Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem

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dc.contributor.author Khosa, Floyd V.
dc.contributor.author Feig, Gregor Timothy
dc.contributor.author Van der Merwe, Martina R.
dc.contributor.author Mateyisi, Mohau Jacob
dc.contributor.author Mudau, Azwitamisi E.
dc.contributor.author Savage, Michael J.
dc.date.accessioned 2019-09-20T10:45:00Z
dc.date.available 2019-09-20T10:45:00Z
dc.date.issued 2019-12
dc.description.abstract Evapotranspiration (ET) plays a crucial role in the land-atmosphere interaction and climate variability, especially in arid and semi-arid areas. Accurate estimates of ET are important in hydrological and climate modeling. This study evaluates eight ET data products from different models used for ET estimation. The data products are classified into three main categories depending on the type of modeling approaches: namely process-based land surface model, empirical models, and satellite data derived estimates. The different model estimates are evaluated against in situ measurements from the Skukuza flux tower which is situated in a semi-arid savanna in South Africa. The correlation score and cantered root mean square error computed on monthly ET averages indicate that the satellite-derived model and land surface model estimates are closer to the observed ET signal for the Skukuza site, both in-phase and magnitude. The empirical models' outputs tend to reflect a relatively pronounced departure from observations in magnitude. The normalised mean bias computed for different seasons reveals that the estimates from all modeling approaches are close to the observed signal during the transition period (March–May) to the austral summer. In general, all models overestimate ET during summer and underestimate it in winter. A qualitative analysis of the year-to-year variation for different seasons reveals that all model estimates are qualitatively consistent with the observed seasonal pattern of the signal. Satellite and process-based land surface models (LSMs) also show a response to extremes events such as drought years. The study identifies satellite-derived model outputs as a candidate for understanding spatio-temporal variability of ET across different landscapes within the study region, and process-based models to potentially be used for climate change impact studies on ET. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.librarian hj2019 en_ZA
dc.description.sponsorship The Council for Scientific and Industrial Research [project number EEGC030]. en_ZA
dc.description.uri https://www.elsevier.com/locate/agrformet en_ZA
dc.identifier.citation Khosa, F.V., Feig, G.T., Van der Merwe, M.R. et al. 2019, 'Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem', Agricultural and Forest Meteorology, vol. 279, art. 107706, pp. 1-20. en_ZA
dc.identifier.issn 0168-1923 (print)
dc.identifier.issn 1873-2240 (online)
dc.identifier.other 10.1016/j.agrformet.2019.107706
dc.identifier.uri http://hdl.handle.net/2263/71431
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2019 The Authors. Published by Elsevier B.V. Open access article publish under a Creative Commons license. en_ZA
dc.subject Evapotranspiration (ET) en_ZA
dc.subject Land surface model (LSM) en_ZA
dc.subject Brutsaert–Strickler en_ZA
dc.subject Commmunity atmosphere biosphere land exchange (CABLE) en_ZA
dc.subject Global land evaporation Amsterdam model (GLEAM) en_ZA
dc.subject Granger–Gray en_ZA
dc.subject Szilagyi–Jozsa en_ZA
dc.subject Complementary relationship en_ZA
dc.subject Actual evapotranspiration (AET) en_ZA
dc.subject South Africa (SA) en_ZA
dc.subject Semi-arid savanna ecosystem en_ZA
dc.title Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem en_ZA
dc.type Article en_ZA


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