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

doi:10.16258/j.cnki.1674-5906.2023.05.010

Ecology and Environment ›› 2023, Vol. 32 ›› Issue (5): 920-932.DOI: 10.16258/j.cnki.1674-5906.2023.05.010

• Research Articles • Previous Articles Next Articles

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
Contact: ZHAO Longlong

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

通讯作者: 赵龙龙
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)

Abstract

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

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

CLC Number:

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.

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.

Figures/Tables 15

Figure 1 Geographic location of the study area

Figure 2 Consistency test of precipitation data

Figure 3 Technology roadmap

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

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

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

Figure 4 Multi-scale SPEI time series

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

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

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

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

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

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

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a Reliability verification of SPEI results

References 44

[1]	ALTMAN D G, BLAND J M, 1983. Measurement in medicine: the analysis of method comparison studies[J]. Journal of the Royal Statistical Society: Series D (The Statistician), 32(3): 307-317.
[2]	BAI L, SHI C X, LI L H, et al., 2018. Accuracy of CHIRPS satellite-rainfall products over mainland China[J]. Remote Sensing, 10(3): 362. DOI URL
[3]	BLAND J M, ALTMAN D G, 1995. Comparing methods of measurement: Why plotting difference against standard method is misleading[J]. The lancet, 346(8982): 1085-1087. DOI URL
[4]	HAN L Y, ZHANG Q, MA P L, et al., 2016. The spatial distribution characteristics of a comprehensive drought risk index in southwestern China and underlying causes[J]. Theoretical and Applied Climatology, 124(3-4): 517-528. DOI URL
[5]	HAN L Y, ZHANG Q, ZHANG Z C, et al., 2021. Drought area, intensity and frequency changes in China under climate warming, 1961-2014[J]. Journal of Arid Environments, 193: 104596.
[6]	JIN X Y, QIANG H F, ZHAO L, et al., 2020. SPEI-based analysis of spatio-temporal variation characteristics for annual and seasonal drought in the Zoige Wetland, Southwest China from 1961 to 2016[J]. Theoretical and Applied Climatology, 139(1-2): 711-725. DOI
[7]	KIM T K, 2015. T test as a parametric statistic[J]. Korean journal of anesthesiology, 68(6): 540-546. DOI PMID
[8]	MCLEOD A I, 2005. Kendall rank correlation and Mann-Kendall trend test[J]. R Package Kendall, 602: 1-10.
[9]	OHLSON J A, KIM S, 2015. Linear valuation without OLS: the Theil-Sen estimation approach[J]. Review of Accounting Studies, 20(1): 395-435. DOI URL
