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DOI	10.5194/hess-24-4061-2020
	Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments
	Terzago S.; Andreoli V.; Arduini G.; Balsamo G.; Campo L.; Cassardo C.; Cremonese E.; Dolia D.; Gabellani S.; Von Hardenberg J.; Morra Di Cella U.; Palazzi E.; Piazzi G.; Pogliotti P.; Provenzale A.
发表日期	2020
ISSN	1027-5606
起始页码	4061
结束页码	4090
卷号	24 期号:8
英文摘要	Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160ma.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediatecomplexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- A nd 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density. © 2020 American Society of Civil Engineers (ASCE). All rights reserved.
语种	英语
scopus关键词	Digital storage; Input output programs; Interpolation; Quality control; Snow melting systems; Snowfall measurement; Uncertainty analysis; Degrees of complexity; Extracting information; High-quality measurements; Meteorological forcing; Meteorological input; Short-wave radiation; Snow water equivalent; Spatial interpolation; Snow; accuracy assessment; climate forcing; climate modeling; data quality; experimental study; hydrological modeling; in situ measurement; interpolation; mountain environment; sensitivity analysis; snow; snowpack; spatial analysis; temporal analysis; Alps; Italy
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159325
作者单位	Terzago, S., Institute of Atmospheric Sciences and Climate, National Research Council, Turin, Italy; Andreoli, V., Department of Physics and Natrisk Center, University of Torino, Turin, Italy; Arduini, G., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; Balsamo, G., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; Campo, L., CIMA Research Foundation, International Centre on Environmental Monitoring, Savona, Italy; Cassardo, C., Department of Physics and Natrisk Center, University of Torino, Turin, Italy; Cremonese, E., Environmental Protection Agency of Aosta Valley, Aosta, Italy; Dolia, D., CIMA Research Foundation, International Centre on Environmental Monitoring, Savona, Italy; Gabellani, S., CIMA Research Foundation, International Centre on Environmental Monitoring, Savona, Italy; Von Hardenberg, J., Institute of Atmospheric Sciences and Climate, National Research Council, Turin, Italy, Department of Environment, Land and Infrastructure Engineer...
推荐引用方式 GB/T 7714	Terzago S.,Andreoli V.,Arduini G.,et al. Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments[J],2020,24(8).
APA	Terzago S..,Andreoli V..,Arduini G..,Balsamo G..,Campo L..,...&Provenzale A..(2020).Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments.Hydrology and Earth System Sciences,24(8).
MLA	Terzago S.,et al."Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments".Hydrology and Earth System Sciences 24.8(2020).