不同温升情景下中国旱灾风险变化评估

doi:10.12118/j.issn.1000-6060.2023.448

干旱区地理 ›› 2024, Vol. 47 ›› Issue (3): 369-379.doi: 10.12118/j.issn.1000-6060.2023.448

不同温升情景下中国旱灾风险变化评估

卢冬燕¹^,²(), 朱秀芳¹^,²^,³(), 唐明秀¹^,², 郭春华¹^,², 刘婷婷¹^,²

1.北京师范大学环境演变与自然灾害教育部重点实验室，北京 100875
2.北京师范大学地理科学学部遥感科学与工程研究院，北京 100875
3.北京师范大学遥感科学国家重点实验室，北京 100875

收稿日期:2023-08-23 修回日期:2023-10-11 出版日期:2024-03-25 发布日期:2024-03-29
通讯作者: 朱秀芳（1982-），女，博士，教授，主要从事遥感应用及灾害相关的研究. E-mail: zhuxiufang@bnu.edu.cn
作者简介:卢冬燕（1999-），女，硕士研究生，主要从事气象灾害风险评估研究. E-mail: 202221051098@mail.bnu.edu.cn
基金资助:
国家自然科学基金面上基金项目(42077436);国家重点研发计划项目(2019YFA0606900)

Assessment of drought risk changes in China under different temperature rise scenarios

LU Dongyan¹^,²(), ZHU Xiufang¹^,²^,³(), TANG Mingxiu¹^,², GUO Chunhua¹^,², LIU Tingting¹^,²

1. Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing 100875, China
2. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

Received:2023-08-23 Revised:2023-10-11 Published:2024-03-25 Online:2024-03-29

摘要/Abstract

摘要：

干旱是致灾最为严重的极端气候事件之一，研究未来气候变暖背景下旱灾风险的变化有利于科学推进防灾减灾工作部署。利用第六次国际耦合模式比较计划的20个气候模式数据计算了标准化降水蒸散指数，提取了基准期及全球2 ℃、3 ℃、4 ℃温升情景下中国的干旱特征变量并计算干旱危险性指数，基于承灾体预估数据计算干旱暴露度指数和干旱脆弱性指数，综合计算旱灾风险指数，从而分析中国旱灾风险分布格局并基于地理探测器对未来旱灾风险变化进行空间归因分析。结果表明：干旱危险性指数、干旱暴露度指数和干旱脆弱性指数的空间分布分别表现为西北和东南相对较高、东高西低、西高东低；旱灾风险指数具有东高西低的分布特点，呈现以高值集聚和低值集聚为主的空间正相关；随着温升水平的升高，未来旱灾风险以增加为主，东部沿海地区增加最为明显；人口数量变化、GDP变化和耕地占比变化是影响旱灾风险变化的主导因素。

关键词: 旱灾风险, CMIP6, 温升情景, 空间自相关, 地理探测器

Abstract:

Drought is one of the most disastrous extreme climate events. Studying the changes in drought risk against the background of future global warming is beneficial for scientifically advancing disaster prevention and reduction work deployment. The standardized precipitation evapotranspiration index was calculated using data from 20 climate models from the sixth phase of the Coupled Model Intercomparison Project. Drought characteristic variables were extracted for the baseline period and global temperature rise scenarios of 2 ℃, 3 ℃, and 4 ℃ in China, and the drought hazard index (DHI) was calculated. Based on the disaster-bearing body projection data, the drought exposure index (DEI), drought vulnerability index (DVI), and drought risk index (DRI) were comprehensively calculated. The distribution pattern of drought risk in China was further analyzed, and a spatial attribution analysis of future drought risk changes was performed using a geodetector. The results showed that the spatial distribution of DHI, DEI, and DVI exhibited higher values in the northwest and southeast, a pattern of being high in the east and low in the west, and a trend of being high in the west and low in the east, respectively. Based on this, the DRI specially showed a spatial positive correlation dominated by high- and low-value clustering. With an increase in the temperature rise level, the future drought risk will mainly increase across China, and the increase in the eastern coastal areas would be the most obvious. Changes in population, gross domestic product, and the proportion of cultivated land were found to be the main factors affecting changes in drought risk.

