Recorded presentation10.5
Modeling of maquis and grape vineyard fluxes with ACASA
Serena Marras, University of Sassari, Sassari, Italy; and D. Spano, C. Sirca, P. Duce, R. L. Snyder, K. T. Paw U, and D. Pyles
The Advanced-Canopy-Atmosphere-Surface Algorithm (ACASA) is a higher-order closure model that uses third-order equations to estimate fluxes of heat, water vapor, and momentum within and above canopy. It also estimates turbulent profiles of velocity, temperature, and humidity within and above canopy. ACASA uses twenty atmospheric layers (ten within the canopy and ten above the canopy) and 15 layers into the soil. Surface energy fluxes are estimated either for wet or dry canopies elements, and they are estimated for nine sunlit angle classes and one shaded class within each canopy layer. The model includes plant physiological components that account for water stress effects on stomata, transpiration, and CO2 assimilation. ACASA also estimates plant and soil respiration, so it can be used with input weather or climate data to estimate CO2 fluxes for use in long-term Net Ecosystem Exchange (NEE), Gross Primary Production (GPP), and ecosystem respiration (Reco). ACASA has been tested over forest ecosystem, but there has been limited application to other ecosystems. The information needed to run the ACASA model includes (1) plant and soil characterization data, (2) 30-minute time-step meteorological data, and (3) initial soil condition data. Input data are either from in situ measurements or were selected from the literature when observations were unavailable. The plant inputs include LAI by canopy layer, leaf optical properties, canopy drag coefficients, basal respiration rate for leaf, stem, roots and microbes, and Q10 values. Soil type is specified using one of 16 USDA classifications, and the wilting point is input. A stress factor is input to provide information on tolerance to water stress. ACASA flux outputs were compared with field measurements from three consecutive years (2004-2006) of measurements over Mediterranean maquis in North-western Sardinia and for two different seven-day periods (2005) and about one month (2006) over a wine grape vineyard in Tuscany near Montelcino, Italy. Modeled data showed a good energy balance closure for both maquis and grapevines ecosystems. Net radiation (Rn) showed the best results when compared with measured values. ACASA's estimates of H flux were good with only small differences between modeled and observed data over maquis. For grapevines, ACASA generally predicted higher H and lower LE than the observations. The ACASA soil heat flux (G) was generally lower than observed in both ecosystems. Since both canopies are sparse and the model assumes a closed canopy, this may be the reason for underestimation of the diurnal cycle. Regarding CO2 flux, the model predictions were good with both positive and negative fluxes well predicted by the model. For both ecosystems, the difference between simulated and observed NEE was low. In maquis, the ACASA model usually was able to capture the seasonal variation in CO2 flux. NEE showed the typical summer decrease due to drought induced water stress, and the simulations predicted the lower CO2 flux. The comparison with observed data was good, and future adjustment of the stress factor inputs may lead to further improvement. The model was also able to capture the increase in respiration (positive NEE values), which occurred after rainfall events in both ecosystems. The use of ACASA to predict energy and mass fluxes between the vegetation and atmosphere is promising. Future results can be improved by making changes to input parameters and by refining the model code to account for ecosystems with sparse canopies. In particular, improvements can be made on the response of the model to water stress and on the estimation of G and LE fluxes. Recorded presentation
Session 10, Global Change Series on Biosphere-Atmosphere Exchange of Water, Carbon and Energy in Managed Ecosystems: Agriculture and Forestry
Friday, 2 May 2008, 9:00 AM-12:00 PM, Floral Ballroom Jasmine
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