Modeling the impacts of future climate change on irrigation over China: Sensitivity to adjusted projections

Journal of Hydrometeorology

Volumn 15, Issue 5, 2014, Pages 2085-2103

  Leng, Guoyong ^a   Tang, Qiuhong ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; CLIMATE EFFECT; DROUGHT; GENERAL CIRCULATION MODEL; IRRIGATION SYSTEM; PRECIPITATION (CLIMATOLOGY); WATER DEMAND; WATER SUPPLY;

CHINA;

EID: 84907478671     PISSN: 1525755X     EISSN: 15257541     Source Type: Journal    
DOI: 10.1175/JHM-D-13-0182.1     Document Type: Article

References (82)

1. Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential impacts of a warming climate on water availability in snowdominated regions. Nature, 438, 303-309, doi:10.1038/nature04141.
2. Benestad, R. E., 2006: Can we expect more extreme precipitation on the monthly time scale? J. Climate, 19, 630-637, doi:10.1175/JCLI3656.1.
3. Chen, J., F. P. Brissette, and R. Leconte, 2011: Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J. Hydrol., 401, 190-202, doi:10.1016/j.jhydrol.2011.02.020.
4. Chung, S. O., and T. Nkomozepi, 2012: Uncertainty of paddy irrigation requirement estimated from climate change projections in the Geumho River basin, Korea. Paddy Water Environ., 10, 175-185, doi:10.1007/s10333-011-0305-z.
5. de Silva, C. S., E. K. Weatherhead, J.W. Knox, and J.A. Rodríguez-Díaz, 2007: Predicting the impacts of climate change-A case study of paddy irrigation water requirements in Sri Lanka. Agric. Water Manage., 93, 19-29, doi:10.1016/j.agwat.2007.06.003.
6. Döll, P., 2002: Impact of climate change and variability on irrigation requirements: A global perspective. Climatic Change, 54, 269-293, doi:10.1023/A:1016124032231.
7. Döll, P., 2009: Vulnerability to the impact of climate change on renewable groundwater resources: A global-scale assessment. Environ. Res. Lett., 4, 035006, doi:10.1088/1748-9326/4/3/035006.
8. Döll, P., and S. Siebert, 2002: Global modeling of irrigation water requirements. Water Resour. Res., 38, 1037, doi:10.1029/2001WR000355.
9. Drewniak, B., J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob, 2013: Modeling agriculture in the Community Land Model. Geosci. Model Dev., 6, 495-515, doi:10.5194/gmd-6-495-2013.
10. Elgaali, E., L. A. Garcia, and D. S. Ojima, 2007: High resolution modeling of the regional impacts of climate change on irrigation water demand. Climatic Change, 84, 441-461, doi:10.1007/s10584-007-9278-8.
11. Elliott, J., and Coauthors, 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl. Acad. Sci. USA, 111, 3239-3244, doi:10.1073/pnas.1222474110.
12. Famiglietti, J. S., and Coauthors, 2011: Satellites measure recent rates of groundwater depletion in California's Central Valley. Geophys. Res. Lett., 38, L03403, doi:10.1029/2010GL046442.
13. Fischer, G., F. N. Tubiello, H. van Velthuizen, and D. A. Wiberg, 2007: Climate change impacts on irrigation water requirements: Effects of mitigation, 1990-2080. Technol. Forecasting Soc. Change, 74, 1083-1107, doi:10.1016/j.techfore.2006.05.021.
14. Frolking, S., J. Qiu, S. Boles, X. Xiao, J. Liu, Y. Zhuang, C. Li, and X. Qin, 2002: Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Global Biogeochem. Cycles, 16, 1091, doi:10.1029/2001GB001425.
15. Gent, P., S. Yeager, R. Neale, S. Levis, and D. Bailey, 2010: Improvements in a half degree atmosphere/land version of the CCSM. Climate Dyn., 34, 819-833, doi:10.1007/s00382-009-0614-8.
16. Haddeland, I., D. P. Lettenmaier, and T. Skaugen, 2006: Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. J. Hydrol., 324, 210-223, doi:10.1016/j.jhydrol.2005.09.028.
17. Hagemann, S., C. Chen, J. O. Haerter, J. Heinke, D. Gerten, and C. Piani, 2011: Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J. Hydrometeor., 12, 556-578, doi:10.1175/2011JHM1336.1.
18. Hagemann, S., and Coauthors, 2013: Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst. Dyn., 4, 129-144, doi:10.5194/esd-4-129-2013.
