@incollection{fdi:010084199,
  title = {{S}cenarios of hydrometeorological variables based on auxiliary data for water stress retrieval in {C}entral {T}unisia},
  author = {{F}arhani, {N}. and {C}arreau, {J}ulie and {B}oulet, {G}illes and {K}assouk, {Z}. and {M}ougenot, {B}. and {L}e {P}age, {M}ichel and {C}habaane, {Z}.{L}. and {Z}itouna, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}haracterization of plant water use, generally determined from evapotranspiration, together with water stress, derived from remote sensing data in the thermal infrared domain, are needed to better manage water resources. {E}vapotranspiration and water stress can be simulated by a dual source energy balance model that combines satellite and in situ hydrometeorological information. {A}vailable hydrometeorological observations are often insufficient to account for the spatial and temporal variability of the area of interest. {T}o address this issue, we developed a stochastic weather generator that relies on {ERA}5 reanalyses and provides spatio-temporal scenarios of multiple hydrometeorological variables. {T}he generator is evaluated and compared with two bias correction methods in terms of their ability to reproduce both observed hydrometeorological variables and simulated evapotranspiration and water stress in central {T}unisia. {O}ur analyses show that the stochastic generator offers interesting advantages to perform gap-filling and to extend the hydrometeorological time series in the past.},
  keywords = {{TUNISIE} ; {KAIROUAN} ; {ZONE} {ARIDE}},
  booktitle = {2020 {M}editerranean and {M}iddle-{E}ast {G}eoscience and {R}emote {S}ensing {S}ymposium ({M}2{GARSS}) : proceedings},
  numero = {},
  pages = {293--296},
  address = {{P}iscataway},
  publisher = {{IEEE}},
  series = {},
  year = {2020},
  DOI = {10.1109/{M}2{GARSS}47143.2020.9105287},
  ISBN = {978-1-7281-2191-8},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084199},
}