Poyang Lake, the largest freshwater lake in China, and its surrounding sub-basins have suffered frequent floods and droughts in recent decades. To better understand and quantitatively assess hydrological impacts of climate change in the region, this study adopted the Statistical Downscaling Model (SDSM) to downscale the outputs of a Global Climate Model (GCM) under three scenarios (RCP2.6, RCP4.5 and RCP8.5) as recommended by the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) during future periods (2010–2099) in the Poyang Lake Basin. A semi-distributed two-parameter monthly water balance model was also used to simulate and predict projected changes of runoff in the Ganjiang sub-basin. Results indicate that: 1) SDSM can simulate monthly mean precipitation reasonably well, while a bias correction procedure should be applied to downscaled extreme precipitation indices (EPI) before being employed to simulate future precipitation; 2) for annual mean precipitation, a mixed pattern of positive or negative changes are detected in the entire basin, with a slightly higher or lower trend in the 2020s and 2050s, with a consistent increase in the 2080s; 3) all six EPI show a general increase under RCP4.5 and RCP8.5 scenarios, while a mixed pattern of positive and negative changes is detected for most indices under the RCP2.6 scenario; and 4) the future runoff in the Ganjiang sub-basin shows an overall decreasing trend for all periods but the 2080s under the RCP8.5 scenario when runoff is more sensitive to changes in precipitation than evaporation.
V Aich, S Liersch, T Vetter, S Huang, J Tecklenburg, P Hoffmann, H Koch, S Fournet, V Krysanova, E N Müller, F F Hattermann (2014). Comparing impacts of climate change on streamflow in four large African river basins. Hydrol Earth Syst Sci, 18(4): 1305–1321 https://doi.org/10.5194/hess-18-1305-2014
2
R Alkama, L Marchand, A Ribes, B Decharme (2013). Detection of global runoff changes: results from observations and CMIP5 experiments. Hydrol Earth Syst Sci, 17(7): 2967–2979 https://doi.org/10.5194/hess-17-2967-2013
3
N W Arnell (2003). Effects of IPCC SRES* emissions scenarios on river runoff: a global perspective. Hydrol Earth Syst Sci, 7(5): 619–641 https://doi.org/10.5194/hess-7-619-2003
4
B C Bates, Z W Kundzewicz, S, Wu, JP Palutikof (eds)2008. Climate change and water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva, pp 15–18
5
M B Butts, J T Payne, M Kristensen, H Madsen (2004). An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J Hydrol (Amst), 298(1‒4): 242–266 https://doi.org/10.1016/j.jhydrol.2004.03.042
6
H Chen, S Guo, C Xu, V P Singh (2007). Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J Hydrol (Amst), 344(3‒4): 171–184 https://doi.org/10.1016/j.jhydrol.2007.06.034
7
H Chen, C Xu, S Guo (2012). Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol (Amst), 434‒435: 36–45 https://doi.org/10.1016/j.jhydrol.2012.02.040
8
N S Christensen, D P Lettenmaier (2007). A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol Earth Syst Sci, 11(4): 1417–1434 https://doi.org/10.5194/hess-11-1417-2007
9
J T Chu, J Xia, C Xu, V P Singh (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 https://doi.org/10.1007/s00704-009-0129-6
10
Y B Dibike, P Coulibaly (2005). Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol (Amst), 307(1‒4): 145–163 https://doi.org/10.1016/j.jhydrol.2004.10.012
11
D R Easterling, J L Evans, P Y Groisman, T R Karl, K E Kunkel, P Ambenje (2000). Observed variability and trends in extreme climate events: a brief review. Bull Am Meteorol Soc, 81(3): 417–425 https://doi.org/10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2
12
H J Fowler, C G Kilsby, J Stunell (2007). Modelling the impacts of projected future climate change on water resources in north-west England. Hydrol Earth Syst Sci, 11(3): 1115–1126 https://doi.org/10.5194/hess-11-1115-2007
13
