REFERENCES

• Abbott , M. B. , Bathurst , J. C. , Cunge , J. A. , O'Connell , P. E. and Rasmussen , J. 1986 . An introduction to the European hydrological system—Système Hydrologique Européen, SHE. 1: History and philosophy of a physically-based, distributed modeling system. . J. Hydrol. , 87 : 45 – 59 .
   Web of Science ®Google Scholar
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology . 2000 . Artificial neural networks in hydrology. II: Hydrologic applications. . J. Hydrol. Engng , 5 ( 2 ) : 124 – 137 .
   Web of Science ®Google Scholar
• Babovic , V. and Abbott , M. B. 1997 . Evolution of equation from hydraulic data: Part I-Theory. . J. Hydraul. Res. , 35 ( 3 ) : 1 – 14 .
   Web of Science ®Google Scholar
• Babovic , V. and Keijzer , M. 2000 . Genetic programming as model induction engine. . J. Hydroinformatics , 2 : 35 – 60 .
   Google Scholar
• Biftu , G. F. and Gan , T. Y. 2000 . Assessment of evapotranspiration models applied to a watershed of Canadian Prairies with mixed land-uses. . Hydrol. Processes , 14 : 1305 – 1325 .
   Web of Science ®Google Scholar
• Blaney , H. F. and Criddle , W. D. 1950 . “ Determining water requirements in irrigated area from climatological irrigation data ” . In Soil Conservation Service Technical Paper no. 96. Washington, DC, , USA : US Department of Agriculture .
   Google Scholar
• Bowden , G. J. , Dandy , G. C. and Maier , H. R. 2005 . Input determination for neural network models in water resources applications. Part 1—background and methodology. . J. Hydrol. , 301 : 75 – 92 .
   Web of Science ®Google Scholar
• Choudhury , B. J. and Monteith , J. L. 1988 . A four-layer model for the heat budget of homogeneous land surfaces. . Quart. J. Roy. Met. Soc. , 114 : 373 – 398 .
   Web of Science ®Google Scholar
• Coulibaly , P. 2004 . Downscaling daily extreme temperatures with genetic programming. . Geophys. Res. Lett. , 31 : L16203 doi:10.1029/2004GL020075
   Web of Science ®Google Scholar
• Demuth , H. and Beale , M. 2001 . Neural Network Toolbox Learning. for Use with MATLAB. Massachusetts, , USA : The Math Works Inc, Natick .
   Google Scholar
• Drexler , J. Z. , Snyder , R. L. , Spano , D. and Paw , K. T. 2004 . A review of models and micrometeorological methods used to estimate wetland evapotranspiration. . Hydrol. Processes , 18 : 2071 – 2101 .
   Web of Science ®Google Scholar
• Eltahir , E. A. B. 1998 . A soil moisture—rainfall feedback mechanism. 1: Theory and observations. . Water Resour. Res. , 34 ( 4 ) : 765 – 776 .
   Web of Science ®Google Scholar
• Flerchinger , G. N. , Hanson , C. L. and Wight , J. L. 1996 . Modeling evapotranspiration and surface energy budgets across a watershed. . Water Resour. Res. , 32 ( 8 ) : 2539 – 2548 .
   Web of Science ®Google Scholar
• Fogel , L. J. , Owens , A. J. and Walsh , M. J. 1966 . Artificial Intelligence through Simulated Evolution. New York, , USA : John Wiley .
   Google Scholar
• French , M. N. , Krajewski , W. F. and Cuykendall , R. R. 1992 . Rainfall forecasting in space and time using a neural network. . J. Hydrol. , 137 : 1 – 31 .
   Web of Science ®Google Scholar
• Granger , R. J. and Gray , D. M. 1989 . Evaporation from natural non-saturated surface. . J. Hydrol. , 111 : 21 – 29 .
