气候变化领域集成服务门户(CCPortal): Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction

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DOI	10.1016/j.advwatres.2019.103473
	Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction
	Ryken A.; Bearup L.A.; Jefferson J.L.; Constantine P.; Maxwell R.M.
发表日期	2020
ISSN	0309-1708
卷号	135
英文摘要	The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs), yet these ecosystems play an important role in the storage and land-atmosphere exchange of water. As much of the western United States’ water comes from water stored in the snowpack (snow water equivalent, SWE), model representation of these regions is important. This study assesses how uncertainty in both model parameters and forcing affect simulated snow processes through sensitivity analysis (active subspaces) on model inputs (meteorological forcing and model input parameters) for a widely used snow model. Observations from an AmeriFlux tower at the Niwot Ridge research site are used to force an integrated, single-column hydrologic model, ParFlow-CLM. This study finds that trees can mute the effects of snow albedo causing the evergreen needleleaf scenarios to be sensitive primarily to hydrologic forcing while bare ground simulations are more sensitive to the snow parameters. The bare ground scenarios are most sensitive overall. Both forcing and model input parameters are important for obtaining accurate hydrologic model results. © 2019
关键词	Earth atmosphere Sensitivity analysis Snow melting systems Uncertainty analysis Active subspaces Hydrologic modeling Meteorological forcing Model representation Mountainous regions Parameter uncertainty Snow water equivalent Western United States Snow computer simulation hydrological modeling numerical model prediction sensitivity analysis snow water equivalent uncertainty analysis
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131884
推荐引用方式 GB/T 7714	Ryken A.,Bearup L.A.,Jefferson J.L.,et al. Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction[J]. Advances in Water Resources,2020,135.
APA	Ryken A.,Bearup L.A.,Jefferson J.L.,Constantine P.,&Maxwell R.M..(2020).Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction.,135.
MLA	Ryken A.,et al."Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction".135(2020).