干旱区研究

Arid Zone Research >

2024 , Vol. 41 >Issue 12: 2045 - 2055

DOI: https://doi.org/10.13866/j.azr.2024.12.07

Land and Water Resources

Trend change and prediction of blue-green water in the Jinghe River Basin under climate change

  • ZHANG Jiaqi ,
  • LIU Zhao ,
  • HAN Zhongqing ,
  • WANG Lixia ,
  • ZHANG Jinxia ,
  • YUE Jiayin ,
  • GUAN Zilong
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  • 1. School of Water and Environment, Chang’an University, Xi’an 710054, Shaanxi, China
    2. Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, Shaanxi, China
    3. Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources, Chang’an University, Xi’an 710054, Shaanxi, China
    4. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, Shaanxi, China
    5. Powerchina Northwest Engineering Corporation Limited, Xi’an 710065, Shaanxi, China

Received date: 2024-04-21

  Revised date: 2024-09-21

  Online published: 2024-12-20

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Abstract

In this study, we analyzed meteorological data from 1980 to 2020, hydrological runoff data, and future climate models from CMIP6 in the Jinghe River Basin. The CMIP6 climate data was processed using the delta downscaling method and coupled with the Soil and Water Assessment Tool hydrological model to investigate the variations in blue-green water due to climate change in the basin. The results showed that under the SSP1-2.6 pathway, the blue-green water content in the study area exhibited an insignificant upward trend. Under the SSP3-7.0 pathway, the blue water content showed an insignificant downward trend, while the green water content showed a significant upward trend. Similarly, under the SSP5-8.5 pathway, the blue water content showed an insignificant downward trend, and the green water content also exhibited an insignificant upward trend. The average annual blue water volume under the three pathways decreased compared to the historical period, with annual averages of 128.8 mm, 117.2 mm, and 126 mm, respectively. Conversely, the average annual green water volume increased, recording values of 372.7 mm, 369.3 mm, and 372.1 mm, resulting in a green water coefficient higher than that of the historical period. The spatial distribution of blue-green water increased from northwest to southeast, with consistent spatial distribution characteristics across each pathway.

Key words： SWAT model; Delta downscaling; climate change; CMIP6 climate model; blue-green water; Jinghe River Basin

Cite this article

ZHANG Jiaqi , LIU Zhao , HAN Zhongqing , WANG Lixia , ZHANG Jinxia , YUE Jiayin , GUAN Zilong . Trend change and prediction of blue-green water in the Jinghe River Basin under climate change[J]. Arid Zone Research, 2024 , 41(12) : 2045 -2055 . DOI: 10.13866/j.azr.2024.12.07

