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
{"title":"量化也门极端气候的随机趋势:利用ERA5数据进行综合评估","authors":"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","doi":"10.1007/s00477-024-02772-6","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"4 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data\",\"authors\":\"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\",\"doi\":\"10.1007/s00477-024-02772-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. 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Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data
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.
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.