Hydroclimatic Extremes: Modeling, Forecasting, and Assessment

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 658

Special Issue Editor


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Guest Editor
Faculty of Sciences, Department of Physics and Astronomy, Universiteit Gent, Ghent, Belgium
Interests: climate change; precipitation extremes; flood; drought; water availability

Special Issue Information

Dear Colleagues,

Hydroclimatic extremes such as floods, droughts, and compound hot dry events are intensifying under anthropogenic climate change, posing severe threats to water security, ecosystem resilience, infrastructure integrity, and human wellbeing worldwide. Despite advances in process-based and statistical modeling and advanced forecasting techniques, substantial uncertainties hinder accurate prediction and risk assessment. A comprehensive understanding of the drivers, spatial temporal dynamics, and cascading impacts of these extremes is essential for developing robust adaptation, mitigation, and early warning systems.

This Special Issue of Climate seeks to assemble high quality original research and critical reviews on the modeling, forecasting, and impact assessment of hydroclimatic extremes. Aligned with the journal’s focus on climate variability, extreme event analysis, and resilience, it aims to foster interdisciplinary collaboration and advance science-based solutions that inform water management and policy decisions.

Contributions may address process-based and statistical modeling; uncertainty quantification and bias correction; compound and multi-hazard event analysis; advanced forecasting methods leveraging data assimilation and machine learning; downscaling and projection evaluation; and the design of early warning or decision support tools. We welcome original research articles, comprehensive reviews that demonstrate innovative methodologies, regional applications, or adaptation pathways.

Dr. Parisa Hosseinzadehtalaei
Guest Editor

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Keywords

  • hydroclimatic extremes
  • extreme precipitation, droughts, and floods
  • compound extreme events
  • climate modeling
  • uncertainty quantification
  • early warning systems
  • adaptation strategies

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Published Papers (1 paper)

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Research

28 pages, 2140 KiB  
Article
Application of the GEV Distribution in Flood Frequency Analysis in Romania: An In-Depth Analysis
by Cristian Gabriel Anghel and Dan Ianculescu
Climate 2025, 13(7), 152; https://doi.org/10.3390/cli13070152 - 18 Jul 2025
Viewed by 374
Abstract
This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may [...] Read more.
This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article
(This article belongs to the Special Issue Hydroclimatic Extremes: Modeling, Forecasting, and Assessment)
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