References

[1] 左其亭, 胡德胜, 窦明, 等. 基于人水和谐理念的最严格水资源管理制度研究框架及核心体系[J]. 资源科学, 2014, 36(5): 906-912.
  [Zuo Qiting, Hu Desheng, Dou Ming, et al. Framework and core system of the most stringent water resource management system based on the concept of human-water harmony[J]. Resources Science, 2014, 36(5): 906-912. ]
[2] Falkenmark M, Rockstrom J. Building water resilience in the face of global change: From a blue-only to a green-blue water approach to land-water management[J]. Journal of Water Resources Planning and Management, 2010, 136(6): 606-610.
[3] 司鹏, 郝立生, 傅宁, 等. 河北保定百年均一化逐日气温序列的建立及其气候变化特征[J]. 大气科学学报, 2023, 46(2): 297-309.
  [Si Peng, Hao Lisheng, Fu Ning, et al. Establishment of homogenized daily temperature series for Baoding in Hebei Province and its climate characteristics over a century-long scale[J]. Transactions of Atmospheric Sciences, 2023, 46(2): 297-309. ]
[4] 高姗. 中国经济发展中的生态环境保护——“金山银山不如绿水青山”的辩证思考[J]. 中国国际财经(中英文), 2018(9): 266.
  [Gao Shan. Ecological environment protection in China’s economic development: A dialectical reflection on “Gold and Silver Mountains are Better than Green Waters and Green Mountains”[J]. China International Finance (in both Chinese and English), 2018(9): 266. ]
[5] Veettil A V, Mishra A K. Water security assessment using blue and green water footprint concepts[J]. Journal of Hydrology, 2016, 542: 589-602.
[6] 童凯, 赵银军, 胡宝清, 等. 基于SWAT模型的典型厄尔尼诺/拉尼娜年广西北部湾南流江蓝绿水评估[J]. 水电能源科学, 2023, 41(10): 46-49.
  [Tong Kai, Zhao Yinjun, Hu Baoqing, et al. Assessment of blue/green water in typical El Niňo/La Niňa years in the Nanliu River Basin, Beibu Gulf Based on SWAT model[J]. Water Resources and Power, 2023, 41(10): 46-49. ]
[7] 康文东, 倪福全, 邓玉, 等. 利用SWAT模型分析乌江流域蓝绿水时空分布特征[J]. 中国农业气象, 2023, 44(6): 469-478.
  [Kang Wendong, Ni Fuquan, Deng Yu, et al. Spatial and temporal variation characteristics of blue-green water resources in Wujiang River Basin based on SWAT model[J]. Chinese Journal of Agrometeorology, 2023, 44(6): 469-478. ]
[8] 齐文华, 金艺华, 尹振浩, 等. 基于SWAT模型的图们江流域蓝绿水资源供需平衡分析[J]. 生态学报, 2023, 43(8): 3116-3127.
  [Qi Wenhua, Jin Yihua, Yin Zhenhao, et al. Analysis of blue and green water scarcity based on SWAT model in the Tumen River Basin[J]. Acta Ecologica Sinica, 2023, 43(8): 3116-3127. ]
[9] 康文东, 倪福全, 邓玉, 等. 乌江流域蓝绿水对气候和土地利用变化的响应[J]. 农业工程学报, 2023, 39(19): 131-140.
  [Kang Wendong, Ni Fuquan, Deng Yu, et al. Response of blue and green water to climate and land use changes: A study in the Wujiang River Basin,China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(19): 131-140. ]
[10] 崔周宇, 杨肖丽, 李文婷, 等. 气候与土地利用变化下黄河源区蓝绿水量的时空响应研究[J]. 水文, 2023, 43(2): 66-71, 7.
  [Cui Zhouyu, Yang Xiaoli, Li Wenting, et al. Responses of blue and green water to climate change and landuse change in the source region of the Yellow River[J]. Journal of China Hydrology, 2023, 43(2): 66-71, 7. ]
[11] Zhang Y F, Tang C J, Ye A Z, et al. Impacts of climate and landuse change on blue and green water: A study case of the upper Ganjiang River Basin[J]. Water, 2020, 12(10): 2661.
[12] 戴军, 胡海珠, 毛晓敏, 等. 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, et al. Future climate change trends in the Shiyang River Basin based on the CMIP6 muti-model estimation data[J]. Arid Zone Research, 2023, 40(10): 1547-1562. ]
