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中国农学通报 ›› 2022, Vol. 38 ›› Issue (23): 102-110.doi: 10.11924/j.issn.1000-6850.casb2021-1012

所属专题: 生物技术 农业气象

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

1960—2020年松嫩平原干湿时空演变及其与涛动指数的关系

钟鑫1(), 刘柏鑫2(), 丁伟3   

  1. 1沈阳城市建设学院,沈阳 110167
    2吉林省气候中心,长春 130062
    3辽宁省城乡建设集团有限责任公司,沈阳 110006
  • 收稿日期:2021-10-24 修回日期:2021-12-08 出版日期:2022-08-15 发布日期:2022-08-08
  • 通讯作者: 刘柏鑫
  • 作者简介:钟鑫,女,1984年出生,辽宁沈阳人,副教授,硕士研究生,研究方向:气候变化与城乡规划。通信地址:110167 辽宁省沈阳市浑南区白塔街380号,Tel:024-31919038,E-mail: zhongxinclimate@163.com
  • 基金资助:
    吉林省气象局科研课题(201914)

Spatiotemporal Evolution of Extremely Dry/Wet Events and Its Correlation with Oscillation Indexes in the Songnen Plain

ZHONG Xin1(), LIU Baixin2(), DING Wei3   

  1. 1Shenyang Urban Construction University, Shenyang 110167
    2Climate Center of Jilin Province, Changchun 130062
    3Liaoning Urban and Rural Construction Group Co., Ltd., Shenyang 110006
  • Received:2021-10-24 Revised:2021-12-08 Online:2022-08-15 Published:2022-08-08
  • Contact: LIU Baixin

摘要:

松嫩平原是中国重要的商品粮基地和湿地分布区,干湿演变关乎区域粮食安全和生态安全。基于松嫩平原及其周边36个气象站逐日的气温、降水、风速等气象资料,采用Penman-Monteith公式计算了各站点月地表湿润指数,标准化后进一步计算了极端干湿事件频次;然后运用趋势分析法、克里金插值法探究了极端干湿事件频次的时空演变特征,并采用交叉小波分析了干湿事件演变与涛动指数的关系。结果表明:(1)1960—2020年松嫩平原年极端干旱事件频发,过去60年均值为7.86次,而年极端湿润事件较为少发;年极端干旱事件频次呈明显的下降趋势,年极端湿润事件频次呈上升趋势,其气候倾向率分别为-0.10次/10 a和0.02次/10 a。(2)空间上,松嫩平原大部分地区极端干旱事件频发且呈不明显的减弱趋势,极端湿润事件少发且变化趋势呈明显的东增西减的变化趋势特征。(3)松嫩平原干湿演变与涛动指数密切相关,年极端干旱事件频次与北极涛动指数、大西洋北美涛动指数和北大西洋涛动指数呈正相关,年极端湿润事件频次与三者主要呈负相关;极端干湿事件与南方涛动指数在年际尺度上和年代尺度上位相关系有明显的差异。(4)极端干湿事件频次演变与区域社会经济部门用水量密切相关,影响区域的水安全。本研究可以为气候变化松嫩平原农业-湿地系统水资源规划和管理提供支撑。

关键词: 松嫩平原, 极端干湿, 湿润指数, 时空演变, 涛动指数

Abstract:

The Songnen Plain is an important commercial grain base and wetland distribution area in China, and the evolution of dry and wet events is closely related to regional food security and ecological safety. Based on the daily meteorological data including temperature, precipitation and wind speed from 36 meteorological stations in and nearby the Songnen Plain, the monthly surface wetness index of each station was calculated by the Penman-Monteith equation, and the frequency of extremely wet and dry events was further calculated after standardization. Then, the trend analysis and Kriging interpolation method were used to explore the spatiotemporal evolution characteristics of extremely dry and wet events, and the squared wavelet coherence spectrum was adopted to detect the potential annual-scale correlation between the extremely dry and wet events and the Oscillation indexes. The results showed that: (1) annual extremely dry events occurred frequently in the Songnen Plain from 1960 to 2020, with an average value of 7.86 times in the past 60 years, while annual extremely wet events were less frequent; the annual extremely dry event frequency showed an obvious decreasing trend and the annual extremely wet event frequency showed an increasing trend, with the climate tendency rate of -0.10 times/10 a and 0.02 times/10 a, respectively; (2) spatially, the frequency of extremely dry events in most areas of the Songnen Plain was not significantly weakening, while the frequency of extremely wet events was low and the trend was obviously increasing in the east and decreasing in the west; (3) the evolution of dry and wet events in the Songnen Plain was closely related to the Oscillation index, the annual frequency of extremely dry events was positively correlated with the Arctic Oscillation index, the Atlantic North American Oscillation index and the North Atlantic Oscillation index; however, the annual frequency of extremely wet events was mainly negatively correlated with them; in addition, the relationship between the extremely dry and wet events and the Southern Oscillation index exhibited obvious differences on interannual and interdecadal scales; (4) the evolution of the frequency of extremely dry and wet events was closely related to the regional socio-economic sector water consumption, which affected regional water security. This study could provide support for water resource planning and management of the agricultural-wetland system in the Songnen Plain to adapt to climate change.

Key words: Songnen Plain, extremely dry/wet events, humid index, spatiotemporal evolution, Oscillation index

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