REFERENCES

• Aalderink, R.H., et al., 1996. Effect of input uncertainties upon scenario predictions for the River Vecht. Water Science and Technology, 33 (2), 107–118. doi:10.1016/0273-1223(96)00193-X.
   Google Scholar
• Bengtsson, L. and Hellström, T., 1992. Wild-induced resuspension in a small shallow lake. Hydrobiologia, 241 (3), 163–172. doi:10.1007/BF00028639.
   Web of Science ®Google Scholar
• Carniello, L., D’Alpaos, A., Defina, A., 2011. Modeling wind waves and tidal flows in shallow micro-tidal basins. Estuarine Coastal and Shelf Science, 92 (2), 263–276. doi:10.1016/j.ecss.2011.01.001.
   Web of Science ®Google Scholar
• Chapra, S., et al., 2007. QUAL2K: a modeling framework for simulating river and stream water quality version 2.07: documentation and user’s manual. Medford, MA: Civil and Environmental Engineering Department, Tufts University.
   Google Scholar
• Chen, Y.D., et al., 2008. Inversing wind drag coefficient by the adjoint data assimilation method. Journal of Nanjing Institute of Meteorology, 31 (6), 879–882 (in Chinese).
   Google Scholar
• Craig, P.M., 2011. User’s manual for EFDC_Explorer: a pre/post processor for the environmental fluid dynamics code. Knoxville, TN: Dynamic Solutions-International, LLC.
   Google Scholar
• Cryer, S.A. and Havens, P.L., 1999. Regional sensitivity analysis using a fractional factorial method for the USDA model GLEAMS. Environmental Modelling & Software, 14 (6), 613–624. doi:10.1016/S1364-8152(99)00003-1.
   Web of Science ®Google Scholar
• DHI, 2003. MIKE 11: a modeling system for rivers and channels: short introduction tutorial. Hørsholm, Denmark: DHI Software.
   Google Scholar
• Fagherazzi, S. and Wiberg, P.L., 2009. Importance of wind conditions, fetch, and water levels on wave-generated shear stresses in shallow intertidal basins. Journal of Geophysical Research – Earth Surface, 114 (F3), F03022. doi:10.1029/2008JF001139.
   Google Scholar
• Francis, J.R.D., 1959. Wind action on a water surface. ICE proceedings, 12 (2), 197–216. doi:10.1680/iicep.1959.12126
   Google Scholar
• Geernaert, G.L., et al., 1986. Variation of the drag coefficient and its dependence on sea state. Journal of Geophysical Research-Ocean, 91 (C6), 7667–7679. doi:10.1029/JC091iC06p07667.
   Web of Science ®Google Scholar
• Hamrick, J.M., 1994. Application of the EFDC environmental fluid dynamics computer code to SFWMD water conservation area 2A. Williamsburg, VA: J.M. Hamrick and Associates, Report JMH-SFWMD-94-01, 126pp.
   Google Scholar
• Helton, J.C., 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering & System Safety, 42 (2–3), 327–367. doi:10.1016/0951-8320(93)90097-I.
   Web of Science ®Google Scholar
• Helton, J.C. and Davis, F.J., 2002. Illustration of sampling-based methods for uncertainty and sensitivity analysis. Risk Analysis, 22 (3), 591–622. doi:10.1111/0272-4332.00041.
   PubMed Web of Science ®Google Scholar
• Helton, J.C., Davis, F.J., and Johnson, J.D., 2005. A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling. Reliability Engineering & System Safety, 89 (3), 305–330. doi:10.1016/j.ress.2004.09.006.
   Web of Science ®Google Scholar
• Helton, J.C., et al., 2006. Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering & System Safety, 91 (10–11), 1175–1209. doi:10.1016/j.ress.2005.11.017.
   Web of Science ®Google Scholar
• Hu, W.P., et al., 2006. A vertical-compressed three-dimensional ecological model in Lake Taihu, China. Ecological Modelling, 190 (3–4), 367–398. doi:10.1016/j.ecolmodel.2005.02.024.
   Web of Science ®Google Scholar
• HydroQual Inc, 2001. A primer for ECOMSED version 1.2: user manual. Mahwah, NJ: HydroQual, Inc.
   Google Scholar
• Jacques, J., Lavergne, C., and Devictor, N., 2006. Sensitivity analysis in presence of model uncertainty and correlated inputs. Reliability Engineering & System Safety, 91 (10–11), 1126–1134. doi:10.1016/j.ress.2005.11.047.
   Web of Science ®Google Scholar
• Kroes, H.W., 1986. Restoration of shallow lake ecosystems, with emphasis on Loosdrecht Lakes. Hydrobiological Bulletin, 20 (1–2), 5–7. doi:10.1007/BF02291145.
   Google Scholar
• Li, S.J., et al., 1998. Numerical simulation of influence of hilly relief on wind-driven current in Dianchi Lake. Journal of Lake Sciences, 10 (1), 5–10 (in Chinese).
