基于CMIP6多模式预估数据的石羊河流域未来气候变化趋势分析

doi:10.13866/j.azr.2023.10.02

干旱区研究 ›› 2023, Vol. 40 ›› Issue (10): 1547-1562.doi: 10.13866/j.azr.2023.10.02 cstr: 32277.14.j.azr.2023.10.02

基于CMIP6多模式预估数据的石羊河流域未来气候变化趋势分析

戴君^1,²(),胡海珠^1,²(),毛晓敏^2,³,张霁^2,⁴

1.内蒙古大学生态与环境学院，内蒙古自治区河流与湖泊生态重点实验室，内蒙古呼和浩特 010020
2.甘肃武威绿洲农业高效用水国家野外科学观测研究站，甘肃武威 733009
3.中国农业大学水利与土木工程学院，北京 100083
4.武威市水务局，甘肃武威 733099

收稿日期:2023-02-27 修回日期:2023-05-05 出版日期:2023-10-15 发布日期:2023-11-01
作者简介:戴君（1998-），女，硕士研究生，主要从事水文与水资源研究. E-mail: 1317330302@qq.com
基金资助:
国家自然科学基金重大项目课题(51790535);甘肃武威绿洲农业高效用水国家野外科学观测研究站开放课题(KF2021007)

Future climate change trends in the Shiyang River Basin based on the CMIP6 multi-model estimation data

DAI Jun^1,²(),HU Haizhu^1,²(),MAO Xiaomin^2,³,ZHANG Ji^2,⁴

1. School of Ecology and Environment, Inner Mongolia Key Laboratory of River and Lake Ecology, Inner Mongolia University, Hohhot 010020, Inner Mongolia, China
2. National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733009, Gansu, China
3. College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
4. Wuwei Municipal Water Bureau, Wuwei 733099, Gansu, China

Received:2023-02-27 Revised:2023-05-05 Published:2023-10-15 Online:2023-11-01

摘要/Abstract

摘要：

石羊河流域地处我国西北干旱区和季风区边缘，流域内绿洲农业的快速发展导致水资源开发利用程度极高，生态环境脆弱，未来气候变化加剧了流域水资源的不确定性，对粮食安全与经济发展构成威胁。本文基于观测数据，评估第6次国际耦合模式比较计划（CMIP6）的11个气候模式在石羊河流域的模拟能力，用等距离累积分布函数法对气候数据进行降尺度，得到该流域的未来气候变化趋势。结果表明：（1）CMIP6模式数据在石羊河流域具有良好的适用性，多模式集合平均数据对石羊河流域降水和气温的模拟性能均优于其他模式。（2）未来不同情景下（2023—2100年），流域内降水量、气温和潜在蒸散发量均呈显著上升趋势，且随着辐射强迫增加而增大。（3）未来时期石羊河流域的干燥度指数整体减小，流域气候趋向暖湿化，且民勤盆地是流域内对气候变化最敏感的地区。研究结果对于石羊河流域应对气候变化、保障经济和农业可持续发展具有重要的参考价值。

关键词: CMIP6, 石羊河流域, 区域气候变化, 干燥度指数, 未来

Abstract:

Due in large part to global climate change, drought, flood, and high temperature events have increased significantly around the world in recent years. The Shiyang River Basin is in Northwest China and fringes onto a monsoon region, and is consequently, highly sensitive to climate change. The rapid development of oasis agriculture has led to high levels of development and the utilization of water resources in fragile ecological environments. Future climate change will aggravate the uncertainty of water resources in the basin, posing a threat to food security and economic development. Coupled General Circulation Models (GCMs) play an important role in the prediction of future climate change and formulation strategies to help devise adjustments accordingly. Based on the observed data in the historical period (1985-2014), the simulation capabilities of 11 climate models from the 6th international Coupled Model Intercomparison Program (CMIP6) in the Shiyang River Basin were evaluated. The equidistant cumulative distribution function method was applied to downscale climate data to obtain the future climate change trend for the basin as presented in this paper. The results show that the CMIP6 multi-model ensemble has good applicability in the Shiyang River Basin, as it accurately depicts the annual and seasonal distribution characteristics of climate factors, including precipitation, temperature, and potential evapotranspiration. The model performs well when simulating temperatures, in comparison to precipitation. While multimodel ensemble mean data perform better when simulating precipitation and temperature in the Shiyang River Basin, in comparison with other models. Under different future scenarios (2023-2100), precipitation, temperature, and potential evapotranspiration in the basin show a significant upward trend and increase with the radiative forcing increase. The late 21 century shows a greater increase in climate factors than the early and middle periods. Compared to the historical period, precipitation in the future could increase by 45.02% in the winter and 0.38% in the summer, and the greatest temperature increases can occur in spring and autumn. In the future, the aridity index of the Shiyang River Basin will decrease overall. The climate of the basin will tend to warm and humidify, with the summer season becoming drier while the other seasons become wetter than those in the historical period. The Minqin Basin located in the lower reaches of the basin is the area most sensitive to climate change. The research results have important reference value as they will help to address future climate change and ensure sustainable economic and agricultural development in the Shiyang River Basin.

