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Geosciences
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Advanced Statistical Modelling in Climate Change
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Advanced Statistical Modelling in Climate Change

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Climate".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 3756

Special Issue Editors

Dr. Sandhya Patidar

E-Mail Website
Guest Editor
School of Energy, Geoscience, Infrastructure and Society, Edinburgh EH14 4AS, UK
Interests: mathematical/statistical modelling; AI; energy; water; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals
Dr. K.S. Kasiviswanathan

E-Mail Website
Guest Editor
Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India
Interests: hydrological modelling; climate change impact; reservoir operation; flood and drought management; optimization and uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Dr. Soundharajan Swamy

E-Mail Website
Guest Editor
Department of Civil Engineering, School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, India
Interests: analysis and assessment of climate change impacts on water resources; irrigation water management; planning and management of water resources systems; urban water systems; water-energy-food nexus
Prof. Dr. Adebayo J. Adeloye

E-Mail Website
Guest Editor
School of the Built Environment, Heriot-Watt University, Edinburgh, Edinburgh, UK
Interests: water resources planning and management; artificial intelligence modelling of environmental systems; climate change impacts on water resources; groudwater evaluation, modelling and management; statistical analysis of floods and low flows; hydro-meteorological data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change is one of the most pressing global issues and it is affecting almost every aspect of our life. To explore the impacts of climate change, recently, substaintial growth in the developments of advanced statistical modelling and data-science approaches has been noted. This Special Issue of Geosciences is aimed to collate high-quality research papers that cover novel developments and applications in the areas of advanced statistical modelling (ASM) approaches or understanding climate change, adaptation, resilience, mitigation strategies and associated wider impacts. In particular, we encourage submissions that highlight multi-disciplinary research work involving ASM for climate change in impact areas of water resources, extreme events, hydrology, machine-learning/artificial intelligence, forecasting, remote sensing and geoinformatics, geology, soil contaminants, ecology and environmental sciences.

I hope you are interested in submitting to the Special Issue collection.

Dr. Sandhya Patidar
Dr. K.S. Kasiviswanathan
Dr. Soundharajan Bankaru Swamy
Prof. Dr. Adebayo J. Adeloye
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. Geosciences 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 1800 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

  • advanced statistical modelling
  • data science
  • climate change
  • forecasting
  • hydrology
  • geology
  • ecology
  • machine-learning/AI
  • remote sensing and geoinformatics
  • environmental sciences

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

12 pages, 3253 KiB  
Article
Neural Network-Based Climate Prediction for the 21st Century Using the Finnish Multi-Millennial Tree-Ring Chronology
by Elena A. Kasatkina, Oleg I. Shumilov and Mauri Timonen
Geosciences 2024, 14(8), 212; https://doi.org/10.3390/geosciences14080212 - 8 Aug 2024
Viewed by 616
Abstract
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records [...] Read more.
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records from five weather stations in northern Fennoscandia (65°–70.4° N) revealed an increasing trend, with a range of 0.09 °C/decade to 0.15 °C/decade. However, due to the short duration of instrumental records, it is not possible to accurately assess and predict climate changes on centennial and millennial timescales. In this study, we used the Finnish super-long (~7600 years) tree-ring chronology to create a climate prediction for the 21st century. We applied a method that combines a long short-term memory (LSTM) neural network with the continuous wavelet transform and wavelet filtering in order to make climate change predictions. This approach revealed a significant decrease in tree-ring growth over the near term (2063–2073). The predicted decrease in tree-ring growth (and regional temperature) is thought to be a result of a new grand solar minimum, which may lead to Little Ice Age-like climatic conditions. This result is significant for understanding current climate processes and assessing potential environmental and socio-economic risks on a global and regional level, including in the area of the Arctic shipping routes. Full article
(This article belongs to the Special Issue Advanced Statistical Modelling in Climate Change)
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25 pages, 5791 KiB  
Article
Quantifying Drought Characteristics in Complex Climate and Scarce Data Regions of Afghanistan
by Rahmatullah Dost, Bankaru-Swamy Soundharajan, Kasiapillai S. Kasiviswanathan and Sandhya Patidar
Geosciences 2023, 13(12), 355; https://doi.org/10.3390/geosciences13120355 - 21 Nov 2023
Cited by 2 | Viewed by 2261
Abstract
Droughts cause critical and major risk to ecosystems, agriculture, and social life. While attempts have been made globally to understand drought characteristics, data scarcity in developing countries often challenges detailed analysis, including climatic, environmental, and social aspects. Therefore, this study developed a framework [...] Read more.
Droughts cause critical and major risk to ecosystems, agriculture, and social life. While attempts have been made globally to understand drought characteristics, data scarcity in developing countries often challenges detailed analysis, including climatic, environmental, and social aspects. Therefore, this study developed a framework to investigate regional drought analysis (RDA) using regional drought intensity-duration-frequency (RD-IDF) curves and regional drought risk assessment (RDRA) based on the drought hazard indicator (DHI) and drought vulnerability indicator (DVI) for scarce data regions in Afghanistan. The drought characteristics were analyzed using the regional standardized-precipitation-index (SPI), and standardized precipitation-deficit distribution (SPDD). Further, L-moment statistics were used to classify different homogenous regions based on regional frequency analysis (RFA). The historical monthly precipitation data from 23 rainfall stations for the years 1970 to 2016 were collected from the Ministry of Water and Energy of Afghanistan. Based on the analysis performed, the area was classified into six homogeneous regions R-1, R-2, R-3, R-4, R-5, and R-6. The drought was very consistent—almost 50% of the years—irrespective of the homogeneous region classified. R-4, located in the northeast of the country, had a one-year extreme drought with high resiliency and low risk to drought compared to other regions. As R-1, R-3 and R-5 are located in the southwest, center and southeast parts of Afghanistan, they experience moderate drought with low resiliency and high drought risk due to long period of droughts. Moreover, the uniform distribution of precipitation deficit (Dm), was less in arid climate regions. In contrast, the semi-arid climate regions showed higher values of Dm. Furthermore, in the results in all the regions, the IDF curves showed a high drought intensity with increasing drought return periods. In contrast, the intensity significantly decreased when the time scale increased, and fewer were enhanced within the increasing drought return period. However, the outcome of this study may contain essential information for end users to make spatially advanced planning for drought effect mitigation in Afghanistan. Full article
(This article belongs to the Special Issue Advanced Statistical Modelling in Climate Change)
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