Analysis of the spatial distribution of snow water equivalent in the watersheds of West Azerbaijan

Document Type : Research Article


1 Ph.D. in water structures, expert in surface water studies, West Azerbaijan Regional Water Company, Urmia, Iran

2 Ph.D. in irrigation and drainage, director of basic studies of water resources, West Azerbaijan Regional Water Company, Urmia, Iran

3 3. Assistant Professor, Agricultural Engineering Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran



A significant part of the precipitation in the upstream areas of the watersheds of West Azerbaijan province is in the form of snow, especially in the cold months of the year. Analyzing the data of these sources is very important for the optimal management of reservoir dams in the region, especially in spring, which is the season of snow melting in the respective basins. In common traditional methods for estimating water equivalent of snow storage, the height gradient relationship (linear regression relationship between water equivalent of snow and the height of snow measuring points from the surface of the oceans) is used, which sometimes due to lack of proper fit, some points are removed from the calculations. In this study, for the data of 65 snow measuring stations in snowy months of the water year 2020-2021, instead of the relation of altitude gradient, the four-variable regression of water equivalent to altitude, longitude and latitude (in the UTM coordinate system) was used. It improved the regression characteristics (an increase of more than 3 times the A.R.S., i.e. the square of the adjusted correlation coefficient, or the coefficient of determination, and a decrease of more than 10,000 times the significance level, i.e. the alpha of the correlation coefficient). Also, in order to further investigate, the simulation of relationships with artificial neural networks (selected network: three-layer perceptron with 3, 8 and 1 neurons in the input, middle and output layers) was used; So that the correlation coefficient of the estimated data with the available observations for the selected artificial neural network model was 0.97.


Main Subjects

[1]. Motamedi A, Sedghi H. Snow hydrology. Arkan Publications, 2013 [Persian].
[2]. Panahi M, Helali J, Moosavi SA, Kabiri Sh. Principles of snow hydrology. Academic Jihad Publications, 2017 [Persian].
[3]. Rasouli, A., Adhami, S. Estimation of Snow Water Equivalent by Processing of MODIS Satellite Imageries. Geography and Development, 2007; 5(10): 23-36 [Persian].
[4]. Jahanbakhsh asl S, Sari Sarraf B, Raziei T, Parandeh khouzani A. An investigation on the spatio-temporal variability of snow season and its start and end dates in the mountainous region of Zagros. Watershed Engineering and Management, 2020; 12(1): 86-106 [Persian].
[5]. Marofi S, Tabari H, Abyaneh HZ. Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water resources management, 2011; 25(5):1417-35.
[6]. Tabari H, Marofi S, Zare Abyaneh H, Sharifi MR. Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Computing and Applications, 2010; 19(4):625-35.
[7]. Sedighi F, Vafakhah M, Javadi MR. Application of artificial neural network for snowmelt-runoff (Case study: Latyan Dam Watershed). Journal of Watershed Management Research, 2016; 6(12):43-54 [Persian].
[8]. Ebdam S, Fathzadeh A, Taghizadeh MR, Mahjoobi J. Digital Mapping of Snow Water Equivalent Using an Artificial Neural Network and Geomorphometric Parameters (Case Study: Sakhvid Watershed, Yazd). Journal of Watershed Management Research, 2016; 7(13):138-149 [Persian].
[9]. Zare Abyaneh H. Estimation of the spatial distribution of snow water equivalent height and snow density in watersheds of West Azarbijan province. Journal of Water Resources Engineering, 2012; 5:1-12 [Persian].
[10]. Rezaei A.M., Mohammadi Meybodi A.M. Statistics and Probabilities, Academic Jihad Publications of Isfahan Industrial Unit, 2010-2011 [Persian]
[11]. Neurosolution software User's Guide, Version  6.01. Neuro Dimension, Inc., USA, 2010.
[12]. Hosseini SA, Mesgari A, Salari Fonoodi MR. Artificial neural networks in hydrology and meteorology. Azarkalk publications, 2016 [Persian].