Key words: CMIP6, Shiyang River Basin, regional climate change, dryness index, future

戴君, 胡海珠, 毛晓敏, 张霁. 基于CMIP6多模式预估数据的石羊河流域未来气候变化趋势分析[J]. 干旱区研究, 2023, 40(10): 1547-1562.

DAI Jun, HU Haizhu, MAO Xiaomin, ZHANG Ji. Future climate change trends in the Shiyang River Basin based on the CMIP6 multi-model estimation data[J]. Arid Zone Research, 2023, 40(10): 1547-1562.

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参考文献 48

[1]	郭静, 王宁, 粟晓玲. 气候变化下石羊河流域上游产流区的径流响应研究[J]. 西北农林科技大学学报(自然科学版), 2016, 44(12): 211-218.
	[Guo Jing, Wang Ning, Su Xiaoling. Response of runoff to climate change in upstream generation area of Shiyang River Basin[J]. Journal of Northwest A & F University, 2016, 44(12): 211-218.]
[2]	IPCC. Climate Change 2021: The Physical Science Basis[M]. Cambridge: Cambridge University Press, 2021.
[3]	赵娜娜, 王贺年, 张贝贝, 等. 若尔盖湿地流域径流变化及其对气候变化的响应[J]. 水资源保护, 2019, 35(5): 40-47.
	[Zhao Nana, Wang Henian, Zhang Beibei, et al. Runoff variation in Zoige Wetland Basin and its response to climate change[J]. Water Resources Protection, 2019, 35(5): 40-47.]
[4]	魏潇娜, 龙爱华, 尹振良, 等. 和田河流域冰川径流对气候变化响应的模拟分析[J]. 水资源保护, 2022, 38(4): 137-144.
	[Wei Xiaona, Long Aihua, Yin Zhenliang, et al. Simulation of response of glacier runoff to climate change in the Hotan River Basin[J]. Water Resources Protection, 2022, 38(4): 137-144.]
[5]	张艳霞, 于瑞宏, 薛浩, 等. 锡林河流域径流量变化对气候变化与人类活动的响应[J]. 干旱区研究, 2019, 36(1): 67-76.
	[Zhang Yanxia, Yu Ruihong, Xue Hao, et al. Response of runoff volume change to climate change and human activities in the Xilin River Basin[J]. Arid Zone Research, 2019, 36(1): 67-76.]
[6]	孙从建, 陈伟, 王诗语. 气候变化下的塔里木盆地西南部内陆河流域径流组分特征分析[J]. 干旱区研究, 2022, 39(1): 113-122.
	[Sun Congjian, Chen Wei, Wang Shiyu. Stream component characteristics of the inland river basin of the Tarim Basin under regional climate change[J]. Arid Zone Research, 2022, 39(1): 113-122.]
[7]	Zhai Jianqing, Mondal S K, Fischer T, et al. Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia[J]. Atmospheric Research, 2020, 246.
[8]	Chen Huopo, Sun Jianqi, Lin Wenqing, et al. Comparison of CMIP6 and CMIP5 models in simulating climate extremes[J]. Science Bulletin, 2020, 65(17): 1415-1418. doi: 10.1016/j.scib.2020.05.015 pmid: 36747394
[9]	宋帅峰, 延晓冬. CMIP6全球气候模式对中国冬季寒潮频次模拟能力的评估[J]. 气候与环境研究, 2022, 27(1): 33-49.
	[Song Shuaifeng, Yan Xiaodong. Evaluation of CMIP6 models performance for winter cold wave frequency in China[J]. Climatic and Environmental Research, 2022, 27(1): 33-49.]
[10]	李玲萍, 卢泰山, 刘明春, 等. 基于标准化流量指数(SDI)的石羊河流域水文干旱特征[J]. 中国沙漠, 2020, 40(4): 24-33. doi: 10.7522/j.issn.1000-694X.2020.00010
	[Li Lingping, Lu Taishan, Liu Mingchun, et al. Characteristics of hydrological drought based on standardized flow index in Shiyang River Basin of China[J]. Journal of Desert Research, 2020, 40(4): 24-33.] doi: 10.7522/j.issn.1000-694X.2020.00010
[11]	Yang Jianxia, Zhao Jun, Zhu Guofeng, et al. Effects of ecological water conveyance on soil salinization in the Shiyang River Basin’s terminal lake-Qingtu Lake-area[J]. Sustainability, 2022, 14(16): 10311. doi: 10.3390/su141610311
[12]	黄菊梅, 周俊菊, 窦娇, 等. 季风边缘区极端降水变化及其影响因素——以石羊河流域为例[J]. 生态学杂志, 2022, 41(3): 536-545.
	[Huang Jumei, Zhou Junju, Dou Jiao, et al. Variation of extreme precipitation and its influencing factors in monsoon marginal region: A case study of Shiyang River Basin[J]. Chinese Journal of Ecology, 2022, 41(3): 536-545.]
[13]	张强, 朱飙, 杨金虎, 等. 西北地区气候湿化趋势的新特征[J]. 科学通报, 2021, 66(Z2): 3757-3771.
	[Zhang Qiang, Zhu Biao, Yang Jinhu, et al. New characteristics about the climate humidification trend in Northwest China[J]. Chinese Science Bulletin, 2021, 66(Z2): 3757-3771.]
[14]	Zhang Gengxi, Su Xiaoling, Ayantobo O O, et al. Remote-sensing precipitation and temperature evaluation using soil and water assessment tool with multiobjective calibration in the Shiyang River Basin, Northwest China[J]. Journal of Hydrology, 2020, 590.
[15]	Huo Zailin, Feng Shaoyuan, Kang Shaozhong, et al. Effect of climate changes and water-related human activities on annual stream flows of the Shiyang River Basin in arid North-west China[J]. Hydrological Processes, 2008, 22(16): 3155-3167. doi: 10.1002/hyp.v22:16
[16]	Tang Zhiguang, Ma Jinhui, Peng Huanhua, et al. Spatiotemporal changes of vegetation and their responses to temperature and precipitation in upper Shiyang River Basin[J]. Advances in Space Research, 2017, 60(5): 969-979. doi: 10.1016/j.asr.2017.05.033
