2011-2013年乌鲁木齐城-郊冬季积雪深度与密度空间分布特征

Spatial distribution of winter snow cover depth and density in rümqi City during 2011- 2013

利用2011-2013年冬季4次地面实测乌鲁木齐城-郊积雪深度与密度数据,应用普通克里格空间插值方法,分析了乌鲁木齐城-郊冬季积雪深度与密度从2011年12月下旬-2012年2月下旬及2012年1月中旬和2013年同期的时空分布特征.结果表明:乌鲁木齐城-郊冬季积雪深度与密度存在显著地区域分布差异及变化特征.整个冬季位于城东北部的米东石化工业园区积雪均较深,尤其12月和2月,在主城区内部又存在不同下垫面下积雪较多的区域.从12月下旬-次年2月下旬,积雪逐渐累积,且积雪深度比密度具有更大的空间变化幅度.除12月下旬大部分主城区雪密度比郊区大之外,1月中旬、2月下旬主城区雪密度均比城东和城东北方向低.2013年1月中旬积雪与2012年同期相比,平均积雪量明显偏厚,约31 cm,但雪密度变化范围不大且深度与密度的空间分布均发生明显改变.本文结果对于了解乌鲁木齐城区积雪的区域差异,为主城区道路积雪清运、保障道路通畅优化方案及春季融雪洪水防御预案的制定提供基础数据支撑,也可以弥补当前气象站点少且空间分布不匀的不足.此外,本文对卫星遥感数据反演的积雪参数精度验证也具有实际参考价值.

In this paper,the spatial and temporal distribution characteristics of snowcover depth and density from late December 2011 to late February 2012,and the same periods from 2012 to 2013 in urban and suburb ofrümqi city were analyzed by using ordinary Kriging interpolation methods,based on data of snowcover depth and density during the winters of 2011- 2013. The results showthat snowcover depth and density between urban and suburb had significant temporal and spatial differences in the city. Snowcover around the Midong Petrochemical Industrial Park often was thicker in winter,especially during December and February. However,there were some places with more snowon different underlying surfaces inside the downtown areas. Snowcover accumulated gradually on the ground from late December to late February,especially in late December. Meanwhile,spatial variation range of snowcover depth was greater than that of density. Snowcover density in the urban centre was lower than that in the east and northeast of the city in mid-January and late February,while higher in the urban centre in late December than that in suburb. Compared to the same period in 2012,the snowcover amount increased in mid-January of 2013,but the mean density of snowcover increased insignificantly,and the spatial distributions of snowcover depth and density in city altered obviously. This study will be of greatly importance for making up the meteorological data,less and uneven distributed,in the study region,and help scientists and policy makers to understand the regional variations of snowand implement plans for road maintains in winter and against snowmelt flood in spring. The measured data also can help check the accuracy of snowparameters inversed from satellite data.