In the sub-Saharan Sahel, energy and water cycling at the land surface is pivotal for the regional climate, water resources and land productivity, yet it is still very poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of long-term average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt: rainfed millet crop and fallow bush. These estimates build on the combination of a 7-year field data set from two typical plots in southwestern Niger with detailed physically based soil–plant–atmosphere modeling, yielding a continuous, comprehensive set of water and energy flux and storage variables over this multiyear period. In the present case in particular, blending field data with mechanistic modeling makes the best use of available data and knowledge for the construction of the multivariate time series. Rather than using the model only to gap-fill observations into a composite series, model–data integration is generalized homogeneously over time by generating the whole series with the entire data-constrained model simulation. Climatological averages of all water and energy variables, with associated sampling uncertainty, are derived at annual to sub-seasonal scales from the time series produced. Similarities and differences in the two ecosystem behaviors are highlighted. Mean annual evapotranspiration is found to represent ~82–85% of rainfall for both systems, but with different soil evaporation/plant transpiration partitioning and different seasonal distribution. The remainder consists entirely of runoff for the fallow, whereas drainage and runoff stand in a 40–60% proportion for the millet field. These results should provide a robust reference for the surface energy- and water-related studies needed in this region. Their significance and the benefits they gain from the innovative data–model integration approach are thoroughly discussed. The model developed in this context has the potential for reliable simulations outside the reported conditions, including changing climate and land cover.

In many tropical areas, evapotranspiration is the most important but least known component of the water cycle. An innovative method, named E3S (for EVASPA S-SEBI Sahel), was developed to provide spatially-distributed estimates of daily actual evapotranspiration (ET_d) from remote sensing data in the Sahel. This new method combines the strengths of a contextual approach that is used to estimate the evaporative fraction (EF) from surface temperature vs. albedo scatterograms and of an ensemble approach that derives ET_d estimates from a weighted average of evapotranspiration estimated from several EF methods. In this work, the two combined approaches were derived from the simplified surface energy balance index (S-SEBI) model and the EVapotranspiration Assessment from SPAce (EVASPA) tool. Main innovative aspects concern (i) ensemble predictions of ET_d through the implementation of a dynamic weighting scheme of several evapotranspiration estimations, (ii) epistemic uncertainty of the estimation of ET_d from the analysis of the variability of evapotranspiration estimates, and (iii) a new cloud filtering method that significantly improves the detection of cloud edges that negatively affect EF determination. E3S was applied to MODIS/TERRA and AQUA datasets acquired during the 2005–2008 period over the mesoscale AMMA-CATCH (Analyse Multidisciplinaire de la Mousson Africaine—Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique) observatory in South-West Niger. E3S estimates of instantaneous and daily available energy, evaporative fraction, and evapotranspiration were evaluated at a local scale based on two field-monitored plots representing the two main ecosystem types in the area—a millet crop and a fallow savannah bush. In addition to these ground-based observations, the local scale evaluation was performed against continuous simulations by a locally-calibrated soil-vegetation-atmosphere transfer model for the two plots. The RMSE (root mean square error) from this comparison for E3S’s ET_d estimates from combined AQUA/TERRA sources was 0.5 mm·day⁻¹, and the determination coefficient was 0.90. E3S significantly improved representation of the evapotranspiration seasonality, compared with a classical implementation of S-SEBI or with the original EVASPA’s non-weighted ensemble scheme. At the mesoscale, ET_d estimates were obtained with an average epistemic uncertainty of 0.4 mm·day⁻¹. Comparisons with the reference 0.25°-resolution GLEAM (global land evaporation Amsterdam model) product showed good agreement. These results suggested that E3S could be used to produce reliable continuous regional estimations at a kilometric resolution, consistent with land and water management requirements in the Sahel. Moreover, all these innovations could be easily transposed to other contextual approaches.

It is crucial to understand the adaptive mechanisms of woody plants facing periodic drought to assess their vulnerability to the increasing climate variability predicted in the Sahel. Guierasenegalensis J.F.Gmel is a semi-evergreen Combretaceae commonly found in Sahelian rangelands, fallows and crop fields because of its value as an agroforestry species. We compared canopy leafing, and allometric measurements of leaf area, stem area and stem length and their relationships with leaf water potential, stomatal conductance (g_s) and soil-to-leaf hydraulic conductance (K_S–L), in mature and current-year resprouts of G. senegalensis in Sahelian Niger. In mature shrubs, seasonal drought reduced the ratio of leaf area to cross-sectional stem area (A_L: A_S), mainly due to leaf shedding. The canopy of the current-year resprouts remained permanently leafed as the shrubs produced leaves and stems continuously, and their A_L: A_S ratio increased throughout the dry season. Their K_S–L increased, whereas g_s decreased. West, Brown and Enquist's (WBE) model can thus describe allometric trends in the seasonal life cycle of undisturbed mature shrubs, but not that of resprouts. Annual clear cutting drives allometric scaling relationships away from theoretical WBE predictions in the current-year resprouts, with scaling exponents 2.5 times greater than those of mature shrubs. High K_S–L (twice that of mature shrubs) supports this intensive regeneration process. The adaptive strategy described here is probably common to many woody species that have to cope with both severe seasonal drought and regular disturbance over the long term.