[17]	Zhou Junju, Li Qiaoqiao, Wang Lanying, et al. Impact of climate change and land-use on the propagation from meteorological drought to hydrological drought in the eastern Qilian Mountains[J]. Water, 2019, 11(8): 1602. doi: 10.3390/w11081602
[18]	宫毓来, 马绍休, 刘伟琦. 机器学习与统计模型在石羊河流域气候降尺度研究中的适用性对比[J]. 中国沙漠, 2022, 42(1): 196-210. doi: 10.7522/j.issn.1000-694X.2021.00210
	[Gong Yulai, Ma Shaoxiu, Liu Weiqi. A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin[J]. Journal of Desert Research, 2022, 42(1): 196-210.] doi: 10.7522/j.issn.1000-694X.2021.00210
[19]	Zhou Junju, Huang Jumei, Zhao Xi, et al. Changes of extreme temperature and its influencing factors in Shiyang River Basin, Northwest China[J]. Atmosphere, 2020, 11(11): 1171. doi: 10.3390/atmos11111171
[20]	Xu Ying, Gao Xuejie, Shen Yan, et al. A daily temperature dataset over China and its application in validating a RCM simulation[J]. Advances in Atmospheric Sciences, 2009, 26(4): 763-772. doi: 10.1007/s00376-009-9029-z
[21]	吴佳, 高学杰. 一套格点化的中国区域逐日观测资料及与其它资料的对比[J]. 地球物理学报, 2013, 56(4): 1102-1111.
	[Wu Jia, Gao Xuejie. A gridded daily observation dataset over China region and comparison with the other datasets[J]. Chinese Journal of Geophysics, 2013, 56(4): 1102-1111.]
[22]	李纯, 姜彤, 王艳君, 等. 基于CMIP6模式的黄河上游地区未来气温模拟预估[J]. 冰川冻土, 2022, 44(1): 171-178. doi: 10.7522/j.issn.1000-0240.2022.0028
	[Li Chun, Jiang Tong, Wang Yanjun, et al. Simulation and estimation of future air temperature in upper basin of the Yellow River based on CMIP6 models[J]. Journal of Glaciology and Geocryology, 2022, 44(1): 171-178.] doi: 10.7522/j.issn.1000-0240.2022.0028
[23]	ONeill B C, Tebaldi C, Van Vuuren D P, et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6[J]. Geoscientific Model Development, 2016, 9(9): 3461-3482. doi: 10.5194/gmd-9-3461-2016
[24]	Li Haibin, Sheffield J, Wood E F. Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching[J]. Journal of Geophysical Research, 2010, 115(D10).
[25]	陈笑晨, 唐振飞, 陈锡宽, 等. 基于CMIP6的福建省极端气温预估[J]. 干旱气象, 2022, 40(3): 415-423. doi: 10.11755/j.issn.1006-7639（2022）-03-0415
	[Chen Xiaochen, Tang Zhenfei, Chen Xikuan, et al. Projection of extreme temperature in Fujian based on CMIP6 output[J]. Journal of Arid Meteorology, 2022, 40(3): 415-423.] doi: 10.11755/j.issn.1006-7639（2022）-03-0415
[26]	江志红, 卢尧, 丁裕国. 基于时空结构指标的中国融合降水资料质量评估[J]. 气象学报, 2013, 71(5): 891-900.
	[Jiang Zhihong, Lu Yao, Ding Yuguo. Analysis of the high-resolution merged precipitation products over China based on the temporal and spatial structure score indices[J]. Acta Meteorologica Sinica, 2013, 71(5): 891-900.]
[27]	Schuenemann K C, Cassano J J. Changes in synoptic weather patterns and Greenland precipitation in the 20th and 21st centuries: 1. Evaluation of late 20th century simulations from IPCC models[J]. Journal of Geophysical Research, 2009, 114(D20).
[28]	Allen R G, Pereira L S, Raes D, et al. Crop evapotranspiration: Guidelines for Computing Crop Water Requirements[R]. Rome: FAO Irrigation and Drainage, 1998.
[29]	Committee of Physical Regionalization of the Chinese Academy of Sciences. Synthetic Physical Regionalization of China[M]. Beijing: Science Press, 1959.
[30]	Wallach D, Martre P, Liu Bing, et al. Multi model ensembles improve predictions of crop-environment-management interactions[J]. Global Change Biology, 2018, 24(11): 5072-5083. doi: 10.1111/gcb.14411 pmid: 30055118
[31]	赵梦霞, 苏布达, 姜彤, 等. CMIP6模式对黄河上游降水的模拟及预估[J]. 高原气象, 2021, 40(3): 547-558. doi: 10.7522/j.issn.1000-0534.2020.00066
	[Zhao Mengxia, Su Buda, Jiang Tong, et al. Simulation and projection of precipitation in the upper Yellow River Basin by CMIP6 multi-model ensemble[J]. Plateau Meteorology, 2021, 40(3): 547-558.] doi: 10.7522/j.issn.1000-0534.2020.00066
[32]	韩林君, 白爱娟, 蒲学敏. 基于CMIP6的祁连山气候变化特征预估[J]. 高原气象, 2022, 41(4): 864-875. doi: 10.7522/j.issn.1000-0534.2021.00037
	[Han Linjun, Bai Aijuan, Pu Xuemin. Projection of climate variation in Qilian Mountains based on CMIP6[J]. Plateau Meteorology, 2022, 41(4): 864-875.] doi: 10.7522/j.issn.1000-0534.2021.00037
[33]	王双双, 谢文强, 延晓冬. CMIP6模式对中国气温日较差的模拟能力评估[J]. 气候与环境研究, 2022, 27(1): 79-93.
	[Wang Shuangshuang, Xie Wenqiang, Yan Xiaodong. Evaluation on CMIP6 model simulation of the diurnal temperature range over China[J]. Climatic and Environmental Research, 2022, 27(1): 79-93.]
