基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估

doi:10.12118/j.issn.1000-6060.2023.536

干旱区地理 ›› 2024, Vol. 47 ›› Issue (5): 798-809.doi: 10.12118/j.issn.1000-6060.2023.536

基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估

向燕芸¹(), 王弋²(), 陈亚宁³, 张齐飞⁴, 张玉杰¹

1.山西财经大学公共管理学院，山西太原 030006
2.华北电力大学水利与水电工程学院，北京 102206
3.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室，新疆乌鲁木齐 830011
4.山西师范大学地理科学学院，山西太原 030031

收稿日期:2023-10-01 修回日期:2023-11-22 出版日期:2024-05-25 发布日期:2024-05-30
通讯作者: 王弋（1977-），女，博士，教授，主要从事气候变化与水文过程研究. E-mail: wangyi28@ncepu.edu.cn
作者简介:向燕芸（1994-），女，博士，讲师，主要从事干旱区水文过程研究. E-mail: xiangyy@sxufe.edu.cn
基金资助:
国家自然科学基金项目(52161145102);山西省高等学校科技创新项目(2022L270)

Prediction of future hydrological drought risk in the Yarkant River Basin based on CMIP6 models

XIANG Yanyun¹(), WANG Yi²(), CHEN Yaning³, ZHANG Qifei⁴, ZHANG Yujie¹

1. School of Public Administration, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China
2. School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
3. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
4. School of Geographical Sciences, Shanxi Normal University, Taiyuan 030031, Shanxi, China

Received:2023-10-01 Revised:2023-11-22 Published:2024-05-25 Online:2024-05-30

摘要/Abstract

摘要：

全球变暖导致干旱等极端事件频发，严重威胁生态安全和社会经济可持续发展，尤其是干旱区这一气候响应敏感区。本研究基于流域站点观测气象、水文等数据和第六次国际耦合模式比较计划（CMIP6）全球气候模式数据，应用分布式水文模型HEC-HMS模型，模拟并预估了塔里木河重要源流——叶尔羌河流域历史（1986—2014年）与未来（2015—2100年）径流变化趋势以及水文干旱风险。结果表明：（1） HEC-HMS模型在干旱区流域适用性较好，未来3个共享社会经济路径（SSPs）下叶尔羌河流域年径流和标准化径流指数（SRI）都将呈显著增加趋势（P<0.1），SRI上升速率约0.13~0.27·(10a)^-1。（2）构建并对比了流域历史和未来时期4个干旱特征变量的边缘分布，未来干旱持续时间、干旱强度和烈度峰值都明显大于历史时期，干旱程度可能将不断加剧。（3）相较于历史时期，叶尔羌河流域未来水文干旱发生的联合概率将持续减小，且未来干旱事件的重现期都将有不同程度的延长。研究结果可为叶尔羌河流域水资源管理与规避水文干旱风险措施的制定提供科学参考。

关键词: 水文干旱, 风险预估, CMIP6, 气候变化

Abstract:

Global warming has led to the increased frequency of extreme events such as droughts, posing significant threats to ecological security and sustainable socioeconomic development, particularly in arid regions, which are highly sensitive and responsive to climate changes. This paper employs the distributed hydrological model HEC-HMS, utilizing observed meteorological and hydrological data from basin stations and global climate model data from the Sixth International Coupled Model Intercomparison Program (CMIP6), to simulate and forecast the historical (1986—2014) and future (2015—2100) runoff trends and hydrological drought risks in the Yarkant River Basin (an essential tributary of the Tarim River), Xinjiang, China. The findings indicate that: (1) The HEC-HMS model is well-suited for arid basin areas. Under the three shared socioeconomic pathways (SSPs) scenarios, the runoff and standardized runoff index (SRI) in the Yarkant River Basin are projected to significantly increase (P<0.1), with the SRI growth rate estimated at approximately 0.13-0.27·(10a)^-1. (2) A comparative analysis of the marginal distributions of four drought characteristic variables in the basin for both historical and future periods reveals that the duration and intensity of future droughts will exceed those in the historical record, with a continuous rise in drought event magnitudes. (3) Moreover, the joint probability of future hydrological droughts in the Yarkant River Basin is expected to decrease relative to the historical period, leading to a prolonged return period for future droughts. The outcomes of this study offer valuable scientific references for water resource management and the development of strategies to mitigate hydrological drought risks in the basin.

