Spatial Prediction of Snow Depth Using Regression Kriging and Terrain Parameters in Sakhvid Region

Snow depth is the most common parameter used for the assessment of water resources in the mountainous areas. Therefore، knowledge about spatial distribution of snow depth is the substantial knowledge of watershed characteristics. At present research، it was tried to estimate the spatial distribution of snow depth using regression kriging based on M5 model tree. Therefore، location of 216 points was selected systematically، and then snow depth was measured with a Monte - Rose sampler in Yazd-Sakhvid region. Then، 30 terrain parameters were derived from a digital elevation model using SAGA software. Our results indicated that channel network base level، stream power and wetness index were the most important parameters in decision-tree model. The correlation coefficient of 90% confirmed the strong performance of regression kriging model. Moreover، this method is very simple، so it is recommended the regression kriging model is being used to estimate spatial distribution of snow depth in other regions.