Abstract
This study analyses the extreme high flows in Jinhua River basin under the impact of climate change for the near future 2011–2040. The objective of this study is to investigate the effect of using the bias corrected RCM outputs as input on the extreme flows by hydrological models. The future projections are obtained through the PRECIS model with resolution of 50 km × 50 km under climate scenario A1B. The daily precipitation from the PRECIS is bias corrected by distribution based scaling method. Afterwards, three hydrological models (GR4J, HBV and Xinanjiang) are calibrated and applied to simulate the daily discharge in the future. The hydrological models are driven with both bias corrected precipitation and raw precipitation from the PRECIS model for 2011–2040. It is found that after bias correction, the amount, frequency, intensity and variance of the precipitation from the regional climate model resemble the observation better. For the three hydrological models, the simulated annual maximum discharges are higher by using the raw precipitation from PRECIS than by bias corrected precipitation at any return period. Meanwhile, the uncertainties from different models cannot be neglected. The largest difference between three models is about 2,100 m3/s.
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Akhtar M, Ahmad N, Booij MJ (2008) The impact of climate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios. J Hydrol 355(1–4):148–163. doi:10.1016/j.jhydrol.2008.03.015
Bartholy J, Pongracz R, Torma C, Pieczka I, Kardos P, Hunyady A (2009) Analysis of regional climate change modelling experiments for the Carpathian basin. Int J Global Warm 1(1–3):238–252
Bergström S (1976) Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Report RHO NO. 7, Norrköping, Sweden
Bergström S (1992) The HBV model - its structure and applications. SMHI Reports RH NO. 4, Norrköping, Sweden
Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320(1–2):18–36
Chen H, Xiang T, Zhou X, Xu CY (2012a) Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China. Stoch Env Res Risk A 26(6):847–858. doi:10.1007/s00477-011-0524-2
Chen X, Yang T, Wang X, Xu CY, Yu Z (2012b) Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows. Water Resour Manag: 1–17. doi:10.1007/s11269-012-0244-5
Chenoweth J, Hadjinicolaou P, Bruggeman A, Lelieveld J, Levin Z, Lange MA, Xoplaki E, Hadjikakou M (2011) Impact of climate change on the water resources of the eastern Mediterranean and Middle East region: modeled 21st century changes and implications. WRR 47
Chu JT, Xia J, Xu CY, Singh VP (2010) Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor Appl Climatol 99(1–2):149–161. doi:10.1007/s00704-009-0129-6
Dakhlaoui H, Bargaoui Z, Bárdossy A (2012) Toward a more efficient calibration schema for HBV rainfall-runoff model. J Hydrol 444–445:161–179. doi:10.1016/j.jhydrol.2012.04.015
Deckers DLEH, Booij MJ, Rientjes THM, Krol MS (2010) Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model. Water Resour Manag 24(14):3961–3985. doi:10.1007/s11269-010-9642-8
Edijatno, Nascimento ND, Yang XL, Makhlouf Z, Michel C (1999) GR3J: a daily watershed model with three free parameters. Hydrolog Sci J 44(2):263–277
Engeland K, Hisdal H (2009) A comparison of low flow estimates in ungauged catchments using regional regression and the HBV-model. Water Resour Manag 23(12):2567–2586. doi:10.1007/s11269-008-9397-7
Engeland K, Renard B, Steinsland I, Kolberg S (2010) Evaluation of statistical models for forecast errors from the HBV model. J Hydrol 384(1–2):142–155. doi:10.1016/j.jhydrol.2010.01.018
Fung F, Watts G, Lopez A, Orr HG, New M, Extence C (2012) Using Large Climate Ensembles to Plan for the Hydrological Impact of Climate Change in the Freshwater Environment. Water Resour Manag:1–22. doi:10.1007/s11269-012-0080-7
Ghosh S, Katkar S (2012) Modeling uncertainty resulting from multiple downscaling methods in assessing hydrological impacts of climate change. Water Resour Manag 26(12):3559–3579. doi:10.1007/s11269-012-0090-5471
Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley centre coupled model without flux adjustments. ClDy 16(2–3):147–168
Gosain AK, Rao S, Basuray D (2006) Climate change impact assessment on hydrology of Indian River basins. CSci 90(3):346–353
Gosain AK, Rao S, Arora A (2011) Climate change impact assessment of water resources of India. CSci 101(3):356–371
Haith DA, Shoemaker LL (1987) Generalized watershed loading functions for stream-flow nutrients. Water Resour Bull 23(3):471–478