P Frich , L V Alexander , P Della-Marta , B Gleason , M Haylock , A M G Klein Tank , T Peterson (2002). Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res, 19(3): 193–212 S N Gosling, R G Taylor, N W Arnell, M C Todd (2011). A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol Earth Syst Sci, 15(1): 279–294 https://doi.org/10.5194/hess-15-279-2011
14
J Guo, H Chen, C Xu, S Guo, J Guo (2012). Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling. Stochastic Environ Res Risk Assess, 26(2): 157–176 https://doi.org/10.1007/s00477-011-0464-x
15
J Guo, S Guo, Y Li, H Chen, Y Li (2013). Spatial and temporal variation of extreme precipitation indices in the Yangtze River basin, China. Stochastic Environ Res Risk Assess, 27(2): 459–475 https://doi.org/10.1007/s00477-012-0643-4
16
S Guo, J Wang, L Xiong, A Ying, D Li (2002). A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China. J Hydrol (Amst), 268(1‒4): 1–15 https://doi.org/10.1016/S0022-1694(02)00075-6
17
I Hanssen-Bauer , C Achberger, R E Benestad, D Chen, E J Forland (2005). Statistical downscaling of climate scenarios over Scandinavia. Clim Res, 29(3): 255–268 https://doi.org/10.3354/cr029255
18
C Harpham, R L Wilby (2005). Multi-site downscaling of heavy daily precipitation occurrence and amounts. J Hydrol (Amst), 312(1-4): 235–255 https://doi.org/10.1016/j.jhydrol.2005.02.020
19
C Hellström, D Chen, C Achberger, J Raisanen (2001). Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Clim Res, 19(1): 45–55 https://doi.org/10.3354/cr019045
20
X Hong, S Guo, J Guo, Y Hou, L Wang (2014). Projected changes of extreme precipitation characteristics in the Poyang Lake Basin based on statistical downscaling model. Journal of Water Resources Research, 3(6): 511–521 (in Chinese) https://doi.org/10.12677/JWRR.2014.36063
21
J T Houghton, Y Ding, D J Griggs, M Noguer, P J van der Linen, X Dai (2001). Climate Change 2001: the Scientific Basis. Cambridge: Cambridge University Press, 1–944
22
A Huang, Y Zhou, Y Zhang, D Huang, Y Zhao, H Wu (2014). Changes of the annual precipitation over central Asia in the twenty-first century projected by multimodels of CMIP5. J Clim, 27(17): 6627–6646 https://doi.org/10.1175/JCLI-D-14-00070.1
23
IPCC (2013). Climate Change 2013: the Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the IPCC. New York: Cambridge University Press
24
S A Islam, M A Bari, A H M F Anwar (2014). Hydrologic impact of climate change on Murray-Hotham catchment of Western Australia: a projection of rainfall–runoff for future water resources planning. Hydrol Earth Syst Sci, 18(9): 3591–3614 https://doi.org/10.5194/hess-18-3591-2014
25
M Jie, H Chen, C Y Xu, Q Zeng, X Tao (2016). A comparative study of different objective functions to improve the flood forecasting accuracy. Hydrology Research, 47(4): 718–735 https://doi.org/10.2166/nh.2015.078
26
Y Kanai, M Ueta, N Germogenov, M Nagendran, N Mita, H Higuchi (2002). Migration routes and important resting areas of Siberian cranes (Grus leucogeranus) between northeastern Siberia and China as revealed by satellite tracking. Biol Conserv, 106(3): 339–346 https://doi.org/10.1016/S0006-3207(01)00259-2
J C J Kwadijk (1993) The impact of climate change on the discharge of the River Rhine, Ph.D. Thesis, Department of Physical Geography, Utrecht University, Netherlands Geographical Studies, 171.
29
J Li, Q Zhang, Y D Chen, V P Singh (2015). Future joint probability behaviors of precipitation extremes across China: spatiotemporal patterns and implications for flood and drought hazards. Global Planet Change, 124: 107–122 https://doi.org/10.1016/j.gloplacha.2014.11.012
30
J Li, Q Zhang, Y D Chen, C Xu, V P Singh (2013). Changing spatiotemporal patterns of precipitation extremes in China during 2071–2100 based on Earth System Models. J Geophys Res, D, Atmospheres, 118(22): 12,537–12,555 https://doi.org/10.1002/2013JD020300
31
D Maraun, F Wetterhall, A M Ireson, R E Chandler, E J Kendon, M Widmann, S Brienen, H W Rust, T Sauter, M Themeßl , V K C Venema , K P Chun , C M Goodness , R G Jones , C Onof , M Vrac , I Thiele-Eich (2010). Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end users. Rev Geophys, 48(3): 1–38 https://doi.org/10.1029/2009RG000314
