Abstract:

Comparative analysis of different drought events occurring in Guizhou Province during the flood season (from June to September) is significant for improving the short-term climate prediction techniques. Based on the precipitation data from 84 meteorological stations in Guizhou Province, the spatial and temporal evolution characteristics of two severe drought events in Guizhou Province during the period of 1981—2023 were characterized statistically, and the causes of the two drought events were revealed using reanalyzed data, and the differences were compared. At the same time, combined with 130 climate indexes of the National Climate Center and machine learning method, the drought event in Guizhou Province was modeled. The results show that the precipitation in flood season in Guizhou Province presented significant inter-decadal variability, and the precipitation was least under the La Niña background in 2011 and 2022. Poor water vapor conditions in flood season in 2011 under the joint influence of the Western Pacific Subtropical High moving to the east and the cyclonic circulation anomalies over the lower South China Sea region led to a widespread drought in Guizhou Province. In 2022, affected by the negative phase of the Tropical Indian Ocean Dipole (TIOD), the Western Pacific Subtropical High was abnormally large, strong and westward, the South Asian High was strong and eastward, the temperature was abnormally high, and there was an anticyclonic circulation anomaly at the lower level over the southern China, and the water vapor conditions were poor and accompanied by continuous high temperature, which resulted in the persistence of the drought in Guizhou Province. Twenty-six algorithms of machine learning were used to build a drought prediction model for Guizhou Province, among which the Linear SVC model had the best prediction effect. The test and evaluation show that this model had a good prediction ability for 2011 and 2022 drought in Guizhou Province.

Key words: drought in Guizhou Province, atmospheric circulation, La Niña event, machine learning

摘要：

对比分析贵州省主汛期（6—9月）不同干旱事件的特征，有助于提升贵州省短期气候预测技术。基于贵州省84个气象台站降水资料，分析贵州省1981—2023年2次严重干旱事件的时空演变特征，并利用再分析数据揭示事件成因，比较两者差异；同时结合国家气候中心130项气候指数和机器学习方法对贵州省干旱事件进行建模。结果表明，贵州省主汛期降水量呈明显年代际变化特征，2011、2022年在拉尼娜背景下降水最少；2011年贵州省大范围干旱的主要原因是西太平洋副热带高压（简称“西太副高”）偏东及低层南海地区气旋式环流异常，导致水汽条件差；2022年在热带印度洋偶极子负位相影响下，西太副高异常偏大、偏西、偏强，南亚高压偏强、偏东，气温异常偏高，中国南方地区低层为反气旋式环流异常，水汽条件差并伴随持续高温，导致贵州省干旱加剧。通过机器学习的26种算法建立贵州省干旱预测模型，其中Linear SVC模型的预测效果最好，检验评估表明，该模型对贵州省2011、2022年的干旱有较好的预测能力。

关键词: 贵州干旱, 大气环流, 拉尼娜事件, 机器学习