{"title":"Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh.","authors":"Asim K Dey, Mohammad Shaha A Patwary","doi":"10.1007/s00477-025-02969-3","DOIUrl":null,"url":null,"abstract":"<p><p>Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990-2015) and Chattogram (1999-2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"39 6","pages":"2281-2296"},"PeriodicalIF":3.9000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12187879/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Environmental Research and Risk Assessment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s00477-025-02969-3","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990-2015) and Chattogram (1999-2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions.
期刊介绍:
Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas:
- Spatiotemporal analysis and mapping of natural processes.
- Enviroinformatics.
- Environmental risk assessment, reliability analysis and decision making.
- Surface and subsurface hydrology and hydraulics.
- Multiphase porous media domains and contaminant transport modelling.
- Hazardous waste site characterization.
- Stochastic turbulence and random hydrodynamic fields.
- Chaotic and fractal systems.
- Random waves and seafloor morphology.
- Stochastic atmospheric and climate processes.
- Air pollution and quality assessment research.
- Modern geostatistics.
- Mechanisms of pollutant formation, emission, exposure and absorption.
- Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection.
- Bioinformatics.
- Probabilistic methods in ecology and population biology.
- Epidemiological investigations.
- Models using stochastic differential equations stochastic or partial differential equations.
- Hazardous waste site characterization.