Hamon WR (1961) Estimating Potential Evapotranspiration. J Hydraul Eng-asce 87 (HY3):107–120
Hay LE, Clark MP, Wilby RL, Gutowski WJ, Leavesley GH, Pan Z, Arritt RW, Takle ES (2002) Use of regional climate model output for hydrologic simulations. J Hydrometeorol 3(5):571–590
Ho TMH, Phan VT, Le NQ, Nguyen QT (2011) Extreme climatic events over Vietnam from observational data and RegCM3 projections. Clim Res 49(2):87–100. doi:10.3354/Cr01021
Huang J, Zhang JC, Zhang ZX, Xu CY, Wang BL, Yao J (2011) Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch Env Res Risk A 25(6):781–792. doi:10.1007/s00477-010-0441-9
Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agr Forest Meteorol 138(1–4):44–53. doi:10.1016/j.agrformet.2006.03.009
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, Pachauri RK and Reisinger A, Geneva, Switzerland
Jiang T, Chen YQD, Xu CYY, Chen XH, Chen X, Singh VP (2007) Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang basin, South China. J Hydrol 336(3–4):316–333
Jiang S, Ren L, Hong Y, Yong B, Yang X, Yuan F, Ma M (2012) Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. J Hydrol 452–453:213–225. doi:10.1016/j.jhydrol.2012.05.055
Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Change 92(1–2):41–63. doi:10.1007/s10584-008-9471-4
Kotlarski S, Block A, BAhm U, Jacob D, Keuler K, Knoche R, Rechid D, Walter A (2005) Regional climate model simulations as input for hydrological applications: evaluation of uncertainties. AdG 5:119–125
Kriauciuniene J, Jakimavicius D, Sarauskiene D, Kaliatka T (2012) Estimation of uncertainty sources in the projections of Lithuanian river runoff. Stoch Env Res Risk A:1–16. doi:10.1007/s00477-012-0608-7
Kundzewicz ZW, Stakhiv EZ (2010) Are climate models “ready for prime time” in water resources management applications, or is more research needed? Hydrolog Sci J 55(7):1085–1089. doi:10.1080/02626667.2010.513211
Leander R, Buishand TA (2007) Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332(3–4):487–496
Lenderink G, Buishand A, van Deursen W (2007) Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol Earth Syst Sc 11(3):1143–1159
Li H, Zhang Y, Chiew FHS, Xu S (2009a) Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index. J Hydrol 370(1–4):155–162
Li L, Hong Y, Wang JH, Adler RF, Policelli FS, Habib S, Irwn D, Korme T, Okello L (2009b) Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, lake Victoria, Africa. Nat Hazards 50(1):109–123
Li HB, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching. JGRD 115(D10). doi:10.1029/2009JD012882
Li H, Zhang Y, Vaze J, Wang B (2012) Separating effects of vegetation change and climate variability using hydrological modelling and sensitivity-based approaches. J Hydrol 420–421:403–418. doi:10.1016/j.jhydrol.2011.12.0331029/2007wr006665
Lindström G, Johansson B, Persson M, Gardelin M, Bergström S (1997) Development and test of the distributed HBV-96 hydrological model. J Hydrol 201(1–4):272–288
Madigan D, Raftery AE, Volinsky C, Hoeting J Bayesian model averaging. In, 1996. pp 77–83
Marengo JA, Jones R, Alves LM, Valverde MC (2009) Future change of temperature and precipitation extremes in South America as derived from the PRECIS regional climate modeling system. Int J Climatol 29(15):2241–2255
Mileham L, Taylor RG, Todd M, Tindimugaya C, Thompson J (2009) The impact of climate change on groundwater recharge and runoff in a humid, equatorial catchment: sensitivity of projections to rainfall intensity. Hydrolog Sci J 54(4):727–738
Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. HyPr 25(18):2814–2826. doi:10.1002/Hyp.8043
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I - a discussion of principles. J Hydrol 10(3):282–290
Oudin L, Andreassian V, Perrin C, Anctil F (2004) Locating the sources of low-pass behavior within rainfall-runoff models. WRR 40(11). doi:10.1029/2004wr003291
Parrish MA, Moradkhani H, DeChant CM (2012) Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation. WRR 48(3):W03519. doi:10.1029/2011WR011116
Payne JT, Wood AW, Hamlet AF, Palmer RN, Lettenmaier DP (2004) Mitigating the effects of climate change on the water resources of the Columbia river basin. Clim Change 62(1–3):233–256
Peng DZ, Xu ZX (2010) Simulating the impact of climate change on streamflow in the Tarim river basin by using a modified semi-distributed monthly water balance model. HyPr 24(2):209–216
Perrin C, Michel C, Andreassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279(1–4):275–289
