Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria
This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climat...
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| description | This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal–Wallis test, and the modified Mann–Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980–2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10 years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment. |
| doi_str_mv | 10.1007/s11356-023-27821-5 |
| format | Article |
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Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal–Wallis test, and the modified Mann–Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980–2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10 years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.</description><identifier>ISSN: 1614-7499</identifier><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-023-27821-5</identifier><identifier>PMID: 37261694</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Climate Change ; Climate models ; Climatic data ; Earth and Environmental Science ; Ecotoxicology ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental impact ; Environmental science ; Forecasting ; Global climate ; Global climate models ; Mathematical models ; Nigeria ; Periodic variations ; Periodicity ; Quasi-biennial oscillation ; Rainfall ; Research Article ; River basins ; Rivers ; Statistical analysis ; Temperature ; Trends ; Variability ; Waste Water Technology ; Water Management ; Water Pollution Control ; Wavelet analysis</subject><ispartof>Environmental science and pollution research international, 2023-07, Vol.30 (31), p.77689-77712</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal–Wallis test, and the modified Mann–Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980–2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10 years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. 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Yunusa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria</atitle><jtitle>Environmental science and pollution research international</jtitle><stitle>Environ Sci Pollut Res</stitle><addtitle>Environ Sci Pollut Res Int</addtitle><date>2023-07-01</date><risdate>2023</risdate><volume>30</volume><issue>31</issue><spage>77689</spage><epage>77712</epage><pages>77689-77712</pages><issn>1614-7499</issn><issn>0944-1344</issn><eissn>1614-7499</eissn><abstract>This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal–Wallis test, and the modified Mann–Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980–2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10 years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37261694</pmid><doi>10.1007/s11356-023-27821-5</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-1490-4479</orcidid></addata></record> |
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| subjects | Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Climate Change Climate models Climatic data Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental impact Environmental science Forecasting Global climate Global climate models Mathematical models Nigeria Periodic variations Periodicity Quasi-biennial oscillation Rainfall Research Article River basins Rivers Statistical analysis Temperature Trends Variability Waste Water Technology Water Management Water Pollution Control Wavelet analysis |
| title | Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria |
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