Combining surface reanalysis and remote sensing data for monitoring evapotranspiration

Abstract

Abstract. Climate change is expected to have the greatest impact on the world's 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 actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.