@article{fdi:010079120,
  title = {{M}odeling land surface fluxes from uncertain rainfall : a case study in the {S}ahel with field-driven stochastic rainfields},
  author = {{C}appelaere, {B}ernard and {F}eurer, {D}enis and {V}ischel, {T}. and {O}ttle, {C}. and {I}ssoufou, {H}. {B}. {A}. and {S}aux-{P}icart, {S}. and {M}ainassara, {I}. and {O}i, {M}onique and {C}hazarin, {J}ean-{P}hilippe and {B}arral, {H}{\'e}l{\`e}ne and {C}oudert, {B}. and {D}emarty, {J}{\'e}rome},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n distributed land surface modeling ({LSM}) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. {T}his is particularly true for applications in the {A}frican {S}ahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. {I}n this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in {S}ahelian ecosystems. {E}nsemble time/space rainfields were generated from field observations of the local {AMMA}-{CATCH}-{N}iger recording raingauge network. {T}he rainfields were then used to force the {SE}t{H}y{S}-{S}avannah {LSM}, yielding an ensemble of time/space simulated fluxes. {T}hrough informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. {S}cale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. {T}he study was performed over a 2530 km(2)domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. {T}he newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. {R}esults show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. {T}hese results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty.},
  keywords = {uncertainty propagation ; uncertainty measures ; ensemble simulation ; water and energy fluxes ; evapotranspiration ; land surface model ; semiarid ; {S}outh-{W}est {N}iger ; {AMMA}-{CATCH} ; {W}est {A}frica ; {NIGER} ; {SAHEL} ; {ZONE} {SEMIARIDE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{A}tmosphere},
  volume = {11},
  numero = {5},
  pages = {465 [35 ]},
  year = {2020},
  DOI = {10.3390/atmos11050465},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079120},
}