DOI: 10.5194/hess-19-3153-2015
Scopus记录号: 2-s2.0-84937675153
论文题名: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework
作者: Raleigh M ; S ; , Lundquist J ; D ; , Clark M ; P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 7 起始页码: 3153
结束页码: 3179
语种: 英语
Scopus关键词: Ablation
; Balloons
; Catchments
; Errors
; Intelligent systems
; Measurement errors
; Monte Carlo methods
; Probability distributions
; Sensitivity analysis
; Snow
; Snow melting systems
; Uncertainty analysis
; Cold region hydrologies
; Error characteristics
; Error probability distribution
; Global sensitivity analysis
; Meteorological variables
; Physically based models
; Physically-based simulation
; Snow water equivalent
; Random errors
; ablation
; atmospheric forcing
; catchment
; data set
; error analysis
; glacial hydrology
; hydrological modeling
; magnitude
; precipitation (climatology)
; sensitivity analysis
; snow cover
; snow water equivalent
; United States
; Utah
英文摘要: Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78469
Appears in Collections: 气候变化事实与影响
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作者单位: National Center for Atmospheric Research, Boulder, CO, United States; Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
Recommended Citation:
Raleigh M,S,, Lundquist J,et al. Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework[J]. Hydrology and Earth System Sciences,2015-01-01,19(7)