[10]	SUN G H, HU Z Y, MA Y M, et al., 2020. Analysis of local land-atmosphere coupling in rainy season over a typical underlying surface in Tibetan Plateau based on field measurements and ERA5[J]. Atmospheric research, 243: 105025.
[11]	VICENTE-SERRANO S M, BEGUERÍA S, LÓPEZ-MORENO J I, 2010. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index[J]. Journal of Climate, 23: 1696-1718. DOI URL
[12]	WANG W, ZHU Y, XU R G, et al., 2015. Drought severity change in China during 1961-2012 indicated by SPI and SPEI[J]. Natural Hazards, 75: 2437-2451. DOI URL
[13]	ZHOU L H, YANG G J, 2006. Ecological economic problems and development patterns of the Arid Inland River Basin in Northwest China[J]. Ambio, 35(6): 316-318. PMID
[14]	白子怡, 薛亮, 张翀, 2019. 基于土壤湿度与植被覆盖变化的黄土高原生态恢复项目适宜性评价[J]. 水土保持研究, 26(4): 292-298, 379.
	BAI Z Y, XUE L, ZHANG C, 2019. Evaluation on Suitability of Ecological Restoration Project in the Loess Plateau Based on Soil Moisture and Vegetation Cover Change[J]. Research of Soil and Water Conservation, 26(4): 292-298, 379.
[15]	蔡思扬, 左德鹏, 徐宗学, 等, 2017. 基于SPEI干旱指数的东北地区干旱时空分布特征[J]. 南水北调与水利科技, 15(5): 15-21.
	CAI S Y, ZUO D P, XU Z X, et al., 2017. Spatial and temporal characteristics of drought in Northeast China based on SPEI[J]. South-to-North Water Transfers and Water Science & Technology, 15(5): 15-21.
[16]	韩兰英, 张强, 姚玉璧, 等, 2014. 近60年中国西南地区干旱灾害规律与成因[J]. 地理学报, 69(5): 632-639. DOI
	HAN L Y, ZHANG Q, YAO Y B, et al., 2014. Laws and causes of drought disasters in Southwest China in recent 60 years[J]. Acta Geographica Sinica, 69(5): 632-639.
[17]	胡光成, 周杰, 卢静, 等, 2020. 中国西南地区历年月度干旱指数(1951-2016)和8天频率土壤湿度(2007-2016)数据集[J]. 全球变化数据学报(中英文), 4(3): 248-256, 248-256.
	HU G C, ZHOU J, LU J, et al., 2020. Data set of monthly drought Index (1951-2016) and 8-day Frequency soil moisture (2007-2016) in Southwest China[J]. Journal of Global Change Data & Discovery, 4(3): 248-256, 248-256.
[18]	黄健, 李谢辉, 王磊, 等, 2020. 基于SPEI指数的西南地区近42 a干旱时空变化分析[J]. 成都信息工程大学学报, 35(3): 359-366.
	HUANG J, LI X H, WANG L, et al., 2020. Analysis of Spatio-temporal variation of drought in Southwest China in recent 42 years based on SPEI Index[J]. Journal of Chengdu University of Information Technology, 35(3): 359-366.
[19]	黄建平, 张国龙, 于海鹏, 等, 2020. 黄河流域近40年气候变化的时空特征[J]. 水利学报, 51(9): 1048-1058.
	HUANG J P, ZHANG G L, YU H P, et al., 2020. Characteristics of climate change in the Yellow River basin during recent 40 years[J]. Journal of Hydraulic Engineering, 51(9): 1048-1058.
[20]	贾艳青, 张勃, 2018. 基于日SPEI的近55 a西南地区极端干旱事件时空演变特征[J]. 地理科学, 38(3): 474-483. DOI
	JIA Y Q, ZHANG B, 2018. Spatial-temporal variability characteristics of extreme drought events based on daily SPEI in the southwest China in recent 55 years[J]. Scientia Geographica Sinica, 38(3): 474-483. DOI
[21]	姜雨彤, 郝增超, 冯思芳, 等, 2022. 长江与黄河流域复合高温干旱事件时空演变特征[J/OL]. 水资源保护, https://kns.cnki.net/kcms/detail/32.1356.TV.20220718.1138.004.html.
	JIANG Y T, HAO Z C, FENG S F, et al., 2022. Spatiotemporal changes in compound hot-dry events in the Yangtze River and Yellow River Basin[J/OL]. Water Resources Protection, https://kns.cnki.net/kcms/detail/32.1356.TV.20220718.1138.004.html.
[22]	靖娟利, 和彩霞, 王永锋, 等, 2022. 西南地区1902-2018年干旱时空演变特征分析[J]. 水土保持研究, 29(3): 220-227.
