorms and show their impacts on Finland's electricity grid by analysing 92 most damaging windstorms (2005–2018). The southwest- and northwest-originating windstorms cause the most damage to the power grid. The most relevant parameters for damage are the wind gust speed and extent of wind gusts. Windstorms are more frequent and damaging in autumn and winter, but weaker wind speeds in summer also cause significant damage.
Taru Olsson, Anna Luomaranta, Kirsti Jylhä, Julia Jeworrek, Tuuli Perttula, Christian Dieterich, Lichuan Wu, Anna Rutgersson, and Antti Mäkelä
Adv. Sci. Res., 17, 87–104, https://doi.org/10.5194/asr-17-87-2020, https://doi.org/10.5194/asr-17-87-2020, 2020
Short summary
Short summary
Statistics of the frequency and intensity of snow bands affecting the Finnish coast during years 2000–2010 was conducted. A set of criteria for meteorological variables favoring the formation of the snow bands were applied to regional climate model (RCA4) data. We found on average three days per year with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline.
Galateia Terti, Isabelle Ruin, Milan Kalas, Ilona Láng, Arnau Cangròs i Alonso, Tommaso Sabbatini, and Valerio Lorini
Nat. Hazards Earth Syst. Sci., 19, 507–533, https://doi.org/10.5194/nhess-19-507-2019, https://doi.org/10.5194/nhess-19-507-2019, 2019
Short summary
Short summary
First applications of the ANYCaRE experiment revealed that multi-model impact-based outputs help forecasters and civil protection to shape a holistic view of the situation and enhance their confidence in specific emergency activities. This interdisciplinary work is conducted in the frame of the ANYWHERE European project, which aims to provide institutions across Europe with a decision-support tool to better anticipate and respond to extreme weather and climate events.
Tiina Ervasti, Hilppa Gregow, Andrea Vajda, Terhi K. Laurila, and Antti Mäkelä
Adv. Sci. Res., 15, 99–106, https://doi.org/10.5194/asr-15-99-2018, https://doi.org/10.5194/asr-15-99-2018, 2018
Short summary
Short summary
An online survey was used to map the needs and preferences of the Finnish general public about extended-range forecasts and their presentation. Survey results guided the co-design process of novel extended-range forecasts in the project. The respondents considered that the tailored extended-range forecasts would be beneficial in planning activities, preparing for weather risks and scheduling everyday life. They also valued impact information higher than advice on how to prepare for the impacts.
Erik Gregow, Antti Pessi, Antti Mäkelä, and Elena Saltikoff
Hydrol. Earth Syst. Sci., 21, 267–279, https://doi.org/10.5194/hess-21-267-2017, https://doi.org/10.5194/hess-21-267-2017, 2017
Short summary
Short summary
A new lightning data assimilation method has been implemented and validated within the Finnish Meteorological Institute – Local Analysis and Prediction System. Lightning data do improve the analysis when no radars are available, and even with radar data, lightning data have a positive impact on the results.
We also investigate the usage of different time integration intervals: 1, 6, 12, 24 h and 7 days, where the 1 h integration time length gives the best results.
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
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
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
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
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
Study on real-time correction of site amplification factor
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Quancai Xie, Qiang Ma, Jingfa Zhang, and Haiying Yu
Nat. Hazards Earth Syst. Sci., 19, 2827–2839, https://doi.org/10.5194/nhess-19-2827-2019, https://doi.org/10.5194/nhess-19-2827-2019, 2019
Short summary
Short summary
This paper evaluates a new method for modeling the site amplification factor. Through implementing this method and making simulations for different cases, we find that this method shows better performance than the previous method and JMA report. We better understand the advantages and disadvantages of this method, although there are some problems that need to be considered carefully and solved; it shows good potential to be used in future earthquake early warning systems.
