Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approachReport as inadecuate




Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach - Download this document for free, or read online. Document in PDF available to download.

Reference: Marshall, M, Tu, K, Funk, C et al., (2013). Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. Hydrology and Earth System Sciences, 17 (3), 1079-1091.Citable link to this page:

 

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

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.

Peer Review status:Peer reviewedPublication status:PublishedVersion:Publisher's version Funder: United States Agency for International Development   Funder: National Aeronautics and Space Administration   Funder: National Aeronautics and Space Administration   Notes:Copyright © Author(s) 2013. This work is distributedunder the Creative Commons Attribution 3.0 License.

Bibliographic Details

Publisher: European Geosciences Union

Publisher Website: http://www.egu.eu/

Publisher: Copernicus Publications

Publisher Website: http://publications.copernicus.org/

Journal: Hydrology and Earth System Sciencessee more from them

Publication Website: http://www.hydrology-and-earth-system-sciences.net/

Issue Date: 2013

pages:1079-1091Identifiers

Urn: uuid:0cced270-a9a1-4c0c-81a2-d35c3ec26e2f

Source identifier: 507351

Eissn: 1607-7938

Doi: https://doi.org/10.5194/hess-17-1079-2013

Issn: 1027-5606 Item Description

Type: Journal article;

Version: Publisher's version Tiny URL: pubs:507351

Relationships





Author: Marshall, M - - - Tu, K - - - Funk, C - - - Michaelsen, J - - - Williams, P - - - Williams, C - - - Ardo, J - - - Boucher, M - -

Source: https://ora.ox.ac.uk/objects/uuid:0cced270-a9a1-4c0c-81a2-d35c3ec26e2f



DOWNLOAD PDF




Related documents