注： ^*表示通过置信度0.95的显著性检验。

气象旱涝急转规律对数据源时空尺度具有相依性，厘清数据源对规律影响意义显著。采用江西省及毗邻区59个气象站、302个雨量站汛期月降水资料和130项气象气候监测指数，综合运用旱涝急转指数（Drought-flood Abrupt Alternation Index，DFAI）、时间序列分析、地统计、机器学习等方法评价汛期旱涝急转时空分布规律、驱动因子及其对数据源的响应。结果表明：两种数据源下汛期旱涝急转主要类型和强度相反，气象站数据源下旱涝急转类型为旱转涝，事件发生频次自1979年增加2.5倍、急转强度降低超10%；雨量站数据源下旱涝急转类型为涝转旱，事件发生频次自1991年增加2.6倍、急转强度增强超69%。两种数据源下旱转涝事件空间分布差异显著，气象站数据源下在年尺度和典型年尺度上旱转涝、涝转旱事件主要集中在研究区东部、南部，而雨量站数据源下在年尺度和典型年尺度上旱转涝、涝转旱事件均主要集中在研究区北部、南部。两种数据源DFAI时程变化驱动因子差异显著。基于随机森林和K近邻算法等适用算法，气象站数据源下西藏高原指数和厄尔尼诺-南方涛动指数是主要驱动因子，相关系数、偏相关系数绝对值分别在0.32~0.35、0.33~0.36间变化，对DFAI时程变化贡献率均大于45%；雨量站数据源下，亚洲经向环流指数是主要驱动因子，相关系数、偏相关系数绝对值为0.32、0.38，对DFAI时程变化贡献率为79%。

The meteorological drought-waterlogging abrupt alternation regulation displays a significant dependency on the spatiotemporal scales of data sources. The response of the regulation on the data resources is very important. This paper evaluated the drought-waterlogging abrupt alternation’s spatiotemporal distribution during the flood season， driving factors and its response to data sources based on the 59 weather stations， 302 rainfall stations， and 130 meteorological-climatical monitoring indexes， using drought-waterlogging abrupt alternation index （DFAI）， time series analysis， geostatistical and machine learning methods. The results show that the main types and intensity of drought-waterlogging abrupt alternation in the flood season are opposite under the two data sources. The drought-to-waterlogging incidents increased by 2.5 times as the main type for the meteorological data， followed by the intensity of abrupt alternation decreasing more than 10%. In comparison， the waterlogging-to-drought incidents increased by 2.6 times as the main type for the precipitation data， in addition to the intensity of abrupt alternation increasing more than 69%. Besides， the drought-to-waterlogging incidents presented a significantly spatial distribution difference under the two types of data resources. The drought-to-waterlogging and waterlogging-to-drought incidents on annual scale and typical annual scale were observed in the eastern and southern part of the study area for the meteorological data， respectively. In comparison， these two types of incidents on the annual scale and the typical annual scale were detected to appear in the northern and southern part of the study area for the precipitation data， respectively. The DFAI’s temporal changes’ driving factors were significantly different for the two types of the data sources. Based on the random forest and K-nearest neighbor algorithms， the Tibet Plateau Index and El Nino and Southern Oscillation indices were detected as the main driving factors under the weather station data source， indicated by the absolute values of correlation coefficient and partial correlation coefficient ranging from 0.32 to 0.35 and 0.33 to 0.36. Their contribution rates were greater than 45% on the temporal changes of the DFAI. Under the rainfall station data source， the Asian Meridional Circulation index was regarded as the main driving factor， indicated by the absolute values of correlation coefficient （0.32） and partial correlation coefficient （0.38）. Its contribution rate was estimated to be 79% on the DFAI’s temporal changes.

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Response of the Meteorological Drought-Waterlogging Abrupt Alternation Regulation on the Different Data Sources in the Flood Season

BAI Hua, XIAO Wen-chang, YANG Xiao-xiao, ZHANG Yang, WEN Zhen-yu, LI Bin, CHEN Jing

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China Rural Water and Hydropower ›› 2025 ›› (3) : 30-36. DOI: 10.12396/znsd.241020

Response of the Meteorological Drought-Waterlogging Abrupt Alternation Regulation on the Different Data Sources in the Flood Season

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Received	Accepted	Published
2024-06-26	2024-11-08	2025-03-15
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2025-03-25

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