19. Hay, L., R. Wilby, and G. Leavesley, 2000: A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Amer. Water Resour. Assoc., 36, 387-397, doi:10.1111/j.1752-1688.2000.tb04276.x.
20. Hempel, S., K. Frieler, L. Warszawski, J. Schewe, and F. Piontek, 2013: A trend-preserving bias correction-The ISI-MIP approach. Earth Syst. Dyn., 4, 219-236, doi:10.5194/esd-4-219-2013.
21. Ho, C. K., D. B. Stephenson, M. Collins, C. A. T. Ferro, and S. J. Brown, 2012: Calibration strategies: A source of additional uncertainty in climate change projections. Bull. Amer. Meteor. Soc., 93, 21-26, doi:10.1175/2011BAMS3110.1.
22. Ke, Y., R. L. Leung, M. Huang, A. M. Coleman, H. Li, and M. S. Wigmosta, 2012: Developing high resolution land surface parameters for Community Land Model. Geosci. Model Dev., 5, 1435-1481, doi:10.5194/gmdd-5-1435-2012.
23. Klemeš, V., 1986: Operational testing of hydrological simulation models. Hydrol. Sci. J., 31, 13-24, doi:10.1080/02626668609491024.
24. Konzmann, M., D. Gertena, and J. Heinkea, 2013: Climate impacts on global irrigation requirements under 19 GCMs, simulated with a vegetation and hydrology model. Hydrol. Sci. J., 58, 88-105, doi:10.1080/02626667.2013.746495.
25. Kueppers, L. M., M. A. Snyder, and L. C. Sloan, 2007: Irrigation cooling effect: Regional climate forcing by land-use change. Geophys. Res. Lett., 34, L03703, doi:10.1029/2006GL028679.
26. Lawrence, D., and Coauthors, 2011: Parameterization Improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, doi:10.1029/2011MS000045.
27. Lenderink, G., A. Buishand, and W. van Deursen, 2007: Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach. Hydrol. Earth Syst. Sci., 11, 1145-1159, doi:10.5194/hess-11-1145-2007.
28. Leng, G., M. Huang, Q. Tang, W. J. Sacks, H. Lei, and L. R. Leung, 2013: Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. J. Geophys. Res. Atmos., 118, 9789-9803, doi:10.1002/jgrd.50792.
29. Leng, G., M. Huang, Q. Tang, H. Gao, and L. R. Leung, 2014a: Modeling the effects of groundwater-fed irrigation on terrestrial hydrology over the conterminous United States. J. Hydrometeor., 15, 957-972, doi:10.1175/JHM-D-13-049.1.
30. Leng, G., Q. Tang, M. Huang, Y. Hong, and L. R. Leung, 2014b: Projected changes in mean and interannual variability of surface water over continental China. Sci. China Earth Sci., in press.
31. Leung, L. R., Y.-H. Kuo, and J. Tribbia, 2006: Research needs and directions of regional climate modeling using WRF and CCSM. Bull. Amer. Meteor. Soc., 87, 1747-1751, doi:10.1175/BAMS-87-12-1747.
32. Levis S., G. B. Bonan, E. Kluzek, P. E. Thornton, A. Jones, W. J. Sacks, and C. J. Kucharik, 2012: Interactive crop management in the Community Earth System Model (CESM1): Seasonal influences on land-atmosphere fluxes. J. Climate, 25, 4839-4859, doi:10.1175/JCLI-D-11-00446.1.
33. Li, H., M. Huang, T. Tesfa, Y. Ke, Y. Sun, Y. Liu, and L. R. Leung, 2013a: A subbasin-based framework to represent land surface processes in an Earth System Model. Geosci. Model Dev. Discuss., 6, 2699-2730, doi:10.5194/gmdd-6-2699-2013.
34. Li, H., M. S. Wigmosta, H. Wu, M. Huang, Y. Ke, A. M. Coleman, and L. Y. R. Leung, 2013b: A physically based runoff routing model for land surface and Earth system models. J. Hydrometeor., 14, 808-828, doi:10.1175/JHM-D-12-015.1.
35. Liu, J., M. Liu, H. Tiana, D. Zhuang, Z. Zhang, W. Zhang, X. Tang, and X. Deng, 2005: Spatial and temporal patterns of China's cropland during 1990-2000: An analysis based on Landsat TM data. Remote Sens. Environ., 98, 442-456, doi:10.1016/j.rse.2005.08.012.
36. Liu, J.-G., and Z.-H. Xie, 2013: Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach. Hydrol. Earth Syst. Sci., 17, 3355-3369, doi:10.5194/hess-17-3355-2013.
37. Lobell, D., G. Bala, A. Mirin, T. Phillips, R. Maxwell, and D. Rotman, 2009: Regional differences in the influence of irrigation on climate. J. Climate, 22, 2248-2255, doi:10.1175/2008JCLI2703.1.
38. Maraun, D., and Coauthors, 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.
39. Moss, R. H., and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747-756, doi:10.1038/nature08823.