Key words: hydrological drought, risk prediction, CMIP6, climate change

向燕芸, 王弋, 陈亚宁, 张齐飞, 张玉杰. 基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估[J]. 干旱区地理, 2024, 47(5): 798-809.

XIANG Yanyun, WANG Yi, CHEN Yaning, ZHANG Qifei, ZHANG Yujie. Prediction of future hydrological drought risk in the Yarkant River Basin based on CMIP6 models[J]. Arid Land Geography, 2024, 47(5): 798-809.

图/表 11

图1

表1

表2

三维阿基米德Copula函数"

Copula函数	公式	参数范围
Gumbel	$E x p - - l n u 1 θ + - l n u 2 θ + - l n u 3 θ 1 / θ$	$1, ∞)$
Frank	$- 1 θ l n 1 + e - θ u 1 - 1 e - θ u 2 - 1 e - θ u 3 - 1 / e - θ - 1 2$	$1, 0), (0, 1$
Clayton	$u 1 - θ + u 2 - θ + u 3 - θ - 2 - 1 / θ$	(0, $∞$ )

表2

图2

表3

图3

图4

图5

表4

图6

表5

叶尔羌河流域历史与未来时期重现期"

时期	干旱变量及相应重现期	单变量重现期（T）
时期	干旱变量及相应重现期	2 a	5 a	10 a	20 a	50 a	100 a
历史时期	Dd/月	2.18	3.85	5.36	7.59	10.00	12.58
	Di	0.81	1.07	1.24	1.42	1.56	1.67
	Dp	0.91	1.43	1.93	2.58	3.19	3.77
	(Dd_Di)T_and/a	2.09	5.91	14.02	37.17	82.25	161.24
	(Dd_Di)T_or/a	1.85	4.33	7.77	13.70	20.90	29.59
	(Di_Dp)T_and/a	2.41	8.33	23.89	76.49	190.98	405.30
	(Di_Dp)T_or/a	1.65	3.57	6.32	11.50	18.26	26.64
	(Dd_Dp)T_and/a	2.27	7.32	19.82	60.32	146.20	304.69
	(Dd_Dp)T_or/a	1.72	3.80	6.69	11.99	18.81	27.23
	(Dd_Di_Dp)T_and/a	2.51	9.40	30.61	122.88	391.81	1000.00
	(Dd_Di_Dp)T_or/a	1.68	3.56	5.92	9.84	14.64	20.41
SSP126	Dd/月	2.29	4.12	5.21	6.97	10.23	18.48
	Di	0.78	1.24	1.45	1.62	1.74	1.83
	Dp	1.01	1.59	1.86	2.15	2.50	3.02
	(Dd_Di)T_and/a	2.19	6.48	15.58	49.05	205.18	1562.50
	(Dd_Di)T_or/a	1.84	4.11	7.06	13.22	27.56	76.45
	(Di_Dp)T_and/a	2.18	6.35	15.08	46.79	192.96	1452.99
	(Di_Dp)T_or/a	1.85	4.16	7.17	13.39	27.79	76.73
	(Dd_Dp)T_and/a	2.23	9.17	28.39	114.86	582.87	5054.03
	(Dd_Dp)T_or/a	1.81	3.46	5.86	11.45	25.35	73.95
	(Dd_Di_Dp)T_and/a	2.51	9.20	24.57	82.12	346.75	2603.72
	(Dd_Di_Dp)T_or/a	1.86	3.51	5.44	9.45	18.92	51.44
SSP245	Dd/月	1.17	4.26	5.88	8.36	15.17	20.03
	Di	1.88	5.07	9.57	17.81	42.62	61.09
	Dp	0.90	1.40	1.73	2.08	2.63	2.88
	(Dd_Di)T_and/a	2.14	6.17	14.95	40.64	203.27	418.16
	(Dd_Di)T_or/a	1.88	4.31	7.67	13.51	31.19	44.92
	(Di_Dp)T_and/a	2.39	11.09	36.93	131.80	863.66	1909.51
	(Di_Dp)T_or/a	1.73	3.29	5.88	10.99	27.92	41.45
	(Dd_Dp)T_and/a	2.20	9.70	33.07	120.80	807.88	1794.67
	(Dd_Dp)T_or/a	1.84	3.43	5.99	11.07	27.98	41.50
	(Dd_Di_Dp)T_and/a	2.40	17.66	169.82	594.86	1161.75	2129.31
	(Dd_Di_Dp)T_or/a	1.74	3.41	5.63	9.46	21.15	30.27
SSP370	Dd/月	1.85	3.77	9.84	22.98	69.41	132.10
	Di	1.10	3.53	10.73	25.50	74.90	138.81
	Dp	0.61	1.12	1.57	1.89	2.25	2.44
	(Dd_Di)T_and/a	3.67	5.27	12.97	36.15	175.58	484.10
	(Dd_Di)T_or/a	3.65	4.71	8.08	13.74	29.96	49.36
	(Di_Dp)T_and/a	3.71	5.18	10.82	23.67	79.29	179.41
	(Di_Dp)T_or/a	3.61	4.79	9.21	17.18	37.80	59.70
	(Dd_Dp)T_and/a	3.72	5.72	13.71	36.16	163.15	435.11
	(Dd_Dp)T_or/a	3.59	4.40	7.81	13.73	30.36	49.93
	(Dd_Di_Dp)T_and/a	3.71	5.42	13.95	40.72	206.81	567.91
	(Dd_Di_Dp)T_or/a	3.59	4.35	7.65	12.87	25.64	39.51

