Evaluation and Projection of Diurnal Temperature Range in Maize Cultivation Areas in China Based on CMIP6 Models

Wenqiang Xie, Shuangshuang Wang and Xiaodong Yan
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Wenqiang Xie: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Shuangshuang Wang: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Xiaodong Yan: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2022, vol. 14, issue 3, 1-17

Abstract: The diurnal temperature range (DTR) is an important meteorological component affecting maize yield. The accuracy of climate models simulating DTR directly affects the projection of maize production. We evaluate the ability of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate DTR during 1961–2014 in maize cultivation areas with the observation (CN05.1), and project DTR under different shared socioeconomic pathway (SSP) scenarios. The root mean square error ( R M S E ), standard deviation ( S D ), Kling-Gupta efficiency ( K G E ) and comprehensive rating index ( C R I ) are used in the evaluation of the optimal model. The results show that CMIP6 models can generally reproduce the spatial distribution. The reproducibility of the annual average DTR in the maize cultivation areas is better than that in China but lower for the maize-growing season. The optimal model (EC-Earth3-Veg-LR) is used in the projection. Under the two SSPs, the DTR decreases compared with the historical period, especially in Northwest and North China. The DTR under SSP245 remains unchanged (annual) or increases slightly (growing season) during 2015–2050, while a significant decreasing trend is observed under SSP585. This highlights the importance of evaluating DTR in maize cultivation areas, which is helpful to further improve the accuracy of maize yield prediction.

Keywords: CMIP6; maize; diurnal temperature range; projection; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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