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Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios
Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially whe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308398/ https://www.ncbi.nlm.nih.gov/pubmed/32572138 http://dx.doi.org/10.1038/s41598-020-67146-8 |
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author | Shiru, Mohammed Sanusi Shahid, Shamsuddin Dewan, Ashraf Chung, Eun-Sung Alias, Noraliani Ahmed, Kamal Hassan, Quazi K. |
author_facet | Shiru, Mohammed Sanusi Shahid, Shamsuddin Dewan, Ashraf Chung, Eun-Sung Alias, Noraliani Ahmed, Kamal Hassan, Quazi K. |
author_sort | Shiru, Mohammed Sanusi |
collection | PubMed |
description | Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall. |
format | Online Article Text |
id | pubmed-7308398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73083982020-06-23 Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios Shiru, Mohammed Sanusi Shahid, Shamsuddin Dewan, Ashraf Chung, Eun-Sung Alias, Noraliani Ahmed, Kamal Hassan, Quazi K. Sci Rep Article Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall. Nature Publishing Group UK 2020-06-22 /pmc/articles/PMC7308398/ /pubmed/32572138 http://dx.doi.org/10.1038/s41598-020-67146-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shiru, Mohammed Sanusi Shahid, Shamsuddin Dewan, Ashraf Chung, Eun-Sung Alias, Noraliani Ahmed, Kamal Hassan, Quazi K. Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title | Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title_full | Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title_fullStr | Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title_full_unstemmed | Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title_short | Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios |
title_sort | projection of meteorological droughts in nigeria during growing seasons under climate change scenarios |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308398/ https://www.ncbi.nlm.nih.gov/pubmed/32572138 http://dx.doi.org/10.1038/s41598-020-67146-8 |
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