表5

参考文献 35

[1]	Ahmed K, Shahid S, Nawaz N. Impacts of climate variability and change on seasonal drought characteristics of Pakistan[J]. Atmospheric Research, 2018, 214: 364-374.
[2]	Damberg L A. Global trends and patterns of drought from space[J]. Theoretical and Applied Climatology, 2013, 117(3-4): 441-448.
[3]	周波涛, 钱进. IPCCAR6报告解读: 极端天气气候事件变化[J]. 气候变化研究进展, 2021, 17(6): 713-718.
	[Zhou Botao, Qian Jin. Changes of weather and climate extremes in the IPCC AR6[J]. Climate Change Research, 2021, 17(6): 713-718. ]
[4]	Dai A. Characteristics and trends in various forms of the Palmer drought severity index during 1900—2008[J]. Journal of Geophysical Research, 2011, 116: D12115, doi: 10.1029/2010JD015541.
[5]	Dai A. Drought under global warming: A review[J]. Wiley Interdisciplinary Reviews: Climate Change, 2011, 2(1): 45-65.
[6]	Oloruntade A J, Mohammad T A, Ghazali A H, et al. Analysis of meteorological and hydrological droughts in the Niger-South Basin, Nigeria[J]. Global and Planetary Change, 2017, 155: 225-233.
[7]	陈亚宁, 李玉朋, 李稚, 等. 全球气候变化对干旱区影响分析[J]. 地球科学进展, 2022, 37(2): 111-119. doi: 10.11867/j.issn.1001-8166.2022.006
	[Chen Yaning, Li Yupeng, Li Zhi, et al. Analysis of the impact of global climate change on dryland areas[J]. Advances in Earth Science, 2022, 37(2): 111-119. ] doi: 10.11867/j.issn.1001-8166.2022.006
[8]	Taskiris G. Drought risk assessment and management[J]. Water Resources Management, 2017, 31(10): 3083-3095.
[9]	Van L, Laaha G. Hydrological drought severity explained by climate and catchment characteristics[J]. Journal of Hydrology, 2015, 526: 3-14.
[10]	Williams A P, Seager R, Abatoglou J T, et al. Contribution of anthropogenic warming to California drought during 2012—2014[J]. Geophysical Research Letters, 2015, 42(16): 6819-6828.
[11]	丁一汇. 构建全球气候变化早期预警和防御系统[J]. 可持续发展经济导刊, 2020, 11(增刊1): 44-45.
	[Ding Yihui. Build an early warning and defense system for global climate change[J]. China Sustainability Tribune, 2020, 11(Suppl. 1): 44-45. ]
[12]	袁星, 马凤, 李华, 等. 全球变化背景下多尺度干旱过程及预测研究进展[J]. 大气科学学报, 2020, 43(1): 225-237.
	[Yuan Xing, Ma Feng, Li Hua, et al. A review on multi-scale drought processes and prediction under global change[J]. Transactions of Atmospheric Sciences, 43(1): 225-237. ]
[13]	向燕芸, 王志成, 张辉, 等. 干旱区融雪径流模拟的研究进展与展望[J]. 冰川冻土, 2017, 39(4): 892-901. doi: 10.7522/j.issn.1000-0240.2017.0099
	[Xiang Yanyun, Wang Zhicheng, Zhang Hui, et al. Study of snowmelt runoff simulation in arid regions: Progress and prospect[J]. Journal of Glaciology and Geocryology, 2017, 39(4): 892-901. ] doi: 10.7522/j.issn.1000-0240.2017.0099
[14]	Azmat M, Choi M, Kim T W, et al. Hydrological modeling to simulate streamflow under changing climate in a scarcely gauged cryosphere catchment[J]. Environmental Earth Sciences, 2016, 75(3): 186, doi: 10.1007/s12665-015-5059-2.
[15]	Xu H, Liu L, Wang Y, et al. Assessment of climate change impact and difference on the river runoff in four basins in China under 1.5 and 2.0 ℃ global warming[J]. Hydrology and Earth System Sciences, 2019, 23(10): 4219-4231.
[16]	Kong Q, Guerreiro S B, Blenkinsop S, et al. Increases in summertime concurrent drought and heatwave in eastern China[J]. Weather and Climate Extremes, 2020, 28: 100242, doi: 10.1016/j.wace.2019.100242.
[17]	Laurent L, Buoncristiani J F, Pohl B, et al. Landscape pattern change research of Yarkant irrigated area[J]. Scientific Reports, 2020, 10: 10420, doi: 10.1038/s41598-020-67379-7. pmid: 32591640
[18]	Han H, Wang R. Landscape pattern change research of Yarkant irrigated area[C]// International Symposium on Geomatics for Integrated Water Resources Management. IEEE, 2012.
[19]	王晨鹏, 黄萌田, 翟盘茂. IPCC AR6 报告关于不同类型干旱变化研究的新进展与启示[J]. 气象学报, 2022, 80(1): 168-175.
	[Wang Chenpeng, Huang Mengtian, Zhai Panmao. New progress and enlightenment on different types of drought changes from IPCC Sixth Assessment Report[J]. Acta Meteorologica Sinica, 2022, 80(1): 168-175. ]
