E3S Web Conf.
Volume 163, 2020IV Vinogradov Conference “Hydrology: from Learning to Worldview” in Memory of Outstanding Russian Hydrologist Yury Vinogradov
|Number of page(s)||6|
|Section||Mathematical Modeling in Hydrology: Problems, Achievements, Practical Application|
|Published online||17 April 2020|
Modelling maximum discharge of the catastrophic flood at the Iya River (Irkutsk region, Russia) in 2019
Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
2 Melnikov Permafrost Institute, 677010 Yakutsk, Russia
3 State Hydrological Institute, 199053 Saint-Petersburg, Russia
4 Perm State University, 614990 Perm, Russia
* Corresponding author: firstname.lastname@example.org
The aim of the study is to estimate the maximum discharge of the catastrophic flood in June 2019 at the Iya River (Irkutsk Region, Russia). The main cause of this flood was extreme precipitation (170 mm for 3 days). The distributed deterministic hydrological model Hydrograph was applied. The schematization of the Iya river basin, parametrization and verification of the Hydrograph model were performed. The median value of the Nash-Sutcliff criteria was 0.69 for the period 1970-1996 for three catchments of the Iya River basin. Based on the data of weather stations and global weather forecast model ICON, maximum daily discharge values of the flood were estimated as 6570 and 4780 m3s-1 respectively with the possible value range assessed by the dependence of Q(H) 6250-7500 m3s-1. The flood hydrograph estimated from weather station data coincides in magnitude of flood peak, but its formation is delayed by 1 day. ICON data underestimates maximum value but provides proper timing of the flood peak. The ensemble of input meteorological data from various sources could potentially be used to satisfactorily predict the magnitude and duration of the catastrophic flood and minimize the consequences of the flood.
© 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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