1983-2020年西南地区气象干旱时空演变趋势及干旱事件识别

doi:10.16258/j.cnki.1674-5906.2023.05.010

生态环境学报 ›› 2023, Vol. 32 ›› Issue (5): 920-932.DOI: 10.16258/j.cnki.1674-5906.2023.05.010

1983-2020年西南地区气象干旱时空演变趋势及干旱事件识别

葛元凯¹^,²(), 赵龙龙¹^,^*(), 陈劲松¹, 任彦霓¹, 李洪忠¹

1.中国科学院深圳先进技术研究院，广东深圳 518055
2.河南理工大学测绘与国土信息工程学院，河南焦作 454150

收稿日期:2022-12-28 出版日期:2023-05-18 发布日期:2023-08-09
通讯作者: *赵龙龙（1988年生），女，博士，副研究员，主要从事生态/农业遥感研究。E-mail: ll.zhao@siat.ac.cn
作者简介:葛元凯（1999年生），男，硕士研究生，研究方向为摄影测量与遥感。E-mail: 212004020008@home.hpu.edu.cn
基金资助:
中国科学院战略性先导科技专项(XDA19030301);国家自然科学基金项目(42171323)

Spatio-temporal Evolution Trend of Meteorological Drought and Identification of Drought Events in Southwest China from 1983 to 2020

GE Yuankai¹^,²(), ZHAO Longlong¹^,^*(), CHEN Jinsong¹, REN Yanni¹, LI Hongzhong¹

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China
2. Henan Polytechnic University, School of Surveying and Land Information Engineering, Jiaozuo 454150, P. R. China

Received:2022-12-28 Online:2023-05-18 Published:2023-08-09

摘要/Abstract

摘要：

全球变暖背景下，区域干湿变化特征不确定性增强，研究不同时间尺度下气象干旱的时空演变趋势并对干旱事件热点区域进行识别，对农业生产和防旱减灾具有重要意义。基于生成的西南地区1983-2020年5.5 km的长时序多时间尺度标准化降水蒸散指数（SPEI）面状数据，构建了干旱最长持续月数Max_mon、年均干旱月数Mean_mon、干旱事件次数C_DE和干旱事件平均持续月数MM_DE 4个指标（干旱事件定义为至少连续3个月1月尺度的SPEI≤-1），并分3个时间尺度（38 a、18 a和10 a）研究了西南地区气象干旱趋势及干旱事件时空演变特征。结果显示：（1）西南地区容易发生月度干旱和季节干旱，1983-2020年整体呈阶段性干湿波动，1983-2000年四川南部和2001-2010年云南南部显著变干，2011-2020年全区整体以变湿为主；（2）构建的干旱强度指标Max_mon和Mean_mon可有效反映西南地区气象干旱的易感性及其空间分布，Max_mon显示云南、四川约50%以上的区域易发生至少连续5个月的持续干旱，Mean_mon显示3个时段内云南的平均干旱强度呈持续增强趋势，而四川、重庆和贵州呈先增强后减弱的趋势；（3）构建的干旱事件指标C_DE和MM_DE可有效识别不同时段内干旱事件的热点区域，1983-2000年干旱事件的热点区域位于四川北部，2001-2010年位于云南中部，2011-2020年位于云南中部、北部和四川南部，云南省在2001-2010年干旱事件最严重，平均MM_DE可达4.70月/次。该文生产的SPEI高分辨率产品可提供精细化的气象干旱分布信息，基于长时序SPEI构建的干旱强度和干旱事件指标可有效识别西南地区气象干旱的常发区和干旱事件的热点区。

关键词: 气象干旱, 标准化降水蒸散指数SPEI, 干旱演变趋势, 干旱强度, 干旱事件, 西南地区

Abstract:

