@article{fdi:010072714,
  title = {{L}arge-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product},
  author = {{E}mery, {C}. {M}. and {P}aris, {A}. and {B}iancamaria, {S}. and {B}oone, {A}. and {C}almant, {S}t{\'e}phane and {G}arambois, {P}. {A}. and da {S}ilva, {J}. {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}and surface models ({LSM}s) are widely used to study the continental part of the water cycle. {H}owever, even though their accuracy is increasing, inherent model uncertainties can not be avoided. {I}n the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. {T}herefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. {T}he objective of this study is to present a data assimilation platform that assimilates into the large-scale {ISBA}-{CTRIP} {LSM} a punctual river discharge product, derived from {ENVISAT} nadir altimeter water elevation measurements and rating curves, over the whole {A}mazon basin. {T}o deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. {T}his assimilation platform is based on the ensemble {K}alman filter and can correct either the {CTRIP} river water storage or the discharge. {R}oot mean square error ({RMSE}) compared to gauge discharges is globally reduced until 21% and at {O}bidos, near the outlet, {RMSE} is reduced by up to 52% compared to {ENVISAT}-based discharge. {F}inally, it is shown that localization improves results along the main tributaries.},
  keywords = {{AMAZONE} {BASSIN} ; {PEROU} ; {BRESIL}},
  booktitle = {},
  journal = {{H}ydrology and {E}arth {S}ystem {S}ciences},
  volume = {22},
  numero = {4},
  pages = {2135--2162},
  ISSN = {1027-5606},
  year = {2018},
  DOI = {10.5194/hess-22-2135-2018},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072714},
}