[34]	康绍忠. 藏粮于水藏水于技——发展高水效农业保障国家食物安全[J]. 中国水利, 2022(13): 1-5.
	[Kang Shaozhong. Store grain in water and technology——development of highly-efficient agricultural water use for ensuring national food security[J]. China water resources, 2022(13): 1-5.]
[35]	赵传燕, 南忠仁, 程国栋, 等. 统计降尺度对西北地区未来气候变化预估[J]. 兰州大学学报(自然科学版), 2008, 44(5): 12-18,25.
	[Zhao Chuanyan, Nan Zhongren, Cheng Guodong, et al. Prediction of the trend of the future climate change in northwestern China by statistical downscaling[J]. Journal of Lanzhou University(Natural Sciences), 2008, 44(5): 12-18, 25.]
[36]	祁晓凡, 李文鹏, 李海涛, 等. 基于CMIP5模式的干旱内陆河流域未来气候变化预估[J]. 干旱区地理, 2017, 40(5): 987-996.
	[Qi Xiaofan, Li Wenpeng, Li Haitao, et al. Future climate change prediction of arid inland river basin based on CMIP5 model[J]. Arid Land Geography, 2017, 40(5): 987-996.]
[37]	王澄海, 张晟宁, 张飞民, 等. 论全球变暖背景下中国西北地区降水增加问题[J]. 地球科学进展, 2021, 36(9): 980-989. doi: 10.11867/j.issn.1001-8166.2021.087
	[Wang Chenghai, Zhang Shengning, Zhang Feimin, et al. On the increase of precipitation in the Northwestern China under the global warming[J]. Advances in Earth Science, 2021, 36(9): 980-989.] doi: 10.11867/j.issn.1001-8166.2021.087
[38]	晋程绣, 姜超, 张曦月. CMIP6模式对中国西南地区气温的模拟与预估[J]. 中国农业气象, 2022, 43(8): 597-611.
	[Jin Chengxiu, Jiang Chao, Zhang Xiyue. Evaluation and projection of temperature in Southwestern China by CMIP6 models[J]. Chinese Journal of Agrometeorology, 2022, 43(8): 597-611.]
[39]	吴健, 夏军, 曾思栋, 等. CMIP6全球气候模式对长江流域气候变化的模拟评估与未来预估[J]. 长江流域资源与环境, 2023, 32(1): 137-150.
	[Wu Jian, Xia Jun, Zeng Sidong, et al. Evaluation of the performance of CMIP6 models and future Changes over the Yangtze River Basin[J]. Resources and Environment in the Yangtze Basin, 2023, 32(1): 137-150.]
[40]	来雪慧, 李丹, 于波峰, 等. 东北农场农作物生长季土壤呼吸对温度和含水量的响应[J]. 水土保持研究, 2016, 23(1): 117-122.
	[Lai Xuehui, Li Dan, Yu Bofeng, et al. Effects of soil temperature and water content on soil respiration rate during the crop growing season in a farm of northern China[J]. Journal of Irrigation and Drainage, 2016, 23(1): 117-122.]
[41]	张成福, 王雨晴, 闫冬, 等. 内蒙古荒漠草原区气候变化及干旱趋势分析[J]. 灌溉排水学报, 2020, 39(S2): 20-25.
	[Zhang Chengfu, Wang Yuqing, Yan Dong, et al. Analysis of climate change and drought trend in desert steppe of Inner Mongolia[J]. Journal of Irrigation and Drainage, 2020, 39(S2): 20-25.]
[42]	刘文斐, 粟晓玲, 张更喜, 等. 中国西北地区未来潜在蒸散发集合预估及不确定性归因[J]. 农业工程学报, 2022, 38(4): 123-132.
	[Liu Wenfei, Su Xiaoling, Zhang Gengxi, et al. Ensemble projection and uncertainty attribution of potential evapotranspiration in Northwest China in the future[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(4): 123-132.]
[43]	张红丽, 韩富强, 张良, 等. 西北地区气候暖湿化空间与季节差异分析[J]. 干旱区研究, 2023, 40(4): 517-531.
	[Zhang Hongli, Han Fuqiang, Zhang Liang, et al. Analysis of spatial and seasonal variations in climate warming and humidification in Northwest China[J]. Arid Zone Research, 2023, 40(4): 517-531.]
[44]	姚俊强, 李漠岩, 迪丽努尔·列吾别克, 等. 不同时间尺度下新疆气候“暖湿化”特征[J]. 干旱区研究, 2022, 39(2): 333-346.
	[Yao Junqiang, Li Moyan, Dilinuer Tuoliewubieke, et al. The assessment on“warming-wetting”trend in Xinjiang at multi-scale during 1961-2019[J]. Arid Zone Research, 2022, 39(2): 333-346.]
[45]	Zhang Qiang, Yang Jinhu, Wang Wei, et al. Climatic warming and humidification in the arid region of Northwest China: Multi-scale characteristics and impacts on ecological vegetation[J]. Journal of Meteorological Research, 2021, 35(1): 113-127. doi: 10.1007/s13351-021-0105-3
[46]	柳利利, 韩磊, 韩永贵, 等. 1989—2019年西北地区干燥度指数时空变化及其对气候因子的响应[J]. 应用生态学报, 2021, 32(11): 4050-4058. doi: 10.13287/j.1001-9332.202111.014
	[Liu Lili, Han Lei, Han Yonggui, et al. Spatio-temporal variations of aridity index and its response to climate factors in Northwest China during 1989—2019[J]. Chinese Journal of Applied Ecology, 2021, 32(11): 4050-4058.] doi: 10.13287/j.1001-9332.202111.014
[47]	Liu Yang, Geng Xiu, Hao Zhixin, et al. Changes in climate extremes in Central Asia under 1.5 and 2 ℃ global warming and their impacts on agricultural productions[J]. Atmosphere, 2020, 11(10): 1076. doi: 10.3390/atmos11101076
[48]	Zhang Zepeng, Wang Qingzheng, Guan Qingyu, et al. Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization[J]. Agricultural Water Management, 2023, 279: 108177. doi: 10.1016/j.agwat.2023.108177

