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基于气候变化下逐日标准化降水蒸散指数的中国东北水稻生育期干旱特征分析

Characterizing Droughts During the Rice Growth Period in Northeast China Based on Daily SPEI Under Climate Change.

作者信息

Nie Tangzhe, Liu Xiu, Chen Peng, Jiang Lili, Sun Zhongyi, Yin Shuai, Wang Tianyi, Li Tiecheng, Du Chong

机构信息

School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, China.

College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China.

出版信息

Plants (Basel). 2024 Dec 25;14(1):30. doi: 10.3390/plants14010030.

DOI:10.3390/plants14010030
PMID:39795291
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11723174/
Abstract

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index () can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (), maximum air temperature () and minimum air temperature () during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration () was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (, , ), and . The results showed that: (1) Under 3 SSP scenarios, showed an increasing trend from the northwest to the southeast, showed an increasing trend from the northeast to the southwest, and higher was mainly distributed in the east and west regions. (2) indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (-0.5 ≥ > -1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (-0.5 ≥ > -2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The was most affected by the , while the was most affected by the . This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future.

摘要

在农业生产中,作物关键生长时期发生的干旱会阻碍作物生长发育,而日尺度标准化降水蒸散指数()可用于准确识别干旱特征。本研究利用统计降尺度方法,获取了CMIP6中SSP1-2.6, SSP2-4.5和SSP5-8.5情景下2015年至2100年黑龙江省水稻生长季的日降水量()、最高气温()和最低气温(),以研究寒地水稻生长季干旱的时空特征及气候变化对干旱特征的影响。采用联合国粮食及农业组织推荐的哈格里夫斯公式回归校正法计算潜在蒸散量(),并计算日 以定量识别干旱等级。利用皮尔逊相关系数分析气象因子(, , )、 和 之间的相关性。结果表明:(1)在3种SSP情景下, 从西北向东南呈增加趋势, 从东北向西南呈增加趋势,较高值主要分布在东部和西部地区。(2) 表明在未来3种SSP情景下总体呈年际上升趋势,较高值主要分布在中部和西部地区。日 均值范围为4.8至6.0毫米/天。(3)在SSP1-2.6情景下,水稻主要经历轻度干旱和中度干旱(-0.5≥ > -1.5)。在SSP2-4.5和SSP8.5情景下(-0.5≥ > -2.0),主要经历的干旱等级为轻度、中度和重度干旱。(4)分蘖期干旱频率和干旱强度最高,最长干旱持续24天。然而,抽穗开花期干旱频率和干旱强度最低。干旱重心主要在铁力和绥化。(5) 受 影响最大,而 受 影响最大。本研究为了解中国东北地区水稻干旱敏感性提供了科学合理的依据,也为未来农业水资源优化调度提供了理论依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/1fd24f3ad30b/plants-14-00030-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/a4aaea221028/plants-14-00030-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/1fd24f3ad30b/plants-14-00030-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/a4aaea221028/plants-14-00030-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/0b7b26f5bf70/plants-14-00030-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/de5bb4eb3114/plants-14-00030-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/56c4473b7af0/plants-14-00030-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/429b7db7a6ed/plants-14-00030-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/d188bb0419b7/plants-14-00030-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bc4/11723174/1fd24f3ad30b/plants-14-00030-g008.jpg

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