40. Narasimhan, T. N., 2009: Groundwater: From mystery to management. Environ. Res. Lett., 4, 035002, doi:10.1088/1748-9326/4/3/035002.
41. Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol., 10, 282-290, doi:10.1016/0022-1694(70)90255-6.
42. O'Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. USA, 106, 14 773-14 777, doi:10.1073/pnas.0907610106.
43. Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-4781STR, 257 pp., doi:10.5065/D6FB50WZ.
44. Ozdogan, M., and G. Gutman, 2008: A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US. Remote Sens. Environ., 112, 3520-3537, doi:10.1016/j.rse.2008.04.010.
45. Ozdogan, M., M. Rodell, H. K. Beaudoing, and D. L. Toll, 2010: Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeor., 11, 171-184, doi:10.1175/2009JHM1116.1.
46. Pfister, S., P. Bayer, A. Koehler, and S. Hellweg, 2011: Projected water consumption in future global agriculture: Scenarios and related impacts. Sci. Total Environ., 409, 4206-4216, doi:10.1016/j.scitotenv.2011.07.019.
47. Piani, C., G. P. Weedon, M. Best, S. M. Gomes, P. Viterbo, S. Hagemann, and J. O. Haerter, 2010: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199-215, doi:10.1016/j.jhydrol.2010.10.024.
48. Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler, 2009: Selecting global climate models for regional climate change studies. Proc. Natl. Acad. Sci. USA, 106, 8441-8446, doi:10.1073/pnas.0900094106.
49. Piontek, F., and Coauthors, 2014: Multisectoral climate impact hotspots in a warming world. Proc. Natl. Acad. Sci. USA, 111, 3233-3238, doi:10.1073/pnas.1222471110.
50. Pokhrel, Y. N., N. Hanasaki, P. J.-F. Yeh, T. Yamada, S. Kanae, and T. Oki, 2012: Model estimates of sea level change due to anthropogenic impacts on terrestrial water storage. Nat. Geosci., 5, 389-392, doi:10.1038/ngeo1476.
51. Portmann, F. T., and Coauthors, 2013: Impact of climate change on renewable groundwater resources: Assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environ. Res. Lett., 8, 024023, doi:10.1088/1748-9326/8/2/024023.
52. Postel, S. L., G. C. Daily, and P. R. Ehrlich, 1996: Human appropriation of renewable freshwater. Science, 271, 785-788, doi:10.1126/science.271.5250.785.
53. Reichler, T., and J. Kim, 2008: How well do coupled models simulate today's climate? Bull. Amer. Meteor. Soc., 89, 303-311, doi:10.1175/BAMS-89-3-303.
54. Rodríguez Díaz, J. A., E. K. Weatherhead, J. W. Knox, and E. Camacho, 2007: Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg. Environ. Change, 7, 149-159, doi:10.1007/s10113-007-0035-3.
55. Rogelj, J., M. Meinshausen, and R. Knutti, 2012: Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Climate Change, 2, 248-253, doi:10.1038/nclimate1385.
56. Rost, S., D. Gerten, A. Bondeau, W. Luncht, J. Rohwer, and S. Schaphoff, 2008: Agricultural green and blue water consumption and its influence on the global water system. Water Resour. Res., 44, W09405, doi:10.1029/2007WR006331.
57. Sacks, W. J., B. I. Cook, N. Buenning, S. Levis, and J.H. Helkowski, 2009: Effects of global irrigation on the near-surface climate. Climate Dyn., 33, 159-175, doi:10.1007/s00382-008-0445-z.
58. Schewe, J., and Coauthors, 2014: Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA, 111, 3245-3250, doi:10.1073/pnas.1222460110.
59. Shahid, S., 2011: Impacts of climate change on irrigation water demand in northwestern Bangladesh. Climatic Change, 105, 433-453, doi:10.1007/s10584-010-9895-5.
60. Shiklomanov, I. A., 2000: Appraisal and assessment of world water resources. Water Int., 25, 11-32, doi:10.1080/02508060008686794.
61. Siebert, S., P. Döll, J. Hoogeveen, J. M. Faures, K. Frenken, and S. Feick, 2005: Development and validation of the global map of irrigation areas. Hydrol. Earth Syst. Sci., 9, 535-547, doi:10.5194/hess-9-535-2005.
62. Sorooshian, S., J. Li, K.-L. Hsu, and X. Gao, 2012: Influence of irrigation schemes used in regional climate models on evapotranspiration estimation: Results and comparative studies from California's Central Valley agricultural regions. J. Geophys. Res., 117, D06107, doi:10.1029/2011JD016978.