	JING J L, HE C X, WANG Y F, et al., 2022. Spatiotemporal evolution characteristics of meteorological drought in southwest China from 1902 to 2018[J]. Research of Soil and Water Conservation, 29(3): 220-227.
[23]	李韵婕, 任福民, 李忆平, 等, 2014. 1960-2010年中国西南地区区域性气象干旱事件的特征分析[J]. 气象学报, 72(2): 266-276.
	LI Y J, REN F M, LI Y P, et al., 2014. A study of the characteristics of the southwestern China regional meteorological drought events during 1960-2010[J]. Acta Meteorologica Sinica, 72(2): 266-276.
[24]	李星, 2018. 基于多源遥感数据的干旱监测方法及生态系统响应研究[D]. 成都: 电子科技大学.
	LI X, 2018. Research on drought monitoring and terrestrial ecosystem response based on multi-source remote sensing data[D]. Chengdu: University of Electronic Science and Technology of China.
[25]	吕爱民, 董延军, 贾春强, 2013. 2009-2011年云南干旱时空演变特征分析[J]. 广东水利水电 (6): 38-41.
	LÜ A M, DONG Y J, JIA C Q, 2013. Analysis on Spatio-temporal Evolution characteristics of drought in Yunnan from 2009 to 2011[J]. Guangdong Water Resources and Hydropower (6): 38-41.
[26]	吕纯月, 管兆勇, 黄垭飞, 2021. 1961-2018年西南地区夏季干旱变化特征及其与环流异常的联系[J]. 大气科学学报, 44(4): 573-584.
	LÜ C Y, GUAN Z Y, HUANG Y F, 2021. Variation characteristics of summer drought in Southwest China and its relationship with circulation anomalies from 1961 and 2018[J]. Transactions of Atmospheric Sciences, 44(4): 573-584.
[27]	石朋, 唐汉, 瞿思敏, 等, 2022. 西南地区气象干旱向水文干旱传播特征[J/OL]. 水资源保护, https://kns.cnki.net/kcms/detail/32.1356.TV.20220329.1815.004.html.
	SHI P, TANG H, QU S M, et al., 2022. Propagation from meteorological drought to hydrological drought in Southwest China[J/OL]. Water Resources Protection, https://kns.cnki.net/kcms/detail/32.1356.TV.20220329.1815.004.html.
[28]	史晓亮, 吴梦月, 丁皓, 2020. SPEI和植被遥感信息监测西南地区干旱差异分析[J]. 农业机械学报, 51(12): 184-192.
	SHI X L, WU M Y, DING H, 2020. Difference analysis of SPEI and vegetation remote sensing information in drought monitoring in southwest China[J]. Transactions of the Chinese Society for Agricultural Machinery, 51(12): 184-192.
[29]	苏布达, 孙赫敏, 李修仓, 等, 2020. 气候变化背景下中国陆地水循环时空演变[J]. 大气科学学报, 43(6): 1096-1105.
	SU B D, SUN H M, LI X C, et al., 2020. Impact of climate change on terrestrial water cycle in China[J]. Transactions of Atmospheric Sciences, 43(6): 1096-1105.
[30]	王东, 张勃, 张调风, 等, 2013. 1960-2011年西南地区干旱时空格局分析[J]. 水土保持通报, 33(6): 152-156, 2, 333.
	WANG D, ZHANG B, ZHANG T F, et al., 2013. Temporal and Spatial Distributions of Droughts in Southwestern China in 1960-2011[J]. Bulletin of Soil and Water Conservation, 33(6): 152-156, 2, 333.
[31]	王东, 张勃, 安美玲, 等, 2014. 基于SPEI的西南地区近53 a干旱时空特征分析[J]. 自然资源学报, 29(6): 1003-1016. DOI
	WANG D, ZHANG B, AN M L, et al., 2014. Analysis of Spatio-temporal characteristics of drought in Southwest China in recent 53 years based on SPEI[J]. Journal of Natural Resources, 29(6): 1003-1016. DOI
[32]	万红莲, 王静, 2018. 多尺度下宝鸡地区干旱动态格局演变及其与植被覆盖的关系[J]. 生态学报, 38(19): 6941-6952.
	WANG H L, WANG J, 2008. Study of dynamic pattern evolution of drought and its correlation with vegetation cover in Baoji area on multi-scale[J]. Acta Ecologica Sinica, 38(19): 6941-6952.
[33]	王永锋, 和彩霞, 靖娟利, 2022. 基于SPEI的西南喀斯特核心分布区干旱时空格局分析[J]. 桂林理工大学学报, 42(1): 131-140.