32
W S Merritt, Y Alila, M Barton, B Taylor, S Cohen, D Neilsen (2006). Hydrologic response to scenarios of climate change in sub-watersheds of the Okanagan basin, British Columbia. J Hydrol (Amst), 326(1-4): 79–108 https://doi.org/10.1016/j.jhydrol.2005.10.025
33
H Middelkoop, K Daamen, D Gellens, W Grabs, J C J Kwadijk, H Lang, B W A H Parmet, B Schädler, J Schulla, K Wilke (2001). Impact of climate change on hydrological regimes and water resources management in the Rhine basin. Clim Change, 49(1/2): 105–128 https://doi.org/10.1023/A:1010784727448
34
R H Moss, J A Edmonds, K A Hibbard, M R Manning, S K Rose, D P van Vuuren, T R Carter, S Emori, M Kainuma, T Kram, G A Meehl, J F B Mitchell, N Nakicenovic, K Riahi, S J Smith, R J Stouffer, A M Thomson, J P Weyant, T J Wilbanks (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282): 747–756 https://doi.org/10.1038/nature08823
T C Peterson, M A Taylor, R Demeritte, D L Duncombe, S Burton, F Thompson, A Porter, M Mercedes, E Villegas, R S Fils, A K Tank, A Martis,Warner R, Joyette A, Mills W, Alexander L, Gleason B (2002). Recent changes in climate extremes in the Caribbean region. Journal of Geophysical Research: Atmospheres (1984–2012), 107 (D21): ACL 16-1–ACL 16-9
37
N Plummer, M J Salinger, N Nicholls, R Suppiah, K J Hennessy, R M Leighton, B Trewin, C M Page , J M Lough (1999). Twentieth century trends in climate extremes over the Australian region and New Zealand. Clim Change, 42(1): 183–202 https://doi.org/10.1023/A:1005472418209
D Raje, R Krishnan (2012). Bayesian parameter uncertainty modeling in a macro-scale hydrologic model and its impact on Indian river basin hydrology under climate change. Water Resour Res, 48(8): W08522 https://doi.org/10.1029/2011WR011123
41
J C Refsgaard, K Havnø, H C Ammentorp, A Verwey (1988). Application of hydrological models for flood forecasting and flood control in India and Bangladesh. Adv Water Resour, 11(2): 101–105 https://doi.org/10.1016/0309-1708(88)90043-7
42
S Sen Roy, R C Balling (2004). Trends in extreme daily precipitation indices in India. Int J Climatol, 24(4): 457–466 doi:10.1002/joc.995
43
M V Shabalova, W P A Van Deursen, T A Buishand (2003). Assessing future discharge of the river Rhine using regional climate model integrations and a hydrological model. Clim Res, 23(3): 233–246 https://doi.org/10.3354/cr023233
44
D Shankman, B D Keim, J Song (2006). Flood frequency in China’s Poyang Lake region: trends and teleconnections. Int J Climatol, 26(9): 1255–1266 https://doi.org/10.1002/joc.1307
45
J Sillmann, V V Kharin, F W Zwiers, X Zhang, D Bronaugh (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J Geophys Res, D, Atmospheres, 118(6): 2473–2493 https://doi.org/10.1002/jgrd.50188
46
S Sun, H Chen, W Ju, M Yu, W Hua, Y Yin (2014). On the attribution of the changing hydrological cycle in Poyang Lake Basin, China. J Hydrol (Amst), 514: 214–225 https://doi.org/10.1016/j.jhydrol.2014.04.013
47
H Tao, K Fraedrich, C Menz, J Zhai (2014). Trends in extreme temperature indices in the Poyang Lake Basin, China. Stochastic Environ Res Risk Assess, 28(6): 1543–1553 https://doi.org/10.1007/s00477-014-0863-x
48
K E Taylor, R J Stouffer, G A Meehl (2012). An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc, 93(4): 485–498 https://doi.org/10.1175/BAMS-D-11-00094.1
49
C Teutschbein, J Seibert (2010). Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geogr Compass, 4(7): 834–860 https://doi.org/10.1111/j.1749-8198.2010.00357.x
50
J M Thibeault, A Seth (2014). Changing climate extremes in the Northeast United States: observations and projections from CMIP5. Clim Change, 127(2): 273–287 https://doi.org/10.1007/s10584-014-1257-2
51
C W Thornthwaite (1948). An approach toward a rational classification of climate. Geogr Rev, 38(1): 55–94 https://doi.org/10.2307/210739
52
G Wang, J Zhang, Y Li, Z Bao, J Jin, X Yan, C Liu (2014). Variation trend of future climate for the Hai River Basin based on multiple GCMs projections. Resources Science, 36(5): 1043–1050 (in Chinese with English abstract)
