Articles | Volume 17, issue 3
Hydrol. Earth Syst. Sci., 17, 1079–1091, 2013
Hydrol. Earth Syst. Sci., 17, 1079–1091, 2013

Research article 12 Mar 2013

Research article | 12 Mar 2013

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

M. Marshall1, K. Tu2, C. Funk1, J. Michaelsen1, P. Williams1, C. Williams3, J. Ardö4, M. Boucher5, B. Cappelaere5, A. de Grandcourt6,7, A. Nickless8, Y. Nouvellon6,7, R. Scholes8, and W. Kutsch9 M. Marshall et al.
  • 1Department of Geography, Climate Hazards Group, UC Santa Barbara, Santa Barbara, CA, USA
  • 2Department of Integrative Biology, 4007 VLSB, UC Berkeley, Berkeley, CA, USA
  • 3Department of Geography, Clark University, Worcester, MA, USA
  • 4Department of Earth and Ecosystem Sciences, Division of Physical Geography and Ecosystem Analysis, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
  • 5Institut de recherche pour le développement, HydroSciences, BP 64501, 34394 Montpellier Cedex 5, France
  • 6CIRAD, Persyst, UPR80, 34398 Montpellier Cedex 5, France
  • 7CRDPI, BP 1291, Pointe-Noire, Republic of Congo
  • 8Natural Resources and the Environment, Council of Scientific and Industrial Research, Pretoria, South Africa
  • 9Johann Heinrich von Thünen Institute, Institute for Agricultural Climate Research, Bundesallee 50, 38116 Braunschweig, Germany

Abstract. 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 National Weather 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.