   Google Scholar
• Li, Y.P., et al., 2010. Modeling water ages and thermal structure of Lake Mead under changing water levels. Lake and Reservoir Management, 26 (4), 258–272. doi:10.1080/07438141.2010.541326.
   Web of Science ®Google Scholar
• Li, Y.P., et al., 2011. Modeling impacts of Yangtze River water transfer on water ages in Lake Taihu, China. Ecological Engineering, 37 (2), 325–334. doi:10.1016/j.ecoleng.2010.11.024.
   Web of Science ®Google Scholar
• Luo, L.C. and Qin, B.Q., 2003. Numerical simulation based on a three-dimensional shallow-water hydrodynamic model in Lake Taihu-Current circulations in Lake Taihu with prevailing wind-forcing. Journal of Hydrodynamics. Ser. A, 18 (6), 686–691 (in Chinese).
   Google Scholar
• Luo, L.C. and Qin, B.Q., 2004. Three-dimensional numerical simulations of wind-induced circulation in Lake Taihu. Journal of Hydrodynamics, Ser. B, 16 (3), 341–349 (in Chinese).
   Web of Science ®Google Scholar
• Manache, G. and Melching, C.S., 2004. Sensitivity analysis of a water-quality model using Latin hypercube sampling. Journal of Water Resources Planning and Management, 130 (3), 232–242. doi:10.1061/(ASCE)0733-9496(2004)130:3(232).
   Web of Science ®Google Scholar
• Manache, G. and Melching, C.S., 2008. Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water quality model parameters. Environmental Modelling & Software, 23 (5), 549–562. doi:10.1016/j.envsoft.2007.08.001.
   Web of Science ®Google Scholar
• McKay, M.D., 1988. Sensitivity and uncertainty analysis using statistical sample of input values. In: Y. Ronen, ed. Uncertainty analysis. Boca Raton, FL: CRC Press, 145–186.
   Google Scholar
• Mckay, M.D., et al., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21 (2), 239–245.
   Web of Science ®Google Scholar
• Mellor, G.L., Ezer, T., and Oey, L., 1994. The pressure gradient conundrum of sigma coordinate ocean models. Journal of Atmospheric and Oceanic Technology, 11 (4), 1126–1134. doi:10.1175/1520-0426(1994)011<1126:TPGCOS>2.0.CO;2.
   Web of Science ®Google Scholar
• Mellor, G.L. and Yamada, T., 1982. Development of a turbulence closure model for geophysical fluid problems. Reviews of Geophysics, 20 (4), 851–875. doi:10.1029/RG020i004p00851.
   Web of Science ®Google Scholar
• Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33 (2), 161–174. doi:10.1080/00401706.1991.10484804.
   Web of Science ®Google Scholar
• Muleta, M.K. and Nicklow, J.W., 2005. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology, 306 (1–4), 127–145. doi:10.1016/j.jhydrol.2004.09.005.
   Web of Science ®Google Scholar
• Munoz-Carpena, R., Fox, G.A., and Sabbagh, G.J., 2010. Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips. Journal of Environmental Quality, 39 (2), 630–641. doi:10.2134/jeq2009.0300.
   PubMed Web of Science ®Google Scholar
• Pan, F., et al., 2009. Numerical evaluation of uncertainty in water retention parameters and effect on predictive uncertainty. Vadose Zone Journal, 8 (1), 158–166. doi:10.2136/vzj2008.0092.
   Web of Science ®Google Scholar
• Pan, F., et al., 2011. Sensitivity analysis of unsaturated flow and contaminant transport with correlated parameters. Journal of Hydrology, 397 (3–4), 238–249. doi:10.1016/j.jhydrol.2010.11.045.
   Web of Science ®Google Scholar
• Pang, Y., et al., 1994. Numerical simulations and their verification with nonuniform wind stress in Taihu Lake. Tansactions of Oceanology & Limnology, 4, 9–15 (in Chinese).
   Google Scholar
• Qin, B.Q., 2009. Process and prospect on the eco-environmental research of Lake Taihu. Journal of Lake Sciences, 21 (4), 445–455 (in Chinese).
   Google Scholar
• Qin, B.Q., et al., 2007. Environmental issues of Lake Taihu, China. Hydrobiologia, 581, 3–14. doi:10.1007/s10750-006-0521-5.
   Web of Science ®Google Scholar
• Ran, L., Wang, S., and Fan, X., 2010. Channel change at Toudaoguai Station and its responses to the operation of upstream reservoirs in the upper Yellow River. Journal of Geographical Sciences, 20 (2), 231–247. doi:10.1007/s11442-010-0231-9.
   Web of Science ®Google Scholar
• Read, J.S., et al., 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling & Software, 26 (11), 1325–1336. doi:10.1016/j.envsoft.2011.05.006.