Refsgaard JC, van der Sluijs JP, Brown J, van der Keur P (2006) A framework for dealing with uncertainty due to model structure error. Adv Water Resour 29(11):1586–1597. doi:10.1016/j.advwatres.2005.11.013
Rojas R, Feyen L, Dassargues A (2008) Conceptual model uncertainty in groundwater modeling: combining generalized likelihood uncertainty estimation and Bayesian model averaging. WRR 44(12):W12418
Senatore A, Mendicino G, Smiatek G, Kunstmann H (2011) Regional climate change projections and hydrological impact analysis for a Mediterranean basin in Southern Italy. J Hydrol 399(1–2):70–92. doi:10.1016/j.jhydrol.2010.12.035
Shi P, Chen C, Srinivasan R, Zhang X, Cai T, Fang X, Qu S, Chen X, Li Q (2011) Evaluating the SWAT model for hydrological modeling in the Xixian watershed and a comparison with the XAJ model. Water Resour Manag 25(10):2595–2612. doi:10.1007/s11269-011-9828-810.1002/hyp.8058
Ueyama H, Adachi S, 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(3–4):421–431
Wang YQ, Zhou L (2005) Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation (vol 32, art no L09707, 2005). GeoRL 32(17). doi:10.1029/2005gl023769
Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the river Thames, UK. WRR 42(2)
Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geog 21(4):530–548
Wilby RL, Hassan H, Hanaki K (1998) Statistical downscaling of hydrometeorological variables using general circulation model output. J Hydrol 205(1–2):1–19
Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan river basin, Colorado. J Hydrol 225(1–2):67–91
Wilby RL, Dawson CW, Barrow EM (2002) SDSM - a decision support tool for the assessment of regional climate change impacts. Environ Modell Softw 17(2):147–159
Willems P, Vrac M (2011) Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change. J Hydrol 402(3–4):193–205. doi:10.1016/j.jhydrol.2011.02.030
Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. JGRD 107(D20). doi:10.1029/2001jd000659
Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62(1–3):189–216
Wu W, Clark JS, Vose JM (2010) Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling. J Hydrol 394(3–4):436–446. doi:10.1016/j.jhydrol.2010.09.017
Xu YL, Huang XY, Zhang Y, Lin WT, Lin ED (2006a) Statistical analyses of climate change scenarios over China in the 21st century. Adv Clim Chang Res 2(1):50–53
Xu YL, Zhang Y, Lin ED, Lin WT, Dong WJ, Jones R, Hassell D, Wilson S (2006b) Analyses on the climate change responses over China under SRES B2 scenario using PRECIS. Chin Sci Bull 51(18):2260–2267
Xu YP, Zhang X, 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
Yuan F, Xie ZH, Liu Q, Xia J (2005) Simulating hydrologic changes with climate change scenarios in the Haihe river basin. Pedosphere 15(5):595–600
Zhang Y, Xu Y, Dong W, Cao L, Sparrow M (2006) A future climate scenario of regional changes in extreme climate events over China using the PRECIS climate model. GeoRL 33(24):L24702
Zhang Q, Singh VP, Sun P, Chen X, Zhang Z, Li J (2011) Precipitation and streamflow changes in China: changing patterns, causes and implications. J Hydrol 410:204–216
Zhang A, Zhang C, Fu G, Wang B, Bao Z, Zheng H (2012a) Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China. Water Resour Manag 26(8):2199–2217. doi:10.1007/s11269-012-0010-8
Zhang D, Zhang L, Guan Y, Chen X (2012b) Sensitivity analysis of Xinanjiang rainfall-runoff model parameters: A case study in Lianghui, Zhejiang province, China. Hydrol Res 43(1–2):123–134. doi:10.2166/nh.2011.131
Zhao RJ (1992) The Xinanjiang model applied in China. J Hydrol 135(1–4):371–381
Zhao G, Hörmann G, Fohrer N, Zhang Z, Zhai J (2010) Streamflow trends and climate variability impacts in Poyang Lake basin, China. Water Resour Manag 24(4):689–706. doi:10.1007/s11269-009-9465-7
Acknowledgments
This study is financially supported by the International Science and Technology Cooperation Program of China (Project No. 2010DFA24320) and the Nature Science Foundation of China (Project No. 50809058). Other supports from Met Office Hadley Centre, UK, Bureau of Hydrology, Zhejiang Province, and Nanjing Hydraulic Research Institute are highly acknowledged. Finally, many thanks are given to two anonymous reviewers for their valuable comments.
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Tian, Y., Xu, YP. & Zhang, XJ. Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models. Water Resour Manage 27, 2871–2888 (2013). https://doi.org/10.1007/s11269-013-0321-4
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DOI: https://doi.org/10.1007/s11269-013-0321-4