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] Assessment of multiple precipitation interpolation methods and uncertainty analysis of hydrological models in Chaohe River basin, China
    Guo, Binbin
    Zhang, Jing
    Xu, Tingbao
    Song, Yongyu
    Liu, Mingliang
    Dai, Zhong
    WATER SA, 2022, 48 (03) : 324 - 334
  • [22] Projecting Rainfall Changes over the South American Altiplano
    Minvielle, Marie
    Garreaud, Rene D.
    JOURNAL OF CLIMATE, 2011, 24 (17) : 4577 - 4583
  • [23] Hydrological projections based on the coupled hydrological-hydraulic modeling in the complex river network region: a case study in the Taihu basin, China
    Liu, Liu
    Xu, Zongxue
    JOURNAL OF WATER AND CLIMATE CHANGE, 2015, 6 (02) : 386 - 399
  • [24] Influence of changes in river system structure on hydrological processes in Taihu Basin, China
    Dai, Xiaoying
    Xu, Youpeng
    Lin, Zhixin
    Wang, Qiang
    Gao, Bin
    Yuan, Jia
    Xiang, Jie
    HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2019, 64 (16): : 2093 - 2104
  • [25] Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China
    Zhu, Honglei
    Li, Ying
    Huang, Yanwei
    Li, Yingchen
    Hou, Cuicui
    Shi, Xiaoliang
    ATMOSPHERIC RESEARCH, 2018, 207 : 28 - 41
  • [26] Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins
    Vetter, Tobias
    Reinhardt, Julia
    Floerke, Martina
    van Griensven, Ann
    Hattermann, Fred
    Huang, Shaochun
    Koch, Hagen
    Pechlivanidis, Ilias G.
    Ploetner, Stefan
    Seidou, Ousmane
    Su, Buda
    Vervoort, R. Willem
    Krysanova, Valentina
    CLIMATIC CHANGE, 2017, 141 (03) : 419 - 433
  • [27] Detection and attribution of hydrological changes in a large Alpine river basin
    Mallucci, S.
    Majone, B.
    Bellin, A.
    JOURNAL OF HYDROLOGY, 2019, 575 : 1214 - 1229
  • [28] Hydrological Responses to Land Use/Cover Changes in the Olifants Basin, South Africa
    Gyamfi, Charles
    Ndambuki, Julius M.
    Salim, Ramadhan W.
    WATER, 2016, 8 (12)
  • [29] River regimes and recent hydrological changes in the Duero basin (Spain)
    Moran-Tejeda, Enrique
    Ignacio Lopez-Moreno, Juan
    Ceballos-Barbancho, Antonio
    Vicente-Serrano, Sergio M.
    JOURNAL OF HYDROLOGY, 2011, 404 (3-4) : 241 - 258
  • [30] Individual and combined impacts of future land-use and climate conditions on extreme hydrological events in a representative basin of the Yangtze River Delta, China
    Wang, Qiang
    Xu, Youpeng
    Wang, Yuefeng
    Zhang, Yuqing
    Xiang, Jie
    Xu, Yu
    Wang, Jie
    ATMOSPHERIC RESEARCH, 2020, 236