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Geosciences 2018, 8(3), 93; https://doi.org/10.3390/geosciences8030093

A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin

1
Hydrometcentre of Russia, Moscow 123242, Russia
2
State Research Centre “Planeta”, Moscow 123242, Russia
*
Authors to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 12 February 2018 / Accepted: 28 February 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Hydrological and Hydrogeological Risks)
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Abstract

This paper presents the basin approach to the design, development, and operation of a hydrological forecasting and early warning system in a large transboundary river basin of high flood potential, where accurate, reliable, and timely available daily water-level and reservoir-inflow forecasts are essential for water-related economic and social activities (the Amur River basin case study). Key aspects of basin-scale system planning and implementation are considered, from choosing efficient forecast models and techniques, to developing and operating data-management procedures, to disseminating operational forecasts using web-GIS. The latter, making the relevant forecast data available in real time (via Internet), visual, and well interpretable, serves as a good tool for raising awareness of possible floods in a large region with transport and industrial hubs located alongside the Amur River (Khabarovsk, Komsomolsk-on-Amur). View Full-Text
Keywords: flood forecasting; early warning system; web-GIS application; hydrologic modeling flood forecasting; early warning system; web-GIS application; hydrologic modeling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Borsch, S.; Khristoforov, A.; Krovotyntsev, V.; Leontieva, E.; Simonov, Y.; Zatyagalova, V. A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin. Geosciences 2018, 8, 93.

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