Infilling Missing Daily Evapotranspiration Data Using Neural Networks
Publication: Journal of Irrigation and Drainage Engineering
Volume 136, Issue 5
Abstract
This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN and readily available weather data where the ET observations exist before and after the gap is a reliable and convenient method. It could be used to obtain continuous ET data for modeling and water management practices.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
Funding for ET measurements was provided in part by New Mexico Office of the State Engineer/Interstate Stream Commission, the United States Bureau of Reclamation, and Unites States Fish and Wildlife—Bosque del Apache National Wildlife Refuge. Our gratitude to Dr. Lloyd Gay (in his memory), Dr. Nabil Shafike, Mr. Steve Hanson, Mr. Steve Bowser, Mr. Brent Tanzy, Mr. John Taylor (in his memory), and Ms. Gina Dello Russo and the NMSU Civil Engineering students for their invaluable contribution to the saltcedar ET project. Our gratitude to anonymous reviewers whose comments greatly improved the quality of the paper.
References
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration: Guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper No. 56, Food and Agricultural Organization of the United Nations, Rome.
Allen, R. G., Walter, I. A., Elliott, R., Howell, T., Itenfisu, D., Jensen, M. E., and Snyder, R. L. (2005). The ASCE standardized reference evapotranspiration equation, ASCE, Reston, Va.
Bawazir, A. S. (2000). “Saltcedar and cottonwood riparian evapotranspiration in the Middle Rio Grande.” Ph.D. dissertation, New Mexico State Univ., Las Cruces, N.M.
Bawazir, A. S., King, J. P., Kidambi, S., Tanzy. B., Nibling, F., Stowe, N. H., and Fahl, M. J. (2006). “A joint investigation of evapotranspiration depletion of treated and non-treated saltcedar at the Elephant Butte Delta, New Mexico.” WRRI Technical Completion Rep. No. 328, New Mexico Water Resources Research Institute, Las Cruces, N.M.
Bawazir, A. S., Samani, Z., Bleiweess, M., Skaggs, R., and Schmugge, T. (2009). “Using ASTER satellite data calculate riparian evapotranspiration in the Middle Rio Grande, New Mexico.” Int. J. Remote Sens., 30(21), 5593–5603.
Bhattacharya, B., Shrestha, D. L., and Solomatine, D. P. (2003). “Neural networks in reconstructing missing wave data in sedimentation modeling.” Water engineering and research in a learning society; Modern developments and traditional concepts (CD-ROM), J. Ganoulis and P. Prinos, eds., Proc., XXXth IAHR Congress, August 2003, International Association of Hydraulic Engineering and Research, Thessaloniki, Greece.
Blaney, H. F., and Criddle, W. D. (1950). “Determining water requirements in irrigated areas from climatological irrigation data.” Tech. Paper 96, U.S. Soil Conservation Service, Washington, D.C.
Coulibaly, P., Anctil, F., and Bobée, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” J. Hydrol., 230(3–4), 244–257.
Coulibaly, P., and Evora, N. D. (2007). “Comparison of neural network methods for infilling missing daily weather records.” J. Hydrol., 341(1–2), 27–41.
Devitt, D., Sala, A., Smith, S., Cleverly, J., Shaulis, L., and Hammet, R. (1998). “Bowen ratio estimates of evapotranspiration for Tamarix ramosissima stands on the Virgin River in southern Nevada.” Water Resour. Res., 34, 2407–2414.
Dibike, Y. B., and Solomatine, D. P. (2001). “River flow forecasting using artificial neural networks.” Phys. Chem. Earth, Part B, 26(1), 1–7.
Elshorbagy, A., Simonovic, S. P., and Panu, U. S. (2002). “Estimation of missing streamflow data using principles of chaos theory.” J. Hydrol., 255, 123–133.
Elshorbagy, A. A., Panu, U. S., and Siminovic, S. P. (2000). “Group-based estimation of missing hydrological data. I: Approach and general methodology.” J. Hydrol. Sci., 45(6), 849–866.
Gatewood, J. S., Robinson, T. W., Colby, B. R., Hem, J. D., and Halfpenny, L. C. (1950). “Use of water by bottom-land vegetation in lower Safford Valley, Arizona.” U.S. Geological Survey Water-supply paper 1103, Washington, D.C., 1–210.
