Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-07-13 DOI:10.1007/s00477-024-02772-6
Ali Salem Al-Sakkaf, Jiahua Zhang, Fengmei Yao, Mohammed Magdy Hamed, Ali R. Al-Aizari, Abdulkarem Qasem Dammag, Yousef A. Al-Masnay, Fursan Thabit, Shamsuddin Shahid
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Abstract

Climate change is worsening existing vulnerabilities in developing countries such as Yemen. This study examined the spatial distribution trends of extreme climate indices defined by ETCCDI (Expert Team on Climate Change Detection and Indices), for precipitation and temperature, from 1988 to 2021. It employed both the classical Mann–Kendall (MK) test as well as its modified (MMK) version that accounts for long-term persistence in hydroclimatic time series, that could otherwise impact the significance of the identified trends. It represents the first country-level investigation of climate extremes in Yemen using ERA5 reanalysis data to overcome the limitations of station data. Results found widespread increases in temperature indices, indicating significant warming nationwide. Minimum temperatures amplified more than maximums, particularly TNn (the minimum of the minimum temperature), with an increasing trend of more than 0.7℃ per decade. Inland cities exhibited more substantial warming than coastal cities. Precipitation trends displayed higher spatial variability, with intensity indices declining across most areas, raising drought concerns. However, Socotra Island presents an exception, with increased precipitation intensity and heightened flood risks. Furthermore, spatial heterogeneity in precipitation indices underscored Yemen’s complex terrain. Fewer trends were significant when applying the MMK test versus MK, confirming the impact of climate variability over the region. This research identifies the most climate-vulnerable regions to prioritise focused adaptation actions. Adaptation strategies are urgently needed, including efficient irrigation, flood assessments for Socotra Island, and investigation of projected climate changes and their implications under diverse topographic and climatic influences.

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量化也门极端气候的随机趋势:利用ERA5数据进行综合评估
气候变化正在加剧也门等发展中国家现有的脆弱性。本研究考察了由 ETCCDI(气候变化检测和指数专家组)定义的极端气候指数的空间分布趋势,包括 1988 年至 2021 年的降水量和温度。研究采用了经典的曼-肯德尔(MK)检验法及其修正版(MMK),该检验法考虑到了水文气候时间序列的长期持续性,否则可能会影响已识别趋势的重要性。这是首次利用ERA5再分析数据对也门极端气候进行国家级调查,以克服站点数据的局限性。结果发现,气温指数普遍上升,表明全国范围内气候显著变暖。最低气温的增幅大于最高气温,尤其是 TNn(最低气温的最小值),每十年的增幅超过 0.7℃。内陆城市比沿海城市的变暖幅度更大。降水趋势显示出更大的空间变异性,大部分地区的降水强度指数都在下降,这引起了人们对干旱的担忧。然而,索科特拉岛是个例外,降水强度增加,洪水风险加大。此外,降水指数的空间异质性凸显了也门复杂的地形。在应用 MMK 检验与 MK 检验时,具有显著性的趋势较少,这证实了气候多变性对该地区的影响。这项研究确定了最易受气候影响的地区,以便优先采取重点适应行动。适应战略迫在眉睫,包括高效灌溉、索科特拉岛洪水评估,以及调查预测的气候变化及其在不同地形和气候影响下的影响。
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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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