[20]	周天军, 邹立维, 陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述[J]. 气候变化研究进展, 2019, 15(5): 455-456.
	[Zhou Tianjun, Zou Liwei, Chen Xiaolong. Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6)[J]. Climate Change Research, 2019, 15(5): 445-456. ]
[21]	Kan B, Su F, Xu B, et al. Generation of high mountain precipitation and temperature data for a quantitative assessment of flow regime in the upper Yarkant Basin in the Karakoram[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(16): 8462-8486.
[22]	刘蛟, 刘铁, 黄粤, 等. 基于遥感数据的叶尔羌河流域水文过程模拟与分析[J]. 地理科学进展, 2017, 36(6): 753-761. doi: 10.18306/dlkxjz.2017.06.010
	[Liu Jiao, Liu Tie, Huang Yue, et al. Simulation and analysis of the hydrological processes in the Yarkant River Basin based on remote sensing data[J]. Progress in Geography, 2017, 36(6): 753-761. ] doi: 10.18306/dlkxjz.2017.06.010
[23]	任才, 龙爱华, 於嘉闻, 等. 气候与下垫面变化对叶尔羌河源流径流的影响[J]. 干旱区地理, 2021, 44(5): 1373-1383.
	[Ren Cai, Long Aihua, Yu Jiawen, et al. Effects of climate and underlying surface changes on runoff of Yarkant River source[J]. Arid Land Geography, 2021, 44(5): 1373-1383. ]
[24]	Xiang Y, Wang Y, Chen Y, et al. Impact of climate change on the hydrological regime of the Yarkant River Basin, China: An assessment using three SSP scenarios of CMIP6 GCMs[J]. Remote Sensing, 2021, 14(1): 115, doi: 10.3390/rs14010115.
[25]	田昊玮, 陈伏龙, 龙爱华, 等. 博尔塔拉河源流区径流对气候变化的响应及预测[J]. 干旱区地理, 2023, 46(9): 1432-1442.
	[Tian Haowei, Chen Fulong, Long Aihua, et al. Response and prediction of runoff to climate change in the headwaters of the Bortala River[J]. Arid Land Geography, 2023, 46(9): 1432-1442. ]
[26]	李昱, 席佳, 张弛, 等. 气候变化对澜湄流域气象水文干旱时空特性的影响[J]. 水科学进展, 2021, 32(4): 508-519.
	[Li Yu, Xi Jia, Zhang Chi, et al. Impact of climate change on the spatio-temporal characteristics of meteorological and hydrological drought over the Lancang-Mekong River Basin[J]. Advances in Water Science, 2021, 32(4): 508-519. ]
[27]	Xiang Y, Wang Y, Chen Y, et al. Hydrological drought risk assessment using a multidimensional Copula function approach in arid inland basins, China[J]. Water, 2020, 12(7): 1888, doi: 10.3390/w12071888.
[28]	Sklar A. Fonctions de Repartition a n Dimensions et Leurs Marges[J]. Publications de l’Institut de statistique de l’Université de Paris, 1959(8): 229-231.
[29]	Hao Z, Aghakouchak A, Nakhjiri N, et al. Global integrated drought monitoring and prediction system[J]. Science Data, 2014, 1: 140001, doi: 10.1038/sdata.2014.1.
[30]	Bushra N, Trepanier J C, Rohli R V. Joint probability risk modelling of storm surge and cyclone wind along the coast of bay of Bengal using a statistical Copula[J]. International Journal of Climatology, 2019, 39(11): 4206-4217.
[31]	Das J, Jha S, Goyal M K. Non-stationary and Copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India[J]. Journal of Hydrology, 2020, 580: 124356, doi: 10.1016/j.jhydrol.2019.124356.
[32]	Lü J Q, Shen B, Li H E. Dynamics of major hydro-climatic variables in the headwater catchment of the Tarim River Basin, Xinjiang, China[J]. Quaternary International, 2015, 380-381: 143-148.
[33]	Gao X, Yang B S, Zhang S Q, et al. Glacier runoff variation and its influence on river runoff during 1961—2006 in the Tarim River Basin, China[J]. Science China Earth Sciences, 2010, 40(5): 544-565.
[34]	Duethmann D T, Bolch D, Farinotti D, et al. Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia[J]. Water Resources Research, 2015, 51: 4727-4750.
[35]	张久丹, 李均力, 包安明, 等. 2013—2020年塔里木河流域胡杨林生态恢复成效评估[J]. 干旱区地理, 2022, 45(6): 1824-1835.
	[Zhang Jiudan, Li Junli, Bao Anming, et al. Effectiveness assessment of ecological restoration of Populus euphratica forest in the Tarim River Basin during 2013—2020[J]. Arid Land Geography, 2022, 45(6): 1824-1835. ]

