dc.contributor.author |
Marshall, M
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|
dc.contributor.author |
Tu, K
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|
dc.contributor.author |
Funk, C
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dc.contributor.author |
Michaelsen, J
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dc.contributor.author |
Williams, P
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|
dc.contributor.author |
Williams, C
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dc.contributor.author |
Ardo, J
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dc.contributor.author |
Boucher, M
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|
dc.contributor.author |
Cappelaere, B
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|
dc.contributor.author |
De Grandcourt, A
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|
dc.contributor.author |
Nickless, A
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|
dc.contributor.author |
Nouvellon, Y
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dc.contributor.author |
Scholes, R
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|
dc.contributor.author |
Kutsch, W
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dc.date.accessioned |
2013-07-18T09:51:20Z |
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dc.date.available |
2013-07-18T09:51:20Z |
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dc.date.issued |
2013-03 |
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dc.identifier.citation |
Marshall, M, Tu K, Funk, C, Michaelsen, J, Williams, P, Williams, C, Ardo, J, Boucher, M, Cappelaere, B, De Grandcourt, A, Nickless, A, Nouvellon, Y, Scholes, R and Kutsch, W. 2013. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. Hydrology and Earth System Sciences, vol. 17(3), pp 1079 - 1091 |
en_US |
dc.identifier.issn |
1027-5606 |
|
dc.identifier.uri |
http://www.hydrol-earth-syst-sci.net/17/1079/2013/hess-17-1079-2013.pdf
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|
dc.identifier.uri |
http://hdl.handle.net/10204/6892
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|
dc.description |
Copyright: 2013 European Geosciences Union. This is an Open Access journal. This journal authorizes the publication of the information herewith contained. Published in Hydrology and Earth System Sciences, vol. 17(3), pp 1079 - 1091 |
en_US |
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
European Geosciences Union |
en_US |
dc.relation.ispartofseries |
Workflow;11224 |
|
dc.subject |
Hydrology |
en_US |
dc.subject |
Earth system sciences |
en_US |
dc.subject |
Evapotranspiration |
en_US |
dc.subject |
Hydrology Research Laboratory |
en_US |
dc.subject |
Food insecurity |
en_US |
dc.title |
Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Marshall, M., Tu, K., Funk, C., Michaelsen, J., Williams, P., Williams, C., ... Kutsch, W. (2013). Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. http://hdl.handle.net/10204/6892 |
en_ZA |
dc.identifier.chicagocitation |
Marshall, M, K Tu, C Funk, J Michaelsen, P Williams, C Williams, J Ardo, et al "Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach." (2013) http://hdl.handle.net/10204/6892 |
en_ZA |
dc.identifier.vancouvercitation |
Marshall M, Tu K, Funk C, Michaelsen J, Williams P, Williams C, et al. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. 2013; http://hdl.handle.net/10204/6892. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Marshall, M
AU - Tu, K
AU - Funk, C
AU - Michaelsen, J
AU - Williams, P
AU - Williams, C
AU - Ardo, J
AU - Boucher, M
AU - Cappelaere, B
AU - De Grandcourt, A
AU - Nickless, A
AU - Nouvellon, Y
AU - Scholes, R
AU - Kutsch, W
AB - 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.
DA - 2013-03
DB - ResearchSpace
DP - CSIR
KW - Hydrology
KW - Earth system sciences
KW - Evapotranspiration
KW - Hydrology Research Laboratory
KW - Food insecurity
LK - https://researchspace.csir.co.za
PY - 2013
SM - 1027-5606
T1 - Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach
TI - Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach
UR - http://hdl.handle.net/10204/6892
ER -
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en_ZA |