TURBULENT FLUX VARIABILITIES OVER THE ARA WATERSHED Moussa Doukouré, Sandrine Anquetin, Jean-Martial Cohard Laboratoire d’étude des Transferts en Hydrologie et Environnement (LTHE) Grenoble, France

INTRODUCTION The determination of turbulent fluxes is very important for the closure of energy balance equation. In an agrometeorological point of view : Assessment of the evapotranspiration term that quantifies water needed by plants ► Evaluate water scarcity and agricultural strategy. In an hydrological point of view : Close the water balance at the watershed scale ► Evaluate the water ressources avalaible for both population needs and energy production Usual technical measurement - Point measurement

Difficulties in measuring Surface heterogeneities (temperature, soil humidity, topography) and atmospheric state generate secondary circulation and rolls that make difficult the accurate measurement of turbulent fluxes with conventional measurements (EC) Simulated water vapour mixing ratio over Hamdallaye watershed (Niger) Simulated organized turbulent structures INTRODUCTION (Steinfeld et al, 2006) (Lothon et al,2007) Problem of averaged data representative of the surfaces Problem of flux sources control for more acurate analyses

Better estimation from the time and space average than from point measurement Need to characterize the variability below the LAS path, wind direction and stability parameter ►Take into account the 3 dimensionnal (3D) behavior of the turbulent fluxes ► Analyse the footprint Another concept of measurement: Large Aperture Scintillometer (LAS) Savannah bushes Savannah trees Cultures and/or bare soils Case Study : The ARA watershed (~12 km 2 ) in Nalohou (North part of Benin) INTRODUCTION Also a problem of representativeness !!!

Topography, atmospheric and ground forcings for a case study of 10/04/2006 ►Use Meso-NH model (Lafore et al, 1998) Use of atmospheric 3D model including Large-Eddy Simulation (LES) approach can resolve part of these problems. Use of an atmospheric model coupled with land surface scheme METHODOLOGY Perform sensitivity studies ► Impact on the turbulent flux variabilities

Δx= Δy=18km Cyclic boundary conditions Δx= Δy=6km Open boundary conditions Δx= Δy=1km Open boundary conditions Δx= Δy=2km Open boundary conditions Δx= Δy=250m Open boundary conditions Altitude (m) MODELING STRATEGY  5 nested domains (Two-way) Topography - SRTM 90m

ECMWF radiation scheme SURFEX soil-vegetation-atmospheric-transfer model (Noilhan and Planton, 1989) ECOCLIMAP (Masson et, al 2003) (1km 2 ) for vegetation parameters LES, sub-grid paramerization of Deardorff Clear sky conditions MODELING STRATEGY Homogeneous sahelian woodland Spot vegetation at 20m resolution ( Zin et al, 2009) Can ECOCLIMAP be usefull for our study ?

Initialization : sounding at Parakou,10/04/2006 at AM MODELING STRATEGY Vertical profil of  v at 10:30, 10/04/ /04/ AM 11/04/ AM Extraction of data at each time step. 20 minutesSimulation strategy 24h simulation, diurnal cycle, radiation scheme active radiation scheme OFF

PRELIMINARY RESULTS: WIND ROTATION Initial South-Westerly wind forcing Final North and North-West wind Δx= Δy=18km Cyclic boundary conditions Dynamic forcing no maintained

PRELIMINARY RESULTS: FLUX VARIABILITY OVER CATCHMENT SCALE Turbulent latent heat flux Turbulent sensible heat flux ► Highest values on the crests and advected in lower zones W/m 2

PRELIMINARY RESULTS: IMPACT OF THE TOPOGRAPHY ON THE FLUX VARIABILITIES m.s -1 Vertical wind over flat terrain Vertical wind over the « real » topography Same meteorological forcings ! Vertical wind organized as convective rolls Vertical motion organized according to orographic structures ► Highest values over flat terrain rather than over « real » topography. ► Vertical motion modified by the orography structure

PRELIMINARY RESULTS: SCINTILLOMETER FOOPRINT ANALYSIS Latent heat flux over flat terrain Latent heat flux with topography Min : 122 W/m2 Max : 137 W/m2 Mean : 127 W/m2 Std : 4.4 W/m2 Min : 153 W/m2 Max : 169 W/m2 Mean : 163 W/m2 Std : 4.4 W/m2 Latent Heat Flux « seen » by the scintillometer The local wind has an impact on the shape of the footprint

CONCLUSION AND PRESPECTIVES Improve the vegetation cover description by using satellite analysis (Zin et al., 2009) ► - Wind rotation probably due to cyclic boundary condition - High values of fluxes observed over crests and advected into the lower zones - Wind circulation influenced by the horizontal gradient of turbulent fluxes Conclusion Perspectives Attempt to improve simulations with OPEN boundary condition that is better to maintain wind direction ECOCLIMAP Satelite data Probably use of ECMWF reanalysis as forcing data !!!

Many thanks !