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Spatial Prediction of Snow Depth Using Regression Kriging and Terrain Parameters in Sakhvid region
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Rohoalla Taghizadeh-Mehrjardi *   , Samane Gharaei , Ali Fathzadeh |
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Abstract: (10934 Views) |
| Snow depth is the most common parameters used for the assessment of water resources in the mountainous areas. Therefore, knowledge about spatial distribution of snow depth is the substantial demand of watershed. At present research, were tried to estimate the spatial distribution of snow depth using regression kriging based on M5 model tree. Therefore, location of 216 points were 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. 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. So using regression kriging model is recommended to estimate spatial distribution of snow depth in other regions. |
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| Keywords: Regression Tree, M5 Algoritm, Snow Depth, DEM, Terrain Parameters |
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Full-Text [PDF 763 kb]
(2020 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2015/07/28 | Accepted: 2015/07/28 | Published: 2015/07/28
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