Disaggregation of air temperature by using fractal and periodic regression in two arid and semi-arid climate

Authors

1 Professor of Water Engineering Department, College of Agriculture, Ferdowsi University, Mashhad, Iran

2 M. Sc. Student of Water Engineering Department, College of Agriculture, Ferdowsi University, Mashhad, Iran

3 Assistant Professor of Water Engineering Department, College of Agriculture, Ferdowsi University, Mashhad, Iran

Abstract

Air temperature is one the most important variables required for environmental and agricultural studies which are not generally available with sufficient spatial and temporal resolution. Thus, the spatial and temporal disaggregation of properties of the catchment is essential for optimal management of the catchment. The common interpolation functions, including fractal, and regression can produce reasonable results. In this research the interpolation functions based on fractal and periodic regression models were used for modeling and disaggregating temperature datasets for the period of 2007- 2009 at Mashhad and 1980-1982 at Kerman Synoptic stations, respectively. At first, two produced daily temperature from daily datasets. Then data with 5-day and 10-day intervals were used to produce daily temperature. Second, we considered data to be missing at random, and then periodic regression and fractal interpolation were adopted to model daily temperature and then to generate 3-hours temperature. On general results showed similar trends in both climates, and 5-day intervals performed more acceptable, such that determination coefficient for Mashhad and Kerman was 0.98 - 0.77 and 0.98 - 0.82, respectively, while RMSE was between 1.52 - 5.81 and 1.19 - 5.48 anddeg;C, respectively. The intercepts and slopes of regression lines between measured and predicted temperatures were not statistically (5% level of significant) different from 0 and 1, respectively. On the overall, fractal interpolation was better than periodic regression.

Keywords

References

Barnsley, M. F. 1988. Fractal everywhere. New York: Academic Pres.567.
Bliss, C. I. 1970. Statistics in Biology. New York: McGraw-Hill Book Company.
Fischer, M. J., Paterson, A. W. 2014. Detecting trends that are nonlinear and asymmetric on diurnal and seasonal time scales. Clim. Dyn., 43: 361–374.
Holder, R. L. 1985. Multiple Regression in Hydrology. Institute of Hydrology Wallingford.
http://en.wikipedia.org/wiki/Kerman.
http://en.wikipedia.org/wiki/Mashhad.
Kolehmainen, M., Martikainen, H., Ruuskanen, J. 2001. Neural networks and periodic components used in air quality forecasting. Atmos. Environ., 35: 815-825.
Li, X. F., Li, X. F. 2008. An explicit fractal interpolation algorithm for reconstruction of seismic data. Chin. Phys. Lett., 25(3): 1157-1168.
Little, T. M., Hills, F. J. 1978. Agricultural Experimentation Design and Analysis. NewYork: John Wiley and Sons, Inc.
Marko, J., Nikolić, E. 1982. Characteristics of yield de velopment of the main field crops in SAP of Vojvodina. Contemp. Agric., 30(2): 87-98.
Mazel, D. S., Hayes, M. H. 1992. Using iterated function systems to model discrete sequences. IEEE Trans. Sig. Proc., 40(7): 1724-1734.
Pathirana, A. 2001. Fractal modeling of rainfall: Disaggregation in time and space for hydrological applications. Ph. D. thesis, University of Tokyo, Japan.
Prudhomme, G., Reed, D. W. 1998. Relationships between extreme daily precipitation and topography in the Mounainous region. A case study in Scotland. Int. J. Climatol., 18: 1439-1453.
Puente, C. E. 1995. Geometric modeling of rainfall fields. Water Resources Center Technical Completion Report W-804. University of California, Davis.
Strahle, W. C. 1991. Turbulent combustion data analysis using fractals. AIAA J., 29(3): 409-417.

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