[13] Eyring V, Bony S, Meehl G A, et al. Overview of the Coupled model Intercomparison Project Phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development, 2016, 9(5): 1937-1958.
[14] 张丽霞, 陈晓龙, 辛晓歌, 等. CMIP6情景模式比较计划(Scenario MIP)概况与评述[J]. 气候变化研究进展, 2019, 15(5): 519-525.
  [Zhang Lixia, Chen Xiaolong, Xin Xiaoge, et al. Short commentary on CMIP6 scenario model intercomparison project (Scenario MIP)[J]. Climate Change Research, 2019, 15(5): 519-525. ]
[15] 闵庆文, 何永涛, 李文华, 等. 基于农业气象学原理的林地生态需水量估算——以泾河流域为例[J]. 生态学报, 2004, 24(10): 2130-2135.
  [Min Qingwen, He Yongtao, Li Wenhua, et al. Estimation of forests’ecological water requirement based on agrometeorology: Taking Jinghe Watershed as an example[J]. Acta Ecologica Sinica, 2004, 24(10): 2130-2135. ]
[16] 郑培龙, 李云霞, 赵阳, 等. 黄土高原泾河流域气候和土地利用变化对径流产沙的影响[J]. 水土保持研究, 2015, 22(5): 20-24.
  [Zheng Peilong, Li Yunxia, Zhao Yang, et al. Effects of climate variation and landuse change on runoff in Jinghe Basin of the Loess Plateau[J]. Research of Soil and Water Conservation, 2015, 22(5): 20-24. ]
[17] 何旭强. 基于SWAT模型的黑河上游径流模拟及其对气候变化的响应[D]. 兰州: 西北师范大学, 2013.
  [He Xuqiang. Runoff Simulation and Response to Climate Change in the Upper Reaches of the HeiHe River Basin Based on SWAT Model[D]. Lanzhou: Northwest Normal University, 2013. ]
[18] Onyutha C, Tabari H, Rutkowska A, et al. Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5[J]. Journal of Hydro-Environment Research, 2016, 12: 31-45.
[19] 吴琛, 王景才, 邵俊博, 等. CMIP6模式对淮河中上游流域气候要素模拟效果的评估研究[J]. 人民珠江, 2023, 44(10): 105-116.
  [Wu Chen, Wang Jingcai, Shao Junbo, et al. Simulation effect evaluation of CMIP6 models on climatic elements in Huai River Basin[J]. Pearl River, 2023, 44(10): 105-116. ]
[20] Yuan Z, Xu J J, Wang Y Q. Historical and future changes of blue water and green water resources in the Yangtze River source region, China[J]. Theoretical and Applied Climatology, 2019, 138(1-2): 1035-1047.
[21] Alashan S. Combination of modified Mann-Kendall method and Sen innovative trend analysis[J]. Engineering Reports, 2020, 2(3): e12131.
[22] Haddad M, Bonaduce A. On the long-term mediterranean sea level variability; proceedings[C]// Hammamet, TUNISIA:The 1st Springer Conference of the Arabian-Journal-of-Geosciences (CAJG), F Nov 12-15, 2018.
[23] 李蓝君. 黄土高原沟壑区典型植被作用下水平衡要素及水相关生态服务的变化机理研究[D]. 西安: 西安理工大学, 2023.
  [Li Lanjun. Study on the Change Mechanism of Water Balance Component and Water-related Ecosystem Services under Typical Vegetation Species in the Gully Region of the Loess Plateau[D]. Xi’an: Xi’an University of Technology, 2023. ]
[24] 豆明玉, 段克勤, 石培宏, 等. 基于CMIP6多模式的黄土高原气温变化模拟评估及情景预估[J]. 水土保持研究, 2024, 31(2): 158-167.
  [Dou Mingyu, Duan Keqin, Shi Peihong, et al. Historical evaluation and scenario prediction of temperature changes the Loess Plateau based on CMIP6 multimodels[J]. Research of Soil and Water Conservation, 2024, 31(2): 158-167. ]
[25] 刘欢. 典型流域径流及其组分变化的归因分析与预测[D]. 西安: 西安理工大学, 2023.
  [Liu Huan. Attributive Analysis and Prediction of Typical Watershed Runoff and Its Component Changes[D]. Xi’an: Xi’an University of Technology, 2023. ]
[26] 朱天生. 未来气候模式下黄河流域径流变化归因研究[D]. 郑州: 郑州大学, 2022.
  [Zhu Tiansheng. Attribution Analysis of Runoff Change in the Yellow River Basin under Climate Model[D]. Zhengzhou: Zhengzhou University, 2022. ]
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