新疆短时强降水时空分布与地理气象因子的相关性OA
Spatiotemporal distribution of short-duration heavy precipitation and its correlation with geographical-meteorological factors in Xinjiang,China
基于2016-2024年5-9月新疆逐小时气象观测数据,分析短时强降水的时空分布特征,并采用MGWR模型,探讨短时强降水量与地理气象因子(海拔、坡度、NDVI、PWEI、平均气温)之间的空间异质性.结果表明:短时强降水呈明显的年际与月际变化特征,日变化的高发时段集中在10:00-12:00.空间分布上,短时强降水量高值区主要分布在天山山脉及其周边,而平原和盆地则显著偏少.通过对比发现,MGWR模型的解释力与拟合度均显著优于OLS和GWR模型,通过对MGWR模型中各因子的标准化回归系数进行分析,发现海拔对短时强降水量的影响最为显著,尤其在1000~2000 m的中海拔区域,坡度在0°~10°范围对短时强降水表现出较高的敏感性,NDVI在高值区同样表现出较强的响应特征,而PWEI与平均气温的影响相对较弱.在所有站点中,海拔作为主导因子的占比最高,达73.43%,坡度与NDVI次之,PWEI与平均气温的主导占比分别仅为1.62%与0.43%.
This study aims to investigate the spatiotemporal distribution characteristics of short-duration heavy precipitation in Xinjiang and explore its spatially heterogeneous relationships with key geographical-meteorologi-cal factors(altitude,slope,NDVI,PWEI,and mean temperature),thereby providing a scientific basis for im-proved meteorological forecasting and disaster prevention in this arid to semiarid region.Using hourly meteoro-logical data from 926 ground meteorological stations across Xinjiang from May to September between 2016 and 2024,we performed a comprehensive analysis of the temporal variations and spatial patterns of short-duration heavy precipitation.A multiscale geographically weighted regression(MGWR)model was employed to quantify the varying influences of geographical-meteorological factors on the amount of short-duration heavy precipita-tion.The results revealed considerable interannual variability in short-duration heavy precipitation events.The frequency exhibited a declining trend from 2016 to 2023,followed by a notable recovery in 2024.Seasonal analy-sis showed a clear unimodal pattern from May to September,with precipitation amount and frequency initially in-creasing,reaching their peak in July,and then gradually decreasing.Diurnally,precipitation events gradually in-creased from 06:00 to 12:00 local time,with a distinct peak during the 10:00-12:00 period,followed by a gradual decline in the afternoon and evening hours.Spatially,the short-duration heavy precipitation distribution is highly heterogeneous.Areas with high values are primarily concentrated in the Tianshan Mountains and their surround-ing regions,whereas plain areas and arid basins experience lower amounts.Model performance comparisons dem-onstrate that the MGWR model provides a substantially better fit than both the ordinary least squares and stan-dard geographically weighted regression models,effectively capturing the spatial heterogeneity of factor influenc-es.Analysis of standardized regression coefficients from the MGWR output indicates that altitude has the most significant impact on precipitation amount,particularly in mid-elevation regions between 1000 and 2000 m.Pre-cipitation shows high sensitivity to slope within the 0°-10°range.It also exhibits increased sensitivity to NDVI in areas with higher values.Meanwhile,the influences of PWEI and mean temperature on precipitation amounts are relatively limited.Spatial analysis of dominant factors across meteorological stations reveals that altitude dominates 73.43%of the stations,primarily located in the central,northern,and southern mountainous regions and their piedmont transition zones.Slope is the dominant factor in 14.26%of the stations,concentrated in west-ern Bortala,the southern foothills of the central Tianshan Mountains,the Bogda Mountain region in Changji,and the eastern Tianshan Mountains in Hami.NDVI dominates 10.26%of the stations,mainly distributed in central Kashgar,where vegetation coverage is relatively high.PWEI and mean temperature dominate only 1.62%and 0.43%of the stations,respectively,indicating their secondary roles in influencing precipitation patterns across most of Xinjiang.
樊星;徐洁;刘兆旭
新疆维吾尔自治区气象服务中心(新疆维吾尔自治区气象灾害防御技术中心),新疆 乌鲁木齐 830002新疆维吾尔自治区气象服务中心(新疆维吾尔自治区气象灾害防御技术中心),新疆 乌鲁木齐 830002新疆维吾尔自治区气象服务中心(新疆维吾尔自治区气象灾害防御技术中心),新疆 乌鲁木齐 830002
短时强降水时空分布地理气象因子多尺度地理加权回归新疆
short-duration heavy precipitationspatiotemporal distributiongeographical-meteorological factorsmultiscale geographically weighted regressionXinjiang
《干旱区研究》 2026 (3)
478-486,9
新疆维吾尔自治区自然科学基金项目(2023D01B06)
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