Spatial distribution of snow water equivalent modeling based on topography and climatic factors (Case Study: Sohravard watershed, Zanjan Province)

Document Type : Research Article


1 PhD Student, Faculty of Natural Resources, Tarbiat Modarres University

2 Associate Professor, Faculty of Natural Resources, Tarbiat Modarres University

3 Associate Professor, College of Agriculture & Natural Resources, Ardakan University

4 Associate Professor, Department of Watershed Management Engineering, Lorestan University


It is inevitable to obtain necessary data including snow depth, snow density and snow water equivalent (SWE) in order to manage water resources in mountains areas. On the other hand, due to financial constraints, unfair weather and impassability of mountainous areas, measurement is limited to the points, and its generalization to larger areas is associated with large errors. A method for predicting the SWE is investigation the relationship between the SWE and effective factors. Therefore, in this research, mountainous Sohravard watershed located in Zanjan Province was selected as the case study. The required data and maps including Digital Elevation Model (DEM), slope, aspect, northern, eastern, profile curvature, plan curvature, topography position index and solar radiation maps were extracted. Then, during the peak of snowfall in the area, snow depth of 150 points and snow density of 18 points were measured using Latin Hypercube and random sampling methods, respectively. The calculation of upwind slope was carried out for the measured snow points. In the next step, the quantitative relation between the SWE and effective factors was determined by fitting a regression relationship. The efficiency of the created models was evaluated by statistical criteria including mean bias error, mean absolute error, root mean square error and correlation coefficient(R). The results showed that SWE in the studied watershed could be estimated by using stepwise regression.  As the results show, although climate factor of upwind slope requires high computing, its incorporation in the model can lead to increased model efficiency in the SWE estimation. The SWE had the highest significant correlation equal to 0.607 with the elevation, and the lowest significant correlation equal to 0.204 to the northern part of the study area. Correlation coefficient between the dependent variable SWE and independent variable upwind slope shows that 300 meters distance is the most effective distance of the interaction of wind and terrain in creation of wind sheltering and wind deflation. Coefficient of variation in snow depth and snow density measurements is 54.14% and 7.89%, respectively.


Main Subjects

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Volume 4, Issue 3
September 2017
Pages 673-686
  • Receive Date: 30 December 2016
  • Revise Date: 10 June 2017
  • Accept Date: 10 June 2017
  • First Publish Date: 23 September 2017
  • Publish Date: 23 September 2017