Editorial Type:
Article
Article Type:
Research Article

Uncertainty in Future High Flows in Qiantang River Basin, China

Ye Tian Institute of Hydrology and Water Resources, Department of Civil Engineering, Zhejiang University, Hangzhou, Zhejiang, China

Search for other papers by Ye Tian in
Current site
Google Scholar
PubMed Close
,
Yue-Ping Xu Institute of Hydrology and Water Resources, Department of Civil Engineering, Zhejiang University, Hangzhou, Zhejiang, China

Search for other papers by Yue-Ping Xu in
Current site
Google Scholar
PubMed Close
,
Martijn J. Booij Department of Water Engineering and Management, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands

Search for other papers by Martijn J. Booij in
Current site
Google Scholar
PubMed Close
, and
Guoqing Wang State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China

Search for other papers by Guoqing Wang in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2015
DOI:
https://doi.org/10.1175/JHM-D-13-0136.1
Page(s):
363–380
Restricted access

Abstract

Uncertainties in high flows originating from greenhouse gas emissions scenarios, hydrological model structures, and their parameters for the Jinhua River basin, China, were assessed. The baseline (1961–90) and future (2011–40) climates for A1B, A2, and B2 scenarios were downscaled from the general circulation model (GCM) using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate model with a spatial resolution of 50 km × 50 km. Bias-correction methods were applied to the PRECIS-derived temperature and precipitation. The bias-corrected precipitation and temperature were used as inputs for three hydrological models [modèle du Génie Rural à 4 paramètres Journalier (GR4J), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Xinanjiang] to simulate high flows. The parameter uncertainty was considered and quantified in the hydrological model calibration by means of the generalized likelihood uncertainty estimation (GLUE) method for each hydrological model for the three emissions scenarios. It was found that, compared with the high flows in the baseline period, the high flows in the future tended to decrease under scenarios A1B, A2, and B2. The largest uncertainty was observed in HBV, and GR4J had the smallest uncertainty. It was found that the major source of uncertainty in this study was from parameters, followed by the uncertainties from the hydrological model structure, and the emissions scenarios have the smallest uncertainty contribution to high flows in this study.

Corresponding author address: Dr. Yue-Ping Xu, Institute of Hydrology and Water Resources, Department of Civil Engineering, Zhejiang University, Yuhangtang Road 388, Hangzhou, Zhejiang 310058, China. E-mail: yuepingxu@zju.edu.cn; m.j.booij@utwente.nl

Abstract

Uncertainties in high flows originating from greenhouse gas emissions scenarios, hydrological model structures, and their parameters for the Jinhua River basin, China, were assessed. The baseline (1961–90) and future (2011–40) climates for A1B, A2, and B2 scenarios were downscaled from the general circulation model (GCM) using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate model with a spatial resolution of 50 km × 50 km. Bias-correction methods were applied to the PRECIS-derived temperature and precipitation. The bias-corrected precipitation and temperature were used as inputs for three hydrological models [modèle du Génie Rural à 4 paramètres Journalier (GR4J), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Xinanjiang] to simulate high flows. The parameter uncertainty was considered and quantified in the hydrological model calibration by means of the generalized likelihood uncertainty estimation (GLUE) method for each hydrological model for the three emissions scenarios. It was found that, compared with the high flows in the baseline period, the high flows in the future tended to decrease under scenarios A1B, A2, and B2. The largest uncertainty was observed in HBV, and GR4J had the smallest uncertainty. It was found that the major source of uncertainty in this study was from parameters, followed by the uncertainties from the hydrological model structure, and the emissions scenarios have the smallest uncertainty contribution to high flows in this study.

Corresponding author address: Dr. Yue-Ping Xu, Institute of Hydrology and Water Resources, Department of Civil Engineering, Zhejiang University, Yuhangtang Road 388, Hangzhou, Zhejiang 310058, China. E-mail: yuepingxu@zju.edu.cn; m.j.booij@utwente.nl
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Akhtar, M., Ahmad N. , and Booij M. J. , 2008: The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. J. Hydrol., 355, 148–163, doi:10.1016/j.jhydrol.2008.03.015.

    • Search Google Scholar
    • Export Citation

  • Akhtar, M., Ahmad N. , and Booij M. J. , 2009: Use of regional climate model simulations as input for hydrological models for the Hindukush–Karakorum–Himalaya region. Hydrol. Earth Syst. Sci., 13, 1075–1089, doi:10.5194/hess-13-1075-2009.

    • Search Google Scholar
    • Export Citation

  • Arnell, N. W., 2003: Effects of IPCC SRES* emissions scenarios on river runoff: A global perspective. Hydrol. Earth Syst. Sci., 7, 619–641, doi:10.5194/hess-7-619-2003.

