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

Marshall, M. ; Tu, K. ; Funk, C. ; Michaelsen, J. ; Williams, P. ; Williams, C. ; Ardö, Jonas LU ; Boucher, M. ; Cappelaere, B. and de Grandcourt, A. , et al. (2013) In Hydrology and Earth System Sciences 17(3). p.1079-1091
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... (More)
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. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Hydrology and Earth System Sciences
volume
17
issue
3
pages
1079 - 1091
publisher
European Geophysical Society
external identifiers
  • wos:000316961300015
  • scopus:84879051189
ISSN
1607-7938
DOI
10.5194/hess-17-1079-2013
language
English
LU publication?
yes
id
b7871180-a286-4eee-9eaf-97078ab31694 (old id 3749507)
date added to LUP
2016-04-01 09:49:41
date last changed
2020-12-08 03:06:08
@article{b7871180-a286-4eee-9eaf-97078ab31694,
  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.},
  author       = {Marshall, M. and Tu, K. and Funk, C. and Michaelsen, J. and Williams, P. and Williams, C. and Ardö, Jonas and Boucher, M. and Cappelaere, B. and de Grandcourt, A. and Nickless, A. and Nouvellon, Y. and Scholes, R. and Kutsch, W.},
  issn         = {1607-7938},
  language     = {eng},
  number       = {3},
  pages        = {1079--1091},
  publisher    = {European Geophysical Society},
  series       = {Hydrology and Earth System Sciences},
  title        = {Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach},
  url          = {http://dx.doi.org/10.5194/hess-17-1079-2013},
  doi          = {10.5194/hess-17-1079-2013},
  volume       = {17},
  year         = {2013},
}