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Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at NationalWeather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices. [ABSTRACT FROM AUTHOR]

Significant, adverse climatic change and drastically increased demographic pressure have strongly affected, in recent years, the hydrology and environment in the semi-arid Sahel region of West Africa. Marked rain deficits have coincided with increased water runoff, meaning less water availability for the vegetation. Conversely, changes in vegetation cover have had strong repercussions on the hydrologic cycle. To study these phenomena, the coupling of two explicit, process-based models, of catchment hydrology and of mixed vegetation cover, respectively, has been undertaken and applied to a 2 km2 site in Niger. Some of the first significant results are presented herein. Some are consistent with intuitive judgments that can be made in the absence of a coupled model, others are much less so and show that representation through model coupling of hydrosphere/biosphere interactions is essential to produce more reliable analyses and projections. In particular, it is found that the relation of biomass productivity to rainfall under this dry, water-limited climate is not as straightforward as one would expect, more specifically, that its main control may not be the total season rainfall. [ABSTRACT FROM AUTHOR]

Abstract: MOD17A2 provides operational gross primary production (GPP) data globally at 1km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (εmax). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate εmax and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred εmax calculated from tower data was higher than the εmax prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2. [Copyright &y& Elsevier]

Abstract: One of the most frequently applied methods for integrating controls on primary production through satellite data is the light use efficiency (LUE) approach, which links vegetation gross or net primary productivity (GPP or NPP) to remotely sensed estimates of absorbed photosynthetically active radiation (APAR). Eddy covariance towers provide continuous measurements of carbon flux, presenting an opportunity for evaluation of satellite estimates of GPP. Here we investigate relationships between eddy covariance estimated GPP, environmental variables derived from flux towers, Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GPP across African savanna ecosystems. MODIS GPP was found to underestimate GPP at the majority of sites, particularly at sites in the Sahel. EVI was found to correlate well with estimated GPP on a site-by-site basis. Combining EVI with tower-measured PAR and evaporative fraction (EF, a measure of water sufficiency) improved the direct relationship between GPP and EVI at the majority of the sites. The slope of this relationship was strongly related to site peak leaf area index (LAI). These results are promising for the extension of GPP through the use of remote sensing data to a regional or even continental scale. [Copyright &y& Elsevier]