53
F Wetterhall, A Bárdossy, D Chen, S Halldin, C Xu (2006). Daily precipitation downscaling techniques in three Chinese regions. Water Resour Res, 42(11): W11423 https://doi.org/10.1029/2005WR004573
54
E Widén-Nilsson, S Halldin, C Y Xu (2007). Global water-balance modelling with WASMOD-M: parameter estimation and regionalization. J Hydrol (Amst), 340(1-2): 105–118 https://doi.org/10.1016/j.jhydrol.2007.04.002
55
R L Wilby, C W Dawson, E M Barrow (2002). SDSM- a decision support tool for the assessment of regional climate change impacts. Environ Model Softw, 17(2): 145–157 https://doi.org/10.1016/S1364-8152(01)00060-3
56
R L Wilby, L E Hay, G H Leavesley (1999). A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol (Amst), 225(1‒2): 67–91 https://doi.org/10.1016/S0022-1694(99)00136-5
57
R L Wilby, O J Tomlinson, C W Dawson (2003). Multi-site simulation of precipitation by conditional resampling. Clim Res, 23(3): 183–194 https://doi.org/10.3354/cr023183
58
R L Wilby, T M L Wigley, D Conway, P D Jones, B C Hewitson, J Main, D S Wilks (1998). Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res, 34(11): 2995–3008 https://doi.org/10.1029/98WR02577
59
D S Wilks (1989). Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour Res, 25(6): 1429–1439 https://doi.org/10.1029/WR025i006p01429
60
X Xin, T Wu, J Li, Z Wang, W Li, F Wu (2013). How well does BCC_CSM1. 1 reproduce the 20th century climate change over China? Atmospheric and Oceanic Science Letters, 6(1): 21–26 https://doi.org/10.1080/16742834.2013.11447053
C-Y Xu (1999). From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Prog Phys Geogr, 23(2): 229–249 https://doi.org/10.1177/030913339902300204
63
C-Y Xu, V P Singh (2001). Evaluation and generalization of temperature-based methods for calculating evaporation. Hydrol Processes, 15(2): 305–319 https://doi.org/10.1002/hyp.119
64
C-Y Xu, E Widén, S Halldin (2005). Modelling hydrological consequences of climate change- progress and challenges. Adv Atmos Sci, 22(6): 789–797 https://doi.org/10.1007/BF02918679
65
Y Xu, C Xu, X Gao, Y Luo (2009). Projected changes in temperature and precipitation extremes over the Yangtze River basin of China in the 21st century. Quat Int, 208(1-2): 44–52 https://doi.org/10.1016/j.quaint.2008.12.020
66
X Ye, Y Li, X Li, C-Y Xu, Q Zhang (2015). Investigation of the variability and implications of meteorological dry/wet conditions in the Poyang Lake catchment, China, during the period 1960‒2010. Adv Meteorol, 2015: 1–11 https://doi.org/10.1155/2015/928534
67
X Ye, J Liu, X Li, Q Zhang (2013). Effects of climate variability and human activities on runoff variation of Ganjiang river basin. Journal of Hohai University (Natural Sciences), 41(3): 196–203 (in Chinese with English abstract)
68
X Ye, Q Zhang, L Bai, Q Hu (2011). A modeling study of catchment discharge to Poyang Lake under future climate in China. Quat Int, 244(2): 221–229 https://doi.org/10.1016/j.quaint.2010.07.004
69
Q Zhang, Y Liu, G Yang, Z Zhang (2011a). Precipitation and hydrological variations and related associations with large-scale circulation in the Poyang Lake Basin, China. Hydrol Processes, 25(5): 740–751 https://doi.org/10.1002/hyp.7863
70
Q Zhang, P Sun, X Chen, T Jiang (2011b). Hydrological extremes in the Poyang Lake Basin, China: changing properties, causes and impacts. Hydrol Processes, 25(20): 3121–3130 https://doi.org/10.1002/hyp.8031
71
Q Zhang, M Xiao, J Li, V P Singh, Z Wang (2014a). Topography-based spatial patterns of precipitation extremes in the Poyang Lake Basin, China: Changing properties and causes. J Hydrol (Amst), 512: 229–239 https://doi.org/10.1016/j.jhydrol.2014.03.010
72
Q Zhang, M Xiao, V P Singh, Y D Chen (2014b). Max-stable based evaluation of impacts of climate indices on extreme precipitation processes across the Poyang Lake Basin, China. Global Planet Change, 122: 271–281 https://doi.org/10.1016/j.gloplacha.2014.09.005
73
X Zhang, F Yang (2004). RClimDex (1.0) user manual. Climate Research Branch Environment, Canada, pp22