Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems
- Publication Type:
- Journal Article
- Citation:
- Ecohydrology, 2018, 11 (5)
- Issue Date:
- 2018-07-01
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| 2018-ECOHY-GAN-RONG.pdf | Published Version | 1.35 MB |
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Copyright © 2018 John Wiley & Sons, Ltd. Accurate quantification of terrestrial evapotranspiration and ecosystem productivity is of significant merit to better understand and predict the response of ecosystem energy, water, and carbon budgets under climate change. Existing diagnostic models have different focus on either water or carbon flux estimates with various model complexity and uncertainties induced by distinct representation of the coupling between water and carbon processes. Here, we propose a diagnostic model to estimate evapotranspiration and gross primary production that is based on biophysical mechanism yet simple for practical use. This is done by coupling the carbon and water fluxes via canopy conductance used in the Penman–Monteith–Leuning equation (named as PML_V2 model). The PML_V2 model takes Moderate Resolution Imaging Spectrometer leaf area index and meteorological variables as inputs. The model was tested against evapotranspiration and gross primary production observations at 9 eddy-covariance sites in Australia, which are spread across wide climate conditions and ecosystems. Results indicate that the simulated evapotranspiration and gross primary production by the PML_V2 model are in good agreement with the measurements at 8-day timescale, indicated by the cross site Nash–Sutcliffe efficiency being 0.70 and 0.66, R2 being 0.80 and 0.75, and root mean square error being 0.96 mm d−1 and 1.14 μmol m−2 s−1 for evapotranspiration and gross primary production, respectively. As the PML_V2 model only requires readily available climate and Moderate Resolution Imaging Spectrometer vegetation dynamics data and has few parameters, it can potentially be applied to estimate evapotranspiration and carbon assimilation simultaneously at long-term and large spatial scales.
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