Cited articles
Allen, M., Fernandez, S., Omitaomu, O., and Walker, K.: Application of hybrid
geo-spatially granular fragility curves to improve power outage predictions,
J. Geogr. Nat. Disast., 4, 1–6, https://doi.org/10.4172/2167-0587.1000127, 2014. a
Autonomio: Talos (software), available at: http://github.com/autonomio/talos., last access: 22 June 2020. a
Barredo, J. I.: Normalised flood losses in Europe: 1970–2006, Nat. Hazards Earth Syst. Sci., 9, 97–104, https://doi.org/10.5194/nhess-9-97-2009, 2009. a
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization,
J. Mach. Learn. Res., 13, 281–305, 2012. a
Brown, I. and Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring data sets, Exp. Syst. Appl., 39, 3446–3453, 2012. a
Campbell, R. J. and Lowry, S.: Weather-related power outages and electric
system resiliency, Tech. rep., Congressional Research Service, Library of
Congress, Washington, D.C., 2012. a
Chan, T. F., Golub, G. H., and LeVeque, R. J.: Updating formulae and a pairwise algorithm for computing sample variances, in: COMPSTAT 1982 5th Symposium held at Toulouse 1982, 30–41, https://doi.org/10.1007/978-3-642-51461-6, 1982. a
Chang, C.-C. and Lin, C.-J.: LIBSVM: A library for support vector machines, ACM Trans. Intel. Syst. Technol., 2, 27, https://doi.org/10.1145/1961189.1961199, 2011. a
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: SMOTE:
synthetic minority over-sampling technique, J. Artif. Intel. Res., 16, 321–357, 2002. a
Chen, P.-C. and Kezunovic, M.: Fuzzy logic approach to predictive risk analysis in distribution outage management, IEEE T. Smart Grid, 7, 2827–2836, 2016. a
Chen, S. H., Jakeman, A. J., and Norton, J. P.: Artificial intelligence
techniques: an introduction to their use for modelling environmental systems,
Math. Comput. Simul., 78, 379–400, 2008. a
Csilléry, K., Kunstler, G., Courbaud, B., Allard, D., Lassegues, P.,
Haslinger, K., and Gardiner, B.: Coupled effects of wind-storms and drought
on tree mortality across 115 forest stands from the Western Alps and the Jura
mountains, Global Change Biol., 23, 5092–5107, https://doi.org/10.1111/gcb.13773, 2017. a
Donat, M. G., Leckebusch, G. C., Wild, S., and Ulbrich, U.: Future changes in
European winter storm losses and extreme wind speeds inferred from GCM and
RCM multi-model simulations, Nat. Hazards Earth Syst. Sci., 11, 1351–1370, https://doi.org/10.5194/nhess-11-1351-2011, 2011. a
Dozat, T.: Incorporating nesterov momentum into adam, in: 4th International Conference on Learning Representations, 2–4 May 2016, San Juan, Puerto Rico, available at: https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ (last access: 10 February 2021), 2016. a
Eskandarpour, R. and Khodaei, A.: Machine Learning Based Power Grid Outage
Prediction in Response to Extreme Events, IEEE T. Power Syst., 32, 3315–3316, 2017. a
European Centre for Medium-Range Weather Forecasts: ERA5: large 10 m winds,
available at: https://confluence.ecmwf.int/display/CKB/ERA5:+large+10m+winds (last access: 10 May 2020), 2019. a
Finnish Energy: Energy distribution interruptions 2011 – Finnish Energy,
Tech. rep., Finnish Energy, available at:
https://energia.fi/files/605/Keskeytystilasto_2011.pdf (last access: 16 February 2020), 2011. a
Finnish Energy: Energy distribution interruptions 2013 – Finnish Energy,
Tech. rep., Finnish Energy, available at:
https://energia.fi/files/605/Keskeytystilasto_2013.pdf (last access: 4 June 2020), 2013. a
Finnish Meteorological Institute: Climate Statistics Report (December 2011), Helsinki, Finland, 1–24, 2011. a
