• Abbaspour, K. C., M. Faramarzi, S. S. Ghasemi, and H. Yang, 2009: Assessing the impact of climate change on water resources in Iran. Water Resour. Res., 45, W10434, https://doi.org/10.1029/2008WR007615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alfieri, L., P. Burek, L. Feyen, and G. Forzieri, 2015: Global warming increases the frequency of river floods in Europe. Hydrol. Earth Syst. Sci., 19, 22472260, https://doi.org/10.5194/hess-19-2247-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnell, N. W., 1999: Climate change and global water resources. Global Environ. Change, 9, S31S49, https://doi.org/10.1016/S0959-3780(99)00017-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnell, N. W., 2004: Climate change and global water resources: SRES emissions and socio–economic scenarios. Global Environ. Change, 1, 3152, https://doi.org/10.1016/j.gloenvcha.2003.10.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aryal, A., S. Shrestha, and M. S. Babel, 2019: Quantifying the sources of uncertainty in an ensemble of hydrological climate–impact projections. Theor. Appl. Climatol., 135, 193209, https://doi.org/10.1007/s00704-017-2359-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bastola, S., C. Murphy, and J. Sweeney, 2011: The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv. Water Resour., 34, 562576, https://doi.org/10.1016/j.advwatres.2011.01.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennett, K. E., J. R. Urrego Blanco, A. Jonko, T. J. Bohn, A. L. Atchley, N. M. Urban, and R. S. Middleton, 2018: Global sensitivity of simulated water balance indicators under future climate change in the Colorado Basin. Water Resour. Res., 54, 132149, https://doi.org/10.1002/2017WR020471.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bianchi Janetti, E., L. Guadagnini, M. Riva, and A. Guadagnini, 2019: Global sensitivity analyses of multiple conceptual models with uncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer. J. Hydrol., 574, 544556, https://doi.org/10.1016/j.jhydrol.2019.04.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosshard, T., M. Carambia, K. Goergen, S. Kotlarski, P. Krahe, M. Zappa, and C. Schär, 2013: Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour. Res., 49, 15231536, https://doi.org/10.1029/2011WR011533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., F. P. Brissette, and R. Leconte, 2011a: Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J. Hydrol., 401, 190202, https://doi.org/10.1016/j.jhydrol.2011.02.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., F. P. Brissette, A. Poulin, and R. Leconte, 2011b: Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour. Res., 47, W12509, https://doi.org/10.1029/2011WR010602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., F. P. Brissette, D. Chaumont, and M. Braun, 2013: Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J. Hydrol., 479, 200214, https://doi.org/10.1016/j.jhydrol.2012.11.062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., F. P. Brissette, P. Liu, and J. Xia, 2017: Using raw regional climate model outputs for quantifying climate change impacts on hydrology. Hydrol. Processes, 31, 43984413, https://doi.org/10.1002/hyp.11368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu-Agor, M. L., R. Muñoz-Carpena, G. Kiker, A. Emanuelsson, and I. Linkov, 2011: Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analysis. Environ. Modell. Software, 26, 593604, https://doi.org/10.1016/j.envsoft.2010.12.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, H., and M. Ye, 2015: Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation. J. Hydrol., 528, 286300, https://doi.org/10.1016/j.jhydrol.2015.06.034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, H., X. Chen, M. Ye, X. Song, and J. M. Zachara, 2017a: A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling. Water Resour. Res., 53, 43274343, https://doi.org/10.1002/2016WR019756.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, H., M. Ye, A. P. Walker, and X. Chen, 2017b: A new process sensitivity index to identify important system processes under process model and parametric uncertainty. Water Resour. Res., 53, 34763490, https://doi.org/10.1002/2016WR019715.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, H., X. Chen, M. Ye, X. Song, G. Hammond, B. Hu, and J. M. Zachara, 2019: Using Bayesian networks for sensitivity analysis of complex biogeochemical models. Water Resour. Res., 55, 35413555, https://doi.org/10.1029/2018WR023589.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demaria, E. M., B. Nijssen, and T. Wagener, 2007: Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model. J. Geophys. Res., 112, D11113, https://doi.org/10.1029/2006JD007534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., 2007: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values. Global Planet. Change, 57, 1626, https://doi.org/10.1016/j.gloplacha.2006.11.030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dick, J., F. Y. Kuo, and I. H. Sloan, 2013: High-dimensional integration: The quasi-Monte Carlo way. Acta Numer., 22, 133288, https://doi.org/10.1017/S0962492913000044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dobler, C., S. Hagemann, R. L. Wilby, and J. Stotter, 2012: Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed. Hydrol. Earth Syst. Sci., 16, 43434360, https://doi.org/10.5194/hess-16-4343-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gädeke, A., H. Hölzel, H. Koch, I. Pohle, and U. Grünewald, 2014: Analysis of uncertainties in the hydrological response of a model-based climate change impact assessment in a subcatchment of the Spree River, Germany. Hydrol. Processes, 28, 39783998, https://doi.org/10.1002/hyp.9933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giuntoli, I., J.-P. Vidal, C. Prudhomme, and D. M. Hannah, 2015: Future hydrological extremes: The uncertainty from multiple global climate and global hydrological models. Earth Syst. Dyn., 6, 267285, https://doi.org/10.5194/esd-6-267-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gou, J., C. Miao, Q. Duan, Q. Tang, Z. Di, W. Liao, J. Wu, and J. Wu, 2020: Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China. Water Resour. Res., 56, e2019WR025968, https://doi.org/10.1029/2019WR025968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guth, P. A., V. Kaarnioja, F. Y. Kuo, C. Schillings, and I. H. Sloan 2019: A quasi-Monte Carlo method for an optimal control problem under uncertainty. ArXiv, 34 pp., https://arxiv.org/abs/1910.10022.

