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Atmosphere
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Natural Disasters and Hazards in the Geographical Environment
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Natural Disasters and Hazards in the Geographical Environment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 23275

Special Issue Editor

Dr. Tin Lukić

E-Mail Website1 Website2
Guest Editor
Department of Geography, Tourism and Hotel Management, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad 21000, Serbia
Interests: physical geography; natural hazards; rainfall erosivity; climate extremes; climatology; meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards mostly occur as sudden, disastrous events with a high possibility of causing harm to various aspects of human life. Both natural hazard research and natural disaster research in geography have a long history and have evolved to incorporate the complexities of physical and human environments, as well as their interactions. Our understanding of the dimensions of hazards continues to be ever changing. The study of these phenomena strives to encompass all casualties in the environment, especially within the geosphere.

While natural hazards present a threat to humans and their environment, they do not necessarily result from natural and environmental processes and causes alone. Processes of interaction between different systems also bring about hazards. In the era of pronounced climate variability, understanding and predicting future hazard variability and occurrence patterns, as well as consequences in the geographical environment, are scientific challenges crucial to the development and implementation of sustainable management practices and policies.

This Special Issue of Atmosphere encompasses papers that present interdisciplinary concepts, methods, and case studies in the prediction, characterization, monitoring, mapping, communication, risk management, and mitigation of hydro-meteorological hazards and disasters (extreme climate events, wildfires, droughts, floods, mass movements (wet), rainfall erosivity, etc.). All types and sub-types of hazards and disasters associated with the atmosphere, hydrosphere, and land, as well as those induced by climate change and variability, will be considered. The main topics of interest include (but are not limited to) the environmental, socio-economic, and health aspects of hydro-meteorological hazards and disasters in various geographical settings, quantitative and qualitative hazard and risk assessment, multi-hazard risk assessment, multi-vulnerability risk assessment, multi-hazard early warning systems, advances in hazard and disaster visualization, applications of new techniques in hazard and disaster research, and the spatial–temporal effects on hazard and risk assessment at local to regional scales.

Dr. Tin Lukić
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural hazards and disasters
  • hydro-meteorological hazard assessment
  • climate change impacts
  • socio-economic impacts
  • vulnerability of weather-dependent economic sectors
  • risk analysis and management
  • land–climate interactions
  • spatial–temporal analysis
  • mapping and visualization
  • early warning systems

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Published Papers (7 papers)

