Abstract
The present study describes the assimilation of discharge in situ data for operational flood forecasting. The study was carried out on the Marne River (France) catchment where lateral inflows’ uncertainty is important due to karstic areas. This source of error was partly accounted for using an Extended Kalman Filter (EKF) algorithm built on the top of a mono-dimensional hydraulic model. The lateral inflows were sequentially adjusted over a sliding 48 h time window. The correction leads to a significant improvement in the simulated water level and discharge in re-analysis and forecast modes. These results pave the way for the operational use of the data assimilation (DA) procedure for real-time forecasting at the French flood forecasting service.
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References
Service de l’Observation et des Statistiques—Commissariat Général au Développement Durable (SOeS—CGDD). (2009). Croissance du nombre de logements en zones inondables, Le point sur, 6, Février.
Coccia, G., & Todini, E. (2008). Recent developments in predictive uncertainty assessment based on the model conditional processor approach. Hydrology and Earth System Sciences, 7, 9219–9270.
Weerts, A. H. & Liu, Y. (2011). Advances in data assimilation for operational hydrologic forecasting. EOS Transactions of the AGU, 92.
Duan, Q., Gupta, V., Sorooshian, S., Rousseau, A. N. & Turcotte, R. (2003). Preface in calibration of watershed Models. In Q. Duan et al. (Eds.), Water Science Appli Ser (vol. 6). Washington, DC: AGU.
Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2003). A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrological parameters. Water Resources Research, 39(8), 1–14.
Schumann, G., Bates, P.D., Horritt, M.S., Matgen, P. & Pappenberger, F. (2009). Progress in integration of remote sensing derived flood extend and stage data and hydraulic models. Reviews of Geophysics, 47.
Bouttier, F., & Courtier, P. (1999). Data assimilation concepts and methods. ECMWF Lecture Note.
Liu, Y., & Gupta, H. V. (2007). Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resource Research, 43, W07401.
Maclaughlin, D., & Townley, L. (1996). A reassessment of the groundwater inverse problem. Water Resources Research, 32, 1131–1161.
McLaughlin, D. (2002). An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering. Advances in Water Resources, 25, 1275–1286.
Reichle, R. H. (2008). Data assimilation methods in the Earth sciences. Advances in Water Resources, 31, 1411–1418.
Drogue, G., Pfister, L., Leviandier, T., El Idrissi, A., Iffly, J. F., Matgen, P., et al. (2004). Simulating the spatio-temporal variability of streamflow response to climate change scenarios in a mesoscale basin. Journal of Hydrology, 293, 255–269.
Thirel, G., Martin, E., Mahfouf, J. F., Massart, S., Ricci, S., & Habets, F. (2010). A past discharges assimilation system for ensemble streamflow forecasts over France, Part 1: Description and validation of the assimilation system. Hydrology and Earth System Sciences, 14, 1623–1637.
Thirel, G., Martin, E., Mahfouf, J. F., Massart, S., Ricci, S., Regimbeau, F., et al. (2010). A past discharges assimilation system for ensemble streamflow forecasts over France, Part 2: Impact on the streamflow forecasts. Hydrology and Earth System Sciences, 14, 1639–1653.
Dechant, C. M., & Moradkhani, H. (2011). Radiance data assimilation for operational snow and streamflow forecasting. Advances in Water Resources Research, 34, 351–364.
Seo, D. J., Cajina, L., Corby, R., & Howieson, T. (2009). Automatic state updating for operational streamflow forecasting via variational data assimilation. Journal of Hydrology, 367, 255–275.
Seo, D. J., Koren, V., & Cajina, N. (2003). Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting. Journal of Hydrometeorology, 4, 627–641.
Gelb, A. (1974). Applied optimal estimation. Cambridge Mass.: MIT Press.
Evensen, G. (1994). Sequential data assimilation with a non linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99, 10143–10162.
Jean-Baptiste, N., Malaterre, P. O., Dorée, C., & Sau, J. (2009). Data assimilation for real-time estimation of hydraulics states and unmeasured perturbations in a 1D hydrodynamic model. Mathematics and Computers in Simulation, 81, 2201–2214.
Malaterre, P. O., Baume, J.P. & Jean-Baptiste, N. (2010). Calibration of open channel flow models: A system analysis and control engineering approach. SimHydro.
Moradkhani, H., Sorooshian, S., Gupta, H., & Houser, P. (2005). Dual-state parameters estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28, 135–147.
Ricci, S., Piacentini, A., Thual, O., Le Pape, E., & Jonville, G. (2011). Correction of upstream flow and hydraulics state with data assimilation in the context of flood forecasting. Hydrology and Earth System Sciences, 15, 1–21.
Goutal, N., & Maurel, F. (2002). A finite volume solver for 1D shallow water equations applied in an actual river. International Journal for Numerical Methods in Fluids, 38(2), 1–19.
Buis, S., Piacentini, A., & Déclat, D. (2006). PALM: A computational framework for assembling high performance computing applications. Concurrency and Computation: Practice and Experience, 18(2), 247–262.
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Habert, J. et al. (2014). Estimation of Lateral Inflows Using Data Assimilation in the Context of Real-Time Flood Forecasting for the Marne Catchment in France. In: Gourbesville, P., Cunge, J., Caignaert, G. (eds) Advances in Hydroinformatics. Springer Hydrogeology. Springer, Singapore. https://doi.org/10.1007/978-981-4451-42-0_8
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DOI: https://doi.org/10.1007/978-981-4451-42-0_8
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