globalchange  > 气候变化与战略
DOI: 10.5194/hess-24-4061-2020
论文题名:
Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments
作者: 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.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期:8
起始页码: 4061
结束页码: 4090
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
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162615
Appears in Collections:气候变化与战略

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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 Engineering, Politecnico di Torino, Turin, Italy; Morra Di Cella, U., Environmental Protection Agency of Aosta Valley, Aosta, Italy; Palazzi, E., Institute of Atmospheric Sciences and Climate, National Research Council, Turin, Italy; Piazzi, G., CIMA Research Foundation, International Centre on Environmental Monitoring, Savona, Italy, IRSTEA, Hydrology Research Group, UR HYCAR, Antony, 92761, France; Pogliotti, P., Environmental Protection Agency of Aosta Valley, Aosta, Italy; Provenzale, A., Institute of Geosciences and Earth Resources, National Research Council, Pisa, Italy

Recommended Citation:
Terzago S.,Andreoli V.,Arduini G.,et al. Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments[J]. Hydrology and Earth System Sciences,2020-01-01,24(8)
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