气候变化领域集成服务门户(CCPortal): Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

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DOI	10.5194/tc-11-857-2017
	Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods
	Wainwright H.M.; Liljedahl A.K.; Dafflon B.; Ulrich C.; Peterson J.E.; Gusmeroli A.; Hubbard S.S.
发表日期	2017
ISSN	19940416
卷号	11 期号:2
英文摘要	This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS).We found that GPR data provided highprecision estimates of snow depth (RMSE D2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSED6.0 cm) and a fine spatial sampling (4cm-4cm). We then investigated the spatial variability of snow depth and its correlation to micro-and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSED6.0 cm), at 0.5m resolution and over the lidar domain (750m × 700m).
学科领域	Bayesian analysis; digital elevation model; lidar; observational method; photogrammetry; polygon; precision; snow cover; spatial variation; topographic mapping; tundra; Alaska; United States; Linanthus dichotomus
语种	英语
scopus关键词	Bayesian analysis; digital elevation model; lidar; observational method; photogrammetry; polygon; precision; snow cover; spatial variation; topographic mapping; tundra; Alaska; United States; Linanthus dichotomus
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119418
作者单位	Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720-8126, United States; Water and Environmental Research Center, University of Alaska Fairbanks, 306 Tanana Loop, Fairbanks, AK 99775-5860, United States; International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, United States
推荐引用方式 GB/T 7714	Wainwright H.M.,Liljedahl A.K.,Dafflon B.,et al. Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods[J],2017,11(2).
APA	Wainwright H.M..,Liljedahl A.K..,Dafflon B..,Ulrich C..,Peterson J.E..,...&Hubbard S.S..(2017).Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods.Cryosphere,11(2).
MLA	Wainwright H.M.,et al."Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods".Cryosphere 11.2(2017).