   Web of Science ®Google Scholar
• Hameed , T. , Mariño , M. A. and Shumway , R. H. 1995 . Evapotranspiration transfer-function-noise modeling. . J. Irrig. Drain. Engng , 121 ( 2 ) : 159 – 169 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural Networks: A Comprehensive Foundation , (second edn) New York, , USA : MacMillan .
   Google Scholar
• Holland , J. H. 1975 . Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan, , USA : University of Michigan Press .
   Google Scholar
• Hong , Y. S. and Rosen , M. R. 2002 . Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling. . J. Hydrol. , 259 : 89 – 104 .
   Web of Science ®Google Scholar
• Hong , Y. S. T. , White , P. A. and Scott , D. M. 2005 . Automatic rainfall recharge model induction by evolutionary computational intelligence. . Water Resour. Res. , 41 : W08422 doi:10.1029/2004WR003577
   Web of Science ®Google Scholar
• Hsu , K. L. , Gupta , H. V. and Sorooshian , S. 1995 . Artificial neural network modeling of the rainfall—runoff process. . Water Resour. Res. , 31 ( 10 ) : 2517 – 2530 .
   Web of Science ®Google Scholar
• Karunanithi , N. , Grenney , W. J. , Whitley , D. and Bovee , K. 1994 . Neural networks for river flow prediction. . J. Comp. Civil Engng ASCE , 8 ( 2 ) : 201 – 220 .
   Web of Science ®Google Scholar
• Khu , S. T. , Liong , S. Y. , Babovic , V. , Madsen , H. and Muttil , N. 2001 . Genetic programming and its application in realtime runoff forecasting. . J. Am. Water Resour. Assoc. ASCE , 8 ( 2 ) : 201 – 220 .
   Google Scholar
• Koza , J. R. 1992 . Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, Massachusetts, , USA : The MIT Press .
   Google Scholar
• Kumar , M. , Raghuwanshi , N. S. , Singh , R. , Wallender , W. W. and Pruitt , W. O. 2002 . Estimating evapotranspiration using artificial neural network. . J. Irrig. Drain. Engng , 128 ( 4 ) : 224 – 233 .
   Web of Science ®Google Scholar
• Lakshmi , V. and Susskind , J. 2001 . Utilization of satellite data in land-surface hydrology: sensitivity and assimilation . Hydrol. Processes , 15 ( 5 ) : 877 – 892 .
   Web of Science ®Google Scholar
• MacKay , D. J. C. 1992 . Bayesian interpolation. . Neural Computation , 4 ( 3 ) : 415 – 447 .
   Web of Science ®Google Scholar
• Maier , H. and Dandy , G. 2000 . Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. . Environ. Modell. Software , 15 ( 1 ) : 101 – 124 .
   Web of Science ®Google Scholar
• Mariño , M. A. , Tracy , J. C. and Taghavi , S. A. 1993 . Forecasting reference crop evapotranspiration. . Agric. Water Manage. , 24 : 163 – 187 .
   Web of Science ®Google Scholar
• Minns , A. W. and Hall , M. J. 1996 . Artificial neural networks as rainfall runoff models. . Hydrol. Sci. J. , 41 ( 3 ) : 399 – 417 .
   Web of Science ®Google Scholar
• Monteith , J. L. 1965 . “ Evaporation and environment in the state and movement of water in living organisms ” . In Proc. Society of Experimental Biology , Symposium no. 19 205 – 234 . Cambridge, , UK : Cambridge University Press .
   Google Scholar
• Parasuraman , K. , Elshorbagy , A. and Carey , S. 2006 . Spiking-modular neural networks: a neural network modeling approach for hydrological processes. . Water Resour. Res. , 42 : W05412 doi:10.1029/2005WR004317
   Web of Science ®Google Scholar
• Penman , H. L. 1948 . Natural evaporation from open water, bare soil and grass. . Proc. Roy. Soc. London , 193 : 120 – 146 .
   Google Scholar
• Priestley , C. H. B. and Taylor , R. J. 1972 . On the assessment of surface heat flux and evaporation using large scale parameters. . Mon. Weather Rev. , 100 : 81 – 92 .