    • Search Google Scholar
    • Export Citation

  • Arnell, N. W., Hudson D. A. , and Jones R. G. , 2003: Climate change scenarios from a regional climate model: Estimating change in runoff in southern Africa. J. Geophys. Res., 108, 4519, doi:10.1029/2002JD002782.

    • Search Google Scholar
    • Export Citation

  • Bastola, S., Murphy C. , and Sweeney J. , 2011: The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv. Water Resour., 34, 562–576, doi:10.1016/j.advwatres.2011.01.008.

    • Search Google Scholar
    • Export Citation

  • Bauwens, A., Sohier C. , and Degré A. , 2011: Hydrological response to climate change in the Lesse and the Vesdre catchments: Contribution of a physically based model (Wallonia, Belgium). Hydrol. Earth Syst. Sci., 15, 1745–1756, doi:10.5194/hess-15-1745-2011.

    • Search Google Scholar
    • Export Citation

  • Bell, J. L., Sloan L. C. , and Snyder M. A. , 2004: Regional changes in extreme climatic events: A future climate scenario. J. Climate, 17, 81–87, doi:10.1175/1520-0442(2004)017<0081:RCIECE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bergström, S., 1976: Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Rep. RHO 7, 133 pp.

  • Bergström, S., 1992: The HBV model—Its structure and applications. SMHI Rep. RH 4, 35 pp.

  • Beven, K., 2006: A manifesto for the equifinality thesis. J. Hydrol.,320, 18–36, doi:10.1016/j.jhydrol.2005.07.007.

  • Beven, K., and Binley A. , 1992: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 6, 279–298, doi:10.1002/hyp.3360060305.

    • Search Google Scholar
    • Export Citation

  • Beven, K., and Freer J. , 2001: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J. Hydrol., 249, 11–29, doi:10.1016/S0022-1694(01)00421-8.

    • Search Google Scholar
    • Export Citation

  • Beven, K., Freer J. , Hankin B. , and Schulz K. , 2000: The use of generalised likelihood measures for uncertainty estimation in high order models of environmental systems. Nonlinear and Nonstationary Signal Processing, W. J. Fitzgerald et al., Eds., Cambridge University Press, 115–151.

  • Booij, M., 2005: Impact of climate change on river flooding assessed with different spatial model resolutions. J. Hydrol., 303, 176–198, doi:10.1016/j.jhydrol.2004.07.013.

    • Search Google Scholar
    • Export Citation

  • Boorman, D. B., and Sefton C. , 1997: Recognising the uncertainty in the quantification of the effects of climate change on hydrological response. Climatic Change, 35, 415–434, doi:10.1023/A:1005372407881.

    • Search Google Scholar
    • Export Citation

  • Buytaert, W., Vuille M. , Dewulf A. , Urrutia R. , Karmalkar A. , and Celleri R. , 2010: Uncertainties in climate change projections and regional downscaling in the tropical Andes: Implications for water resources management. Hydrol. Earth Syst. Sci., 14, 1247–1258, doi:10.5194/hess-14-1247-2010.

    • Search Google Scholar
    • Export Citation

  • Driessen, T., Hurkmans R. , Terink W. , Hazenberg P. , Torfs P. , and Uijlenhoet R. , 2010: The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model. Hydrol. Earth Syst. Sci., 14, 651–665, doi:10.5194/hess-14-651-2010.

    • Search Google Scholar
    • Export Citation

  • Edijatno, N. D. O. N., Yang X. , Makhlouf Z. , and Michel C. , 1999: GR3J: A daily watershed model with three free parameters. Hydrol. Sci. J., 44, 263–277, doi:10.1080/02626669909492221.

    • Search Google Scholar
    • Export Citation

  • Ghosh, S., and Mujumdar P. , 2009: Climate change impact assessment: Uncertainty modeling with imprecise probability. J. Geophys. Res., 114, D18113, doi:10.1029/2008JD011648.

    • Search Google Scholar
    • Export Citation

  • Gordon, C., and Coauthors, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dyn., 16, 147–168, doi:10.1007/s003820050010.

    • Search Google Scholar
    • Export Citation

  • Gosain, A. K., Rao S. , and Arora A. , 2011: Climate change impact assessment of water resources of India. Curr. Sci., 101, 356–371.

  • Hannaford, J., and Marsh T. J. , 2008: High flow and flood trends in a network of undisturbed catchments in the UK. Int. J. Climatol., 28, 1325–1338, doi:10.1002/joc.1643.

    • Search Google Scholar
    • Export Citation

  • Hargreaves, G. H., and Samani Z. A. , 1983: Estimating potential evapotranspiration. J. Irrig. Drain. Div., 108, 225–230.

