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Applied Sciences
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Advances in Environmental Management and Climate Change
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Advances in Environmental Management and Climate Change

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3145

Special Issue Editors

Dr. Joao Carlos Andrade dos Santos

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, CITAB, Universidade de Trás-os-Montes e Alto Douro, UTAD, 5000-801 Vila Real, Portugal
Interests: climate modeling; climate impact research; climate change adaptation; meteorology; crop model simulations
Special Issues, Collections and Topics in MDPI journals
Dr. André Ribeiro Da Fonseca

E-Mail Website
Guest Editor
Department of Physics, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: hydrology; hydrology modelling; climate change; climate downscale; climate modelling; water quality modelling

Special Issue Information

Dear Colleagues,

This is a call for papers on the topic of “Advances in Environmental Management and Climate Change”, which has been designed in order to provide stakeholders and decision-makers with guidelines to proactively address the challenges of climate change. The ongoing global warming crisis has been driving a wide range of impacts and natural disasters that are significantly challenging environmental systems worldwide. Furthermore, in the upcoming decades, it is projected that the magnitude and frequency of extreme events (namely, droughts, flash floods, heat waves, soil erosion, crop destruction and forest fires) will tend to increase due to climate change. Hence, the implementation of timely and effective environmental management strategies is critical to respond to climate change.

In this Special Issue, we invite submissions exploring innovation and originality in design, methods and applications that focus on advanced methodologies in environmental management, such as impact and risk assessments that are relevant responses to climate change impacts.

Potential topics include, but are not limited to:

  • Nature-based solutions to face climate change;
  • Ecosystem services in the context of climate change;
  • Environmental resource management towards climate resiliency;
  • Water management and challenges under changing climates;
  • Climate-smart agricultural and forestry practices and options;
  • Disaster risk reduction and management.

Dr. Joao Carlos Andrade dos Santos
Dr. André Ribeiro Da Fonseca
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • climate change
  • nature-based solutions
  • ecosystem services
  • water resources
  • hydrological processes
  • environmental impact
  • extreme weather events
  • water quality
  • decision making
  • food security
  • adaptation and mitigation strategies

Published Papers (3 papers)

