Geography
Document Type
Article
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. © 2013 Author(s).
Publication Title
Hydrology and Earth System Sciences
Publication Date
2013
Volume
17
Issue
3
First Page
1079
Last Page
1091
ISSN
1027-5606
DOI
10.5194/hess-17-1079-2013
Keywords
biome, calibration, canopy, climate change, data set, error analysis, evapotranspiration, numerical model, remote sensing, vegetation structure, water supply
Repository Citation
Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, Christopher A.; Ardö, J.; Boucher, M.; Cappelaere, B.; De Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; and Kutsch, W., "Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach" (2013). Geography. 899.
https://commons.clarku.edu/faculty_geography/899
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Conditions
Published source must be acknowledged with citation:
Marshall, M., et al. "Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach." Hydrology and Earth System Sciences 17.3 (2013): 1079-1091.