  • Habets, F., and Coauthors, 2013: Impact of climate change on the hydrogeology of two basins in northern France. Climatic Change, 121, 771785, https://doi.org/10.1007/s10584-013-0934-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg, 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21, 13311364, https://doi.org/10.1080/014311600210209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hattermann, F. F., and Coauthors, 2018: Sources of uncertainty in hydrological climate impact assessment: A cross-scale study. Environ. Res. Lett., 13, 015006, https://doi.org/10.1088/1748-9326/aa9938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., T. M. Osborne, C. K. Ho, and A. J. Challinor, 2013: Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agric. For. Meteor., 170, 1931, https://doi.org/10.1016/j.agrformet.2012.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, J., K. Yang, W. Tang, H. Lu, J. Qin, Y. Chen, and X. Li, 2020: The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helton, J. C., and F. J. Davis, 2003: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab. Eng. Syst. Saf., 81, 2369, https://doi.org/10.1016/S0951-8320(03)00058-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoegh-Guldberg, O., and J. F. Bruno, 2010: The impact of climate change on the world’s marine ecosystems. Science, 328, 15231528, https://doi.org/10.1126/science.1189930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iman, R. L., and W. J. Conover, 1980: Small sample sensitivity analysis techniques for computer models with an application to risk assessment. Commun. Stat. Theory Methods, 9, 17491842, https://doi.org/10.1080/03610928008827996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jha, M. K., and P. W. Gassman, 2014: Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models. Hydrol. Processes, 28, 27722781, https://doi.org/10.1002/hyp.9836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, T., Y. Q. Chen, C. Y. Xu, X. H. Chen, X. Chen, and V. P. Singh, 2007: Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. J. Hydrol., 336, 316333, https://doi.org/10.1016/j.jhydrol.2007.01.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, M., and Coauthors, 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951954, https://doi.org/10.1038/nature09396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, A. L., H. N. Davies, V. A. Bell, and R. G. Jones, 2009: Comparison of uncertainty sources for climate change impacts: Flood frequency in England. Climatic Change, 92, 4163, https://doi.org/10.1007/s10584-008-9471-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khorashadi Zadeh, F., J. Nossent, F. Sarrazin, F. Pianosi, A. van Griensven, T. Wagener, and W. Bauwens, 2017: Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model. Environ. Modell. Software, 91, 210222, https://doi.org/10.1016/j.envsoft.2017.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 41514 428, https://doi.org/10.1029/94JD00483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, K., F. Lv, L. Chen, V. P. Singh, Q. Zhang, and X. Chen, 2014: Xinanjiang model combined with Curve Number to simulate the effect of land use change on environmental flow. J. Hydrol., 519, 31423152, https://doi.org/10.1016/j.jhydrol.2014.10.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J., X. Chen, J. Zhang, and M. Flury, 2009: Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting. Hydrol. Processes, 23, 13371348, https://doi.org/10.1002/hyp.7255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, L., T. Fischer, T. Jiang, and Y. Luo, 2013: Comparison of uncertainties in projected flood frequency of the Zhujiang River, South China. Quat. Int., 304, 5161, https://doi.org/10.1016/j.quaint.2013.02.039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier, 1998: Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J., 43, 131141, https://doi.org/10.1080/02626669809492107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, W., W. Wang, Q. Shao, Z. Yu, Z. Hao, W. Xing, B. Yong, and J. Li, 2018: Hydrological projections of future climate change over the source region of Yellow River and Yangtze River in the Tibetan Plateau: A comprehensive assessment by coupling RegCM4 and VIC model. Hydrol. Processes, 32, 20962117, https://doi.org/10.1002/hyp.13145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, J. H., and T. L. Quesne, 2009: Adapting Water Management: A Primer on Coping with Climate Change. WWF Water Security Series 3, 36 pp., http://assets.wwf.org.uk/downloads/water_management.pdf.

