State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China.
State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China.
Sci Total Environ. 2022 Apr 20;818:151746. doi: 10.1016/j.scitotenv.2021.151746. Epub 2021 Nov 19.
Extreme heat events have become more frequent and severe under climate change and seriously threaten rice growth. Most existing crop models tend to underestimate the impacts of heat stress on rice yields. Heat stress modules in crop models have not been extensively explored, particularly on a large scale. This study modeled rice growth under heat stress at the flowering and filling stages through two heat stress models which coupled into the CERES-Rice model. We evaluated the advanced model with provincial statistics and Gridded Observed Rice Yield. Our improved CERES-Rice model produced more accurate estimates on rice yield than the original model evidenced by an increased correlation coefficient (R) of 12.72% and d-index of 0.02%. The RMSE and MAE decreased by 5.94% and 6.01%, respectively. Most pseudo positive correlations between rice yield and the number of heat days were corrected to the negative ones by the improved model. The future projections from the improved model signifies multi-model ensemble yield projection without CO effect (MME-I-NOCO) has an apparent fall from 2020 to 2099 under RCP4.5, RCP6.0 and RCP8.5 with the decreasing percentages of 6%, 14%, and 37%, respectively, whereas the decreasing trend (12%) only occurs under RCP8.5 with CO effect (MME-I-CO). The apparently decreasing trends of yield projection from MME-I-NOCO will occur in most rice-planted regions of China with the decreasing rate < 50 kg/ha/a especially in the central-south and southern cropping regions, and this decreasing trend will be slowed down for MME-I-CO. Relative to rice yield of historical period, rice yield variations of MME-I-NOCO for different growing seasons show a downward trend with the decrease of approximately 54%, 60%, and 43%, respectively. Our study highlights the importance of modeling crop yields under heat stress to food security, agricultural adaptation and mitigation to climate change.
气候变化导致极端高温事件日益频繁和剧烈,严重威胁着水稻生长。大多数现有的作物模型往往低估了热应激对水稻产量的影响。作物模型中的热应激模块尚未得到广泛探索,特别是在大规模上。本研究通过两个热应激模型将水稻在开花和灌浆期的热应激生长建模,这两个模型耦合到 CERES-Rice 模型中。我们利用省级统计数据和网格化观测的水稻产量对改进后的模型进行了评估。与原始模型相比,我们改进后的 CERES-Rice 模型对水稻产量的估计更加准确,相关系数(R)提高了 12.72%,d 指数提高了 0.02%。RMSE 和 MAE 分别降低了 5.94%和 6.01%。通过改进后的模型,大多数与热天数有关的水稻产量伪正相关都被修正为负相关。改进后的模型未来的预测显示,在 RCP4.5、RCP6.0 和 RCP8.5 情景下,不考虑 CO 影响的多模式集合产量预测(MME-I-NOCO)从 2020 年到 2099 年明显下降,分别下降了 6%、14%和 37%,而在 RCP8.5 情景下考虑 CO 影响(MME-I-CO)的情况下,下降趋势仅为 12%。在不考虑 CO 影响的情况下(MME-I-NOCO),中国大部分水稻种植区的产量预测都呈现出明显的下降趋势,下降速度<50kg/ha/a,尤其是在中南和南方种植区,而在考虑 CO 影响的情况下(MME-I-CO),这一下降趋势将会放缓。与历史时期的水稻产量相比,不同生长季节的 MME-I-NOCO 的水稻产量变化呈下降趋势,分别下降了约 54%、60%和 43%。本研究强调了在气候变化背景下,对作物在热应激下的产量进行建模对于粮食安全、农业适应和缓解的重要性。