Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Abstract

Abstract. 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 2160 m a.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, intermediate-complexity 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- and 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.

A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change

“…These findings are confirmed by the work of Terzago et al (2020), where melt models of lower complexity did show higher biases against observations with respect to more complex ones like SNOWPACK. In Terzago et al (2020), high biases for simpler models are related both to their underestimation of snow water equivalent peak values and to the protraction of snow melt season that they reproduce. Maps of summer months in Fig.…”

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

“…This information is crucial to calibrate adaptive strategies to better face climate change effects in alpine ecosystems. It is worth remembering that mountains are among the most sensitive ecosystems to climate change, especially rising temperature, where effects observed more quickly than in other terrestrial habitats [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114] and this study could be considered another one concerning the short-term climate change effect on rangelands' and broad-lived forests' phenology in Aosta Valley. In particular, EO data from public and free archives of ready-to-use products such as MOD13Q1 and MOD16A2, proved to support well this type of analysis in spite of their reduced geometric resolution.…”

Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

“…Wang et al [57], Liu et al [58], Hersbach et al [59], Terzago et al [60], and Orsolini et al [61] conducted a comprehensive assessment of snow cover parameters for this dataset, the findings showed that the ERA5 dataset can well capture the spatial distribution of snow cover and a wide range of characteristics of seasonal changes despite its large positive bias. Wang et al [62], Matveeva and Sidorchuk [63], and Yılmaz et al [64]…”