Evaluation of the Advanced-Canopy-Atmosphere-Surface Algorithm (ACASA Model) Using Eddy Covariance Technique Over Sparse Canopy (Contributo in atti di convegno)

Type
Label
  • Evaluation of the Advanced-Canopy-Atmosphere-Surface Algorithm (ACASA Model) Using Eddy Covariance Technique Over Sparse Canopy (Contributo in atti di convegno) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Alternative label
  • Marras S, Spano D, Sirca C, Duce P, Snyder RL, Pyles RD, Paw U KT (2008)
    Evaluation of the Advanced-Canopy-Atmosphere-Surface Algorithm (ACASA Model) Using Eddy Covariance Technique Over Sparse Canopy
    in American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2008
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Marras S, Spano D, Sirca C, Duce P, Snyder RL, Pyles RD, Paw U KT (literal)
Pagina inizio
  • B31A-0292 (literal)
Pagina fine
  • B31A-0292 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 89 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 53 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Economics and Woody Plant Ecosystem, University of Sassari, Italy; Institute of Biometeorology, National Research Council, Italy; Department of Land, Air and Water Resources, University of California, Davis, United States (literal)
Titolo
  • Evaluation of the Advanced-Canopy-Atmosphere-Surface Algorithm (ACASA Model) Using Eddy Covariance Technique Over Sparse Canopy (literal)
Abstract
  • Land surface models are usually used to quantify energy and mass fluxes between terrestrial ecosystems and atmosphere on micro- and regional scales. One of the most elaborate land surface models for flux modelling is the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) model, which provides micro-scale as well as regional-scale fluxes when imbedded in a meso-scale meteorological model (e.g., MM5 or WRF). The model predicts vegetation conditions and changes with time due to plant responses to environment variables. In particular, fluxes and profiles of heat, water vapor, carbon and momentum within and above canopy are estimated using third-order equations. It also estimates turbulent profiles of velocity, temperature, humidity within and above canopy, and CO2 fluxes are estimated using a combination of Ball-Berry and Farquhar equations. The ACASA model is also able to include the effects of water stress on stomata, transpiration and CO2 assimilation. ACASA model is unique because it separates canopy domain into twenty atmospheric layers (ten layers within the canopy and ten layers above the canopy), and the soil is partitioned into fifteen layers of variable thickness. The model was mainly used over dense canopies in the past, so the aim of this work was to test the ACASA model over a sparse canopy as Mediterranean maquis. Vegetation is composed by sclerophyllous species of shrubs that are always green, with leathery leaves, small height, with a moderately sparse canopy, and that are tolerant at water stress condition. Eddy Covariance (EC) technique was used to collect continuous data for more than 3 years period. Field measurements were taken in a natural maquis site located near Alghero, Sardinia, Italy and they were used to parameterize and validate the model. The input values were selected by running the model several times varying the one parameter per time. A second step in the parameterization process was the simultaneously variation of some parameters. ACASA simulations were compared with measured fluxes of net radiation (Rn), sensible heat (H), latent heat (LE), soil heat (G), and CO2 fluxes at half-hourly time scale. Statistical analysis was made to evaluate model performance. Comparisons between simulated and measured values were evaluated using linear regression, the root mean squared error (RMSE), mean absolute error (RA), and mean bias error (MBE). Modeled data showed a good energy balance closure. ACASA estimates of net radiation were excellent. Sensible (H) and latent heat (LE) flux predictions exhibited only small differences between modeled and observed data. The ACASA model was able to capture the seasonal variation in CO2 flux. Net Ecosystem Exchange (NEE) showed the typical summer decrease due to drought induced water stress, and the simulations predicted the lower CO2 flux. Differences between simulated and observed fluxes were significant at 0.001 probability. ACASA simulations, therefore, are considered good. So, we can say that the use of ACASA to predict energy and mass fluxes between the vegetation and atmosphere is promising, and it could greatly improve our ability to estimate fluxes over natural ecosystems at both local and regional scales. (literal)
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