地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (11): 1721-1734.doi: 10.12082/dqxxkx.2019.190058

• 地理空间分析综合应用 • 上一篇    下一篇

山东半岛降雪时空分布特征

庞海洋1,2,3, 孔祥生1,*(), 贺正洋2,3, 苏晓强4   

  1. 1. 鲁东大学资源与环境工程学院,烟台 264025
    2. 首都师范大学资源环境与旅游学院,北京 100048
    3. 城市环境过程与数字模拟国家重点实验室培育基地,北京 100048
    4. 长治市地质环境监测中心, 长治 046000
  • 收稿日期:2019-02-11 修回日期:2019-05-27 出版日期:2019-10-25 发布日期:2019-12-11
  • 通讯作者: 孔祥生 E-mail:emails305@163.com
  • 作者简介:庞海洋(1991-),男,山东滨洲人,博士生,主要从事遥感科学与技术的科研工作。 E-mail: fuyunmeili@163.com
  • 基金资助:
    国家自然科学基金项目(No.41271342);山东省高等学校科技计划项目(J12LH01)

Spatiotemporal Distribution of the Snowfall in Shandong Peninsula

PANG Haiyang1,2,3, KONG Xiangsheng1,*(), HE Zhengyang2,3, SU Xiaoqiang4   

  1. 1. College of resources and environmental engineering, Ludong University, Yantai 264025, China
    2. College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China;
    3. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Beijing 100048, China
    4. Geological Environment Monitoring Center of Changzhi, Changzhi 046000, China
  • Received:2019-02-11 Revised:2019-05-27 Online:2019-10-25 Published:2019-12-11
  • Contact: KONG Xiangsheng E-mail:emails305@163.com
  • Supported by:
    National Natural Science Foundation of China(No.41271342);Shandong Province Higher Education Science and Technology Projects(J12LH01)

摘要:

以2000-2018年MODIS MOD10A1日产品数据为数据源,结合数字高程模型(DEM)及降水量、风向等气象数据,构建了积雪空间分布模型,能够有效地提取强降雪区域。以此为基础,利用相关分析、缓冲区分析等方法,探究山东半岛降雪时空分布特征,结果表明:① 将NDSI累积量与DEM数据相结合,能够有效构建山东半岛积雪空间分布模型,实现了对山东半岛强、弱降雪区域提取,NDSI累积量≥150的区域中,在强降雪区的面积占降雪范围的79.78%;② 降雪区域存在空间差异,呈现北多南少,东多西少的分布格局,以黄、渤海与山东半岛海陆分界线为基准,离岸距离39.1 km范围内降雪多,离岸距离39.1 km以外降雪少;山脉150 m高程线北侧迎风坡降雪多,南侧背风坡降雪少;③ 山东半岛强降雪年以3-5年为周期存在年际变化。探究山东半岛降雪长时间序列时空分布特征,在收集淡水资源,缓解用水紧张和灾害预防方面具有一定意义。

关键词: 山东半岛, NDSI, 积雪空间分布模型, 时空分布, MODIS, 数据高程模型(DEM)

Abstract:

Snow accumulation is an important freshwater resource to alleviate the current situation of water stress; however, disasters may also happen when there is too much snow. Therefore, snowfall must be monitored. To extract the strong snowfall areas, a snow distribution model was constructed in this paper. MODIS products (MOD10A1) in the past 20 years from 2000 to 2018 were used as the main input data, and the digital elevation model (DEM) of Shandong Peninsula and meteorological data(e.g., precipitation and wind direction) were used as the auxiliary data. The model worked well in distinguishing strong and weak snowfall areas in Shandong Peninsula. Based on the snow covers information that was extracted by the snow distribution model, the spatiotemporal distribution characteristics were statistically analyzed. We found: (1) It can effectively construct the spatial distribution model of snow cover in Shandong Peninsula using the NDSI values accumulated over many years of snow and DEM data. and The boundary between strong and weak snowfall areas was successfully extracted using this model, and heavy snowfall areas covered 79.78% of the research area where the accumulated NDSI was above 150; (2) The spatial distribution of snow cover on the Shandong Peninsula is spatially uneven, which is generally characterized by rich snow on northeast and east, and less snow in south and west regions of Shandong Peninsula. There was more snow at the north side of mountains 150 m above sea level, and there was less snowfall in the south of mountains. Based on the boundary line between the Bohai Sea, Yellow Sea, and Shandong Peninsula, there was more snowfall within 39.1 km offshore distance. There was a correlation between snowfall areas and wind direction, and northerly winds were more likely to cause heavy snowfall. (3) The amount of snowfall in the Shandong Peninsula varied from 3 to 5 years in a cycle, there was a large snowfall every 3-5 years. But there was uncertainty in the snowfall areas, not all areas had strong snowfall. There was also a difference in the snowfall in each month, snowfall was mainly concentrated in December, and snowfall formed a small peak in late January of the following year. Our findings reveal the spatiotemporal distribution characteristics of long-term sequences and extraction methods of the snowfall in Shandong Peninsula, and indicate the cause of the snowfall. This study can helpin collecting fresh water resources and alleviating water stress and disaster prevention.

Key words: Shandong Peninsula, NDSI, snow distribution model, time and space distribution, MODIS, DEM