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Atmosphere
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Practical Implications of Future Changes in Climate Extremes and Natural...
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Practical Implications of Future Changes in Climate Extremes and Natural Hazards

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (21 February 2024) | Viewed by 6974

Special Issue Editors

Dr. Kathleen Beyer

E-Mail Website
Guest Editor
School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, TAS 7001, Australia
Interests: regional climate; climate extremes; natural hazards; climate impacts and adaptation research
Dr. Fei Ji

E-Mail Website
Guest Editor
Department of Planning, Industry and Environment, Parramatta, NSW 2150, Australia
Interests: regional climate simulation and evaluation; climate extreme; climate dynamics; climate change impact assessment;
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to this Special Issue from researchers working on a wide range of interdisciplinary topics and techniques. We seek research papers investigating future extremes and hazards and their relevance for climate change risk management, including studies supporting decision-makers and stakeholders to better plan for and manage risk from climate change at a regional scale. Furthermore, studies that unravel critical issues for sector-specific challenges (for example insurance, finance, energy, etc.) are also encouraged.

We are particularly interested in work across extreme rainfall/flood-producing rainfall, temperature extremes (hot/cold extremes), drought, bushfire, severe storms/wind, hail, lightning, statistical analyses, model performance/evaluation at different scales, etc. Studies addressing future changes in the frequency, intensity, and duration of future hazards and subsequent implications are of interest, as are integrated analytical/modelling approaches such as those linked with other types of modelling systems such as hydrology, air quality, etc.

Dr. Kathleen Beyer
Dr. Fei Ji
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • climate extremes
  • integrate modelling systems
  • regional climate
  • climate impacts

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Published Papers (3 papers)

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Research

22 pages, 6594 KiB  
Article
Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda
by James Kagabo, Giri Raj Kattel, Jonah Kazora, Charmant Nicolas Shangwe and Fabien Habiyakare
Atmosphere 2024, 15(6), 691; https://doi.org/10.3390/atmos15060691 - 6 Jun 2024
Cited by 2 | Viewed by 2096
Abstract
Precipitation is an essential component of the hydrological cycle that directly affects human lives. An accurate and early detection of a future rainfall event can help prevent social, environmental, and economic losses. Traditional methods for accurate rainfall prediction have faltered due to their [...] Read more.
Precipitation is an essential component of the hydrological cycle that directly affects human lives. An accurate and early detection of a future rainfall event can help prevent social, environmental, and economic losses. Traditional methods for accurate rainfall prediction have faltered due to their weakness in quantifying nonlinear climatic conditions as they involve numerical weather prediction using radar to solve complex mathematical equations based on contemporary meteorological data. This study aims to develop a precise rainfall forecast model using machine learning (ML), and this model focuses on long short-term memory (LSTM) to enhance rainfall prediction accuracy. In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting extreme weather phenomena worldwide. For instance, long short-term memory (LSTM) is a forecast model that effectively estimates the amount of precipitation based on historical data. We analyzed 85,470 pieces of daily rainfall data from 1983 to 2021 collected from each of four synoptic stations in Rwanda (Kigali Aero, Ruhengeri Aero, Kamembe Aero, and Gisenyi Aero). Advanced ML algorithms, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and LSTM, were applied to predict extreme rainfall events. LSTM outperforms the CNN and GRU with 99.7%, 99.8%, and 99.7% accuracy. LSTM’s ability to filter out noise showed important patterns by handling irregularities in rainfall data to improve forecast results. Our outcomes have significant implications for disaster preparedness and risk mitigation efforts in Rwanda, where frequent natural disasters, including floods, pose a challenge. Our research also demonstrates the superiority of LSTM-based ML algorithms in predicting extreme rainfall events, highlighting their potential to enhance disaster risk resilience and preparedness strategies in Rwanda. Full article
(This article belongs to the Special Issue Practical Implications of Future Changes in Climate Extremes and Natural Hazards)
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24 pages, 4984 KiB  
Article
Projecting Hydroclimatic Extremes: Climate Change Impacts on Drought in a German Low Mountain Range Catchment
by Paula Farina Grosser and Britta Schmalz
Atmosphere 2023, 14(8), 1203; https://doi.org/10.3390/atmos14081203 - 26 Jul 2023
Cited by 2 | Viewed by 1640
Abstract
Germany’s increasing temperatures and droughts are significantly impacting the hydrological realm. This study examines the implications of climate change on future droughts in a representative catchment within Germany’s low mountain range. Findings of this research shed light on potential impacts on future seasonal [...] Read more.
Germany’s increasing temperatures and droughts are significantly impacting the hydrological realm. This study examines the implications of climate change on future droughts in a representative catchment within Germany’s low mountain range. Findings of this research shed light on potential impacts on future seasonal water availability, aiding decision makers and stakeholders in managing regional climate change risks. Climate and drought indices, as well as the climatic water balance, are computed and analyzed until 2100, relative to a reference period. A high emission scenario (RCP8.5) and a climate protection scenario (RCP2.6) are considered to address uncertainties. Results reveal above-average warming in the study area compared to the national average. Under the RCP8.5 scenario, the far future exhibits an average of 44 annual heat days. Despite wetter winters, extended droughts persist. Water stress intensifies in summer and autumn, with a projected 68% increase in dry period duration. The findings emphasize the necessity of adaptation strategies, as even ambitious global warming mitigation efforts require regional adaptation. The study represents the first application of a Germany-wide, bias-adjusted, and regionalized dataset at catchment level. It contributes novel insights for regional water resources management and advances understanding of climate change impacts in German low mountain range regions. Full article
(This article belongs to the Special Issue Practical Implications of Future Changes in Climate Extremes and Natural Hazards)
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36 pages, 23425 KiB  
Article
Evaluation of Convective Environments in the NARCliM Regional Climate Modeling System for Australia
by Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Nicholas Herold and Kellie Cook
Atmosphere 2023, 14(4), 690; https://doi.org/10.3390/atmos14040690 - 6 Apr 2023
Cited by 2 | Viewed by 2000
Abstract
Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate [...] Read more.
Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate models can resolve the convective environments better than global models. Australia, especially the east and southeast parts of the continent, is a global hot spot for severe thunderstorms. This study evaluates the simulated convective environments from the New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) project based on the parameters of CAPE, CIN, 0–6-km vertical wind shear and storm severity. The ensemble regional downscaling is compared against the fifth-generation European Centre for Medium-range Weather Forecast Reanalysis (ERA5). The results show that although there are apparent biases (generally positive for CAPE and negative for CIN, and slightly overestimated vertical wind shear) in the downscaled storm parameters, the climatology of measures of storm severity over land, including their spatial patterns and seasonality, agree well with ERA5. These results have strong implication on the application of the climate projection to assess future convective environments in the region. Full article
(This article belongs to the Special Issue Practical Implications of Future Changes in Climate Extremes and Natural Hazards)
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