Publications des scientifiques de l'IRD

Paiva R. C. D., Collischonn W., Bonnet M. P., de Goncalves L. G. G., Calmant Stéphane, Getirana A., da Silva J. S. (2013). Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon. Hydrology and Earth System Sciences, 17 (7), p. 2929-2946. ISSN 1027-5606.

Titre du document
Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon
Année de publication
2013
Type de document
Article référencé dans le Web of Science WOS:000322376000037
Auteurs
Paiva R. C. D., Collischonn W., Bonnet M. P., de Goncalves L. G. G., Calmant Stéphane, Getirana A., da Silva J. S.
Source
Hydrology and Earth System Sciences, 2013, 17 (7), p. 2929-2946 ISSN 1027-5606
In this work, we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-mean-squared error Delta rms = -49 %) and also transferring information to ungauged rivers reaches (Delta rms = -16 %). Altimetry data assimilation improves results, in terms of water levels (Delta rms = -44 %) and discharges (Delta rms = -15 %) to a minor degree, mostly close to altimetry sites and at a daily basis, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterisation of model errors and spatial localisation techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. > 90 days at the Solimoes/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote-sensing information.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Hydrologie [062]
Description Géographique
AMAZONE BASSIN
Localisation
Fonds IRD [F B010060541]
Identifiant IRD
fdi:010060541
Contact