Developing a representative snow-monitoring network in a forested mountain watershed
| Autor: | Anne W. Nolin, Kelly E. Gleason, Travis R. Roth |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Watershed
010504 meteorology & atmospheric sciences 0208 environmental biotechnology Terrain 02 engineering and technology Land cover lcsh:Technology 01 natural sciences lcsh:TD1-1066 lcsh:Environmental technology. Sanitary engineering lcsh:Environmental sciences 0105 earth and related environmental sciences lcsh:GE1-350 Hydrology Land use lcsh:T lcsh:Geography. Anthropology. Recreation Elevation 15. Life on land Snowpack Snow 020801 environmental engineering lcsh:G 13. Climate action Environmental science Spatial variability |
| Zdroj: | Hydrology and Earth System Sciences, Vol 21, Iss 2, Pp 1137-1147 (2017) |
| ISSN: | 1607-7938 |
| Popis: | A challenge in establishing new ground-based stations for monitoring snowpack accumulation and ablation is to locate the sites in areas that represent the key processes affecting snow accumulation and ablation. This is especially challenging in forested montane watersheds where the combined effects of terrain, climate, and land cover affect seasonal snowpack. We present a coupled modeling approach used to objectively identify representative snow-monitoring locations in a forested watershed in the western Oregon Cascades mountain range. We used a binary regression tree (BRT) non-parametric statistical model to classify peak snow water equivalent (SWE) based on physiographic landscape characteristics in an average snow year, an above-average snow year, and a below-average snow year. Training data for the BRT classification were derived using spatially distributed estimates of SWE from a validated physically based model of snow evolution. The optimal BRT model showed that elevation and land cover type were the most significant drivers of spatial variability in peak SWE across the watershed (R2 = 0.93, p value |
| Databáze: | OpenAIRE |
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