Linear, Nonlinear and Hybrid Response of Potential Evaporation to Meteorological Variables (Case Study: Tabriz Synoptic Station)

آب و خاک

Article 12, Volume 34, Issue 5 - Serial Number 73, November 2020, Pages 1175-1188   

Document Type: Research Article

DOI: 10.22067/jsw.v34i5.82703

Author

H. Mirhashemi^*

Assistant Professor in Climatology, Geography Sciences Department, Lorestan University, Khorammabad, Iran

Abstract

Introduction: Potential evaporation is the result of the combined effects of several meteorological elements, including air temperature, relative humidity (or vapor pressure for saturation), wind speed, sunshine hours and air pressure. The amount of potential evaporation depends on how these variables interact in each climate region. Potential evaporation response of each of these variables depends on the importance that variable plays in the environment. For example, in windy places, the importance of wind speeds in the potential evaporation rate increases relative to places with calm air. By changing each of these meteorological elements, while the rest of the elements react to the given change, the overall effect of these changes and reactions is reflected in the amount of potential evaporation. It is therefore obvious that the potential evaporation response to meteorological variables due to spatial and time variations of these variables is of a complex nature.
Materials and Methods: For this study, monthly data of air temperature, air pressure at sea level, wind speed, relative humidity and sunshine hours were used as independent variables and monthly data of evaporation pan at Tabriz Synoptic Station as response or dependent variable. In this study, firstly, the nonlinear and linear relationship between meteorological elements and potential evaporation were identified through Generalized Additive Model (GAM), MARSplines Model, and Generalized Linear Model (GLM), respectively. In the next step, by applying the simplex algorithm on the MARSplines model, the evaporation response gradient levels were determined individually for the meteorological variables. Also, to understand the process of pure evaporation response to each of these variables under different climatic conditions, first three weather conditions based on Tabriz Synoptic Station data were defined in three scenarios as S-1, S-2 and S-3. Then, by controlling and maintaining the meteorological variables under these three scenarios and combining the simplex algorithm with the MARSplines Model, the net evaporation reaction curves for the meteorological variables changes were evaluated.
Results and Discussion: The computational results show that in all combinations, the computational error of the GAM model is less than the GLM model. Also considering the significant variables in each model, the combination of temperature, pressure, wind speed and sunshine are considered as the best subset of the effective variables in the distribution of potential evaporation in both models. On the one hand, relative humidity in these two linear and nonlinear models, in combination with other variables, does not show a significant relationship with potential evaporation. The results of the graphs of Splin smoothing components of the GAM model show that the overall effect of temperature on the evaporation is incremental. But the unit amount of this effect increases with increasing temperature. The individual evaporation reaction against air temperature is similar to its combined reaction. It is thus clear that other meteorological variables do not play a significant role in the influence of air temperature on the evaporation gradient. The overall and hybrid effect of air pressure variations on the amount of evaporation is singular and decreasing. Instead, the individual effect of this variable on evaporation is very intense, decreasing, and partly linear. Therefore, the major influence of air pressure on evaporation in the environment is due to the performance of other variables that interfere with the relationship between these two variables. The evaporation hybrid response to wind velocity was also incremental, although the single and nonlinear evaporation response to wind velocity was not significant, but its tendency was to increase its slope with respect to wind velocity changes. Sunny hours also have a net effect on the amount of evaporation. However, the slope of the solitary effect of this variable, like wind speed, is more than its combined effect. Based on the GLM model results, except for relative humidity, the other variables have a significant linear effect on the potential evaporation. Evaporation response to changes in meteorological variables under S-1, S-2 and S-3 scenarios, while accurately determining the interaction of these variables in plotting absolute evaporation, implicitly implying the synergistic role of these variables in determining absolute evaporation. The lowest distance between the absolute values of evaporation under these three scenarios is related to air temperature, which implies less influence of air temperature than the other variables. That is, the effect of each of the meteorological variables on the amount of evaporation depends to a large extent on the relationship of this variable to other meteorological variables, if such a matter is less weighted for temperature.
Conclusion: The results of this study show that, except for air pressure, which has an increment-reducing effect on evaporation, other variables have only an incremental influence on evaporation and the intensity of this relationship has changed. This process has resulted in a nonlinear component in the relation of independent variables to evaporation. Since hybrid spline smoothing graphs determine evapotranspiration response to each of the predictor variables by eliminating the effect of other variables, therefore, consideration of the composition of these meteorological variables provides more accurate information on evaporation behavior against environmental changes. Through individually fitting evaporation against these meteorological elements, one cannot find how evaporation works against environmental changes. Comparing individual and combined evaporation responses to meteorological variables, while identifying the net effect of each of these variables, explains why evaporation responses within a given unit differ from changing meteorological variables over different times and locations.