  • Maurer, E. P., and P. B. Duffy, 2005: Uncertainty in projections of streamflow changes due to climate change in California. Geophys. Res. Lett., 32, L03704, https://doi.org/10.1029/2004GL021462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKay, M. D., R. J. Beckman, and W. J. Conover, 1979: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 239245, https://doi.org/10.2307/1268522.

    • Search Google Scholar
    • Export Citation
  • Minville, M., F. Brissette, and R. Leconte, 2008: Uncertainty of the impact of climate change on the hydrology of a Nordic watershed. J. Hydrol., 358, 7083, https://doi.org/10.1016/j.jhydrol.2008.05.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morris, M. D., 1991: Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161174, https://doi.org/10.1080/00401706.1991.10484804.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Najafi, M. R., H. Moradkhani, and I. W. Jung, 2011: Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol. Processes, 25, 28142826, https://doi.org/10.1002/hyp.8043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neuman, S. P., 2003: Maximum likelihood Bayesian averaging of uncertain model predictions. Stochastic Environ. Res. Risk Assess., 17, 291305, https://doi.org/10.1007/s00477-003-0151-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nijssen, B., D. P. Lettenmaier, X. Liang, S. W. Wetzel, and E. F. Wood, 1997: Streamflow simulation for continental-scale river basins. Water Resour. Res., 33, 711724, https://doi.org/10.1029/96WR03517.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nóbrega, M. T., W. Collischonn, C. E. M. Tucci, and A. R. Paz, 2011: Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil. Hydrol. Earth Syst. Sci., 15, 585595, https://doi.org/10.5194/hess-15-585-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Piao, S., and Coauthors, 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 4351, https://doi.org/10.1038/nature09364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Preston, B. L., 2002: Aquatic ecosystems and global climate change. Limnol. Oceanogr. Bull., 11, 2222, https://doi.org/10.1002/lob.200211122.

  • Prudhomme, C., and H. Davies, 2009: Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: Future climate. Climatic Change, 93, 197222, https://doi.org/10.1007/s10584-008-9461-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raje, D., and P. P. Mujumdar, 2010: Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster–Shafer and Bayesian approaches. Adv. Water Resour., 33, 11761186, https://doi.org/10.1016/j.advwatres.2010.08.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Refsgaard, J. C., J. P. van der Sluijs, A. L. Højberg, and P. A. Vanrolleghem, 2007: Uncertainty in the environmental modelling process – A framework and guidance. Environ. Modell. Software, 22, 15431556, https://doi.org/10.1016/j.envsoft.2007.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, Q. W., Y. B. Chen, and X. J. Shu, 2010: Global sensitivity analysis of Xinanjiang model parameters based on Extend FAST method (in Chinese). Acta Sci. Nat. Univ. Sunyatseni, 49, 127134.

    • Search Google Scholar
    • Export Citation
  • Rubin, Y., X. Chen, H. Murakami, and M. Hahn, 2010: A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields. Water Resour. Res., 46, W10523, https://doi.org/10.1029/2009WR008799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saltelli, A., 2000: What is sensitivity analysis? Sensitivity Analysis, A. Saltelli, K. Chan, and M. Scott, Eds., Wiley, 3–14.

  • Saltelli, A., and I. M. Sobol’, 1995: Sensitivity analysis for nonlinear mathematical models: Numerical experience. Inst. Math. Modell., 7, 1628.