Finnish Meteorological Institute: Rauli nousi myrskyjen raskaaseen sarjaan | Ilmastokatsaus – Ilmatieteen laitos, Ilmastokatsaus,
available at: http://www.ilmastokatsaus.fi/2016/08/29/rauli-nousi-myrskyjen-raskaaseen-sarjaan/
(last access: 15 June 2020), 2016. a
Finnish Meteorological Institute: Kesäkuun 2018 kuukausikatsaus | Ilmastokatsaus – Ilmatieteen laitos, available at:
http://www.ilmastokatsaus.fi/2018/07/04/kesakuun-2018-kuukausikatsaus/ (last access: 15 June 2020), 2018. a
Finnish Meteorological Institute – Twitter: Ilmatieteen laitos Twitterissä: “#Juhannus'aaton-#myrsky #Pauliina lukuina klo 15.30:
kovin puuska 32,5 m/s ja 10min keskituuli 26,5 m/s (Kaskinen Sälgrund),
maa-alueilla Helsinki Kumpula puuska 22,7 m/s. Vaasa Klemettilä 24 h
sademäärä 41 mm.”/Twitter, available at:
https://twitter.com/meteorologit/status/1010144460703969280, last access: 17 June 2020. a
Gardiner, B., Blennow, K., Carnus, J., Fleischner, P., Ingemarson, F.,
Landmann, G., Lindner, M., Marzano, M., Nicoll, B., Orazio, C., Peyron, J.,
Reviron, M., Schelhaas, M., Schuck, A., Spielmann, M., and Usbeck, T.:
Destructive storms in European Forests: Past and Forthcoming Impacts. Final
report to European Commission – DG Environment, European Forest Institute,
Wageningen, the Netherlands, 2010. a, b
Gardiner, B., Schuck, A., Schelhaas, M.-J., Orazio, C., Blennow, K., and
Nicoll, B.: Living with Storm Damage to Forests What Science Can Tell Us What
Science Can Tell Us, European Forest Institute, EFI, Joensuu, Finland,
https://doi.org/10.13140/2.1.1730.2400, 2013. a, b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, in: Deep Learning, MIT Press, Cambridge, Massachusetts, USA, 164–223, 2016. a
Gregow, H., Laaksonen, A., and Alper, M. E.: Increasing large scale windstorm
damage in Western, Central and Northern European forests, 1951–2010, Scient. Rep., 7, 46397, https://doi.org/10.1038/srep46397, 2017. a
Guikema, S. D., Quiring, S. M., and Han, S. R.: Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems, Risk Anal., 30,
1744–1752, https://doi.org/10.1111/j.1539-6924.2010.01510.x, 2010. a
Guikema, S. D., Nateghi, R., Quiring, S. M., Staid, A., Reilly, A. C., and Gao, M.: Predicting Hurricane Power Outages to Support Storm Response Planning, IEEE Access, 2, 1364–1373, https://doi.org/10.1109/ACCESS.2014.2365716, 2014. a
Haarsma, R. J., Hazeleger, W., Severijns, C., De Vries, H., Sterl, A.,
Bintanja, R., Van Oldenborgh, G. J., and Van Den Brink, H. W.: More hurricanes to hit western Europe due to global warming, Geophys. Res. Lett., 40, 1783–1788, https://doi.org/10.1002/grl.50360, 2013. a
Han, S. R., Guikema, S. D., and Quiring, S. M.: Improving the predictive
accuracy of hurricane power outage forecasts using generalized additive
models, Risk Anal., 29, 1443–1453, https://doi.org/10.1111/j.1539-6924.2009.01280.x, 2009. a
Hanewinkel, M.: Neural networks for assessing the risk of windthrow on the
forest division level: a case study in southwest Germany, Eur. J. Forest Res., 124, 243–249, 2005. a
Hanninen, K. and Naukkarinen, J.: 570 000 customers experienced power losses at the end of the year, (Loppuvuoden sähkökatkoista kärsi 570 000 asiakasta – Energiateollisuus, available at:
http://www.mynewsdesk.com/fi/pressreleases/loppuvuoden-saehkoekatkoista-kaersi-570-000-asiakasta-725038 (last access: 11 June 2020), 2012. a
He, J., Wanik, D. W., Hartman, B. M., Anagnostou, E. N., Astitha, M., and
Frediani, M. E.: Nonparametric Tree-Based Predictive Modeling of Storm
Outages on an Electric Distribution Network, Risk Anal., 37, 441–458,
