@article{PAR00013730,
  title = {{SMOS} soil moisture assimilation for improved hydrologic simulation in the {M}urray {D}arling {B}asin, {A}ustralia},
  author = {{L}ievens, {H}. and {T}omer, {S}. {K}. and {A}l {B}itar, {A}. and {D}e {L}annoy, {G}. {J}. {M}. and {D}rusch, {M}. and {D}umedah, {G}. and {F}ranssen, {H}. {J}. {H}. and {K}err, {Y}ann and {M}artens, {B}. and {P}an, {M}. and {R}oundy, {J}. {K}. and {V}ereecken, {H}. and {W}alker, {J}. {P}. and {W}ood, {E}. {F}. and {V}erhoest, {N}. {E}. {C}. and {P}auwels, {V}. {R}. {N}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study explores the benefits of assimilating {SMOS} soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the {M}urray {D}arling {B}asin, {A}ustralia. {I}n this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. {T}he land surface model is the {V}ariable {I}nfiltration {C}apacity ({VIC}) model. {T}he model is calibrated using the available streamflow records of 169 gauge stations across the {M}urray {D}arling {B}asin. {T}he {VIC} soil moisture forecast is sequentially updated with observations from the {SMOS} {L}evel 3 {CATDS} ({C}entre {A}val de {T}raitement des {D}onnees {SMOS}) soil moisture product using the {E}nsemble {K}alman filter. {T}he assimilation algorithm accounts for the spatial mismatch between the model (0.125) and the {SMOS} observation (25 km) grids. {T}hree widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. {T}hese methods match the first, second and higher order moments of the soil moisture distributions, respectively. {I}n this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. {P}reserving the observational variability of the {SMOS} soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. {S}econd or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. {I}n comparison with in situ measurements of {O}z{N}et the assimilation with mean bias correction reduces the root mean square error ({RMSE}) of the modeled soil moisture from 0.058 m(3)/m(3) to 0.046 m(3)/m(3) and increases the correlation from 0564 to 0.714. {T}hese improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. {I}n conclusion, the results of this study clearly demonstrate the merit of {SMOS} data assimilation for soil moisture and streamflow predictions at the large scale.},
  keywords = {{SMOS} ; {D}ata assimilation ; {S}oil moisture ; {S}treamflow ; {M}urray {D}arling {B}asin ; {AUSTRALIE}},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {168},
  numero = {},
  pages = {146--162},
  ISSN = {0034-4257},
  year = {2015},
  DOI = {10.1016/j.rse.2015.06.025},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00013730},
}