@article{fdi:010060541,
  title = {{A}ssimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the {A}mazon},
  author = {{P}aiva, {R}. {C}. {D}. and {C}ollischonn, {W}. and {B}onnet, {M}. {P}. and de {G}oncalves, {L}. {G}. {G}. and {C}almant, {S}t{\'e}phane and {G}etirana, {A}. and da {S}ilva, {J}. {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n 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 {A}mazon {R}iver basin. {W}e used the process-based hydrological model called {MGB}-{IPH} coupled with a river hydrodynamic module using a storage model for floodplains. {T}he {E}nsemble {K}alman {F}ilter technique was used to assimilate information from hundreds of gauging and altimetry stations based on {ENVISAT} satellite data. {M}odel state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. {T}he {E}n{KF} performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-mean-squared error {D}elta rms = -49 %) and also transferring information to ungauged rivers reaches ({D}elta rms = -16 %). {A}ltimetry data assimilation improves results, in terms of water levels ({D}elta rms = -44 %) and discharges ({D}elta 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. {S}ensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. {T}he deterioration of model performance at some unmonitored reaches indicates the need for proper characterisation of model errors and spatial localisation techniques for hydrological applications. {F}inally, we evaluated stream flow forecasts for the {A}mazon 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. {T}he 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 {S}olimoes/{A}mazon main stem. {R}esults encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote-sensing information.},
  keywords = {{AMAZONE} {BASSIN}},
  booktitle = {},
  journal = {{H}ydrology and {E}arth {S}ystem {S}ciences},
  volume = {17},
  numero = {7},
  pages = {2929--2946},
  ISSN = {1027-5606},
  year = {2013},
  DOI = {10.5194/hess-17-2929-2013},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060541},
}