Issue |
E3S Web Conf.
Volume 163, 2020
IV Vinogradov Conference “Hydrology: from Learning to Worldview” in Memory of Outstanding Russian Hydrologist Yury Vinogradov
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Article Number | 06003 | |
Number of page(s) | 6 | |
Section | Methodology of Science and Current Tasks of Hydrological Research | |
DOI | https://doi.org/10.1051/e3sconf/202016306003 | |
Published online | 17 April 2020 |
Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods
1
Lomonosov Moscow State University, Leninskiye Gory 1, 119991, Moscow, Russia
2
Hydrometcenter of Russia, 11-13 Bolshoy Predtechensky per., 123242, Moscow, Russia
* Corresponding author: evgenychur@gmail.com
Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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