Key words: drought risk, CMIP6, temperature rise scenario, spatial autocorrelation, geodetector

卢冬燕, 朱秀芳, 唐明秀, 郭春华, 刘婷婷. 不同温升情景下中国旱灾风险变化评估[J]. 干旱区地理, 2024, 47(3): 369-379.

LU Dongyan, ZHU Xiufang, TANG Mingxiu, GUO Chunhua, LIU Tingting. Assessment of drought risk changes in China under different temperature rise scenarios[J]. Arid Land Geography, 2024, 47(3): 369-379.

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序号	模式名称	机构简称	国家	经纬向格点数
1	ACCESS-CM2	CSIRO-ARCCSS	澳大利亚	192×144
2	ACCESS-ESM1-5	CSIRO	澳大利亚	192×145
3	CanESM5	CCCma	加拿大	128×64
4	CanESM5-1	CCCma	加拿大	128×64
5	CAS-ESM2-0	CAS	中国	256×128
6	CMCC-ESM2	CMCC	意大利	288×192
7	EC-Earth3	EC-Earth-Consortium	欧盟	512×256
8	EC-Earth3-CC	EC-Earth-Consortium	欧盟	512×256
9	EC-Earth3-Veg	EC-Earth-Consortium	欧盟	512×256
10	EC-Earth3-Veg-LR	EC-Earth-Consortium	欧盟	320×160
11	FGOALS-g3	CAS	中国	180×80
12	FIO-ESM-2-0	FIO-QLNM	中国	288×192
13	GFDL-ESM4	NOAA-GFDL	美国	288×180
14	INM-CM4-8	INM	俄罗斯	180×120
15	INM-CM5-0	INM	俄罗斯	180×120
16	IPSL-CM6A-LR	IPSL	法国	144×143
17	MIROC6	MIROC	日本	256×128
18	MPI-ESM1-2-HR	MPI-M	德国	384×192
19	MPI-ESM1-2-LR	MPI-M	德国	192×96
20	MRI-ESM2-0	MRI	日本	320×160

模式名称	年份
模式名称	2 ℃温升情景	3 ℃温升情景	4 ℃温升情景
ACCESS-CM2	2024—2043	2043—2062	2058—2077
ACCESS-ESM1-5	2029—2048	2050—2069	2068—2087
CanESM5	2021—2040	2037—2056	2050—2069
CanESM5-1	2021—2040	2036—2055	2050—2069
CAS-ESM2-0	2029—2048	2047—2066	2063—2082
CMCC-ESM2	2033—2052	2049—2068	2064—2083
EC-Earth3	2026—2045	2048—2067	2064—2083
EC-Earth3-CC	2039—2058	2056—2075	2069—2088
EC-Earth3-Veg	2031—2050	2049—2068	2065—2084
EC-Earth3-Veg-LR	2034—2053	2054—2073	2067—2086
FGOALS-g3	2039—2058	2066—2085	-
FIO-ESM-2-0	2031—2050	2050—2069	2066—2085
GFDL-ESM4	2037—2056	2061—2080	-
INM-CM4-8	2037—2056	2060—2079	-
INM-CM5-0	2035—2054	2063—2082	-
IPSL-CM6A-LR	2029—2048	2045—2064	2060—2079
MIROC6	2037—2056	2062—2081	2080—2099
MPI-ESM1-2-HR	2039—2058	2064—2083	-
MPI-ESM1-2-LR	2040—2059	2063—2082	-
MRI-ESM2-0	2025—2044	2052—2071	2071—2090

不同温升情景下中国旱灾风险变化评估

Assessment of drought risk changes in China under different temperature rise scenarios

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