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Research

24 pages, 7170 KiB  
Article
Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data
by Tanja Micić Ponjiger, Tin Lukić, Robert L. Wilby, Slobodan B. Marković, Aleksandar Valjarević, Slavoljub Dragićević, Milivoj B. Gavrilov, Igor Ponjiger, Uroš Durlević, Miško M. Milanović, Biljana Basarin, Dragan Mlađan, Nikola Mitrović, Vasile Grama and Cezar Morar
Atmosphere 2023, 14(1), 104; https://doi.org/10.3390/atmos14010104 - 3 Jan 2023
Cited by 15 | Viewed by 3797
Abstract
The Western Balkans (WB) region is highly prone to water erosion processes, and therefore, the estimation of rainfall erosivity (R-factor) is essential for understanding the complex relationships between hydro-meteorological factors and soil erosion processes. The main objectives of this study are to (1) [...] Read more.
The Western Balkans (WB) region is highly prone to water erosion processes, and therefore, the estimation of rainfall erosivity (R-factor) is essential for understanding the complex relationships between hydro-meteorological factors and soil erosion processes. The main objectives of this study are to (1) estimate the spatial-temporal distribution R-factor across the WB region by applying the RUSLE and RUSLE2 methodology with data for the period between 1991 and 2020 and (2) apply cluster analysis to identify places of high erosion risk, and thereby offer a means of targeting suitable mitigation measures. To assess R-factor variability, the ERA5 reanalysis hourly data (0.25° × 0.25° spatial resolution) comprised 390 grid points were used. The calculations were made on a decadal resolution (i.e., for the 1990s, the 2000s, and the 2010s), as well as for the whole study period (1991–2020). In order to reveal spatial patterns of rainfall erosivity, a k-means clustering algorithm was applied. Visualization and mapping were performed in python using the Matplotlib, Seaborn, and Cartopy libraries. Hourly precipitation intensity and monthly precipitation totals exhibited pronounced variability over the study area. High precipitation values were observed in the SW with a >0.3 mm h−1 average, while the least precipitation was seen in the Pannonian Basin and far south (Albanian coast), where the mean intensity was less than an average of 0.1 mm h−1. R-factor variability was very high for both the RUSLE and RUSLE2 methods. The mean R-factor calculated by RUSLE2 was 790 MJ mm ha−1·h−1·yr−1, which is 58% higher than the mean R-factor obtained from RUSLE (330 MJ mm ha−1·h−1·yr−1). The analysis of the R-factor at decadal timescales suggested a rise of 14% in the 2010s. The k-means algorithm for both the RUSLE and RUSLE2 methods implies better spatial distribution in the case of five clusters (K = 5) regarding the R-factor values. The rainfall erosivity maps presented in this research can be seen as useful tools for the assessment of soil erosion intensity and erosion control works, especially for agriculture and land use planning. Since the R-factor is an important part of soil erosion models (RUSLE and RUSLE2), the results of this study can be used as a guide for soil control works, landscape modeling, and suitable mitigation measures on a regional scale. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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15 pages, 5793 KiB  
Article
Impact of Human Activities on Hydrological Drought Evolution in the Xilin River Basin
by Wei Li, Wenjun Wang, Yingjie Wu, Qiang Quan, Shuixia Zhao and Weijie Zhang
Atmosphere 2022, 13(12), 2079; https://doi.org/10.3390/atmos13122079 - 10 Dec 2022
Cited by 7 | Viewed by 3273
Abstract
The impact of human activities on the hydrological cycle makes hydrological drought no longer a natural disaster in a strict sense, and influences the stationarity of the hydrologic process. In this context, assessment methods that consider nonstationary conditions are more reasonable in the [...] Read more.
The impact of human activities on the hydrological cycle makes hydrological drought no longer a natural disaster in a strict sense, and influences the stationarity of the hydrologic process. In this context, assessment methods that consider nonstationary conditions are more reasonable in the study of hydrological drought. In this study, we used the SWAT (Soil and Water Assessment Tool) model to reconstruct the historical hydrological conditions during the period affected by human activities (1998–2019) of the Xilin River Basin. After calculating the standardized runoff index (SRI) at multiple time scales, we compared the drought characteristics of the basin under natural conditions and under the influence of human activities. The results show that human activities were the main reason for the significant decrease of runoff in the basin (an obvious change-point for runoff series is identified in 1998), which accounted for 68%. Compared with natural conditions, human activities delayed the occurrence of short-term drought in the basin and changed its seasonal distribution characteristics, resulting in an increase in the frequency of severe and extreme droughts in autumn; the corresponding drought frequency increased by 15% and 60%, respectively. Moreover, human activities have also prolonged drought duration, increased drought intensity, and increased the uncertainty of drought in the basin. The proposed method is demonstrated to be efficient in quantifying the effects of human activities on hydrological drought, and the findings of this study provide a scientific basis for water resource management, drought early warning, and forecasting under a changing environment. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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16 pages, 2902 KiB  
