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||Hazardous Hydrological Phenomena: Predictability and Assessment|
|Published online||17 April 2020|
Automated short-term forecast system based on open-source hydrological models for the Tikhvinka river (Leningrad region of Russia)
State Hydrological Institute, 23 2-nd line, Vasilievsky Island, 199004, St. Petersburg, Russia
2 LLC “LGMI”, 22a Zvenigorodskaya str, 191119, St. Petersburg, Russia
3 ITMO University, 49 Kronversky prospekt, 197101, St. Petersburg, Russia
4 Saint Petersburg State University, 7/9 Universitetskaya nab, 199034, St. Petersburg, Russia
* Corresponding author: firstname.lastname@example.org
In recent decades there has been a trend towards an increase in the number of dangerous hydrological events, especially floods. In order to protect citizens and solve economic problems, it is important to develop and actively introduce into operational practice methods of hydrological forecasting, as well as to build more modern and convenient interfaces of interaction between hydrometeorological services, municipal authorities and citizens. This work discusses a compact automated short-term hydrological forecasting system that uses open-source conceptual models HBV, SimHYD and GR4J as its core. The system is connected to data streams on the observed temperatures and precipitation in the watershed basin, as well as the predicted values of these parameters (in a current implementation, the WRF model with a forecast for 84 hours is used). Also, for operational calibration in daily mode, the system can assimilate (if available) data on observed water levels. Testing of the system is carried out on the example of Tikhvin city (the Tikhvinka river), which in recent years has been characterized by frequent flooding.
© 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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