Estimation of Snow Water Equivalent in Semiarid Zone from Data of Global Numerical Models ICON and GFS/NCEP (Case Study of the Selenga River Basin)

Autor: A. N. Shikhov, V. N. Chernykh, A. A. Aurzhanaev, S. V. Pyankov, R. K. Abdullin
Jazyk: ruština
Rok vydání: 2023
Předmět:
Zdroj: Лëд и снег, Vol 63, Iss 2, Pp 257-270 (2023)
Druh dokumentu: article
ISSN: 2076-6734
2412-3765
20766734
DOI: 10.31857/S2076673423020151
Popis: The possibility to use the global numerical (NWP) models ICON and GFS/NCEP for We consider the applicability of ICON and GFS/NCEP global numerical atmospheric model data for calculating the snow water equivalent (SWE) in the Selenga River basin located the semiarid zone. SWE was calculated for the cold periods of 2020–2022 based on the empirical methodology previously developed for the Kama River basin and adapted to the semiarid conditions. The main components of the SWE balance that are taken into account in the calculation are atmospheric precipitation (liquid or solid phase), snowmelt, sublimation from the snow surface and precipitation interception by vegetation with subsequent sublimation. The validation of the results was performed for the Russian part of the basin using the data of snow surveys carried out in the second half of the winter of 2021/22. In general, reasonable estimates of the SWE spatial distribution were obtained. While in 2021, both overestimation and underestimation by 1–15 mm (20–50%) of the calculated SWE was observed at different sites compared to the measurements, in 2022, its systematic underestimation was observed, especially significant in calculations using the ICON model data. In the steppe zone, SWE is significantly underestimated, which may be due to overestimation of the intensity of sublimation from the snow surface. The comparison of these results with the ERA5-Land reanalysis data and MODIS satellite images showed that the ERA5-Land reanalysis significantly overestimates the SWE and the snow cover area. The simulation results based on the GFS/NCEP and ICON models underestimated the snow cover area in 2022 and reproduced well in 2021, which correlates with the results of the SWE calculation.
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