@article{fdi:010073764,
  title = {{A} new efficient {B}ayesian parameter inference strategy : application to flow and pesticide transport through unsaturated porous media},
  author = {{Y}ounes, {A}nis and {M}ara, {T}. {A}. and {V}oltz, {M}. and {G}uellouz, {L}. and {B}aalousha, {H}. {M}. and {F}ahs, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}tatistical calibration of flow and transport models in unsaturated porous media is often carried out with {M}arkov {C}hain {M}onte {C}arlo ({MCMC}) methods. {H}owever, the practicality of these methods is limited by their computational requirement, particularly when large prior intervals are assigned to the model parameters. {I}n this work, a new operational strategy is investigated to alleviate the computational burden of {MCMC} samplers using results from a preliminary calibration performed with the {F}irst-{O}rder {A}pproximation ({FOA}) method. {W}ith the new strategy, the posterior distribution is approximated using a high-order {P}olynomial {C}haos {E}xpansion ({PCE}) surrogate model constructed over reduced parameter ranges. {T}he latter are obtained from the 99.9 {FOA} confidence intervals. {T}wo challenging test cases are investigated to assess efficiency and accuracy of the new strategy. {T}he first test case considers estimation of flow and pesticide transport parameters from a synthetic infiltration experiment. {T}he second test case deals with the assessment of unsaturated hydraulic soil parameters from a real-word laboratory drainage experiment. {T}he results of the proposed strategy are compared to those of {FOA}, of the standard {MCMC} method and of an improved {MCMC} method in which the sampler is preconditioned with draws from the {FOA} posterior distribution. {F}or both test cases, the new strategy provides accurate mean estimated parameter values and uncertainty regions and is much more efficient than the other {MCMC} methods. {I}t is up to 50 times more efficient than the standard {MCMC} method.},
  keywords = {{U}nsaturated flow ; {P}esticide transport ; {B}ayesian inference ; {C}onfidence intervals ; {P}olynomial {C}haos {E}xpansion ; {DREAM}((zs)) software},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology},
  volume = {563},
  numero = {},
  pages = {887--899},
  ISSN = {0022-1694},
  year = {2018},
  DOI = {10.1016/j.jhydrol.2018.06.043},
  URL = {https://www.documentation.ird.fr/hor/fdi:010073764},
}