Daily Pan Evaporation Modeling in a Hot and Dry Climate
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VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 14, Issue 8
Abstract
Evaporation plays a key role in water resources management in arid and semiarid climatic regions. This is the first time that an artificial neural network (ANN) model is applied to estimate evaporation in a hot and dry region (BWh climate by the Köppen classification). It has been found that ANN works very well at the study site and, further, an integrated ANN and autoregressive with exogeneous inputs can have an improved performance over the traditional ANN. Both models significantly outperformed the two empirical methods. It has been demonstrated that the important weather factors to be included in the model inputs are wind speed, saturation vapor pressure deficit, and relative humidity. This result is different from all those reported in the literature and is interestingly linked with a 1936 study by Anderson, who emphasized the importance of saturation vapor pressure deficit. As evaporation is a nonlinear dynamic process, the selection of suitable input weather variables has been a complicated and time-consuming task for modelers. In this study, a new data analysis tool called the gamma test has been used to identify the best combination of model inputs prior to model construction and evaluation.
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References
Agalbjörn, S., Končar, N., and Jones, A. J. (1997). “A note on the gamma test.” Neural Comput. Appl., 5(3), 131–133.
Alizadeh, A. (2004). Principles of applied hydrology, Imam Reza Univ. (in Persian).
Anderson, D. B. (1936). “Relative humidity or vapor pressure deficit.” Ecology, 17(2), 277–282.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (Rao Govindaraju). (2000a). “Artificial neural networks in hydrology. II: Hydrologic applications. J. Hydrol. Eng., 5(2), 124–137.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (Rao Govindaraju). (2000b). “Artificial neural networks in hydrology. II: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
Blanken, P. D., et al. (2000). “Eddy covariance measurements of evaporation from Great Slave Lake, Northwest Territories, Canada.” Water Resour. Res., 36(4), 1069–1077.
Blodgett, T. A., Lenters, J. D., and Isacks, B. L. (1997). “Constraints on the origin of paleolake expansions in the Central Andes.” Earth Interact., 1.
Bray, M., and Han, D. (2004). “Identification of support vector machines for runoff modeling.” J. Hydroinform., 6(4), 265–280.
Cahoon, J. E., Costello, T. A., and Ferguson, J. A. (1991). “Estimating pan evaporation using limited meteorological observations.” Agric. Forest Meteorol., 55, 181–190.
Christiansen, J. E. (1968). “Pan evaporation and evapotranspiration from climatic data.” J. Irrig. and Drain. Div., 94, 243–265.
Chuzhanova, N. A., Jones, A. J., and Margetts, S. (1998). “Feature selection for genetic sequence classification.” Bioinformatics, 14(2), 139–143.
Clair, T. A., and Ehrman, J. M. (1998). “Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers.” Water Resour. Res., 34(3), 447–455.
Dalton, J. (1802). “Experiment essays on the constitution of mixed gases; on the force of steam of vapor from waters and other liquids in different temperatures, both in a torricellian vacuum and in air; on evaporation; and on the expansion of gases by heat.” Mem. Manch. Lit. Philos. Soc., 5, 535–602.
De Oliveira, A. G. (1999). “Synchronisation of chaos and applications to secure communications.” Ph.D. thesis, Dept. of Computing, Imperial College of Science, Technology and Medicine, Univ. of London, London.
Durrant, P. J. (2001). “winGamma: A non-linear data analysis and modelling tool with applications to flood prediction.” Ph.D. thesis, Dept. of Computer Science, Cardiff Univ., Wales, U.K.
Elshorbagy, A., Simonovic, S. P., and Panu, U. S. (2000). “Performance evaluation of artificial neural networks for runoff prediction.” J. Hydrol. Eng., 5(4), 424–427.
Evans, D., and Jones, A. J. (2002). “A proof of the gamma test.” Proc. R. Soc. London, Ser. A, 458(2027), 2759–2799.
Fernando, D. A., and Jayawardena, A. W. (1988). “Runoff forecasting using RBF network with OLS algorithm.” J. Hydrol. Eng., 3(3), 203–209.