Under the background of global warming, the uncertainty in the characteristics of regional dry and wet changes increases. Studying the spatio-temporal evolution trend of meteorological drought under different time scales and identifying the hot spots of drought events are of great significance for agricultural production and drought prevention. Based on the long-term series and multi-time-scale standardized precipitation evapotranspiration index (SPEI) raster data with resolution of 5.5 km generated for Southwest China from 1983 to 2020, this paper constructed four indicators, the maximum lasted drought months (Max_mon), the mean annual drought months (Mean_mon), the count of drought events (C_DE), and the mean lasted months of drought event (MM_DE) (drought events are defined as at least 3 consecutive months with SPEI-1≤-1). And the spatio-temporal evolution of meteorological drought trends and drought events in Southwest China were studied in three time-scales (38, 18 and 10 years) and for four periods (1983-2020, 1983-2000, 2001-2010, and 2011-2020). The results show that (1) the Southwest China is prone to monthly and seasonal droughts. From 1983 to 2020, the Southwest China had experienced periodic dry-wet fluctuations. From 1983 to 2000, southern Sichuan became significantly drier, and so was southern Yunnan from 2001 to 2010, and from 2011 to 2020, the Southwest China was mainly getting wet. (2) The constructed drought intensity indicators Max_mon and Mean_mon can effectively reflect the susceptibility and spatial distribution of meteorological drought in Southwest China. The Max_mon indicator showed that more than 50% of Yunnan and Sichuan were prone to long-term drought for at least 5 months, and the Mean_mon indicator showed that the mean drought intensity in Yunnan increased continuously during the three periods, while Sichuan, Chongqing and Guizhou showed a trend of first increasing and then weakening. (3) The constructed drought event indicators C_DE and MM_DE can effectively identify the hot spots of drought events in different periods. The results show that the hotspots of drought events were in northern Sichuan from 1983 to 2000, in central Yunnan from 2001 to 2010, and in central and northern Yunnan and southern Sichuan from 2011 to 2020. The drought event in Yunnan was the most serious from 2001 to 2010, with an average MM_DE of 4.70 months per time. The SPEI high-resolution products produced in this paper can provide refined meteorological drought distribution information. The constructed drought intensity and drought event indicators based on long-term SPEI can effectively identify the areas with frequent meteorological drought occurrence and the hot spots of drought events in Southwest China. This study has great practical significance for dealing with meteorological and agricultural droughts.

Key words: meteorological drought, standardized precipitation evapotranspiration index, drought evolution trend, drought intensity, drought event, southwest China

中图分类号:

葛元凯, 赵龙龙, 陈劲松, 任彦霓, 李洪忠. 1983-2020年西南地区气象干旱时空演变趋势及干旱事件识别[J]. 生态环境学报, 2023, 32(5): 920-932.

GE Yuankai, ZHAO Longlong, CHEN Jinsong, REN Yanni, LI Hongzhong. Spatio-temporal Evolution Trend of Meteorological Drought and Identification of Drought Events in Southwest China from 1983 to 2020[J]. Ecology and Environment, 2023, 32(5): 920-932.

图/表 15

图1 研究区地理位置中国地图依据自然资源部标准底图（审图号：GS (2019) 1697号）绘制而成

Figure 1 Geographic location of the study area

图2 降水数据一致性检验

Figure 2 Consistency test of precipitation data

图3 技术路线图

Figure 3 Technology roadmap

表1 SPEI指数干湿等级划分

Table 1 Dry and wet grade division of SPEI

干湿等级	SPEI值
极度干旱	<-2.00
严重干旱	-2.00--1.50
中等干旱	-1.50--1.00
轻度干旱	-1.00--0.50
正常或湿润	>-0.50

表2 t检验显著性判断标准

Table 2 T-test significance test criteria

P	显著性
<0.01	极显著
<0.05	显著
≥0.05	不显著

表3 Mann-Kendall显著性检验判断标准

Table 3 Mann-Kendall significance test criteria

\|Z\|	α	显著性
≥2.58	≤0.01	极显著
≥1.96	≤0.05	显著
<1.96	>0.05	不显著

图4 多尺度SPEI时间序列

Figure 4 Multi-scale SPEI time series

表4 t检验结果

Table 4 The T-test results

SPEI	P	显著性
SPEI-1	0.842	不显著
SPEI-3	0.694	不显著
SPEI-12	0.814	不显著

图5 不同时段SPEI-12时空演变特征

Figure 5 Spatio-temporal evolution characteristics of SPEI-12 in different time periods

图6 不同时段下干旱最长持续月数空间分布及其在各区域面积占比

Figure 6 Spatial distribution maps of the Maxmon indicator in different time periods and the area proportion in each region

图7 不同时段下年均干旱月数空间分布及其在各区域面积占比

Figure 7 Spatial distribution maps of the Meanmon indicator in different periods and the area proportion in each region

图8 不同时段下干旱事件次数空间分布及其在各区域面积占比

Figure 8 Spatial distribution maps of the CDE indicator in different time periods and the area proportion in each region

图9 不同时段下干旱事件平均持续月数空间分布及其在各区域面积占比

Figure 9 Spatial distribution maps of the MMDE indicator in different periods and the area proportion in each region

表5 研究区各区域不同时段下MMDE的区域均值

Table 5 Regional mean MMDE in different periods of the study area

区域	不同时段下MM_DE均值（月/次）
区域	1983-2020	1983-2000	2001-2010	2011-2020
重庆市	3.46	3.07	3.13	3.86
云南省	3.80	3.26	4.70	3.41
四川省	3.66	3.69	3.56	3.83
贵州省	3.23	3.15	3.16	3.47
研究区	3.63	3.45	3.93	3.67

图10 SPEI结果可靠性验证和b分别基于文献李星（2018）和史晓亮等（2020）重绘，图c为本研究结果

图a Reliability verification of SPEI results

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1983-2020年西南地区气象干旱时空演变趋势及干旱事件识别

Spatio-temporal Evolution Trend of Meteorological Drought and Identification of Drought Events in Southwest China from 1983 to 2020

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