极端降水对水稻产量的影响研究综述

极端降水在全球范围内呈现广泛增强的趋势,对农业生态系统的影响不容忽视。水稻作为重要的粮食作物,其产量的年际波动受到极端降水的影响,然而其响应机理和时空敏感性尚未厘清。论文总结了极端降水在水稻主产区的时空格局及对产量的影响程度,梳理了极端降水对水稻产量的生理、化学和物理过程的影响机制,对比分析了多个主流方法(统计模型和作物过程模型)的输入数据和应用上的优缺点。结果表明,极端降水增加1%导致水稻减产0.02%~0.5%,主要通过增加养分流失和造成淹水胁迫等途径。然而当前研究仍难以明确水稻产量如何响应于极端降水的不同特征值(强度、频次、持续时间等)及其敏感性的时空差异,尚未完善极端降水对水稻各产量组成的影响机理,同时缺乏作物模型与统计模型等相结合的研究方法,造成水稻产量预测的不确定性。建议未来相关研究应加强田间观测、控制性实验与模型改进,定量解析极端降水对产量的影响机理,促进模型—数据融合,提高数据精度以更好地模拟极端降水事件下的水稻产量,为优化当前稻作系统和建立气候智能型农业奠定理论基础。

A review of studies on the impacts of extreme precipitation on rice yields

The increasing trend of extreme precipitation has become stronger globally, and is expected to have detrimental impact on agricultural ecosystems. Rice is one of the staple foods, and the inter-annual fluctuation of rice yield is highly affected by extreme precipitation. However, the mechanisms and spatiotemporal sensitivity of rice yield to extreme precipitation have not been clarified. This review summarized the temporal and spatial patterns of extreme precipitation in the main rice-producing regions of the world and its impact on rice yield, and explored the mechanism of extreme precipitation impact on rice growth and yield from the perspective of physiological, chemical, and physical processes. The input data and advantages and disadvantages in application of the main research methods, including statistical model and crop model, were evaluated and compared. The results indicate that an increase of 1% in extreme precipitation led to a decrease in rice yield by 0.02%-0.5%, mainly through increased nutrient loss and flooding. Yet, large uncertainties still exist in rice yield prediction of current studies, because it is difficult to clarify how rice yield responds to different characteristics (intensity, frequency, and duration) of extreme precipitation and its spatiotemporal sensitivity, and the mechanisms of extreme precipitation affecting rice yield components are not well understood. In addition, lacking the integration of crop models and statistical models also introduces uncertainties. We recommend to promote the integration of multi-methods, especially field observation, controlled experiment, and model improvement, to quantitatively analyze the mechanism of extreme precipitation impact on yield components, and to improve data accuracy to better simulate rice yields under extreme precipitation events in the future. Achieving these progresses will lay a foundation for optimizing the current rice cropping system and agricultural management to mitigate the impact of extreme precipitation.