Article
The Extremely Active 2020 Hurricane Season in the North Atlantic and Its Relation to Climate Variability and Change
by José Javier Hernández Ayala and Rafael Méndez-Tejeda
Atmosphere 2022, 13(12), 1945; https://doi.org/10.3390/atmos13121945 - 23 Nov 2022
Cited by 4 | Viewed by 3141
Abstract
The 2020 hurricane season in the North Atlantic basin was the most active on record, with 30 named tropical cyclones. In this study, climate trends in oceanic and atmospheric parameters (including the sea surface temperatures, ocean heat content, cloud cover, mid-level humidity, vertical [...] Read more.
The 2020 hurricane season in the North Atlantic basin was the most active on record, with 30 named tropical cyclones. In this study, climate trends in oceanic and atmospheric parameters (including the sea surface temperatures, ocean heat content, cloud cover, mid-level humidity, vertical wind shear, and sea level pressure) were used to model the tropical cyclone, hurricane, and major hurricane frequency in the post-satellite era (1966–2020). The relationships between storm frequency and climate variability factors (including the El Niño Southern Oscillation, the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation, and the Atlantic Meridional Mode) were also examined. This was performed to determine the factors that exerted the greatest influence on the most active hurricane seasons on record. Mann–Kendall trend tests, Pearson’s correlations tests, stepwise Poisson linear regression models and spatial analysis techniques were used to identify the climate change and variability factors that best explained the tropical cyclone frequency in the North Atlantic. Our results show that hyperactive hurricane seasons, such as that of 2020, tend to be associated with higher cloud cover development, lower sea level pressure patterns, higher sea surface temperatures, positive phases of the Atlantic Multidecadal Oscillation and the Atlantic Meridional Mode, and weaker wind shear environments. Seasons with more major hurricanes had higher ocean heat contents and weaker wind shear environments. The 2020 and 2005 seasons had similar cloud cover and sea level pressure patterns, yet the wind shear was lower in 2020 than in 2005, which was associated with La Niña dominant conditions that could explain why 2020 surpassed 2005 in the total number of storms. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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12 pages, 64032 KiB  
Article
Quantifying the Effects of Drought Using the Crop Moisture Stress as an Indicator of Maize and Sunflower Yield Reduction in Serbia
by Gordan Mimić, Branislav Živaljević, Dragana Blagojević, Branislav Pejak and Sanja Brdar
Atmosphere 2022, 13(11), 1880; https://doi.org/10.3390/atmos13111880 - 10 Nov 2022
Cited by 8 | Viewed by 2257
Abstract
The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an [...] Read more.
The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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20 pages, 5499 KiB  
Article
Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms
by Jesús Soria-Ruiz, Yolanda M. Fernandez-Ordoñez, Juan P. Ambrosio-Ambrosio, Miguel J. Escalona-Maurice, Guillermo Medina-García, Erasto D. Sotelo-Ruiz and Martha E. Ramirez-Guzman
Atmosphere 2022, 13(11), 1852; https://doi.org/10.3390/atmos13111852 - 7 Nov 2022
Cited by 7 | Viewed by 2614
Abstract
Recurrent flooding occurs in most years along different parts of the Gulf of Mexico coastline and the central and southeastern parts of Mexico. These events cause significant economic losses in the agricultural, livestock, and infrastructure sectors, and frequently involve loss of human life. [...] Read more.
Recurrent flooding occurs in most years along different parts of the Gulf of Mexico coastline and the central and southeastern parts of Mexico. These events cause significant economic losses in the agricultural, livestock, and infrastructure sectors, and frequently involve loss of human life. Climate change has contributed to flooding events and their more frequent occurrence, even in areas where such events were previously rare. Satellite images have become valuable information sources to identify, precisely locate, and monitor flooding events. The machine learning models use remote sensing images pixels as input feature. In this paper, we report a study involving 16 combinations of Sentinel-1 SAR images, Sentinel-2 optical images, and digital elevation model (DEM) data, which were analyzed to evaluate the performance of two widely used machine learning algorithms, gradient boosting (GB) and random forest (RF), for providing information about flooding events. With machine learning models GB and RF, the input dataset (Sentinel-1, Sentinel-2, and DEM) was used to establish rules and classify the set in the categories specified by previous tags. Monitoring of flooding was performed by tracking the evolution of water bodies during the dry season (before the event) through to the occurrence of floods during the rainy season (during the event). For detection of bodies of water in the dry season, the metrics indicate that the best algorithm is GB with combination 15 (F1m = 0.997, AUC = 0.999, K = 0.994). In the rainy season, the GB algorithm had better metrics with combination 16 (F1m = 0.995, AUC = 0.999, Kappa = 0.994), and detected an extent of flooded areas of 1113.36 ha with depths of <1 m. The high classification performance shown by machine learning algorithms, particularly the so-called assembly algorithms, means that they should be considered capable of improving satellite image classification for detection of flooding over traditional methods, in turn leading to better monitoring of flooding at local, regional, and continental scales. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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17 pages, 1783 KiB  
