Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh.

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL
Asim K Dey, Mohammad Shaha A Patwary
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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.

利用非平稳极值模型量化孟加拉国两个主要城市的极端降雨。
孟加拉国极易受到气候变化的影响,特别是季风季节的极端降雨,导致严重的洪水和山体滑坡。本研究引入了一个非平稳广义极值(GEV)建模框架,该框架将大气干球温度作为协变量来捕捉极端降雨事件的季节和动态特征。利用达卡(1990-2015)和Chattogram(1999-2015)的日降雨量和温度数据,该研究确定了基于AIC、BIC和拟合优度标准的最佳模型。预测中的不确定性使用delta方法和参数自举方法进行量化。结果表明,与达卡相比,Chattogram中极端降雨事件的可能性更高,这反映在回归水平的预测和概率中。诊断评估证实,这些模式有效地捕捉了季风期间月最大降雨量的变化。这些发现为洪水风险管理、城市规划和备灾提供了有价值的信息。通过纳入温度效应和量化预测不确定性,该研究解决了现有方法的主要局限性。未来的工作将扩展这一框架,以评估孟加拉国的时空降雨变异性,并探索先进的机器学习方法,同时模拟降雨分布的整体和尾部。
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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
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.
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