Fine, T. L. (1999). Feedforward neural network methodology (Statistics for engineering and information science), Springer, New York.
Govindaraju, R. S. (2000). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
Hammond, M., and Han, D. (2006). “Issues of using digital maps for catchment delineation.” Proc., ICE, Water Management, 159(1), 45–51.
Hammond, M., and Han, D. (2008). “Quantization analysis of weather radar data with synthetic rainfall.” Stochastic Environ. Res. Risk Assess., 22(3), 1436–3259.
Han, D., and Bray, M. (2006). “Automated Thiessen polygon generation.” Water Resour. Res., 42.
Han, D., Chan, L., and Zhu, N. (2007a). “Flood forecasting using support vector machines.” J. Hydroinform., 9(4), 267–276.
Han, D., Cluckie, I. D., Griffith, R. J., and Austin, G. (2000). “Rainfall measurements for urban catchments using weather radars.” J. Urban Technol., 7(1), 85–102.
Han, D., Cluckie, I. D., Karbassioun, D., Lawry, J., and Krauskopf, B. (2002). “River flow modelling using fuzzy decision trees.” Water Resour. Manage., 16(6), 431–445.
Han, D., Kwong, T., and Li, S. (2007b). “Uncertainties in real-time flood forecasting with neural networks.” Hydrolog. Process., 21, 223–228.
Han, H., and Felker, P. (1997). “Estimation of daily soil water evaporation using an artificial neural network.” J. Arid Environ., 37, 251–260.
Haykin, S. (1999). Neural networks—A comprehensive foundation, Prentice-Hall, Englewood Cliffs, N.J.
Hostetler, S. W., and Bartlein, P. J. (1990). “Simulation of lake evaporation with application to modeling lake level variations of Harney–Malheur Lake, Oregon.” Water Resour. Res., 26(10), 2603–2612.
Hsu, K., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 159–169.
Ikebuchi, S., Seki, M., and Ohtoh, A. (1988). “Evaporation from Lake Biwa.” J. Hydrol., 102, 427–444.
Imrie, C. E., Durucan, S., and Korre, A. (2000). “River flow prediction using neural networks: Generalization beyond the calibration range.” J. Hydrol. Eng., 233(3–4), 138–154.
Jones, A. J., Tsui, A., and De Oliveira, A. G. (2002). “Neural models of arbitrary chaotic systems: Construction and the role of time delayed feedback in control and synchronization.” Complexity International, 9.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.” J. Comput. Civ. Eng., 8(2), 201–220.
Keshavarz, E., and Roopaei, M. (2006). “Intelligent structures in economical forecasting.” Proc., Int. Conf. on Advanced Technologies in Telecommunications and Control Engineering (ATTCE).
Keskin, M. E., and Terzi, O. (2006). “Artificial neural network models of daily pan evaporation.” J. Hydrol. Eng., 11(1), 65–70.
Keskin, M. E., Terzi, O., and Taylan, D. (2004). “Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey.” Hydrol. Sci. J., 49(6), 1001–1010.
Kisi, O. (2005). “Discussion of ‘Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey.’” Hydrol. Sci. J., 50(4).
Kisi, O. (2006). “Daily pan evaporation modeling using a neuro-fuzzy computing technique.” J. Hydrol., 329.
Končar, N. (1997). “Optimisation methodologies for direct inverse neurocontrol.” Ph.D. thesis, Dept. of Computing, Imperial College of Science, Technology and Medicine, Univ. of London, London.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F. (2006). “World map of the Köppen-Geiger climate classification updated.” Meteorol. Z., 15, 259–263.
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., and Pruitt, W. O. (2002). “Estimating evapotranspiration using artificial neural network.” J. Irrig. Drain. Eng., 128(4), 224–233.
Laird, N. F., and Kristovich, D. A. R. (2002). “Variations of sensible and latent heat fluxes from a great lakes buoy and associated synoptic weather patterns.” J. Hydrometeor., 3(1), 3–12.