Gay, L. W., and Hartman, R. K. (1982). “ET measurements over riparian saltcedar of the Colorado River.” Hydrol. Water Resour. Ariz. Southwest, 12, 9–15.
Hargreaves, G. H., and Samani, Z. A. (1982). “Estimating potential evapotranspiration.” J. Irrig. Drain. Eng., 108(3), 225–230.
Ilunga, M., and Stephenson, D. (2005). “Infilling streamflow data using feed-forward back-propagation (BP) artificial neural networks: Application of standard BP and pseudo MacLaurin power series BP techniques.” Water SA, 31(2), 171–176.
Jensen, M. D., Burman, R. D., and Allen, R. G. (1990). “Evapotranspiration and irrigation water requirements.” ASCE manuals and reports on engineering practice, Vol. 70, ASCE, Reston, Va.
Khalil, M., Panu, U. S., and Lennox, W. C. (2001). “Group and neural networks based streamflow data infilling procedures.” J. Hydrol., 241(3–4), 153–176.
Kim, T., and Valdes, J. B. (2003). “Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks.” J. Hydrol. Eng., 8(6), 319–328.
Kişi, O. (2007). “Evapotranspiration modeling from climatic data using a neural computing technique.” Hydrolog. Process., 21(14) 1925–1934.
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.
Montieth, J. L. (1965). “Evaporation and environment.” The state and movement of water in living organisms, G. E. Fogg, ed., Symp. Soc. Exp. Biol., Vol. 19, Cambridge University Press, Cambridge, U.K., 205–234.
NeuroSolutions getting started manual, version 5. (2009). NeuroDimension, Inc., Gainesville, Fla., ⟨http://www.neurosolutions.com/downloads/documentation.html⟩ (Jan. 12, 2009).
Parasuraman, K., Elshorbagy, A., and Carey, S. K. (2006). “Spiking modular neural networks: A neural network modeling approach for hydrological processes.” Water Resour. Res., 42(5), posted online ahead of print Feb. 12, 2007, ⟨http://www.agu.org/pubs/crossref/2006/2005WR004317.shtml⟩.
Parasuraman, K., Elshorbagy, A., and Carey, S. K. (2007). “Modelling the dynamics of the evapotranspiration process using genetic programming.” Hydrol. Sci. J., 52(3), 563–578.
Penman, H. L. (1948). “Natural evaporation from open water, bare soil and grass.” Proc. R. Soc. London, Ser. A, 193, 120–145.
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.
Robinson, T. W. (1965). “Introduction, spread and areal extent of saltcedar (Tamarix) in the western states.” U.S. Geol. Surv. Prof. Pap., 491-A, 1–12.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2003). “Estimating actual evapotranspiration from limited climatic data using neural computing technique.” J. Irrig. Drain. Eng., 129(3), 214–218.
Tokar, A. S., and Markus, M. (2000). “Precipitation-runoff modeling using artificial neural network and conceptual models.” J. Hydrol. Eng., 5(2), 156–161.
Trajkovic, S., Todorovic, B., and Stankovic, M. (2003). “Forecasting of reference evapotranspiration by artificial neural networks.” J. Irrig. Drain. Eng., 129(6), 454–457.
van Hylckama, T. E. A. (1974). “Water use by saltcedar as measured by the water budget method.” U.S. Geol. Surv. Prof. Pap., 491-E, 1–30.
Weeks, E. P., Weaver, H. L., Campbell, G. S., and Tanner, B. D. (1988). “Water use by saltcedar and by replacement vegetation in the Pecos River floodplain between Acme and Artesia, New Mexico.” U.S. Geol. Surv. Prof. Pap., 491-G, 1–33.
Zanetti, S. S., Sousa, E. F., Oliveira, V. P. S., Almeida, F. T., and Bernardo, S. (2007). “Estimating evapotranspiration using artificial neural network and minimum climatological data.” J. Irrig. Drain. Eng., 133(2), 83–89.
Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). “Forecasting with artificial neural networks: The state of the art.” Int. J. Forecast., 14, 35–62.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 136 • Issue 5 • May 2010
Pages: 317 - 325
Copyright
© 2010 ASCE.
History
Received: Oct 16, 2008
Accepted: Nov 4, 2009
Published online: Nov 6, 2009
Published in print: May 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.