Evaluation of combined models Efficiency for improving the evaporation modeling

Authors

1 Assistant professor, Department of Water Engineering, Razi University, Kermanshah

2 . Assist. Prof. Watershed management Department, Agricultural faculty, University of Gonbad Kavoos, Gonbad, Iran

3 Watershed management Department, Agricultural faculty, University of Gonbad Kavoos, Gonbad, Iran

Abstract

In this research by combining individual models outputs, the strengths of each single model are used to make a new model that has better performance than each single model. In this study the efficiency of nonparametric K nearest neighbor and BMA model were compared with BGA , GRA,  AICA, BICA, equal weights averaging and lasso methods. For this purpose, the evaporation rate from the water level was simulated at three stations of Marwa Tappeh, Gonbad and Gorgan, using the individual models of the US Land Development Organization, Tchymyorov, Ivanov, Hanfer, Shahten, Marciano and Meyer. Then each combination of models was implemented to combine the outputs of each single model. The results showed that during the calibration and validation period, the best performance was related to the experimental model of the US Land Development Organization. The best performance of the combination models for the calibration period and validation period at the Marah-e-Tappeh station in GRA in Gonbad and Gorgan stations. Regarding the error indices, the best performance of calibration period of GRA method and validation data is related to the GRA, BICA and AICA methods and the worst the function is related to the EWA method. For the validation period, the nearest KNN neighbor method has a better performance than other combination methods.  Bayesian average results showed that in all three stations, in the case of gamma distribution, different modes of its variance modeling were similar and in all stations of normal distribution modes, they had better performance than gamma distribution modes, as well as the range of uncertainty in normal distribution mode, it was smaller than gamma distribution. On the other hand, in the estimation of the point, Bayesian average modification with normal distribution after gra method performed better than other combination models.

Keywords


Arsenault, R., P. Gatien, B. Renaud, F. Brissette, and J. L.  Martel. 2015. A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation. Journal of Hydrology, 529, 754-767.
   Buckland, S. T., K. P. Burnham, and N. H.  Augustin. 1997. Model selection: an integral part of inference. Biometrics, 603-618.
   Burnham, K. P. and D. R. Anderson. 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological methods & research, 33(2), 261-304.
   Chen, Y., W. Yuan, J. Xia, J. B. Fisher, W. Dong, X. Zhang and J. Feng. 2015. Using Bayesian model averaging to estimate terrestrial evapotranspiration in China. Journal of Hydrology, 528, 537-549.
   Duan, Q., and T. J. Phillips. 2010. Bayesian estimation of local signal and noise in multimodel simulations of climate change. Journal of Geophysical Research: Atmospheres, 115(D18)., D18123. http://dx.doi.org/10.1029/2009JD013654.
   Duan, Q., N. K. Ajami, X. GAO, and S. Sorooshian.2007. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Advances in Water Resources, 30(5), 1371-1386.
   Diks, C. G., and J. A. Vrugt. 2010. Comparison of point forecast accuracy of model averaging methods in hydrologic applications. Stochastic Environmental Research and Risk Assessment, 24(6), 809-820.
   Draper, D.­1995. Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 45-70.
   Doane, D. P., and L. W. Seward. 2011. Applied statistics in business and economics. New York, NY: McGraw-Hill/Irwin,.
   Eamus, D.­2003. How does ecosystem water balance affect net primary productivity of woody ecosystems? Functional Plant Biology, 30(2), 187-205. http://dx.doi.org/10.1071/FP02084.
   Fernandez, C., E. Ley, and M. F. Steel. 2001. Model uncertainty in cross‐country growth regressions. Journal of applied Econometrics, 16(5), 563-576.
   Field, A. 2013. Discovering statistics using IBM SPSS statistics. 4th edition. Sage, London, 856 pp.
  Granger, C. W., and R. Ramanathan. 1984. Improved methods of combining forecasts. Journal of forecasting, 3(2), 197-204.
  Hagedorn, R., Doblas-Reyes, F. J., and T. N.  Palmer. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus A: Dynamic Meteorology and Oceanography, 57(3), 219-233.
  Hoeting, J. A., D. Madigan, A. E. Raftery,and C. T. Volinsky.1999. Bayesian model averaging: a tutorial. Statistical science, 382-401.
   Jimenez, C., C. Prigent, B. Mueller, S. I. Seneviratne, M. F. McCabe, E. F. Wood, and J. B. Fisher. 2011. Global intercomparison of 12 land surface heat flux estimates. Journal of Geophysical Research: Atmospheres, 116(D2).
  Karamouz, M., and S. H.  Araghinejad. 2005. Advanced hydrology. Amirkabir University of Technology.
  Keane, R. E., K. C. Ryan, T. T. Veblen, C. D. Allen, J. Logan, and B. Hawkes. 2002. Cascading effects of fire exclusion in Rocky Mountain ecosystems: a literature review. The Bark Beetles, Fuels, and Fire Bibliography, 52.
                                                     
  Kisi, O. 2015. An innovative method for trend analysis of monthly pan evaporations. Journal of Hydrology, 527, 1123-1129.
  Li, W., and A. Sankarasubramanian. 2012. Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination. Water Resources Research, 48(12).
 
   Li, Y., H. E. Andersen, and R. McGaughey. 2008. A comparison of statistical methods for estimating forest biomass from light detection and ranging data. Western Journal of Applied Forestry, 23(4), 223-231.
  Najafi, M. R., H. Moradkhani, and I. W. Jung. 2011. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrological processes, 25(18), 2814-2826.
http://dx.doi.org/10.1002/hyp.8043.
   Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review, 133(5), 1155-1174.
  Raupach, M. R. 2001. Combination theory and equilibrium evaporation. Quarterly Journal of the Royal Meteorological Society, 127(574), 1149-1181.
 http://dx.doi.org/10.1002/qj.49712757402.
  Singh, H., and A. Sankarasubramanian. 2014. Systematic uncertainty reduction strategi