https://doi.org/10.1111/risa.12652, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, 2018. a, b
Hersbach, H., Bell, W., Berrisford, P., Horányi, A. J. M.-S., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global
reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF newsletter, European Centre for Medium-Range Weather Forecasts, Reading, UK, 17–24,
https://doi.org/10.21957/vf291hehd7, 2019. a, b
Holdaway, M. R.: Spatial modeling and interpolation of monthly temperature
using kriging, Clim. Res., 6, 215–225, 1996. a
Ilta-Sanomat: Rauli-myrsky repii Suomea: lähes 200000 taloutta ilman
sähköjä – Kotimaa – Ilta-Sanomat, available at:
https://www.is.fi/kotimaa/art-2000001248969.html (last access: 15 June 2020), 2016. a
IPCC: Impacts of 1.5 ∘C global warming on natural and human systems, Global warming of 1.5 ∘C, An IPCC Special Report, Intergovernmental Panel on Climate Change, Geneva, Swizerland, 2018. a
Jokinen, P., Vajda, A., and Gregow, H.: The benefits of emergency rescue and
reanalysis data in decadal storm damage assessment studies, Adv. Sci. Res., 12, 97–101, https://doi.org/10.5194/asr-12-97-2015, 2015. a
Kankanala, P., Pahwa, A., and Das, S.: Regression models for outages due to
wind and lightning on overhead distribution feeders, in: 2011 IEEE Power and Energy Society General Meeting, 24–28 July 2011, Detroit, Michigan, USA, 1–4, 2011. a
Kankanala, P., Pahwa, A., and Das, S.: Estimation of Overhead Distribution
System Outages Caused by Wind and Lightning Using an Artificial Neural
Network, in: International Conference on Power System Operation & Planning, 12–14 November 2012, Las Vegas, USA, 2012. a
Kankanala, P., Das, S., and Pahwa, A.: AdaBoost+: An Ensemble Learning Approach for Estimating Weather-Related Outages in Distribution Systems, IEEE T. Power Syst., 29, 359–367, 2014. a
Karthick, S., Malathi, D., Sudarsan, J., and Nithiyanantham, S.: Performance,
evaluation and prediction of weather and cyclone categorization using various
algorithms, Model. Earth Syst. Environ., https://doi.org/10.1007/s40808-020-00887-7, in press, 2020. a, b
Korhonen, K., Packalen, T., and Kangas, A.: The National Forest Inventory (NFI), available at: http://kartta.luke.fi/index-en.html, last access: 20 February 2019. a
Kossin, J. P. and Sitkowski, M.: An objective model for identifying secondary
eyewall formation in hurricanes, Mon. Weather Rev., 137, 876–892, 2009. a
Kron, W. and Schuck A.: After the floods, Tech. rep., Münchener
Rückversicherungs-Gesellschaft (Munich Reinsurance Company), available at:
https://www.munichre.com/content/dam/munichre/global/content-pieces/documents/302-08121_en.pdf/_jcr_content/renditions/original./302-08121_en.pdf
(last access: 5 June 2020), 2013. a
Kufeoglu, S. and Lehtonen, M.: Cyclone Dagmar of 2011 and its impacts in
Finland, in: IEEE PES Innovative Smart Grid Technologies Conference Europe,
vol. 2015-January, IEEE Computer Society, Istanbul, Turkey, https://doi.org/10.1109/ISGTEurope.2014.7028868, 2015. a, b
Lagerquist, R., McGovern, A., and Smith, T.: Machine learning for real-time
prediction of damaging straight-line convective wind, Weather Forecast., 32, 2175–2193, 2017. a
Lamb, H. and Knud, F.: Historic Storms of the North Sea, British Isles and
Northwest Europe – Google Books, available at:
https://books.google.fi/books?hl=en&lr=&id=P4n1z9rOh5MC&oi=fnd&pg=PR8&ots=ewtYPSlQ1H&sig=VJJ00ehiPL0d1oaTDfa14nFb3zI&redir_esc=y#v=onepage&q=duration&f=false (last access: 30 September 2020), 1991. a