Article
Risky Travel? Subjective vs. Objective Perceived Risks in Travel Behaviour—Influence of Hydro-Meteorological Hazards in South-Eastern Europe on Serbian Tourists
by Ivana Blešić, Milan Ivkov, Jelena Tepavčević, Jovanka Popov Raljić, Marko D. Petrović, Tamara Gajić, Tatiana N. Tretiakova, Julia A. Syromiatnikova, Dunja Demirović Bajrami, Milica Aleksić, Duško Vujačić, Emina Kričković, Milan Radojković, Cezar Morar and Tin Lukić
Atmosphere 2022, 13(10), 1671; https://doi.org/10.3390/atmos13101671 - 13 Oct 2022
Cited by 13 | Viewed by 3196
Abstract
In terms of climate related security risks, the region of South-Eastern Europe (SEE) can be identified as one of the world’s hot spots. As weather-related hazards continue to increase in numbers and spatial distribution, risk perception in the tourism industry becomes even more [...] Read more.
In terms of climate related security risks, the region of South-Eastern Europe (SEE) can be identified as one of the world’s hot spots. As weather-related hazards continue to increase in numbers and spatial distribution, risk perception in the tourism industry becomes even more important. Additionally, people’s perception of natural hazards is one of the key elements in their decision-making process when choosing a travel destination. Although a vast number of studies have examined aspects of risk perception, an integrated approach which considers both objective and subjective factors related to the tourism industry and hydro-meteorological hazards remains relatively scarce. This pioneering study inspects the causality between objective perceived risks, as well as subjective risk factors. A methodological approach and the obtained results present a certain novelty since the previous conceptualized Psychological Preparedness for Disaster Threat Scale (PPDTS) was applied for the first time in the tourism industry. The obtained results reveal the presence of a statistically significant relationship between objective risks and certain subjective risk factors (gender, age, education, prior experience, anticipation, and awareness). Therefore, this study may offer a conceptual platform for both theoretical and practical implications for enhanced approaches oriented toward more qualitative risk management at a given travel destination, in regions prone to hydro-meteorological hazards. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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17 pages, 5061 KiB  
Article
Spatiotemporal Variations and Causes of Wind/Rainfall Erosion Climatic Erosivity in Qinghai Province, China
by Yihua Liu, Ge Gao, Hongmei Li, Lüliu Liu, Zong Fan and Tingting Wen
Atmosphere 2022, 13(10), 1649; https://doi.org/10.3390/atmos13101649 - 10 Oct 2022
Cited by 5 | Viewed by 1606
Abstract
Wind and rainfall climatic erosivities are important parameters with which to assess the possible effects of climatic conditions on erosion. In this study, wind erosion climatic erosivity (C-factor) and rainfall erosivity (Rday-factor) were calculated for the period 1970–2020 based on data [...] Read more.
Wind and rainfall climatic erosivities are important parameters with which to assess the possible effects of climatic conditions on erosion. In this study, wind erosion climatic erosivity (C-factor) and rainfall erosivity (Rday-factor) were calculated for the period 1970–2020 based on data from 50 meteorological stations in Qinghai Province. The Mann–Kendall test, trend analysis, and K-means clustering method were used to explore the spatiotemporal characteristics of regional wind/rainfall climatic erosivity. Results showed that the annual mean value of the C-factor was 25.8 over the past 51 years, with an obvious trend of decline of 6.5/10a. The mean annual value of the Rday-factor was 491.6 MJ·mm/(hm2·h·a), with an obvious trend of increasing of 24.0 MJ·mm/(hm2·h·10a). Strong seasonality was found in both the C-factor and the Rday-factor. The highest values of the C-factor were found in late winter and spring, accounting for a substantial proportion of the annual C-factor (48.6%). Rainfall erosivity occurred mainly April–October, with the highest values in summer, accounting for a substantial proportion of the annual Rday-factor (72.9%). Wind-erosion climatic erosivity and rainfall erosivity were obviously asynchronous on an annual basis, and the period of their combination extended the time of soil erosion. Through k-means clustering analysis, climatic erosivity in Qinghai Province was divided into three regions: the first dominated by wind-erosion climatic erosivity, the second dominated by rainfall erosivity, and the third dominated by their combination. The most serious land erosion occurred in the third region, accounting for 34.3% of the entire land area of Qinghai Province, where annual rainfall was found to be broadly consistent at 300–400 mm. Wind speed, temperature, rainfall, and sunshine duration are key factors known to impact the variation in wind-erosion climatic erosivity, while annual erosive rainfall, number of rainy days, and sunshine duration are the main factors known to impact the variation in rainfall erosivity. The findings of this study represent a robust reference for ecoenvironmental protection, sustainable development, and soil protection. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment)
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