Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach
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 NationalWeather 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.
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dig-cirad-fr-5680612024-12-20T10:32:59Z http://agritrop.cirad.fr/568061/ http://agritrop.cirad.fr/568061/ Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach. Marshall K., Tu Kevin P., Funk C., Michaelsen J., Williams P., Williams Christopher, Ardö Junas, Boucher M., Cappelaere Bernard, De Grandcourt Agnès, Nickless A., Nouvellon Yann, Scholes Robert J., Kutsch Werner L.. 2013. Hydrology and Earth System Sciences, 17 : 1079-1091.https://doi.org/10.5194/hess-17-1079-2013 <https://doi.org/10.5194/hess-17-1079-2013> Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach Marshall, K. Tu, Kevin P. Funk, C. Michaelsen, J. Williams, P. Williams, Christopher Ardö, Junas Boucher, M. Cappelaere, Bernard De Grandcourt, Agnès Nickless, A. Nouvellon, Yann Scholes, Robert J. Kutsch, Werner L. eng 2013 Copernicus GmbH Hydrology and Earth System Sciences U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 Sahel Afrique au sud du Sahara http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 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 NationalWeather 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/568061/1/document_568061.pdf application/pdf Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.5194/hess-17-1079-2013 10.5194/hess-17-1079-2013 info:eu-repo/semantics/altIdentifier/doi/10.5194/hess-17-1079-2013 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.5194/hess-17-1079-2013 |
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U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 |
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U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 Marshall, K. Tu, Kevin P. Funk, C. Michaelsen, J. Williams, P. Williams, Christopher Ardö, Junas Boucher, M. Cappelaere, Bernard De Grandcourt, Agnès Nickless, A. Nouvellon, Yann Scholes, Robert J. Kutsch, Werner L. Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
description |
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 NationalWeather 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. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche F60 - Physiologie et biochimie végétale P40 - Météorologie et climatologie télédétection modèle mathématique évapotranspiration transpiration changement climatique couverture végétale écosystème végétation échange d'énergie technique de prévision http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_2741 http://aims.fao.org/aos/agrovoc/c_7871 http://aims.fao.org/aos/agrovoc/c_1666 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_2482 http://aims.fao.org/aos/agrovoc/c_8176 http://aims.fao.org/aos/agrovoc/c_2568 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_166 |
author |
Marshall, K. Tu, Kevin P. Funk, C. Michaelsen, J. Williams, P. Williams, Christopher Ardö, Junas Boucher, M. Cappelaere, Bernard De Grandcourt, Agnès Nickless, A. Nouvellon, Yann Scholes, Robert J. Kutsch, Werner L. |
author_facet |
Marshall, K. Tu, Kevin P. Funk, C. Michaelsen, J. Williams, P. Williams, Christopher Ardö, Junas Boucher, M. Cappelaere, Bernard De Grandcourt, Agnès Nickless, A. Nouvellon, Yann Scholes, Robert J. Kutsch, Werner L. |
author_sort |
Marshall, K. |
title |
Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
title_short |
Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
title_full |
Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
title_fullStr |
Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
title_full_unstemmed |
Improving operational land surface model canopyevapotranspiration in Africa using a direct remote sensing approach |
title_sort |
improving operational land surface model canopyevapotranspiration in africa using a direct remote sensing approach |
publisher |
Copernicus GmbH |
url |
http://agritrop.cirad.fr/568061/ http://agritrop.cirad.fr/568061/1/document_568061.pdf |
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