   Web of Science ®Google Scholar
• Savic , D. A. , Walters , G. A. and Davidson , J. W. 1999 . A genetic programming approach to rainfall—runoff modelling. . Water Resour. Manage. , 13 : 219 – 231 .
   Web of Science ®Google Scholar
• Schwefel , H. P. 1981 . Numerical Optimization of Computer Models. Chichester, , UK : Wiley .
   Google Scholar
• Shamseldin , A. Y. 1997 . Application of neural network technique to rainfall—runoff modeling. . J. Hydrol. , 199 : 272 – 294 .
   Web of Science ®Google Scholar
• Shuttleworth , J. W. and Wallace , J. S. 1985 . Evaporation from sparse crops-an energy combination theory. . Quart. J. Roy. Met. Soc. , 111 : 839 – 855 .
   Web of Science ®Google Scholar
• Shuttleworth , J. W. 1991 . “ Evaporation models in hydrology ” . In Land Surface Evaporation Edited by: Schmugge , T. J. and André , J. 93 – 120 . New York, , USA : Springer-Verlag .
   Google Scholar
• Silva, S., 2005 GPLAB—a genetic programming toolbox for MATLAB. http://gplab.sourceforge.net.
   Google Scholar
• Singh , V. P. 1989 . Hydrologic Systems: Watershed Modeling. Vol. II , New Jersey, , USA : Prentice-Hall .
   Google Scholar
• Stephens , J. C. and Stewart , E. H. 1963 . “ A comparison of procedures for computing evaporation and evapotranspiration ” . In Publication 62, International Association of Scientific Hydrology 123 – 133 . Berkeley, California, , USA : International Union of Geodesy and Geophysics .
   Google Scholar
• Souch , C. , Wolfe , C. P. and Grimmond , C. S. B. 1996 . Wetland evaporation and energy partitioning: Indiana Dunes National Lakeshore. . J. Hydrol. , 184 : 189 – 208 .
   Web of Science ®Google Scholar
• Sudheer , K. P. , Gosain , A. K. and Ramasastri , K. P. 2003 . Estimating actual evapotranspiration from limited climatic data using neural computing technique. . J. Irrig. Drain. Engng , 129 ( 3 ) : 214 – 218 .
   Web of Science ®Google Scholar
• Temesgen , B. , Eching , S. , Davidoff , B. Z. and Frame , K. 2005 . Comparison of some reference evapotranspiration equations for California. . J. Irrig. Drain. Engng , 131 ( 1 ) : 73 – 84 .
   Web of Science ®Google Scholar
• Tracy , J. C. , Mariño , M. A. and Taghavi , S. A. 1992 . “ Predicting water demand in agricultural regions using time series forecasts of reference crop evapotrasnpiration ” . In Water Resources Planning and Management: Saving a Threatened Resource-In Search of Solutions Edited by: Karamouz , M. 50 – 55 . New York, , USA : ASCE .
   Google Scholar
• Trajkovic , S. , Todorovic , B. and Stankovic , M. 2003 . Forecasting of reference evapotranspiration by artificial neural networks. . J. Irrig. Drain. Engng , 129 ( 6 ) : 454 – 457 .
   Web of Science ®Google Scholar
• Whigham , P. A. and Crapper , P. F. 2001 . Modelling rainfall—runoff using genetic programming. . Math. Comput. Modell. , 33 : 707 – 721 .
   Web of Science ®Google Scholar
• Wang , J. , Salvucci , G. D. and Bras , R. L. 2004 . An extremum principle of evaporation. . Water Resour. Res. , 40 : W09303 doi:10.1029/2004WR003087.
   Web of Science ®Google Scholar
• Zhang , M. , Fulcher , J. and Scofield , R. A. 1997 . Rainfall estimation using artificial neural network group. . Neurocomputing , 16 : 97 – 115 .
   Web of Science ®Google Scholar