Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the 'Adour Maritime' river
| Autor: | Sébastien Barthélémy, Olivier Thual, Sophie Ricci, E. Le Pape, Mélanie C. Rochoux |
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| Přispěvatelé: | Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Flood forecasting
010504 meteorology & atmospheric sciences Flood myth Meteorology 0208 environmental biotechnology Univariate 02 engineering and technology Ingénierie de l'environnement Covariance 01 natural sciences 020801 environmental engineering Water level Data assimilation Climatology Environmental science Ensemble Kalman filter Data assimilation Uncertainty reduction Hydraulic modeling Lead time 0105 earth and related environmental sciences Water Science and Technology Ensemble approach |
| Popis: | This study presents the implementation and the merits of an Ensemble Kalman Filter (EnKF) algorithm with an inflation procedure on the 1D shallow water model MASCARET in the framework of operational flood forecasting on the “Adour Maritime” river (South West France). In situ water level observations are sequentially assimilated to correct both water level and discharge. The stochastic estimation of the background error statistics is achieved over an ensemble of MASCARET integrations with perturbed hydrological boundary conditions. It is shown that the geometric characteristics of the network as well as the hydrological forcings and their temporal variability have a significant impact on the shape of the univariate (water level) and multivariate (water level and discharge) background error covariance functions and thus on the EnKF analysis. The performance of the EnKF algorithm is examined for observing system simulation experiments as well as for a set of eight real flood events (2009–2014). The quality of the ensemble is deemed satisfactory as long as the forecast lead time remains under the transfer time of the network, when perfect hydrological forcings are considered. Results demonstrate that the simulated hydraulic state variables can be improved over the entire network, even where no data are available, with a limited ensemble size and thus a computational cost compatible with operational constraints. The improvement in the water level Root-Mean-Square Error obtained with the EnKF reaches up to 88% at the analysis time and 40% at a 4-h forecast lead time compared to the standalone model. |
| Databáze: | OpenAIRE |
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