    • Search Google Scholar
    • Export Citation
  • Saltelli, A., S. Tarantola, and K. Chad, 1998: Presenting results from model based studies to decision makers: Can sensitivity analysis be a defogging agent? Risk Anal., 18, 799803, https://doi.org/10.1111/j.1539-6924.1998.tb01122.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saltelli, A., S. Tarantola, and P. S. Chan, 1999: A quantitative model independent method for global sensitivity analysis of model output. Technometrics, 41, 3956, https://doi.org/10.1080/00401706.1999.10485594.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola, 2010: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun., 181, 259270, https://doi.org/10.1016/j.cpc.2009.09.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, M., J. Chen, M. Zhuan, H. Chen, C. Y. Xu, and L. Xiong, 2018: Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. J. Hydrol., 556, 1024, https://doi.org/10.1016/j.jhydrol.2017.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobol’, I. M., 1993: Sensitivity analysis for nonlinear mathematical models. Math. Model. Comput. Exp., 1, 407414.

  • Song, X., F. Kong, C. Zhan, J. Han, and X. Zhang, 2013: Parameter identification and global sensitivity analysis of Xin’anjiang model using meta-modeling approach. Water Sci. Eng., 6, 117, https://doi.org/10.3882/j.issn.1674-2370.2013.01.001.

    • Search Google Scholar
    • Export Citation
  • Song, X., J. Zhang, C. Zhan, Y. Xuan, M. Ye, and C. Xu, 2015: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. J. Hydrol., 523, 739757, https://doi.org/10.1016/j.jhydrol.2015.02.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, C. M., F. M. Johnson, and L. A. Marshall, 2018: Implications of future climate change for event–based hydrologic models. Adv. Water Resour., 119, 95110, https://doi.org/10.1016/j.advwatres.2018.07.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, B., J. Huang, X. Zeng, C. Gao, and T. Jiang, 2017: Impacts of climate change on streamflow in the upper Yangtze River basin. Climatic Change, 141, 533546, https://doi.org/10.1007/s10584-016-1852-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., Z. Zeng, D. Long, X. Guo, B. Yong, W. Zhang, and Y. Hong, 2016: Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: Is Day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeor., 17, 121137, https://doi.org/10.1175/JHM-D-15-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tartakovsky, D. M., 2013: Assessment and management of risk in subsurface hydrology: A review and perspective. Adv. Water Resour., 51, 247260, https://doi.org/10.1016/j.advwatres.2012.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teng, J., J. Vaze, F. H. Chiew, B. Wang, and J. Perraud, 2012: Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J. Hydrometeor., 13, 122139, https://doi.org/10.1175/JHM-D-11-058.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teutschbein, C., F. Wetterhall, and J. Seibert, 2011: Evaluation of different downscaling techniques for hydrological climate–change impact studies at the catchment scale. Climate Dyn., 37, 20872105, https://doi.org/10.1007/s00382-010-0979-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, J. R., A. J. Green, D. G. Kingston, and S. N. Gosling, 2013: Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. J. Hydrol., 486, 130, https://doi.org/10.1016/j.jhydrol.2013.01.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valkó, É., T. Varga, A. S. Tomlin, Á. Busai, and T. Turányi, 2018: Investigation of the effect of correlated uncertain rate parameters via the calculation of global and local sensitivity indices. J. Math. Chem., 56, 864889, https://doi.org/10.1007/s10910-017-0836-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Griensven, A., T. Meixner, S. Grunwald, T. Bishop, M. Diluzio, and R. Srinivasan, 2006: A global sensitivity analysis tool for the parameters of multi-variable catchment models. J. Hydrol., 324, 1023, https://doi.org/10.1016/j.jhydrol.2005.09.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Vuuren, D. P. V., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 531, https://doi.org/10.1007/s10584-011-0148-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vetter, T., S. Huang, V. Aich, T. Yang, X. Wang, V. Krysanova, and F. Hattermann, 2015: Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth Syst. Dyn., 6, 1743, https://doi.org/10.5194/esd-6-17-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vetter, T., and Coauthors, 2017: Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change, 141, 419433, https://doi.org/10.1007/s10584-016-1794-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., P. Green, J. Salisbury, and R. B. Lammers, 2000: Global water resources: Vulnerability from climate change and population growth. Science, 289, 284288, https://doi.org/10.1126/science.289.5477.284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wada, Y., and Coauthors, 2013: Multimodel projections and uncertainties of irrigation water demand under climate change. Geophys. Res. Lett., 40, 46264632, https://doi.org/10.1002/grl.50686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G. Q., and Coauthors, 2012: Assessing water resources in China using PRECIS projections and a VIC model. Hydrol. Earth Syst. Sci., 16, 231240, https://doi.org/10.5194/hess-16-231-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., and Coauthors, 2011: The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J., 56, 8498, https://doi.org/10.1080/02626667.2010.543087.