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Research

20 pages, 9901 KiB  
Article
Artificial Neural Networks for Determining the Empirical Relationship between Meteorological Parameters and High-Level Cloud Characteristics
by Olesia Kuchinskaia, Maxim Penzin, Iurii Bordulev, Vadim Kostyukhin, Ilia Bryukhanov, Evgeny Ni, Anton Doroshkevich, Ivan Zhivotenyuk, Sergei Volkov and Ignatii Samokhvalov
Appl. Sci. 2024, 14(5), 1782; https://doi.org/10.3390/app14051782 - 22 Feb 2024
Viewed by 506
Abstract
The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do [...] Read more.
The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do not take into account a number of atmospheric effects, in particular, the orientation of crystalline ice particles due to the simplified physical description of the medium, or within the framework of these models, accounting for such dependencies becomes a highly nontrivial task. Neural networks are able to take into account the complex interaction of meteorological parameters with each other, as well as reconstruct almost any dependence of the HLC characteristics on these parameters. In the process of prototyping the software product, the greatest difficulty was in determining the network architecture, the loss function, and the method of supplying the input parameters (attributes). Each of these problems affected the most important issue of neural networks—the overtraining problem, which occurs when the neural network stops summarizing data and starts to tune to them. Dependence on meteorological parameters was revealed for the following quantities: the altitude of the cloud center; elements m22 and m44 of the backscattering phase matrix (BSPM); and the m33 element of BSPM requires further investigation and expansion of the analyzed dataset. Significantly, the result is not affected by the compression method chosen to reduce the data dimensionality. In almost all cases, the random forest method gave a better result than a simple multilayer perceptron. Full article
(This article belongs to the Special Issue Advances in Environmental Management and Climate Change)
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12 pages, 3551 KiB  
Article
Climate Change and the Dung Beetle: Evaluation of Global Warming Impact on the Distribution of Phyllognathus excavatus (Forster, 1771) through the Mediterranean Region
by Adel Mamoun A. Fatah, Samy Zalat, Shereen M. Elbanna, Areej A. Al-Khalaf and Mohamed Nasser
Appl. Sci. 2023, 13(22), 12107; https://doi.org/10.3390/app132212107 - 07 Nov 2023
Cited by 1 | Viewed by 943
Abstract
Climate change poses a significant threat to ecosystems, food security, and human well-being. This study focuses on the Phyllognathus excavatus beetle, an important insect species in the Mediterranean region with ecological importance in nature recycling of organic wastes. The aim of this study [...] Read more.
Climate change poses a significant threat to ecosystems, food security, and human well-being. This study focuses on the Phyllognathus excavatus beetle, an important insect species in the Mediterranean region with ecological importance in nature recycling of organic wastes. The aim of this study is to assess its current habitat suitability and predict its distribution under future climate scenarios. The beetle’s occurrence records were gathered and climate information, including 19 bioclimatic variables, was retrieved from the Global Biodiversity Informatic Facility (GBIF) and WorldClim depository, respectively. The MaxEnt algorithm was used to calculate habitat appropriateness using geographic information systems (GISs) and species distribution modeling (SDM) with an accuracy of 0.907 using the AUC test. The findings show that the annual mean temperature is the most important factor, with the beetle flourishing in temperatures between 13.9 and 19.1 °C. The distribution is greatly impacted by the mean temperature of the warmest quarter. Future projections using different climate scenarios suggest potential changes in the beetle’s distribution. By integrating climate data and occurrence records, this study provides insights into the vulnerability of Phyllognathus excavatus to climate change and identifies regions where its habitat may be at risk as 81% of its current habitat will be lost. The research helps to prioritize efforts to reduce the harmful effects of climate change on insect biodiversity and to design effective conservation strategies. Overall, this study advances our knowledge of the Phyllognathus excavatus beetle’s present and projected distribution patterns in the Mediterranean region under the influence of climate change. It illustrates the significance of taking into account how climate change would affect insect populations and the use of SDM and GIS tools for researching and protecting insect biodiversity. Full article
(This article belongs to the Special Issue Advances in Environmental Management and Climate Change)
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26 pages, 4828 KiB  
Article
Assessment of Temperature, Precipitation, and Snow Cover at Different Altitudes of the Varzob River Basin in Tajikistan
by Nekruz Gulahmadov, Yaning Chen, Manuchekhr Gulakhmadov, Zulqarnain Satti, Muhammad Naveed, Rashid Davlyatov, Sikandar Ali and Aminjon Gulakhmadov
Appl. Sci. 2023, 13(9), 5583; https://doi.org/10.3390/app13095583 - 30 Apr 2023
Cited by 1 | Viewed by 1296
Abstract
The analysis of precipitation, snow cover, and temperature based on measured data is important for many applications in hydrology, meteorology, climatology, disaster management, and human activities. In this study, we used long-term historical datasets from the Varzob River Basin (VRB) in Tajikistan to [...] Read more.
The analysis of precipitation, snow cover, and temperature based on measured data is important for many applications in hydrology, meteorology, climatology, disaster management, and human activities. In this study, we used long-term historical datasets from the Varzob River Basin (VRB) in Tajikistan to evaluate the trend and magnitudinal changes in temperature, precipitation, and snow cover area in the Anzob (upstream), Maykhura (midstream), and Hushyori (downstream) regions of the VRB using the original Mann–Kendall test, modified Mann–Kendall test, Sen’s slope test, and Pettitt test. The results revealed a decreasing trend in the mean monthly air temperature at Anzob station in the upstream region for all months except January, February, and December between 1960 and 2018 and 1991 to 2018. In each of the three regions, the mean annual temperature indicated a clear upward trend. Seasonal precipitation indicated a large increasing trend in January and February at the Anzob station from 1960 to 2018, but a significant downward trend in April in the upstream, midstream, and downstream regions between 1960 and 1990 and from 1991 to and 2018. In the VRB, almost all stations exhibited a downward trend in annual precipitation across all periods, whereas the upstream region showed a non-significant upward trend between 1960 and 1990. The monthly analysis of snow cover in the VRB based on ground data showed that the maximum increase in snow cover occurs in April at the Anzob station (178 cm) and in March at Maykhura (138 cm) and Hushyori stations (54 cm). The Mann–Kendall test, based on MODIS data, revealed that the monthly snow cover in the VRB increased in April and July while a decrease was recorded in February, September, November, and December from 2001 to 2022. The trend’s stable pattern was observed in March, May, August, and October. Full article
(This article belongs to the Special Issue Advances in Environmental Management and Climate Change)
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