School of Environment, Northeast Normal University, Changchun 130024, China.
School of Environment, Northeast Normal University, Changchun 130024, China; Jilin Province Science and Technology Innovation Center of Agro-Meteorological Disaster Risk Assessment and Prevention, Northeast Normal University, Changchun 130024, China; Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China; State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun 130024, China.
Sci Total Environ. 2024 Dec 1;954:176684. doi: 10.1016/j.scitotenv.2024.176684. Epub 2024 Oct 4.
Cold damage caused by low temperatures is known as chilling injury (CI), and it has consistently been one of the primary meteorological disasters affecting maize. With ongoing global climate change, the issue of chilling injury is becoming more prominent, exhibiting new characteristics and presenting new challenges. Consequently, understanding the disaster process and conducting a more refined real-time chilling injury identification have become significant challenges. In this study, we divided maize planting areas into seven maturity types based on the accumulated temperature, constructed a standard curve of the daily accumulated temperature from 1991 to 2020, proposed real-time identification indicators based on the CI process, and developed a real-time CI hazard assessment model. The results indicated that the model can capture independent CI events and rapidly determine the location, intensity, duration and scope of CIs, thereby providing a basis for accurately understanding the impact of chilling injury and taking timely countermeasures. The combination of accumulated temperature standard curves for seven maturity types of maize and the CI curve was used to construct the CI daily scale identification indicator, ΔEAT. Judgment thresholds for the CI identification indicators at various maturity levels were obtained by correlating them with historical disaster data. The frequency and intensity of maize CI gradually increased from the extremely late-maturing zone to the extremely early-maturing zone, with the seeding and emergence periods being the peak periods for CI. The spatiotemporal evolution characteristics of the three different degrees of CI events in 1992, 2004, and 2017 were consistent with the historical disaster records. Northeastern Inner Mongolia and most of Heilongjiang were found to be high-hazard areas for maize CIs. The constructed daily CI identification indicators can accurately and rapidly identify maize CIs, providing practical and targeted guidance for combating these injuries.
低温导致的冷害被称为寒害（CI），它一直是影响玉米的主要气象灾害之一。随着全球气候变化的持续，寒害问题日益突出，表现出新的特点和新的挑战。因此，了解灾害过程并进行更精细的实时寒害识别已成为重大挑战。在本研究中，我们根据积温将玉米种植区分为七种成熟类型，构建了 1991 年至 2020 年的日积温标准曲线，基于寒害过程提出了实时识别指标，并开发了实时寒害风险评估模型。结果表明，该模型可以捕捉独立的寒害事件，并快速确定寒害的位置、强度、持续时间和范围，从而为准确了解寒害的影响和及时采取对策提供依据。将七种成熟类型玉米的积温标准曲线与寒害曲线相结合，构建了寒害日尺度识别指标ΔEAT。通过将其与历史灾害数据相关联，获得了不同成熟度水平的寒害识别指标的判断阈值。玉米寒害的频率和强度从极晚熟区到极早熟区逐渐增加，播种和出苗期是寒害的高峰期。1992 年、2004 年和 2017 年三种不同程度寒害事件的时空演变特征与历史灾害记录一致。内蒙古东北部和黑龙江大部分地区是玉米寒害的高风险区。构建的日寒害识别指标可以准确、快速地识别玉米寒害，为防治寒害提供实用的、有针对性的指导。