Leckebusch, G. C., Renggli, D., and Ulbrich, U.: Development and application of an objective storm severity measure for the Northeast Atlantic region,
Meteorol. Z., 17, 575–587, https://doi.org/10.1127/0941-2948/2008/0323, 2008. a
Li, Z., Singhee, A., Wang, H., Raman, A., Siegel, S., Heng, F.-L., Mueller, R., and Labut, G.: Spatio-temporal forecasting of weather-driven damage in a
distribution system, in: 2015 IEEE Power & Energy Society General Meeting,
26–30 July 2015, Denver, CO, USA, 1–5, 2015. a
Liu, Y., Zhong, Y., and Qin, Q.: Scene Classification Based on Multiscale
Convolutional Neural Network, IEEE T. Geosci. Remote Sens., 56, 7109–7121, https://doi.org/10.1109/TGRS.2018.2848473, 2018. a
Mäkisara, K., Katila, M., Peräsaari, J., and Tomppo, E.: The multi-source national forest inventory of Finland – methods and results 2013, Natural Resources Institute Finland (Luke), Helsinki, Finland, 215 pp., ISBN 978-952-326-186-0, 2016. a
Masson-Delmotte, T., Zhai, P., Pörtner, H., Roberts, D., Skea, J., Shukla, P., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterfield, T.: IPCC, 2018: Summary for Policymakers, in: Global warming of 1.5 ∘C. An IPCC Special
Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global, Geneva, Switzerland, 2018. a
myrskyvaroitus.com: 2018 Syystrombeja, available at:
https://myrskyvaroitus.com/index.php/myrskytieto/myrskyhistoria/202-2018-syystrombeja
(last access: 18 June 2020), 2018. a
Nateghi, R., Guikema, S., and Quiring, S. M.: Power Outage Estimation for
Tropical Cyclones: Improved Accuracy with Simpler Models, Risk Anal., 34,
1069–1078, https://doi.org/10.1111/risa.12131, 2014. a
Niemelä, E.: KESKEYTYSTILASTO 2017 (i), Tech. Rep. 2018-06-14
11:51:52.916, Energiateollisuus Ry, Helsinki, Finland, available at:
https://energia.fi/files/2785/Sahkon_keskeytystilasto_2017.pdf (last access: 16 February 2020), 2018. a
Nurmi, V., Pilli-Sihvola, K., Gregow, H., and Perrels, A.: Overadaptation to
Climate Change? The Case of the 2013 Finnish Electricity Market Act,
Econ. Disast. Clim. Change, 3, 161–190, https://doi.org/10.1007/s41885-018-0038-1, 2019. a
Peltola, H., Kellomäki, S., and Väisänen, H.: Model Computations of the Impact of Climatic Change on the Windthrow Risk of Trees, Climatic Change, 41, 17–36, https://doi.org/10.1023/A:1005399822319, 1999. a, b
Pinto, J. G., Bellenbaum, N., Karremann, M. K., and Della-Marta, P. M.: Serial clustering of extratropical cyclones over the North Atlantic and Europe under recent and future climate conditions, J. Geophys. Res.-Atmos., 118, 12476–12485, https://doi.org/10.1002/2013JD020564, 2013. a
Ramon, J., Lledó, L., Torralba, V., Soret, A., and Doblas‐Reyes, F. J.: What global reanalysis best represents near‐surface winds?, Natural Disasters
and Adaptation to Climate Change, 145, 3236–3251, https://doi.org/10.1002/qj.3616, 2019. a
Rasmussen, C. E.: Gaussian processes in machine learning, in: Summer School on Machine Learning, Springer, Cambridge, Massachusetts, USA, 63–71, 2003. a
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F.:
Cross-validation strategies for data with temporal, spatial, hierarchical, or
phylogenetic structure, Ecography, 40, 913–929, https://doi.org/10.1111/ecog.02881, 2017. a
Rupp, M.: Machine learning for quantum mechanics in a nutshell, Int. J. Quant. Chem., 115, 1058–1073, 2015. a
Schelhaas, M.-J.: Impacts of natural disturbances on the development of
European forest resources: application of model approaches from tree and
stand levels to large-scale scenarios, Dissertationes Forestales, Alterra, Wageningen, the Netherlands, https://doi.org/10.14214/df.56, 2008. a