  • Harlan, D., Wangsadipura M. , and Munajat C. M. , 2010: Rainfall–runoff modeling of Citarum Hulu River basin by using GR4J. Proc. World Congress on Engineering 2010, London, United Kingdom, International Association of Engineers, 1607–1611.

  • Houghton, J. T., Ding Y. , Griggs D. J. , Noguer M. , van der Linden P. J. , Dai X. , Maskell K. , and Johnson C. A. , Eds., 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.

  • Jin, X., Xu C. Y. , Zhang Q. , and Singh V. , 2010: Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J. Hydrol., 383, 147–155, doi:10.1016/j.jhydrol.2009.12.028.

    • Search Google Scholar
    • Export Citation

  • Jones, R. G., Noguer M. , Hassell D. C. , Hudson D. , Wilson S. S. , Jenkins G. J. , and Mitchell J. F. B. 2004: Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre Rep., 40 pp.

    • Search Google Scholar
    • Export Citation

  • Jones, R. N., Chiew F. H. S. , Boughton W. C. , and Zhang L. , 2006: Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Adv. Water Resour., 29, 1419–1429, doi:10.1016/j.advwatres.2005.11.001.

    • Search Google Scholar
    • Export Citation

  • Kay, A. L., Davies H. N. , Bell V. A. , and Jones R. G. , 2009: Comparison of uncertainty sources for climate change impacts: Flood frequency in England. Climatic Change, 92, 41–63, doi:10.1007/s10584-008-9471-4.

    • Search Google Scholar
    • Export Citation

  • Kerkhoven, E., and Gan T. Y. , 2013: Differences in the potential hydrologic impact of climate change to the Athabasca and Fraser River basins of Canada with and without considering shifts in vegetation patterns induced by climate change. J. Hydrometeor., 14, 963–976, doi:10.1175/JHM-D-12-011.1.

    • Search Google Scholar
    • Export Citation

  • Leander, R., Buishand T. A. , van den Hurk B. J. J. M. , and de Wit M. J. M. , 2008: Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. J. Hydrol., 351, 331–343, doi:10.1016/j.jhydrothrl.2007.12.020.

    • Search Google Scholar
    • Export Citation

  • Li, H., Zhang Y. , Chiew F. H. S. , and Xu S. , 2009: Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index. J. Hydrol., 370, 155–162, doi:10.1016/j.jhydrol.2009.03.003.

    • Search Google Scholar
    • Export Citation

  • Li, Z., Shao Q. , Xu Z. , and Cai X. , 2010: Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: A case study of SWAT model applied to Yingluoxia watershed in northwest China. J. Hydrol., 385, 76–83, doi:10.1016/j.jhydrol.2010.01.025.

    • Search Google Scholar
    • Export Citation

  • Lindström, G., Johansson B. , Persson M. , Gardelin M. , and Bergström S. , 1997: Development and test of the distributed HBV-96 hydrological model. J. Hydrol., 201, 272–288, doi:10.1016/S0022-1694(97)00041-3.

    • Search Google Scholar
    • Export Citation

  • Middelkoop, H., and Coauthors, 2001: Impact of climate change on hydrological regimes and water resources management in the Rhine basin. Climatic Change, 49, 105–128, doi:10.1023/A:1010784727448.

    • Search Google Scholar
    • Export Citation

  • Najafi, M. R., Moradkhani H. , and Jung I. W. , 2011: Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol. Processes, 25, 2814–2826, doi:10.1002/hyp.8043.

    • Search Google Scholar
    • Export Citation

  • Nakićenović, N., and Swart R. , Eds., 2000: Special Report on Emissions Scenarios. Cambridge University Press, 570 pp.

  • New, M., and Hulme M. , 2000: Representing uncertainty in climate change scenarios: A Monte-Carlo approach. Integr. Assess.,1, 203–213, doi:10.1023/A:1019144202120.

  • Pachauri, R. K., and Reisinger A. , Eds., 2007: Climate Change 2007: Synthesis Report. Intergovernmental Panel on Climate Change, 104 pp.

  • Parrish, M. A., Moradkhani H. , and DeChant C. M. , 2012: Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation. Water Resour. Res., 48, W03519, doi:10.1029/2011WR011116.

    • Search Google Scholar
    • Export Citation

  • Perrin, C., Michel C. , and Andreassian V. , 2003: Improvement of a parsimonious model for streamflow simulation. J. Hydrol., 279, 275–289, doi:10.1016/S0022-1694(03)00225-7.

    • Search Google Scholar
    • Export Citation

  • Raftery, A. E., Gneiting T. , Balabdaoui F. , and Polakowski M. , 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174, doi:10.1175/MWR2906.1.