模式名称	国家	所属机构	分辨率（纬度×经度）
ACCESS-ESM1-5	澳大利亚	英联邦科学和工业研究组织（CSIRO）	1.875°×1.24°
CanESM5	加拿大	加拿大气候建模和分析中心（CCCma）	2.8125°×2.8125°
EC-Earth3	瑞典	欧共体地球联合会（EC）	0.703°×0.703°
FGOALS-g3	中国	中国科学院大气物理研究所（CAS）	2.0°×2.0°
GFDL-ESM4	美国	美国国家海洋和大气管理局地球物理流体动力学实验室（GFDL）	1.25°×1.0°
INM-CM4-8	俄罗斯	俄罗斯科学院数值数学研究所（INMRAS）	2.0°×1.5°
IPSL-CM6A-LR	法国	皮埃尔-西蒙拉普拉斯学院（IPSL）	2.5°×1.25°
MIROC6	日本	日本海洋地球科学技术厅（JAMSTEC）	1.40625°×1.40625°
MPI-ESMl-2-LR	德国	马克斯普朗克气象研究所（MPI-M）	1.875°×1.875°
MRI-ESM2-0	日本	日本气象厅气象研究所（JMA）	1.125°×1.126°
NorESM2-MM	挪威	挪威气候中心（NorCC）	1.25°×0.9375°

