Abstract
The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, the general circulation model (GCM), which is widely used to simulate future climate scenario, operates on a coarse scale and does not provide reliable data on local or regional scale for hydrological modeling. Therefore the outputs from GCM have to be downscaled to obtain the information fit for hydrologic studies. The variable infiltration capacity (VIC) distributed hydrological model with 9×9 km2 grid resolution was applied and calibrated in the Hanjiang Basin. Validation results show that SSVM can approximate observed precipitation and temperature data reasonably well, and that the VIC model can simulate runoff hydrograph with high model efficiency and low relative error. By applying the SSVM model, the trends of precipitation and temperature (including daily mean temperature, daily maximum temperature and daily minimum temperature) projected from CGCM2 under A2 and B2 scenarios will decrease in the 2020s (2011–2040), and increase in the 2080s (2071–2100). However, in the 2050s (2041–2070), the precipitation will be decreased under A2 scenario and no significant changes under B2 scenario, but the temperature will be not obviously changed under both climate change scenarios. Under both climate change scenarios, the impact analysis of runoff, made with the downscaled precipitation and temperature time series as input of the VIC distributed model, has resulted in a decreasing trend for the 2020s and 2050s, and an overall increasing trend for the 2080s.
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References
Bate B C, Kundzewicz Z W, Wu S, et al. Climate Change and Water. Technical Report, Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva. 2008
Xu C Y. From GCMs to river flow: A review of downscaling methods and hydrologic modelling approaches. Prog Phys Geog, 1999, 23(2): 229–249
Wilby R L, Wigley T M L, Conway D, et al. Statistical downscaling of general circulation model output: A comparison of methods. Water Resour Res, 1998, 34(11): 2995–3008
Lee Y, Hsieh W, Huang C. ε-SSVR: A smooth support vector machine for insensitive regression. IEEE T Knowl Data Eng, 2005, 17(5): 678–685
Chen H, Guo J, Xiong W, et al. 2009: Downscaling GCMs using the Smooth Support Vector Machine Method to predict daily precipitation in the Hanjiang Basin. Adv Atmos Sci, 2008, doi: 10.1007/s00376-009-8071-1
Huth R. Statistical downscaling of daily temperature in Central Europe. J Climate, 2002, 15(13): 1731–1742
Liang X, Lettenmaier D P, Wood E F, et al. A simple hydrologically based model of land surface water and energy fluxes for GSMs. J Geophys Res, 1994, 99(14): 415–428
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Supported by the National Natural Science Foundation of China (Grant Nos. 50679063, 50809049), the International Cooperation Research Fund of China (Grant No. 2005DFA20520) and the Research Fund for the Doctoral Program of Higher Education (Grant No. 200804861062)
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Guo, S., Guo, J., Zhang, J. et al. VIC distributed hydrological model to predict climate change impact in the Hanjiang basin. Sci. China Ser. E-Technol. Sci. 52, 3234–3239 (2009). https://doi.org/10.1007/s11431-009-0355-2
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DOI: https://doi.org/10.1007/s11431-009-0355-2