348
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

1D and 2D model coupling approach for the development of operational spatial flood early warning system

, , , &
Pages 4390-4405 | Received 30 Jul 2020, Accepted 24 Jan 2021, Published online: 18 Feb 2021
 

Abstract

Main aim of the paper is to emphasise the advantage of 1 D and 2 D hydrodynamic models coupling in simulating flood inundations using high resolution Digital Terrain Models. The developed flood early warning model was calibrated and validated thoroughly at critical locations using historic observed discharge data and point rainfall data of about 350 stations. The flood inundation simulation model was prepared using flexible mesh approach and compared with the satellite imagery of Radarsat2. 1 D hydrodynamic model was integrated with 2 D hydrodynamic model by standard links for seamless hydrodynamic modelling. It is found that computational time has reduced drastically due to integration of 1 D and 2 D hydrodynamic models. Web-enabled spatial flood early warning system is developed to disseminate flood advisories in real-time. Computed peak discharge is found to be more than 87% accurate with forecast lead time of 52 hours when compared with field measurements subsequently.

Additional information

Funding

The authors sincerely acknowledge the support and guidance provided by Sri Santanu Chowdhury, Director, NRSC and Dr. P.V.N Rao, Deputy Director, Remote Sensing Applications, Area, NRSC. Field data support provided by Superintending Engineer, Godavari circle, Central Water Commission and Director, India Meteorological Department, Hyderabad is greatly acknowledged.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.