Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield

Rice and wheat, two staple food grain crops, play a key role in farmers' income and food security. The response of these crops towards climate change is heterogeneous and uncertain. Therefore, it becomes essential to analyse the impact of climate change on these crops. An investigation was perf...

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Autores principales:	Madhuri Dubey, Ashok Mishra, Rajendra Singh
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ceres global climate model quantile mapping method Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/47e9729cd194441fa101651a1d1ded23
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spelling	oai:doaj.org-article:47e9729cd194441fa101651a1d1ded232021-11-05T18:52:37ZClimate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield2040-22442408-935410.2166/wcc.2020.191https://doaj.org/article/47e9729cd194441fa101651a1d1ded232021-06-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/4/1282https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Rice and wheat, two staple food grain crops, play a key role in farmers' income and food security. The response of these crops towards climate change is heterogeneous and uncertain. Therefore, it becomes essential to analyse the impact of climate change on these crops. An investigation was performed to analyse the impact of climate change on rice and wheat yield and to quantify the uncertainties in the yield predictions in West Bengal, India. The climatic projections from eight global climate models were used to simulate the rice and wheat yields in all districts of West Bengal. A quantile mapping method was used to correct systematic biases of daily rainfall, solar radiation and temperature. The corrected data were then used for driving crop environment and resource synthesis models for yield simulations. Results reveal that rice yield is expected to reduce by 7–9% in the 2020s, 8–14% in the 2050s and 8–15% in the 2080s, whereas wheat yield is expected to go down by 18–20% in the 2020s, 20–28% in the 2050s and 18–33% in the 2080s. These reductions signify that rice and wheat yield is more likely to decline under the future climate change condition, which may affect the regional food sustainability. HIGHLIGHTS GCMs are used to assess the effect of climate change on rice and wheat yield.; Quantile mapping method is used to correct bias of GCMs outputs.; DSSAT-CERES for rice and wheat is used for yield prediction.; Rice and wheat yield is expected to reduce, respectively, up to 15 and 33% by the end of the 21st century in West Bengal.; Study prompts to develop adaptation for regional food sustainability.;Madhuri DubeyAshok MishraRajendra SinghIWA Publishingarticleceresglobal climate modelquantile mapping methodEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 4, Pp 1282-1296 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ceres global climate model quantile mapping method Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	ceres global climate model quantile mapping method Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Madhuri Dubey Ashok Mishra Rajendra Singh Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
description	Rice and wheat, two staple food grain crops, play a key role in farmers' income and food security. The response of these crops towards climate change is heterogeneous and uncertain. Therefore, it becomes essential to analyse the impact of climate change on these crops. An investigation was performed to analyse the impact of climate change on rice and wheat yield and to quantify the uncertainties in the yield predictions in West Bengal, India. The climatic projections from eight global climate models were used to simulate the rice and wheat yields in all districts of West Bengal. A quantile mapping method was used to correct systematic biases of daily rainfall, solar radiation and temperature. The corrected data were then used for driving crop environment and resource synthesis models for yield simulations. Results reveal that rice yield is expected to reduce by 7–9% in the 2020s, 8–14% in the 2050s and 8–15% in the 2080s, whereas wheat yield is expected to go down by 18–20% in the 2020s, 20–28% in the 2050s and 18–33% in the 2080s. These reductions signify that rice and wheat yield is more likely to decline under the future climate change condition, which may affect the regional food sustainability. HIGHLIGHTS GCMs are used to assess the effect of climate change on rice and wheat yield.; Quantile mapping method is used to correct bias of GCMs outputs.; DSSAT-CERES for rice and wheat is used for yield prediction.; Rice and wheat yield is expected to reduce, respectively, up to 15 and 33% by the end of the 21st century in West Bengal.; Study prompts to develop adaptation for regional food sustainability.;
format	article
author	Madhuri Dubey Ashok Mishra Rajendra Singh
author_facet	Madhuri Dubey Ashok Mishra Rajendra Singh
author_sort	Madhuri Dubey
title	Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
title_short	Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
title_full	Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
title_fullStr	Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
title_full_unstemmed	Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
title_sort	climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/47e9729cd194441fa101651a1d1ded23
work_keys_str_mv	AT madhuridubey climatechangeimpactanalysisusingbiascorrectedmultipleglobalclimatemodelsonriceandwheatyield AT ashokmishra climatechangeimpactanalysisusingbiascorrectedmultipleglobalclimatemodelsonriceandwheatyield AT rajendrasingh climatechangeimpactanalysisusingbiascorrectedmultipleglobalclimatemodelsonriceandwheatyield
_version_	1718444099303899136

Climate change impact analysis using bias-corrected multiple global climate models on rice and wheat yield

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