Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China

被引:28
|
作者
Yuan, Fei [1 ]
Zhao, Chongxu [1 ]
Jiang, Yong [2 ]
Ren, Liliang [1 ]
Shan, Hongcui [3 ]
Zhang, Limin [1 ]
Zhu, Yonghua [1 ]
Chen, Tao [1 ]
Jiang, Shanhu [1 ]
Yang, Xiaoli [1 ]
Shen, Hongren [1 ]
机构
[1] Hohai Univ, Coll Hydrol & Water Resources, State Key Lab Hydrol Water Resources & Hydraul En, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China
[2] Water Resources Serv Ctr Jiangsu Prov, 5 Shanghai Rd, Nanjing 210029, Jiangsu, Peoples R China
[3] Hunan Water Resources & Hydropower Res Inst, 370 North Shaoshan Rd, Changsha 410007, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Uncertainty; Climate change; Emission scenario; Climate model; Statistical downscaling method; Hydrological model; Extreme flow frequency analysis; CLIMATE-CHANGE IMPACTS; FLOOD FREQUENCY; QUANTIFYING UNCERTAINTY; GLOBAL CLIMATE; ZHUJIANG RIVER; RUNOFF MODEL; PRECIPITATION; CATCHMENT; SCENARIOS; ENSEMBLE;
D O I
10.1016/j.jhydrol.2017.08.034
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMS), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July-September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:434 / 450
页数:17
相关论文
共 50 条
  • [21] Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin
    Sun, Fengyun
    Mejia, Alfonso
    Zeng, Peng
    Che, Yue
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 696
  • [22] Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China
    Touseef, Muhammad
    Chen, Lihua
    Yang, Kaipeng
    Chen, Yunyao
    ADVANCES IN METEOROLOGY, 2020, 2020
  • [23] Correlation between hydrological drought, climatic factors, reservoir operation, and vegetation cover in the Xijiang Basin, South China
    Lin, Qingxia
    Wu, Zhiyong
    Singh, Vijay P.
    Sadeghi, S. H. R.
    He, Hai
    Lu, Guihua
    JOURNAL OF HYDROLOGY, 2017, 549 : 512 - 524
  • [24] Temporal and Spatial Changes of Hydrological Processes in the Jinsha River Basin, China
    Zeng, Xiao-fan
    Ye, Lei
    Zhao, Na
    Mai, Zi-jun
    2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY AND ENVIRONMENTAL ENGINEERING (SEEE 2016), 2016,
  • [25] Changes and Relationships of Climatic and Hydrological Droughts in the Jialing River Basin, China
    Zeng, Xiaofan
    Zhao, Na
    Sun, Huaiwei
    Ye, Lei
    Zhai, Jianqing
    PLOS ONE, 2015, 10 (11):
  • [26] Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China
    Tao Chen
    Liliang Ren
    Fei Yuan
    Tiantian Tang
    Xiaoli Yang
    Shanhu Jiang
    Yi Liu
    Chongxu Zhao
    Limin Zhang
    Stochastic Environmental Research and Risk Assessment, 2019, 33 : 1893 - 1905
  • [27] Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China
    Li, Xue
    Chen, Yangbo
    Deng, Xincui
    Zhang, Yueyuan
    Chen, Lingfang
    REMOTE SENSING, 2021, 13 (05) : 1 - 23
  • [28] Evaluation of Precipitation Products by Using Multiple Hydrological Models over the Upper Yellow River Basin, China
    Guan, Xiaoxiang
    Zhang, Jianyun
    Yang, Qinli
    Tang, Xiongpeng
    Liu, Cuishan
    Jin, Junliang
    Liu, Yue
    Bao, Zhenxin
    Wang, Guoqing
    REMOTE SENSING, 2020, 12 (24) : 1 - 27
  • [29] Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China
    Ren, Peizhen
    Li, Jianzhu
    Feng, Ping
    Guo, Yuangang
    Ma, Qiushuang
    WATER, 2018, 10 (06)
  • [30] Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China
    Chen, Tao
    Ren, Liliang
    Yuan, Fei
    Tang, Tiantian
    Yang, Xiaoli
    Jiang, Shanhu
    Liu, Yi
    Zhao, Chongxu
    Zhang, Limin
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2019, 33 (10) : 1893 - 1905