EMPIRICAL MODEL OF SUSPENDED SEDIMENTS VERTICAL DISTRIBUTION FOR BIG RIVERS
Authors
- Victor A. Ivanov Lomonosov MSU, Moscow, Russia , МГУ имени М.В. Ломоносова, г. Москва, Россия
- Sergey R. Chalov Lomonosov MSU, Moscow, Russia; Kazan Federal University, Kazan, Russia , МГУ имени М.В. Ломоносова, г. Москва, Россия; Казанский (Приволжский) федеральный университет, г. Казань, Россия
DOI:
https://doi.org/10.34753/HS.2022.4.2.149+ Keywords
+ Abstract
The suspended sediment concentration (SSC) is one of the main characteristics of the water flow. This parameter is a necessary water management characteristic of the river flow for forecasting channel transformations. Furthermore, SSC is a normative characteristic of water quality, because of many pathogenic microorganisms are transported with suspended particles and heavy metals are sorbed, and increased SSC values are pure for many types of aquatic organisms. This high socio-economic and hydrological-ecological significance actualizes the study of the SSC accuracy of estimates and their spatial distribution within the cross-section of the river. This study presents the distribution of water turbidity and hydraulic parameters on the Russia rivers (Lena, Ob, Yenisei, Kolyma, Don, Kuban, Terek, Selenga).
78 measurements of river cross-sections made by the Acoustic Doppler Current Profiler RiverRay
600 kHz were made, that gave 40,079 vertical distributions of backscatter intensity, calculated into units of suspended sediment concentration. Thus, for all cross-sections and verticals the Rouse number was calculated. 78 models of vertical suspended sediments concentration distribution as a function of depth, flow velocity and particle diameter were obtained. For 8 models, the correlation coefficients to the measured values were more than 0.9. For these calculations the authors developed a software in the programming language R, which input parameters were raw data of measurements by ADCP, and the result of calculations was suspended sediments and hydraulic characteristics of the flow. These models, that was developed by machine learning technics, are recommended for further use in studying the features of the movement of suspended sediments of large rivers.
+ Author Biographies
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