Mathematical Modeling and Advanced Statistics of Climate Change

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1091

Special Issue Editor


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Guest Editor
Department of Economics, Sapporo Gakuin University, Ebetsu 069-8555, Japan
Interests: statistics of climate variability; nonlinear phenomena in physics; solitons and solitary waves; information theory; complex systems
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Special Issue Information

Dear Colleagues,

Climate variability, which operates across diverse timescales that range from seasons to millennia, presents complex patterns that require rigorous statistical analysis to differentiate natural fluctuations from anthropogenic signals and to improve predictive capabilities. This Special Issue focuses on the application of advanced statistical methodologies for the characterization, modeling, and prediction of climate variability. We welcome contributions that explore novel statistical techniques for analyzing climate data, including time series analysis, change point detection, the minimum description length principle, Tikhonov’s regularization, the penalty function method, spatiotemporal statistics, statistical information theory, ordinal symbolic mapping, principal component analysis, and machine learning. The scope of this Special Issue encompasses the detection and attribution of climate variability drivers, the development of statistical climate models, the analysis of climate extremes and their statistical properties, and the use of statistical approaches to enhance climate forecasts and projections across different spatiotemporal scales.

Prof. Dr. Kazuya Hayata
Guest Editor

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Keywords

  • climate variability
  • statistical methods
  • time series analysis
  • nonlinear regression
  • extreme events
  • detection and attribution
  • climate modeling
  • spatiotemporal statistics

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Published Papers (1 paper)

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Research

15 pages, 1628 KB  
Article
Comparative Performance of the Halphen-A and Pearson Type III Distributions in Modeling Annual Maximum Discharges in Romania
by Dan Ianculescu and Cristian Gabriel Anghel
Climate 2026, 14(2), 56; https://doi.org/10.3390/cli14020056 - 14 Feb 2026
Viewed by 581
Abstract
This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from [...] Read more.
This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from the Siret, Ialomița, and Danube rivers are analyzed, covering diverse hydroclimatic conditions. Distribution parameters are estimated using the method of moments and maximum likelihood estimation. Model performance is evaluated using RMSE and MAE, complemented by an analysis of extreme quantile behavior. The results show that both distributions fit the observed data well, with only minor differences in global error metrics. However, for high return periods (T > 100 years), Halphen-A exhibits smoother extrapolation and yields more stable extreme quantile estimates, particularly when estimated by MLE. Although Pearson III often achieves slightly lower metrics values, its upper tail is more constrained and sensitive to skewness and record length. The study concludes that classical goodness-of-fit measures alone are insufficient for selecting models for design floods and that Halphen-A provides a robust complementary alternative for extreme flood estimation. Full article
(This article belongs to the Special Issue Mathematical Modeling and Advanced Statistics of Climate Change)
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