Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition

Topic Information

Dear Colleagues,

The incidence of hazardous events has increased in recent years. While some hazardous events are triggered by natural processes, others are induced by human-related activities such as urbanization, industrialization, construction, negligence, etc. In this Topic, the role/contribution of humans in the occurrence of hazards and the impacts of these incidents on humanity and the environment are examined. Covering both natural and human-induced hazards, this Topic aims to publish a collection of innovative original research papers, case studies, and review papers addressing a wide range of concerns regarding natural hazards and the challenges they pose to man and the environment. We welcome multidisciplinary submissions on erosion, landslides, flooding, extreme temperatures, droughts, wildfire, tsunamis, volcanic eruptions, earthquakes, coastal hazards, subsidence, sinkholes, windstorms, tornadoes, etc. The incorporation of geospatial, (geo)statistical, numerical or index-based, and soft computational modelling techniques is highly encouraged. The themes that will be considered in this topic include, but are not limited to, the following:

• The modelling, prediction, characterization, and risk assessment of natural hazards.
• Anthropogenic and technological hazards in the present age: insights on the role of man.
• The impacts of natural hazard occurrences on man and the environment.
• Emerging technologies and sensors for geohazard data processing and interpretation.
• Climate change and the occurrence of mass movement and hydrometeorological hazards.
• Engineering failures related to water: their monitoring, assessment, and risk reduction.
• Extreme temperatures and environmental sustainability.
• The multi-hazard analysis and multiscale modelling of natural hazards.
• Seismic-related hazards and human settlement/displacement challenges.
• Age and gender exposure, response, and resilience to natural hazards.
• Social impacts of natural hazards in urban, semi-urban, and rural regions.
• Earth’s resources and hazards: the interactions of the Earth’s systems in hazardous events.
• Land use/land conservation strategies in the face of natural hazards.
• Planning, policymaking, and management strategies for hazard mitigation.
• The deterministic, computational, and stochastic modelling of hazard impacts.

The participating journals are Water, Geosciences, Geohazards, Environments and Atmosphere. We are delighted to invite you to submit your high-quality manuscript(s) to our Topic.

Dr. Quoc Bao Pham
Dr. Eyyüp Ensar Başakın
Prof. Dr. Hone-Jay Chu
Dr. Ömer Ekmekcioglu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.3	4.9	2010	19.7 Days	CHF 2400	Submit
GeoHazards geohazards	1.6	2.2	2020	20.1 Days	CHF 1400	Submit
Geosciences geosciences	2.1	5.1	2011	23.6 Days	CHF 1800	Submit
Water water	3.0	6.0	2009	18.9 Days	CHF 2600	Submit
Environments environments	3.7	5.7	2014	19.2 Days	CHF 1800	Submit

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Published Papers (4 papers)

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All Atmosphere GeoHazards Geosciences Water Environments

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Open AccessReview

The Snow Avalanches That Hit Longyearbyen in 2015 and 2017 Led to Better Forecasts and Physical Barriers

by Ole Arve Misund, Marius O. Jonassen and Jan Otto Larsen

GeoHazards 2025, 6(4), 84; https://doi.org/10.3390/geohazards6040084 - 17 Dec 2025

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Abstract

On 19 December 2015 and 21 February 2017, Longyearbyen was hit by major avalanches from the steep hillside of the mountain Sukkertoppen. In this article, we specifically consider the 2015 avalanche that destroyed eleven houses and buried nine people; seven were located and [...] Read more.

On 19 December 2015 and 21 February 2017, Longyearbyen was hit by major avalanches from the steep hillside of the mountain Sukkertoppen. In this article, we specifically consider the 2015 avalanche that destroyed eleven houses and buried nine people; seven were located and rescued, while two died. We describe the meteorological conditions leading up to the avalanche, the rescue operation, the media coverage, and the immediate aftermath of the catastrophe. Both events came as a result of warming, strong easterly winds, and drifting snow, with the December 2015 event being the most extreme. The 2017 avalanche damaged two houses, but no people were hurt. We analyse the catastrophes in relation to the knowledge of the risks and impacts of avalanches in Longyearbyen, as provided through field-based student courses at the University Centre of Svalbard (UNIS). To protect against further avalanche accidents, parts of Longyearbyen have been restructured, and physical barriers against avalanches have been installed on the hillside of Sukkertoppen. Now there are snow drift fences to reduce snow accumulation in the release areas, avalanche protection fences mounted in the hillside, and a large wall at the foot of the mountain to catch avalanche debris in the future. In hindsight, the accidents have contributed to an increased national awareness of the danger of severe weather events. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition)

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17 pages, 2380 KB  

Open AccessArticle

Utilizing Geoparsing for Mapping Natural Hazards in Europe

by Tinglei Yu, Xuezhen Zhang and Jun Yin

Water 2025, 17(24), 3520; https://doi.org/10.3390/w17243520 - 12 Dec 2025

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Abstract

Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, [...] Read more.

Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, the release of such information from the narrative format is encouraging. Natural Language Processing (NLP), a technique demonstrated to be capable of automated data extraction, provides a useful tool in establishing a structured dataset on hazard occurrences. In our study, we utilize scattered textual records of historical natural hazard events to create a novel dataset and explore the applicability of NLP in parallel. We put forward a standard list of toponyms based on manual annotation of a compilation of disaster-related texts, all of which were references in an authoritative publication in the field. The final natural hazards dataset comprised location data, which referred to a specific hazard report in Europe during 1301–1500, together with its geocoding result, year of occurrence and detailed event(s). We evaluated the performance of four pre-trained geoparsing tools (Flair, Stanford CoreNLP, spaCy and Irchel Geoparser) for automated toponym extraction in comparion with the standard list. All four tested methods showed a high precision (above 0.99). Flair had the best overall performance (F1 score 0.89), followed by Stanford CoreNLP (F1 score 0.83) and Irchel Geoparser (F1 score 0.82), while spaCy had a poor recall (0.5). Then we divided natural hazards into six categories: extreme heat, snow and ice, wind and hails, rainstorms and floods, droughts, and earthquakes. Finally, we compared our newly digitized natural hazard dataset to a geocoded version of the dataset provided by Harvard University, thus providing a comprehensive overview of the spatial–temporal characteristics of European hazard observations. The statistical outcomes of the present investigation demonstrate the efficacy of NLP techniques in text information extraction and hazard dataset generation, offering references for collaborative and interdisciplinary efforts. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition)

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15 pages, 3877 KB  

Open AccessArticle

Erosive Wind Characteristics and Aeolian Sediment Transport and Dune Formation in Makran Region of Baluchistan, Iran

by Hamidreza Abbasi, Azadeh Gohardoust, Fazeh Mohammadpour, Mohammad Khosroshahi, Michael Groll and Christian Opp

Atmosphere 2025, 16(6), 650; https://doi.org/10.3390/atmos16060650 - 27 May 2025

Cited by 1 | Viewed by 2181

Abstract

Understanding aeolian sediment transport and wind erosion enhances our knowledge of desert dune formation and sand migration. The Makran region of southern Sistan and Baluchistan is prone to wind-driven erosion alongside frequent sand and dust storms (SDSs). Hourly wind data from two meteorological [...] Read more.

Understanding aeolian sediment transport and wind erosion enhances our knowledge of desert dune formation and sand migration. The Makran region of southern Sistan and Baluchistan is prone to wind-driven erosion alongside frequent sand and dust storms (SDSs). Hourly wind data from two meteorological stations spanning 1994–2020 were analyzed to study erosive winds and sand transport. Wind energy analysis using drift potential (DP) indicated low energy (DP < 200 in vector unit) and minimal spatial variation across the Makran dune fields. The effective winds transporting sand particles were towards the east from November to May, and in the northwestern direction from June to October. The DP showed a gradual decline in the study area from 1990 to 2022, with no significant temporal trends. The sand dune morphology analysis indicates that bimodal wind regimes primarily form linear dunes and sand sheets, while crescentic, transverse, and topographic dunes are also present. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition)

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25 pages, 3496 KB  

Open AccessArticle

Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis

by Kayhan Bayhan, Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Quoc Bao Pham and Hone-Jay Chu

Water 2025, 17(7), 936; https://doi.org/10.3390/w17070936 - 23 Mar 2025

Cited by 1 | Viewed by 2390

Abstract

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled water production, and commercial use. In recent years, due to pressures from climate change and excessive urbanization, a noticeable decline [...] Read more.

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled water production, and commercial use. In recent years, due to pressures from climate change and excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly in arid and semi-arid regions. The corresponding changes have been analyzed using a diverse range of methodologies, including data-driven modeling techniques. Recent evidence has shown a notable acceleration in the utilization of such advanced techniques, demonstrating significant attention by the research community. Therefore, the major aim of the present study is to conduct a bibliometric analysis to investigate the application and evolution of machine learning (ML) techniques in groundwater research. In this sense, studies published between 2000 and 2023 were examined in terms of scientific productivity, collaboration networks, research themes, and methods. The findings revealed that ML techniques offer high accuracy and predictive capacity, especially in water quality, water level estimation, and pollution modeling. The United States, China, and Iran stand out as leading countries emphasizing the strategic importance of ML in groundwater management. However, the outcomes demonstrated that a low level of international cooperation has led to deficiencies in solving transboundary water problems. The study aimed to encourage more widespread and effective use of ML techniques in water management and environmental planning processes and drew attention to the importance of transparent and interpretable algorithms, with the potential to yield rewarding opportunities in increasing the adoption of corresponding technologies by decision-makers. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition)

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