Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025030 |
Modeling the Snow Depth Variability With a High-Resolution Lidar Data Set and Nonlinear Terrain Dependency | |
Skaugen, T.; Melvold, K. | |
2019-11-23 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
文章类型 | Article;Early Access |
语种 | 英语 |
国家 | Norway |
英文摘要 | In the mountains of Norway, snow depth (SD) is highly variable due to strong winds and open terrain. To investigate snow conditions on one of Europe's largest mountain plateaus, Hardangervidda, we conducted snow measurement campaigns in spring 2008 and 2009 using airborne lidar scanning at the approximate time of annual snow maximum (mid-April). From 658 empirical distributions of SD at Hardangervidda, each comprised about 4,000 SD values sampled from a grid cell of 0.5 km(2), quantitative tests have shown that the gamma distribution is a better fit for SD than the normal and log-normal distributions. When aggregating snow and terrain data from 10 x 10 m to 0.5 km(2), we find that the standard deviation of the terrain parameter squared slope, land cover, and the mean SD are highly correlated (0.7, 0.52, and 0.89) to the standard deviation of SD. A model for SD variance is proposed that, in addition to addressing the dependencies between the variability of SD and the terrain characteristics, also takes into account the observed nonlinear relationship between the mean and the standard deviation of SD. When validated against observed SD variance retrieved from the same area, the model explains 81-83% of the observed variance for spatial scales of 0.5 and 5.1 km(2), which compares favorably to previous models. The model parameters can be determined from a GIS analysis of a detailed digital terrain and land cover model and will hence not increase the number of calibration parameters when implemented in environmental models. |
英文关键词 | Snow depth distribution Laser scan of snow depth snow depth distribution model |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000498054100001 |
WOS关键词 | SPATIAL VARIABILITY ; WATER EQUIVALENT ; TEMPORAL VARIABILITY ; ARCTIC TUNDRA ; PARAMETERIZATION ; DISTRIBUTIONS ; VEGETATION ; DEPLETION ; FOREST ; WIND |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/223927 |
专题 | 资源环境科学 |
作者单位 | Norwegian Water Resources & Energy Directorate, Hydrol Dept, Oslo, Norway |
推荐引用方式 GB/T 7714 | Skaugen, T.,Melvold, K.. Modeling the Snow Depth Variability With a High-Resolution Lidar Data Set and Nonlinear Terrain Dependency[J]. WATER RESOURCES RESEARCH,2019. |
APA | Skaugen, T.,&Melvold, K..(2019).Modeling the Snow Depth Variability With a High-Resolution Lidar Data Set and Nonlinear Terrain Dependency.WATER RESOURCES RESEARCH. |
MLA | Skaugen, T.,et al."Modeling the Snow Depth Variability With a High-Resolution Lidar Data Set and Nonlinear Terrain Dependency".WATER RESOURCES RESEARCH (2019). |
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