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Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space-time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), PrecipitationEstimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05 • ) rainfall data for 15 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) over East Africa. The products' assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes.

Hydrology and crop water management require daily values of evapotranspiration ET at different time-space scale. Sun synchronous optical remote sensing, which allows for the assessment of ET with high to moderate spatial resolution, provides instantaneous estimates during satellites overpass. Then, usual solutions consist of extrapolating instantaneous to daily values by assuming that evaporative fraction EF is constant throughout the day, providing that daily available energy AE is known. The current study aims at deriving daily ET values from ASTER derived instantaneous estimates, over an olive orchard in a semi-arid region of Moroccan. It has been shown that EF is almost constant under dry conditions, but it depicts a pronounced concave up shape under wet conditions. A new heuristic parameterization is then proposed, which is based on the combination of routine daily meteorological data for characterizing atmospheric dependence, and on optical remote sensing based estimates of instantaneous EF values to take into account the dependence on soil and vegetation conditions. Using the same type of approach, a similar parameterization is next developed for AE. The validation of both approaches shows good performances. The overall method is finally applied to ASTER data. Though performances are reasonably good, their moderate reduction is ascribed to errors on remotely sensed variables. Future works will focus on method portability since its empirical formulation does not account for the direct stomatal response to water availability, as well as on application over different surface and climate conditions. ª

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Eddy covariance (EC) and large aperture scintillometer (LAS) measurements were collected over an irrigated olive orchard near Marrakech, Morocco. The tall, sparse vegetation in the experimental site was relatively homogeneous, but during irrigation events spatial variability in soil humidity was large. This heterogeneity caused large differences between the source area characteristics of the EC system and the LAS, resulting in a large scatter when comparing sensible heat fluxes obtained from LAS and EC. Radiative surface temperatures were retrieved from thermal infrared satellite images from the Landsat Enhanced Thematical Mapperϩ and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellites. Using these images in combination with an analytical footprint model, footprint-weighted radiative surface temperatures for the footprints of the LAS and the EC system were calculated. Comparisons between the difference in measured sensible heat fluxes and the difference in footprint-weighted radiative surface temperature showed that for differences between the footprint-weighted radiative surface temperatures larger than Ϯ0.5 K, correlations with the difference in measured sensible heat flux were good. It was found that radiative surface temperatures, obtained from thermal infrared satellite imagery, can provide a good indication of the spatial variability of soil humidity, and can be used to identify differences between LAS and EC measurements of sensible heat fluxes resulting from this variability.