Sub-daily stochastic weather generator based on reanalyses for water stress retrieval in central Tunisia
2020
Farhani, Nesrine | Carreau, Julie | Kassouk, Zeineb | Mougenot, Bernard | Le Page, Michel | -Chabaane, Lili | Zitouna, Rim | Boulet, Gilles | Institut National Agronomique de Tunisie (INAT) | Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Hydrosciences Montpellier (HSM) ; Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS) | Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]) | Institut National de Recherche en Génie Rural Eaux et Forêts (INRGREF) ; Ecole Nationale du Génie Rural, des Eaux et des Forêts (ENGREF)-Institution de la Recherche et de l'Enseignement Supérieur Agricoles [Tunis] (IRESA)
In semi-arid areas, evapotranspiration that characterizes plant water use and water stress are needed to better manage water resources and agrosystem health. They both can be simulated by a dual source energy balance model that relies on hydro-meteorological variables and satellite data. Available hydro-meteorological observations may often be insufficient to account for the variability present in the study area. Our aim is to adapt a stochastic weather generator (SWG) driven by large-scale reanalysis data to semi-arid climates and to the sub-daily resolution. The SWG serves to perform consistent gap-filling and temporal extension of multiple hydro-meteorological variables. It is compared with two state-of-the-art bias correction methods applied to large-scale reanalysis data. The surrogate series that are either produced by the SWG and the bias correction methods with a cross-validation scheme or taken as the un-processed reanalysis data, are evaluated in terms of their ability to reproduce the statistical properties of the hydro-meteorological observations. They are also used to constrain a dual source energy balance model and compared in terms of estimated evapotranspiration and water stress.
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