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Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse

Abstract : In arid and semi-arid areas, water is a major limitation factor for agricultural production. Indeed, these areas are characterized by a short rainy season and strong irregularity in time and space of precipitation events. This induces more frequent annual and intra-seasonal droughts. 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 meteorological variables (air temperature, relative humidity, wind speed and global radiation) and satellite data (surface temperature, NDVI, albedo and LAI). These variables might be simulated for a long period in order to be adequate for drought studies purposes. However, available meteorological observations may often be insufficient to account for the temporal variability present in the study area (sparsity of gauged networks, the lack of long observation periods and the presence of numerous gaps). Our first objective is then to adapt a stochastic weather generator "MetGen" driven by large-scale reanalysis data (about 31 km of spatial resolution) to semi-arid climates and to the sub-daily resolution. MetGen serves to fill in missing data and to provide a temporal extension of multiple meteorological variables. It is compared with two state-of-the-art bias correction methods, univariate and multivariate methods, applied to large-scale reanalysis data. The surrogate series that are either produced by MetGen and the bias correction methods or taken as the un-processed reanalysis data, are evaluated in terms of their ability (1) to reproduce the statistical properties of the meteorological observations and (2) to reproduce energy balance outputs when constrained by observations series. The evaluation of these different statistical methods is performed on a validation period which included the observation period (2011-2016). Then, we used MetGen and the unprocessed reanalyses data to generate meteorological data during the whole study period (2000-2019). These surrogate series are used therefore to constrain the dual-source model Soil Plant Atmosphere and Remote Evapotranspiration (SPARSE) in order to simulate water stress indices SI(SWG) and SI(ERA5) from MetGen and ERA5 reanalyses successively. Stress index anomalies retrieved from SPARSE are then compared to anomalies in other wave lengths in order to assess their consistency, reliability and capacity to detect incipient water stress and early droughts at the kilometer resolution. Those are the root zone soil moisture at low resolution derived from the microwave domain, active vegetation fraction cover deduced from NDVI time series and a uniformized precipitation index UPI as a reference for these analyses. Both thermal stress indices show a good performance to detect water status, especially using SI(SWG) which show more precision and ability to identify incipient water stress. Our analyses are carried on in the Kairouan area in central Tunisia which is subject to semi-arid climate.
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Submitted on : Wednesday, July 6, 2022 - 11:49:13 AM
Last modification on : Friday, July 8, 2022 - 3:21:02 AM


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  • HAL Id : tel-03715302, version 1


Nesrine Farhani. Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse. Météorologie. Université Paul Sabatier - Toulouse III; Université de Carthage (Tunisie), 2022. Français. ⟨NNT : 2022TOU30022⟩. ⟨tel-03715302⟩



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