    • Search Google Scholar
    • Export Citation

  • Raje, D., and Mujumdar P. , 2010: Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling. Water Resour. Res., 46, W07543, doi:10.1029/2009WR008425.

    • Search Google Scholar
    • Export Citation

  • Rebora, N., and Coauthors, 2013: Extreme rainfall in the Mediterranean: What can we learn from observations? J. Hydrometeor., 14, 906–922, doi:10.1175/JHM-D-12-083.1.

    • Search Google Scholar
    • Export Citation

  • Refsgaard, J. C., van der Sluijs J. P. , Brown J. , and van der Keur P. , 2006: A framework for dealing with uncertainty due to model structure error. Adv. Water Resour., 29, 1586–1597, doi:10.1016/j.advwatres.2005.11.013.

    • Search Google Scholar
    • Export Citation

  • Seibert, J., 1997: Estimation of parameter uncertainty in the HBV model. Nord. Hydrol., 28, 247–262.

  • Seibert, J., 2000: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrol. Earth Syst. Sci., 4, 215–224, doi:10.5194/hess-4-215-2000.

    • Search Google Scholar
    • Export Citation

  • Tang, B., 1993: Orthogonal array-based Latin hypercubes. J. Amer. Stat. Assoc., 88, 1392–1397, doi:10.1080/01621459.1993.10476423.

  • Teutschbein, C., and Seibert J. , 2010: Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. Geogr. Compass, 4, 834–860, doi:10.1111/j.1749-8198.2010.00357.x.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., Xu Y.-P. , and Zhang X.-J. , 2013: Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour. Manage., 27, 2871–2888, doi:10.1007/s11269-013-0321-4.

    • Search Google Scholar
    • Export Citation

  • Ueyama, H., Adachi S. , and Kimura F. , 2010: Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts. Theor. Appl. Climatol., 101, 421–431, doi:10.1007/s00704-009-0228-4.

    • Search Google Scholar
    • Export Citation

  • Vrugt, J. A., Gupta H. V. , Bastidas L. A. , Bouten W. , and Sorooshian S. , 2003: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res., 39, 1214, doi:10.1029/2002WR001746.

    • Search Google Scholar
    • Export Citation

  • Wen, K. G., Xi G. Y. , and Xu W. N. , 2006: Dictionary of Chinese Meteorological Disaster (in Chinese). China Meteorology Press, 287 pp.

  • Wigley, T., Jones P. , Briffa K. , and Smith G. , 1990: Obtaining sub-grid-scale information from coarse-resolution general circulation model output. J. Geophys. Res., 95, 1943–1953, doi:10.1029/JD095iD02p01943.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., Hassan H. , and Hanaki K. , 1998: Statistical downscaling of hydrometeorological variables using general circulation model output. J. Hydrol., 205, 1–19, doi:10.1016/S0022-1694(97)00130-3.

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., Hay L. E. , and Leavesley G. H. , 1999: A comparison of downscaled and raw GCM output: Implications for climate change scenarios in the San Juan River basin, Colorado. J. Hydrol., 225, 67–91, doi:10.1016/S0022-1694(99)00136-5.

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., Dawson C. W. , and Barrow E. M. , 2002: SDSM—A decision support tool for the assessment of regional climate change impacts. Environ. Modell. Software, 17, 145–157, doi:10.1016/S1364-8152(01)00060-3.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., Maurer E. P. , Kumar A. , and Lettenmaier D. P. , 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation

  • Xu, Y.-P., Zhang X. , and Tian Y. , 2012: Impact of climate change on 24‐h design rainfall depth estimation in Qiantang River basin, east China. Hydrol. Processes, 26, 4067–4077, doi:10.1002/hyp.9210.

    • Search Google Scholar
    • Export Citation

  • Xu, Y.-P., Zhang X. , Ran Q. , and Tian Y. , 2013: Impact of climate change on hydrology of upper reaches of Qiantang River basin, east China. J. Hydrol., 483, 51–60, doi:10.1016/j.jhydrol.2013.01.004.

    • Search Google Scholar
    • Export Citation

  • Zhang, Q., Singh V. P. , Sun P. , Chen X. , Zhang Z. , and Li J. , 2011: Precipitation and streamflow changes in China: Changing patterns, causes and implications. J. Hydrol., 410, 204–216, doi:10.1016/j.jhydrol.2011.09.017.

    • Search Google Scholar
    • Export Citation

  • Zhao, R.-J., 1992: The Xinanjiang model applied in China. J. Hydrol., 135, 371–381, doi:10.1016/0022-1694(92)90096-E.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 411 100 3
PDF Downloads 254 91 3