Keywords

Evaporation; Hybrid model; Nonlinear; scenario; Simplex Algorithm; Smoothing component

References

1. 1- Aguado E., and Burt J.E. 2013. Understanding weather and climate. Pearson. 608.

   2- 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 56. Rome, Italy: Fao.

   3- Dehghani A.A., Piri M., Hesam M., and Dehghani N. 2012. Estimation of Daily Pan Evaporation By Using MLP,RBF and Recuurent Neural Networks. Journal of Water and Soil Conservation 17(2): 49-67. (In Persian with English abstract).

   4- Guisan A., Edwards Jr T.C., and Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157(2-3): 89-100.

   5- Gundalia Manoj J., and Dholakia Mrugen B. 2013. Dependence of evaporation on meteorological variables at daily time-scale and estimation of pan evaporation in Junagadh region. American Journal Eng Research 10(2): 354-362.

   6- Hastie T., and Tibshirani R. 1986. Generalized Additive Models. Statistical Science 1(3): 297-310.

   7- Hastie T., and Tibshirani R. 1987. Non-parametric logistic and proportional odds regression. Journal of the Royal Statistical Society: Series C (Applied Statistics) 36(3): 260-276.

   8- Isazadeh M., ahmadzadeh h., and Ghorbani M.A. 2016. Assessment of Kernel Functions Performance in River Flow Estimation using Support Vector Machine. Journal of Water and Soil Conservation 23(3): 69-89. (In Persian with English abstract)

   9- Jahanbakhsh S., Khorshiddoust A.M., Mirhashemi H., Khorrami H., and Tadayoni M. 2014. Trending Changes of the Reference Crop Water Requirement and its Associated Meteorological Variables in East Azerbaijan. Water and Soil 28(2): 296-306. (In Persian with English abstract)

   10- Kay A., and Davies H. 2008. Calculating potential evaporation from climate model data: A source of uncertainty for hydrological climate change impacts. Journal of Hydrology 358(3-4): 221-239.

   11- Khorshiddoust A.M., Jahanbakhsh Asl S., Mirhashemi H., and Farzin S. 2016. Modeling and Analysis of the Combined Effects of Meteorological Variables on Atmospheric Evaporative Potential Using Structural Equation (Case Study: Tabriz City). Water and Soil Science 25(4/2): 257-270. (In Persian with English abstract)

   12- Lutgens F.K., and Tarbuck E.J. 2016. Atmosphere, The: An Introduction to Meteorology. 13 ed.: Pearson.

   13- O'Neill R.t. 1971. Algorithm AS 47: function minimization using a simplex procedure. Journal of the Royal Statistical Society. Series C (Applied Statistics) 20(3): 338-345.

   14- Rahimikhoob A., and Mahmoodi A. 2012. Estimating Actual Evapotranspiration in a Catchment Using Artificial Neural Networks with Minimum Climatic Data Case Study: Emame Representative Catchment. Iran-Water Resources Research 7(4): 51-61. (In Persian with English abstract)

   15- Rakhecha P., and Singh V.P. 2009. Applied hydrometeorology. Springer Science & Business Media.

   16- Remenieras G. 1984. Principles of Engineering Hydrology. Vol. 1r, Iran: Ministry of Power. 400.

   17- Ren J., Li Q., Yu M., and Li H. 2012. Variation trends of meteorological variables and their impacts on potential evaporation in Hailar region. Water Science and Engineering 5(2): 137-144.

   18- Shadmani M., and Marofi S. 2011. Comparison of Some Methods for Estimation of Daily Pan Evaporation: Case Study in Kerman Region. Journal of Water and Soil Science 15(55): 69-84.(In Persian with English abstract)

   19- Shi H., Li T., and Wang G. 2017. Temporal and spatial variations of potential evaporation and the driving mechanism over Tibet during 1961–2001. Hydrological Sciences Journal 62(9): 1469-1482.

   20- Valizadeh Kamran H. 2014. Estimation of Potential Evapotranspiration with Estefnz Method and GIS Techniques in Eastern Azerbaijan. Geography and Planning 18(49): 317-334. (In Persian)

   21- Van Bavel C. 1966. Potential evaporation: the combination concept and its experimental verification. Water Resources Research 2(3): 455-467.

   22- Xu C.Y., and Singh V. 1998. Dependence of evaporation on meteorological variables at different time‐scales and intercomparison of estimation methods. Hydrological Processes 12(3): 429-442.

   23- Yan Z., Wang S., Ma D., Liu B., Lin H., and Li S. 2019. Meteorological Factors Affecting Pan Evaporation in the Haihe River Basin, China. Water 11(2): 317.