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Journals
Geosciences
Special Issues
Applications of Mathematical/Statistical Techniques to Extreme Events
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Applications of Mathematical/Statistical Techniques to Extreme Events

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 7330

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

Extreme events (such as flooding, droughts, heatwaves, cyclones, wildfires, earthquakes, and volcanic eruptions) occur randomly in nature and are of high importance. Growing scientific evidence indicates that it is becoming increasingly likely that the frequency, intensity, severity, duration, temporal ranges, and spatial extent of extreme events will change considerably in response to future climate change. The Special Issue “Applications of Mathematical/Statistical Techniques to Extreme Events” of Geosciences invites original research articles, review papers, experimental work, case studies, and technical notes that feature novel applications of novel mathematical/statistical techniques for the analysis, visualisation, modelling, and forecasting of extreme events.

Wider topics covering the application of mathematical/statistical approaches can include the following (though not limited to):

  • Extreme events and projections of future climate change;
  • Risk assessment/management for extreme events;
  • Big data for extreme events;
  • Clustering/cascading effects of extreme events;
  • Socioeconomic impacts of extreme events, such as on critical infrastructures, the environment, longevity, pollution, and health and safety.

If your research work fits the scope of the Special Issue, please send a short abstract (300–500 words) providing a brief explanation regarding the motivation, novelty of research methodology, key outcomes, and wider impact. If your abstract is approved by our panel, you will be invited to submit the full paper.

 

Assoc. Prof. Sandhya Patidar
Guest Editor

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

  • mathematics/statistical modelling of extreme events
  • extreme events and climate change
  • Big Data for extreme events
  • risk assessment and management for extreme events
  • clustering of extreme events
  • extreme events and impacts

Published Papers (2 papers)

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Research

27 pages, 5808 KiB  
Article
Associating Climatic Trends with Stochastic Modelling of Flow Sequences
by Sandhya Patidar, Eleanor Tanner, Bankaru-Swamy Soundharajan and Bhaskar SenGupta
Geosciences 2021, 11(6), 255; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11060255 - 13 Jun 2021
Cited by 3 | Viewed by 2492
Abstract
Water is essential to all lifeforms including various ecological, geological, hydrological, and climatic processes/activities. With the changing climate, associated El Niño/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across [...] Read more.
Water is essential to all lifeforms including various ecological, geological, hydrological, and climatic processes/activities. With the changing climate, associated El Niño/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across the globe. Changes in P and EV patterns are highly sensitive to temperature (T) variation and thus also affect natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework (HMM_GP) that integrates a hidden Markov model (HMM) with a generalised Pareto (GP) distribution for simulating synthetic flow sequences. The GP distribution within the HMM_GP model aims to improve the model’s efficiency in effectively simulating extreme events. This paper further investigated the potential of generalised extreme value distribution (GEV) coupled with an HMM model within a regression-based scheme for associating the impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic was thoroughly assessed for its suitability to generate and predict synthetic river flow sequences for a set of future climatic projections, specifically during ENSO events. The new modelling schematic can be adapted for a range of applications in hydrology, agriculture, and climate change. Full article
(This article belongs to the Special Issue Applications of Mathematical/Statistical Techniques to Extreme Events)
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20 pages, 5334 KiB  
Article
Quantifying Uncertainty in the Modelling Process; Future Extreme Flood Event Projections Across the UK
by Cameron Ellis, Annie Visser-Quinn, Gordon Aitken and Lindsay Beevers
Geosciences 2021, 11(1), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11010033 - 08 Jan 2021
Cited by 4 | Viewed by 4216
Abstract
With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK [...] Read more.
With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK by the 2080s (2069–2098). Capturing uncertainty in the hydroclimatic modelling chain, flow projections were extracted from the EDgE (End-to-end Demonstrator for improved decision-making in the water sector in Europe) multi-model ensemble: five Coupled Model Intercomparison Project (CMIP5) General Circulation Models and four hydrological models forced under emissions scenarios Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 (5 × 4 × 2 chains). Uncertainty in extreme value parameterisation was captured through consideration of two methods: generalised extreme value (GEV) and generalised logistic (GL). The method was applied across 192 catchments and aggregated to eight regions. The results suggest that, by the 2080s, many regions could experience large increases in extreme runoff, with a maximum mean change signal of +34% exhibited in East Scotland (1:2-year RP). Combined with increasing urbanisation, these estimates paint a concerning picture for the future UK flood landscape. Model chain uncertainty was found to increase by the 2080s, though extreme value (EV) parameter uncertainty becomes dominant at the 1:30-year RP (exceeding 60% in some regions), highlighting the importance of capturing both the associated EV parameter and ensemble uncertainty. Full article
(This article belongs to the Special Issue Applications of Mathematical/Statistical Techniques to Extreme Events)
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