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中国农学通报 ›› 2016, Vol. 32 ›› Issue (33): 187-192.doi: 10.11924/j.issn.1000-6850.casb16080014

• 资源 环境 生态 土壤 气象 • 上一篇    下一篇

汛期辽宁各流域的降水量变化及预测研究

张运福,赵连伟,李菲   

  1. 辽宁省气象服务中心,沈阳区域气候中心,沈阳区域气候中心
  • 收稿日期:2016-08-02 修回日期:2016-10-10 接受日期:2016-10-11 出版日期:2016-11-25 发布日期:2016-11-25
  • 通讯作者: 张运福
  • 基金资助:
    辽宁省科技厅农业攻关及产业化项目“辽宁省气象干旱应用研究”(2015103038);沈阳区域气候中心科学技术研究项目“月动力延伸期预 测模式业务系统DERF2.0 数据在东北地区气候预测中的应用”(201504)。

Precipitation Change and Forecast in River Basins of Liaoning in Flood Season

  • Received:2016-08-02 Revised:2016-10-10 Accepted:2016-10-11 Online:2016-11-25 Published:2016-11-25

摘要: 为了准确预测汛期辽宁省的洪涝灾害趋势,满足日益增长的服务需求,建立汛期辽宁省各流域的洪涝灾害趋势客观化预报方法,以便为实际气候预测服务工作提供有价值的参考依据。以辽宁省53个测站的1961~2011年6-8月平均的降水量资料开展研究。结果表明:辽宁省内流域都属于面积较小的流域,可以用算术平均法作为汛期各流域的降水量的计算方法。除浑河流域汛期降水量略有增加趋势外,其他流域都是下降趋势。各流域在60年代中期前后发生了突变。1月500hPa位势高度场西太平洋副高系统、1月太平洋海温场Nino3-4区、2月海平面气压场西伯利亚高压系统、 2月东南太平洋区域海温场与辽宁省各流域汛期降水相关显著。所建立的各流域汛期总降水量多元回归预报方程都通过了0.01信度的显著性检验,说明这些回归方程有显著意义。

关键词: 新品系, 新品系, 品质性状, 相关分析

Abstract: The paper aims to predict the trend of flood disaster in Liaoning in flood season accurately, and better serve agriculture and meet the increasing service requirement. The authors set up the objective forecast method of flood disaster trend of each river basin in Liaoning, to provide valuable reference to the practical climate prediction for serving agriculture. The study was based on the precipitation data from June to August of 53 observational stations in Liaoning during 1961-2011. The results showed that: river basins in Liaoning were all small area watershed, so the authors used arithmetic average method to calculate the precipitation of each river basin; except Hunhe watershed’s precipitation increased slightly, the other river basins’precipitation decreased; all the basins’mutation point occurred in the middle of 1960s; the 500 hPa geopotential height field in January, the Pacific SST field in Nino3-4 in January, the Siberian high pressure system of SLP field in February and the southeast Pacific SST field in February had close relationship with the precipitation of each river basin in Liaoning; all the multiple regression prediction equations for each river basin’s precipitation passed the significance test of 0.01 reliability, indicating these regression equations had significant meanings.

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