	WANG Y F, HE C X, JING J L, 2022. Analysis of Spatio-temporal pattern of drought in Karst Core Distribution area of Southwest China based on SPEI[J]. Journal of Guilin University of Technology, 42(1): 131-140.
[34]	文佐, 2022. 2019年3-6月云南省高温干旱复合事件过程及机理研究[D]. 南京: 南京信息工程大学.
	WEN Z, 2022. Study on the process and Mechanism of compound events of High temperature and drought in Yunnan Province from March to June 2019[D]. Nanjing: Nanjing University of Information Science and Technology.
[35]	吴秋洁, 2019. 近55年西南地区干旱气候特征及成因分析[D]. 成都: 成都信息工程大学.
	WU Q J, 2019. Analysis on the characteristics and causes of drought Climate in Southwest China in recent 55 years[D]. Chengdu: Chengdu University of Information Technology.
[36]	谢清霞, 谷晓平, 万雪丽, 等, 2020. 西南地区干旱的变化特征及其与大气环流的关系[J]. 干旱区地理, 43(1): 79-86.
	XIE Q X, GU X P, WANG X L, et al., 2020. Variation characteristics of drought in Southwest China and its relationship with Atmospheric Circulation[J]. Arid Land Geography, 43(1): 79-86.
[37]	许凯, 2015. 我国干旱变化规律及典型引黄灌区干旱预报方法研究[D]. 北京: 清华大学.
	XU K, 2015. Drought spatio-temporal variation in China and drought forecast in a typical irrigation area of the Yellow River[D]. Beijing: Tsinghua University.
[38]	徐翔宇, 刘昀竺, 汪党献, 等, 2022. 干旱灾害风险管理的战略思考[J]. 灾害学, 37(2): 1-5.
	XU X Y, LIU Y L, WANNG D X, et al., 2022. Strategic thinking on risk Management of drought disaster[J]. Journal of Catastrophology, 37(2): 1-5.
[39]	姚玉璧, 张强, 王劲松, 等, 2014. 中国西南干旱对气候变暖的响应特征[J]. 生态环境学报, 23(9): 1409-1417.
	YAO Y B, ZHANG Q, WANG J S, et al., 2014. The Response of Drought to Climate Warming in Southwest in China[J]. Ecology and Environmental Sciences, 23(9): 1409-1417.
[40]	张远东, 张笑鹤, 刘世荣, 2011. 西南地区不同植被类型归一化植被指数与气候因子的相关分析[J]. 应用生态学报, 22(2): 323-330.
	ZHANG Y D, ZHANG X H, LIU S R, 2011. Correlation analysis on normalized difference vegetation index (NDVI) of different vegetations and climatic factors in Southwest China[J]. Chinese Journal of Applied Ecology, 22(2): 323-330.
[41]	张琪, 李跃清, 2014. 近48年西南地区降水量和雨日的气候变化特征[J]. 高原气象, 33(2): 372-383. DOI
	ZHANG Q, LI Y Q, 2014. Climate change characteristics of precipitation and rainy days in Southwest China in recent 48 years[J]. Plateau Meteorology, 33(2): 372-383. DOI
[42]	张玉静, 王春乙, 张继权, 2015. 基于SPEI指数的华北冬麦区干旱时空分布特征分析[J]. 生态学报, 35(21): 7097-7107.
	ZHANG Y J, WANG C Y, ZHANG J Q, 2015. Analysis of the spatial and temporal characteristics of drought in the North China plain based on standardized precipitation evapotranspiration index[J]. Acta Ecologica Sinica, 35(21): 7097-7107.
[43]	张旭煜, 2022. 基于SPI和SPEI中国秋季干旱检测与归因[D]. 南京: 南京信息工程大学.
	ZHANG X Y, 2022. Detection and Attribution of Autumn drought in China based on SPI and SPEI[D]. Nanjing: Nanjing University of Information Science and Technology.
[44]	赵兰兰, 闻童, 赵兵, 等, 2021. 西南地区近50年干旱趋势及特征分析[J]. 水文, 41(6): 91-95, 59.
	ZHAO L L, WEN T, ZHAO B, et al., 2021. Analysis of drought trend and characteristics in Southwest China in recent 50 years[J]. Journal of China Hydrology, 41(6): 91-95, 59.

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

1983-2020年西南地区气象干旱时空演变趋势及干旱事件识别

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