Back to Search Start Over

Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation.

Authors :
Ziliani, Matteo G.
Ghostine, Rabih
Ait-El-Fquih, Boujemaa
McCabe, Matthew F.
Hoteit, Ibrahim
Source :
Journal of Hydrology. Oct2019, Vol. 577, pN.PAG-N.PAG. 1p.
Publication Year :
2019

Abstract

• Implementation of a flood forecasting system based an ensemble Kalman filter. • Forecasting system is validated with a real test case of the Toce river valley. • Assimilation of real data improved the model prediction skills. • Simultaneously estimation of 2D Manning coefficient further improves performances. Accurate water level forecasts during flood events are crucial to mitigate the loss of human lives and economic damages. However, the accuracy of flood models can be affected by various factors, including the complexity of the terrain geometry and bathymetry, imperfect physics as well as uncertainties in the inflows and parameters. This paper describes a practical implementation of an ensemble Kalman filter (EnKF) based data assimilation system that is aimed towards enhancing the forecasting skill of flood models. The system was implemented and tested with a real world dam break flood, based on the experimentally scaled Toce River valley flood that occurred on July 8th, 1996. Water depth data are available for assimilation from a network of 21 sensors distributed across the domain. Our results demonstrate that assimilating data into the flood model significantly improves the model prediction by up to 90% after assimilation and 60% during forecasting. Assimilating the data more frequently significantly enhances the system performances. Estimating the two-dimensional Manning coefficient together with the model's dynamical variables (water depth and velocities) further improves the model prediction skill. Overall, our results suggest that assimilating data into the flood model, while jointly inferring the state and (poorly known) parameters, using an EnKF may provide an efficient framework for developing an operational flood forecasting system. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00221694
Volume :
577
Database :
Academic Search Index
Journal :
Journal of Hydrology
Publication Type :
Academic Journal
Accession number :
141612075
Full Text :
https://doi.org/10.1016/j.jhydrol.2019.123924