Schelhaas, M.-J., Nabuurs, G.-J., and Schuck, A.: Natural disturbances in the
European forests in the 19th and 20th centuries, Global Change Biol., 9,
1620–1633, https://doi.org/10.1046/j.1365-2486.2003.00684.x, 2003. a
Seidl, R., Schelhaas, M., Rammer, W., and Verkerk, P.: Increasing forest
disturbances in Europe and their impact on carbon storage, Nat. Clim. Change, 4, 806–810, https://doi.org/10.1038/NCLIMATE2318, 2014. a
Shawe-Taylor, J. and Cristianini, N.: Kernel methods for pattern analysis, Cambridge University Press, Cambridge, 296–297, 2004. a
Shield, S.: Predictive Modeling of Thunderstorm-Related Power Outages, MS Thesis, The Ohio State University, Columbus, Ohio, USA, 2018. a
Singhee, A. and Wang, H.: Probabilistic forecasts of service outage counts from severe weather in a distribution grid, in: 2017 IEEE Power & Energy Society General Meeting, 16–20 July 2017, Chicago, IL, USA, 1–5, 2017. a
Suvanto, S., Henttonen, H. M., Nöjd, P., and Mäkinen, H.: Forest
susceptibility to storm damage is affected by similar factors regardless of
storm type: Comparison of thunder storms and autumn extra-tropical cyclones
in Finland, Forest Ecol. Manage., 381, 17–28, https://doi.org/10.1016/j.foreco.2016.09.005, 2016. a, b
Tervo, R.: SASSE polygon process, Zenodo, https://doi.org/10.5281/zenodo.4525234, 2020 a
Tervo, R. and Sirviö, T: SmartMet Server setup for ERA5, Zenodo, https://doi.org/10.5281/zenodo.4525236, 2020a. a
Ukkonen, P., Manzato, A., and Mäkelä, A.: Evaluation of thunderstorm
predictors for Finland using reanalyses and neural networks, J. Appl. Meteorol. Clim., 56, 2335–2352, 2017. a
Ulbrich, U., Pinto, J. G., Kupfer, H., Leckebusch, G., Spangehl, T., and
Reyers, M.: Changing Northern Hemisphere storm tracks in an ensemble of IPCC
climate change simulations, J. Climate, 21, 1669–1679, 2008. a
Ulbrich, U., Leckebusch, G. C., and Pinto, J. G.: Extra-tropical cyclones in
the present and future climate: A review, in: Theoretical and Applied
Climatology, vol. 96, Springer, Vienna, 117–131,
https://doi.org/10.1007/s00704-008-0083-8, 2009. a
Virot, E., Ponomarenko, A., Dehandschoewercker, Quéré, D., and Clanet, C.: Critical wind speed at which trees break, Phys. Rev. E, 93, 023001, https://doi.org/10.1103/PhysRevE.93.023001, 2016. a
Wang, G., Xu, T., Tang, T., Yuan, T., and Wang, H.: A Bayesian network model
for prediction of weather-related failures in railway turnout systems, Exp. Sys. Appl., 69, 247–256, https://doi.org/10.1016/j.eswa.2016.10.011, 2017. a
Wilks, D. S. Statistical methods in the atmospheric sciences, in: Vol. 100, Academic Press, Cambridge, Massachusetts, Yhdysvallat, ISBN 978-0-12-385022-5, 2011. a
Williams, C. K. and Rasmussen, C. E.: Gaussian processes for machine learning, in: vol. 2, MIT Press, Cambridge, MA, 2006. a
Yang, F., Wanik, D. W., Cerrai, D., Bhuiyan, M. A. E., and Anagnostou, E. N.:
Quantifying uncertainty in machine learning-based power outage prediction
model training: A tool for sustainable storm restoration, Sustainability, 12,
1525, https://doi.org/10.3390/su12041525, 2020. a
Yue, M., Toto, T., Jensen, M. P., Giangrande, S. E., and Lofaro, R.: A
Bayesian approach-based outage prediction in electric utility systems using
radar measurement data, IEEE T. Smart Grid, 9, 6149–6159,
https://doi.org/10.1109/TSG.2017.2704288, 2018. a
Short summary
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.
Predicting the number of power outages caused by extratropical storms is a key challenge for...
Altmetrics
Final-revised paper
Preprint