干湿状态	分级标准
湿润	AI≤ 1.0
半湿润	1.0<AI≤ l.5
半干旱	1.5<AI≤ 4.0
干旱	4.0<AI≤ l6.0
极干旱	AI>16.0

排放情景	降水变化 /[mm·（10a）^-1]	气温变化 /[℃·（10a）^-1]	潜在蒸散发变化 /[mm·（10a）^-1]
SSP1-2.6	2.19^*（-11.63~7.98）	0.04^*（-0.02~0.14）	0.44^*（-3.73~5.01）
SSP2-4.5	6.70^**（0.29~14.86）	0.24^**（0.08~0.42）	3.27^**（-1.03~8.06）
SSP3-7.0	8.27^**（0.57~17.77）	0.51^**（0.36~0.86）	8.38^**（2.70~21.14）
SSP5-8.5	10.75^**（0.65~23.48）	0.67^**（0.43~1.11）	11.35^**（-0.21~18.77）

基于CMIP6多模式预估数据的石羊河流域未来气候变化趋势分析

Future climate change trends in the Shiyang River Basin based on the CMIP6 multi-model estimation data

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模式	降水					气温					综合排名
模式	r	RMSE	SD	S_T	M_R	r	RMSE	SD	S_T	M_R	综合排名
ACCESS-ESM1-5	11	7	2	10	9	6	6	8	6	7	8
CanESM5	2	8	11	1	5	12	10	12	10	11	9
EC-Earth3	7	4	6	4	3	8	4	4	3	4	2
FGOALS-g3	8	3	3	7	4	5	3	2	5	3	3
GFDL-ESM4	6	5	7	6	6	3	5	9	4	5	4
INM-CM4-8	9	12	12	12	12	2	2	5	2	2	6
IPSL-CM6A-LR	5	1	5	2	2	10	11	10	11	10	5
MIROC6	4	10	9	8	10	9	12	11	12	12	12
MPI-ESMl-2-LR	12	11	8	11	11	7	9	3	8	8	11
MRI-ESM2-0	10	6	1	9	7	11	7	6	7	9	10
NorESM2-MM	3	9	10	5	8	4	8	1	9	6	7
MME	1	2	4	3	1	1	1	7	1	1	1

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