Linacre, E. T. (1977). “A simple formula for estimating evaporation rates in various climates, using temperature data alone.” Agric. Forest Meteorol., 18, 409–424.
Linacre, E. T. (1994). “Estimating U.S. Class A pan evaporation from few climate data.” Water Int., 19, 5–14.
Liong, S. Y., Khu, S. T., and Chan, W. T. (2001). “Derivation of pareto front with genetic algorithm and neural network.” J. Hydrol. Eng., 6(1), 52–61.
Ljung, L. (1999). System identification. Theory for the user, Prentice-Hall PTR, Englewood Cliffs, N.J.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environ. Modell. Software, 15, 101–124.
McCuen, R. H. (1998). Hydrologic analysis and design, Prentice-Hall, Englewood Cliffs, N.J.
McCulloch, W. S., and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity.” Bull. Math. Biophys., 5, 115–133.
Mosner, M. S., and Aulenbach, B. T. (2003). “Comparison of methods used to estimate lake evaporation for a water budget of Lake Seminole, southwestern Georgia and northwestern Florida.” Proc., 2003 Georgia Water Resources Conf.
Penman, H. L. (1948). “Natural evapotranspiration from open water, bare soil and grass.” Proc. R. Soc. London, Ser. A, 193, 120–145.
Remesan, R., Shamim, M. A., and Han, D. (2008). “Model data selection using gamma test for daily solar radiation estimation.” Hydrol. Process., in press.
Rohwer, C. (1931). “Evaporation from free water surfaces.” U.S. Dept. of Agriculture Technical Bulletin No. 271, Washington, D.C.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by back propagation, D. E. Rumelhart, J. L. McClelland, and PDP Research Group, eds., MIT, Cambridge, Mass., 316–382.
Smith, M. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
Sudheer, K. P., Gosain, A. K., Rangan, D. M., and Saheb, S. M. (2002). “Modelling evaporation using an artificial neural network algorithm.” Hydrolog. Process., 16, 3189–3202.
Tan, S. B. K., Shuy, E. B., and Chua, L. H. C. (2007). “Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate.” Hydrolog. Process., 21(486–499).
Terzi, O., and Keskin, M. E. (2005). “Modelling of daily pan evaporation.” J. Appl. Sci., 5(2), 368–372.
Terzi, O., Keskin, M. E., and Taylan, E. D. (2006). “Estimating evaporation using ANFIS.” J. Irrig. Drain. Eng., 132(5), 503–507.
Tokar, A. S., and Johnson, P.A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 4(3), 232–239.
Tsui, A. P. M. (1999). “Smooth data modelling and stimulus-response via stabilisation of neural chaos.” Ph.D. thesis, Dept. of Computing, Imperial College of Science, Technology and Medicine, Univ. of London, London.
Tsui, A. P. M., Jones, A. J., and De Oliveira, A. G. (2002). “The construction of smooth models using irregular embeddings determined by a gamma test analysis.” Neural Comput. Appl., 10(4), 318–329.
Van Zyl, W. H., De Jager, J. M., and Maree, C. J. (1989). “The relationship between daylight evaporation from short vegetation and the USWB Class A pan.” Agric. Forest Meteorol., 46, 107–118.
Wang, Y. C., Han, D., Yu, P. S., and Cluckie, I. D. (2006). “Comparative modeling of two catchments in Taiwan and England.” Hydrolog. Process., 4335–4349.
Warnaka, K., and Pochop, L. (1988). “Analyses of equations for free water evaporation estimates.” Water Resour. Res., 24(7), 979–984.
Winter, T. C. (1981). “Uncertainties in estimating the water balance of lakes.” Water Resour. Bull., 17(1), 82–115.
Yu, Y. S., and Knapp, H. V. (1985). “Weekly, monthly, and annual evaporations for Elk City Lake.” J. Hydrol., 80, 93–110.
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Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 8 • August 2009
Pages: 803 - 811
Copyright
© 2009 ASCE.
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Received: May 12, 2008
Accepted: Nov 27, 2008
Published online: Feb 12, 2009
Published in print: Aug 2009
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