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., R. Ranasinghe, S. Maskey, P. H. A. J. M. van Gelder, and J. K. Vrijling, 2016: Comparison of empirical statistical methods for downscaling daily climate projections from CMIP5 GCMs: A case study of the Huai River Basin, China. Int. J. Climatol., 36, 145164, https://doi.org/10.1002/joc.4334.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, P. L., and R. J. Zhao, 1989: Examination of parameters of Xinanjiang model (3 components) (in Chinese). J. Hohai Univ., 17, 1621.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., 2005: Uncertainty in water resource model parameters used for climate change impact assessment. Hydrol. Processes, 19, 32013219, https://doi.org/10.1002/hyp.5819.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., and I. Harris, 2006: A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK. Water Resour. Res., 42, W02419, https://doi.org/10.1029/2005WR004065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C., G. Huang, H. Yu, Z. Chen, and J. Ma, 2014: Impact of climate change on reservoir flood control in the upstream area of the Beijiang River basin, South China. J. Hydrometeor., 15, 22032218, https://doi.org/10.1175/JHM-D-13-0181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. H., G. R. Huang, and H. J. Yu, 2015: Prediction of extreme floods based on CMIP5 climate models: A case study in the Beijiang River basin, South China. Hydrol. Earth Syst. Sci., 19, 13851399, https://doi.org/10.5194/hess-19-1385-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. H., P. J.-F. Yeh, Y. Y. Chen, B. X. Hu, and G. R. Huang, 2020: Future precipitation-driven meteorological drought changes in the CMIP5 multimodel ensembles under 1.5°C and 2°C global warming. J. Hydrometeor., 21, 21772196, https://doi.org/10.1175/JHM-D-19-0299.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, Z., F. Yuan, Q. Duan, J. Zheng, M. Liang, and F. Chen, 2007: Regional Parameter estimation of the VIC land surface model: Methodology and application to river basins in China. J. Hydrometeor., 8, 447468, https://doi.org/10.1175/JHM568.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, K., B. Xu, J. Ju, C. Wu, H. Dai, and B. X. Hu, 2019: Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China. Atmos. Res., 226, 122137, https://doi.org/10.1016/j.atmosres.2019.04.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Y. P., X. Zhang, Q. Ran, and Y. Tian, 2013: Impact of climate change on hydrology of upper reaches of Qiantang River Basin, East China. J. Hydrol., 483, 5160, https://doi.org/10.1016/j.jhydrol.2013.01.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, X., Y. Hong, A. S. Limaye, J. J. Gourley, G. J. Huffman, S. I. Khan, C. Dorji, and S. Chen, 2013: Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J. Hydrol., 499, 9199, https://doi.org/10.1016/j.jhydrol.2013.06.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, J., 2011: Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environ. Modell. Software, 26, 444457, https://doi.org/10.1016/j.envsoft.2010.10.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, C., K. Zhang, Z. Yu, Z. Li, and Q. Li, 2014: Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph. J. Hydrol., 517, 10351048, https://doi.org/10.1016/j.jhydrol.2014.06.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeng, X., D. Wang, and J. Wu, 2012: Sensitivity analysis of the probability distribution of groundwater level series based on information entropy. Stochastic Environ. Res. Risk Assess., 26, 345356, https://doi.org/10.1007/s00477-012-0556-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., G. H. Huang, D. Wang, and X. Zhang, 2011: Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. J. Hydrol., 396, 94103, https://doi.org/10.1016/j.jhydrol.2010.10.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, R., Y. Zhuang, L. Fang, X. Liu, and Q. Zhang, 1980: The Xinanjiang model. IAHS Publ., 129, 351356.

  • Zhao, R. J., 1992: The Xinanjiang model applied in China. J. Hydrol., 135, 371381, https://doi.org/10.1016/0022-1694(92)90096-E.

  • Zhao, R. J., and X. R. Liu, 1995: The Xinanjiang model. Computer Models of Watershed Hydrology, V. Singh, Ed., Water Resources Publications, 215–232.

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Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

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  • 1 School of Water Resources and Environment, China University of Geosciences, Beijing, China
  • 2 State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, China
  • 3 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
  • 4 Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, China
  • 5 Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida
  • 6 Pacific Northwest National Laboratory, Richland, Washington
  • 7 Cele National Station of Observation and Research for Desert–Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
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Abstract

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

These authors contributed equally to this work.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0016.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chuanhao Wu, wuch0907@jnu.edu.cn

Abstract

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

These authors contributed equally to this work.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0016.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chuanhao Wu, wuch0907@jnu.edu.cn

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