标准化径流指数（SRI）	干旱等级
(-1, -0.5]	轻度干旱
(-1.5, -1]	中度干旱
(-2, -1.5]	重度干旱
≤-2	极端干旱

时段	年份	NSE	PBIAS/%	R²
率定期	2005—2010	0.80	5.86	0.81
验证期	1986—2004	0.72	16.56	0.70
验证期	2011—2014	0.83	9.74	0.85

时期	函数	参数	Dd&Di	Di&Dp	Dd&Dp	Dd&Di&Dp
控制期	Gumbel	θ	1.34	3.32	1.80	2.93^#
		RMSE	0.12	0.09	0.11	0.05
		AIC	-52.38	-61.21	-55.18	-342.51
	Frank	θ	2.57^#	13.42^#	4.50^#	9.41
		RMSE	0.12	0.08	0.11	0.05
		AIC	-52.45	-64.16	-54.99	-334.57
	Clayton	θ	0.30	3.50	0.53	3.85
		RMSE	0.13	0.09	0.12	0.06
		AIC	-50.84	-61.34	-54.42	-323.45
SSP126	Gumbel	θ	2.59	2.15	1.57	3.51
		RMSE	0.05	0.04	0.06	0.09
		AIC	-347.79	-381.25	-333.49	-264.46
	Frank	θ	11.14^#	10.28^#	4.80	12.08
		RMSE	0.04	0.03	0.04	0.10
		AIC	-375.28	-408.83	-360.19	-261.79
	Clayton	θ	0.05	0.08	1.94^#	5.03^#
		RMSE	0.14	0.11	0.04	0.09
		AIC	-226.00	-253.63	-362.21	-271.12
SSP245	Gumbel	θ	1.14	2.72	1.29	2.23
		RMSE	0.04	0.04	0.05	0.11
		AIC	-348.89	-334.98	-306.04	-231.66
	Frank	θ	1.86^#	11.99^#	2.89	6.44^#
		RMSE	0.03	0.03	0.04	0.11
		AIC	-355.62	-349.88	-327.94	-232.54
	Clayton	θ	0.02	0.07	1.32^#	2.45
		RMSE	0.04	0.13	0.04	0.12
		AIC	-331.18	-213.75	-339.59	-221.57
SSP370	Gumbel	θ	1.46	4.15	1.75	3.00
		RMSE	0.07	0.05	0.07	0.06
		AIC	-118.79	-138.45	-123.35	-130.76
	Frank	θ	4.39^#	14.43	6.44^#	9.97^#
		RMSE	0.06	0.05	0.05	0.06
		AIC	-126.57	-136.16	-134.79	-133.06
	Clayton	θ	0.86	4.44^#	1.16	4.00
		RMSE	0.07	0.04	0.07	0.06
		AIC	-120.65	-143.51	-119.68	-128.56