63. Tang, Q., T. Oki, S. Kanae, and H. Hu, 2007: The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydrometeor., 8, 499-512, doi:10.1175/JHM589.1.
64. Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climatechange impact studies: Review and evaluation of different methods. J. Hydrol., 456-457, 12-29, doi:10.1016/j.jhydrol.2012.05.052.
65. Teutschbein, C., and J. Seibert, 2013: Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions? Hydrol. Earth Syst. Sci., 17, 5061-5077, doi:10.5194/hess-17-5061-2013.
66. Themeßl, M. J., A. Gobiet, and A. Leuprecht, 2011: Empiricalstatistical downscaling and error correction of daily precipitation from regional climate models. Int. J. Climatol., 31, 1530-1544, doi:10.1002/joc.2168.
67. Tilman, D., K. G. Cassman, P. A. Matson, R. Naylor, and S. Polasky, 2002: Agricultural sustainability and intensive production practices. Nature, 418, 671-677, doi:10.1038/nature01014.
68. Tilman, D., C. Balzer, J. Hill, and B. L. Befort, 2011: Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci.USA, 108, 20 260-20 264, doi:10.1073/pnas.1116437108.
69. van Vuuren, D. P., and Coauthors, 2012:A comprehensive view on climate change: Coupling of earth system and integrated assessment models. Environ. Res. Lett., 7, 024012, doi:10.1088/1748-9326/7/2/024012.
70. Voisin, N., L. Liu, M. I. Hejazi, T. K. Tesfa, H. Li, M. Huang, Y. Liu, and L. Leung, 2013: One-way coupling of an integrated assessment model and a water resources model: Evaluation and implications of future changes over the US Midwest. Hydrol. Earth Syst. Sci., 17, 4555-4575, doi:10.5194/hess-17-4555-2013.
71. Vormoor, K., and T. Skaugen, 2013: Temporal disaggregation of daily temperature and precipitation grid data for Norway. J. Hydrometeor., 14, 989-999, doi:10.1175/JHM-D-12-0139.1.
72. Wada, Y., L. P. H. van Beek, and M. F. P. Bierkens, 2011a: Modelling global water stress of the recent past: On the relative importance of trends in water demand and climate variability. Hydrol. Earth Syst. Sci., 15, 3785-3808, doi:10.5194/hess-15-3785-2011.
73. Wada, Y., L. P. H. van Beek, D. Viviroli, H. H. Dürr, R. Weingartner, and M. F. P. Bierkens, 2011b: Global monthly water stress: 2. Water demand and severity of water. Water Resour. Res., 47, W07518, doi:10.1029/2010WR009792.
74. Wada, Y., L. P. H. van Beek., F. C. Sperna Weiland, B. F. Chao, Y.-H. Wu, and M. F. P. Bierkens, 2012: Past and future contribution of global groundwater depletion to sea-level rise. Geophys. Res. Lett., 39, L09402, doi:10.1029/2012GL051230.
75. Wada, Y., and Coauthors, 2013: Multi-model projections and uncertainties of irrigation water demand under climate change. Geophys. Res. Lett., 40, 4626-4632, doi:10.1002/grl.50686.
76. Wang, A., and X. Zeng, 2011: Sensitivities of terrestrial water cycle simulations to the variations. J. Geophys. Res., 116, D02107, doi:10.1029/2010JD014659.
77. Warszawski, L., K. Frieler, V. Huber, F. Piontek, O. Serdeczny, and J. Schewe, 2014: The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): Project framework. Proc. Natl. Acad. Sci. USA, 111, 3228-3232, doi:10.1073/pnas.1312330110.
78. Weedon, G. P., and Coauthors, 2011: Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeor., 12, 823-848, doi:10.1175/2011JHM1369.1.
79. 2. Climatic Change, 102, 651-670, doi:10.1007/s10584-009-9701-4.
80. Wisser, D., S. Frolking, E. M. Douglas, B. M. Fekete, C. J. Vörösmarty, and A. H. Schumann, 2008: Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets. Geophys. Res. Lett., 35, L24408, doi:10.1029/2008GL035296.
81. Xiao, X., and Coauthors, 2003: Uncertainties in estimates of cropland area in China: A comparison between an AVHRRderived dataset and a Landsat TM-derived dataset. Global Planet. Change, 37, 297-306, doi:10.1016/S0921-8181(02)00202-3.
82. Yano, T., M. Aydin, and T. Haraguchi, 2007: Impact of climate change on irrigation demand and crop growth in a Mediterranean environment of Turkey. Sensors, 7, 2297-2315, doi:10.3390/s7102297.