   Web of Science ®Google Scholar
• Saltelli, A., et al., 2000. Sensitivity analysis. Chichester: John Wiley & Sons.
   Google Scholar
• Saltelli, A., et al., 2008. Global sensitivity analysis: The Paimer. Chichester: John Wiley & Sons.
   Google Scholar
• Smagorinsky, J., 1963. General circulation experiments with the primitive equations: I. The basic experiment. Monthly Weather Review, 91 (3), 99–164. doi:10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2.
   Google Scholar
• Straten, G.T. and Keesman, K.J., 1991. Uncertainty propagation and speculation in projective forecasts of environmental change: a lake-eutrophication example. Journal of Forecasting, 10 (1–2), 163–190. doi:10.1002/for.3980100110.
   Web of Science ®Google Scholar
• TBA Taihu Basin Authority of Ministry of Water Resources, Shanghai, China, 2009. Database of water diversion from Yangtze River to Lake Taihu [online]. Available from: http://www.tba.gov.cn/ztbd/yjjt/xyfx/yq detail th.asp. [Accessed 15 August].
   Google Scholar
• Tsvetova, E.A., 1999. Mathematical modelling of Lake Baikal hydrodynamics. Hydrobiologia, 407, 37–43. doi:10.1023/A:1003766220781.
   Web of Science ®Google Scholar
• USEPA, 2011. United States Environmental Protection Agency: Hydrodynamic models [online]. Available from: http://www.epa.gov/athens/wwqtsc/html/hydrodynamic_models.html [Accessed 17 November, 2011].
   Google Scholar
• Van, G. and Meixner, T., 2006. Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Science and Technology, 53 (1), 51–59. doi:10.2166/wst.2006.007.
   Web of Science ®Google Scholar
• Wang, C.L., et al., 2010. Divergence characteristics and formation mechanism of wind field appropriate for the cyanbacteria bloom in Taihu Lake. China Environment and Science, 30 (9), 1168–1176 (in Chinese).
   Google Scholar
• Wang, H.Z., et al., 2001. A quasi-3D numerical model of wind-driven current in Taihu Lake considering the variation of vertical coefficient of eddy viscosity. Journal of Lake Sciences, 13 (3), 233–239 (in Chinese).
   Google Scholar
• Wool, T.A., et al., 2007. Water Quality Analysis Simulation Program (WASP) version 6.0 draft: user’s manual. Atlanta, GA: US Environmental Protection Agency–Region 4.
   Google Scholar
• Wüest, A. and Lorke, A., 2003. Small-scale hydrodynamics in lakes. Annual Review of Fluid Mechanics, 35, 373–412. doi:10.1146/annurev.fluid.35.101101.161220.
   Web of Science ®Google Scholar
• Xu, C.G. and Gertner, G.Z., 2008. Uncertainty and sensitivity analysis for models with correlated parameters. Reliability Engineering & System Safety, 93 (10), 1563–1573. doi:10.1016/j.ress.2007.06.003.
   Web of Science ®Google Scholar
• Xu, X.F. and Liu, Q.Q., 2009. Numerical study on the characteristics of wind-induced current in Taihu Lake. Chinese Journal of Hydrodynamics, 24 (4), 512–518 (in Chinese).
   Google Scholar
• Ye, M., et al., 2007. Assessment of radionuclide transport uncertainty in the unsaturated zone of Yucca Mountain. Advances in Water Resources, 30 (1), 118–134. doi:10.1016/j.advwatres.2006.03.005.
   Web of Science ®Google Scholar
• Zhang, Y.C., et al., 2008. Field Measurement and analysis on diurnal stratification in Taihu Lake. Environmental Science and Management, 33 (6), 117–121.
   Google Scholar
• Zhao, D.H., et al., 2003. 2-D depth-averaged follow-pollutions model for Jiangsu reaches in Yangtze River. Shuili Xuebao, 6, 72–77 (in Chinese).
   Google Scholar
• Zhao, L., et al., 2011a. Thermal stratification and its influence factors in a large-sized and shallow Lake Taihu. Advance in water science, 22 (6), 845–850 (in Chinese).
   Google Scholar
• Zhao, X., et al., 2011b. Key uncertainty sources analysis of water quality model using the first order error method. International Journal of Environmental Science and Technology, 8 (1), 137–148. doi:10.1007/BF03326203.
   Web of Science ®Google Scholar
• Zhou, J., et al., 2009. Influence of wind drag coefficient on wind-drived flow simulation. Chinese Journal of Hydrodynamics, 24 (4), 440–447 (in Chinese).
   Google Scholar
• Zhu, G.W., et al., 2007. Effects of hydrodynamics on phosphorus concentrations in water of Lake Taihu, a large, shallow, eutrophic lake of China. Hydrobiologia, 194, 53–61. doi:10.1007/978-1-4020-6158-5_6.
   Google Scholar