基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估

Prediction of future hydrological drought risk in the Yarkant River Basin based on CMIP6 models

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 35

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	赵明杰, 王宁练, 石晨烈, 侯靖琪. 2000—2020年中亚大型湖泊湖冰物候时空变化[J]. 干旱区地理, 2024, 47(4): 561-575.
[2]	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.
[3]	王淑芝, 温得平. 青藏高原大通河流域径流变化归因分析[J]. 干旱区地理, 2024, 47(2): 203-213.
[4]	常学向, 赵文智, 田全彦. 干旱区气候变化及其对山地森林生态系统稳定性和水文过程影响研究进展[J]. 干旱区地理, 2024, 47(2): 228-236.
[5]	隋露, 闫志明, 李开放, 何佩恩, 马英杰, 张汝萃. 人类活动及气候变化影响下伊犁河谷生境质量预测研究[J]. 干旱区地理, 2024, 47(1): 104-116.
[6]	田昊玮, 陈伏龙, 龙爱华, 刘静, 海洋. 博尔塔拉河源流区径流对气候变化的响应及预测[J]. 干旱区地理, 2023, 46(9): 1432-1442.
[7]	卢冬燕, 朱秀芳, 刘婷婷, 张世喆. 2 ℃温升情景下中国气象干旱特征变化[J]. 干旱区地理, 2023, 46(8): 1227-1237.
[8]	艾丽亚, 王永芳, 郭恩亮, 银山, 顾锡羚. 基于GEE的大青山国家级自然保护区NDVI变化及影响因素分析[J]. 干旱区地理, 2023, 46(8): 1279-1290.
[9]	成硕, 李艳忠, 星寅聪, 于志国, 王渊刚, 黄曼捷. 遥感降水产品对黄河源区水文干旱特征的模拟性能分析[J]. 干旱区地理, 2023, 46(7): 1063-1072.
[10]	高晓宇, 郝海超, 张雪琪, 陈亚宁. 中国西北干旱区植被水分利用效率变化对气象要素的响应——以新疆为例[J]. 干旱区地理, 2023, 46(7): 1111-1120.
[11]	顾朝林, 苏鹤放, 顾江, 高喆, 陈乐琳, 郭力. 论地球科学的新时代[J]. 干旱区地理, 2023, 46(7): 1176-1195.
[12]	陈淑君,许国昌,吕志平,马铭悦,李晗羽,朱玉岩. 中国植被覆盖度时空演变及其对气候变化和城市化的响应[J]. 干旱区地理, 2023, 46(5): 742-752.
[13]	李娜,武永利,赵桂香,钱锦霞,李芬,赵海英,韩普. 近60 a山西省极端气温事件的年际变化及其对区域增暖的响应[J]. 干旱区地理, 2023, 46(3): 337-348.
[14]	任涛涛,李双双,段克勤,何锦屏. 黄土高原热浪型和缺水型骤旱时空变化特征及其影响因素[J]. 干旱区地理, 2023, 46(3): 360-370.
[15]	晋子振, 秦翔, 赵求东, 李延召, 刘宇硕, 陈记祖, 王利辉, 王强. 祁连山西段老虎沟流域消融季径流变化特征研究[J]. 干旱区地理, 2023, 46(2): 178-190.