Snow Characterizes Mapping of North-West Basins

Snow is an important hydrological phenomenon and snow water equivalent is suitable water resource in many parts of the world. Snow and snow water equivalent have a significant contribution in streamflow and groundwater recharge. For this reason, it is important modeling of snow accumulation and melting. So, estimation of snow spatial distribution in different time scales is one of key stages in water resources studies.
Due to the successful application of geostatistical methods in different sciences, the purpose of this study is mapping of snow characteristics. In this study, the spatial analysis of snow water equivalent, snow depth and snow density, which is one of major components of the water balance, is evaluated in watershed of the country north-west.
In this study, using geostatistical methods, spatial distribution of snow height, snow density and snow water equivalent were estimated. So, by measurement data of three Azabbayjan- Sharghi, Azarbayjan- Gharbi and Ardebi provinces during four years (2008-2012) in north-west, capability of Kriging, radial basis function and inverse distance weight method were evaluated. The Figure 1 shows location of study area and used data.
For estimation of snow characterizes in non-measurement estimated points was used evaluation, longitude and latitude parameters. The results comparison of each geostatistical methods was done by the Normal Root Mean Square Error (NRMSE) criterion.
(1) where Xi , Yi: ith estimated snow data , n: number examples. The drawing of zoning maps was done by ArcGIS software.
The before zoning, correlation coefficient value of snow density, snow height and snow water equivalent as depended with geographic characterizes was obtain in SPSS software (Table 1).
Table 1. the correlation coefficient matrix of used variables
Longitude Latitude Elevation Snow density Snow water equivalent Snow height
Longitude 1
Latitude -0.456** 1
Elevation 0.276* 0.105 1
Snow density 0.167 -0.053 0.221 1
Snow water equivalent 0.270* -0.107 0.489** 0.410** 1
Snow height 0.218 -0.103 0.500** 0.035 0.893** 1
In addition to Table 2, elevation and longitude with correlation coefficient of 0.489 and 0.270, respectively, have the most effect on snow water equivalent. the positive sign indicates straight relative between elevation and longitude with snow water equivalent.
As a general result, each three snow characterizes have positive relative with elevation. it is because of elevation is an important topography factor and increased height leads decreased air temperature and enhancement snow.
The results indicated that in 100% cases, the ordinary kriging method with Gaussian semi variogram had the best results than all methods. the results of inverse distance weight method showed that this method had the least accurate in zoning. Even, power increased dont lead than accurate enhancement. Nikbakht and Delbari (2013) applied different interpolation methods for estimated ground water table in Zahedan plain. in this study reported preference of kriging method with Gaussian semi variogram. McKenna (2000) reported that kriging geostatistical method is suitable, also can be used low number data. The results indicated that mean accurate of kriging method with Guassian semi variogram for snow density, snow water equivalent during four years on base Normal Root Mean Square Error (NRMSE) were 6.76, 3.18 and 10.57, respectively. the results of mapping showed that the most zones of snow characterizes were located in two middle classis and minimum and maximum zones were as small spots.
The purpose of this research was to develop interpolation methods to assess the estimation snow components in the non-measurement points. In addition to equipment and preparation problems of snow stations, it is necessary to use modern methods to inform from the snow spatial distribution. The results of this study showed that in the study area and in four years time, ordinary kriging gave better results than other methods. the different interpolation methods had different accurate and can not extend results of an area easily to other areas. But the advantage of this research was in using four years data to an area with 100,861 km2 zone and for three snow important features. Other features of the study was in using longitude, latitude and elevation in non-measured points. Since the used independent variables located in readily available variables categories (access quick to the data at a lower cost and higher accuracy), so we can expect good results with high accurate.