1. Effects of Intra-Biome Variations in the Tropical Rainforest Biophysical Parameters on the Fluxes Between the Surface and the Atmosphere Hewlley Acioli Imbuzeiro 1, Gleidson Chales Botelho Baleeiro 1, Marcos Heil Costa 1 1 Department of Agricultural Engineering, Federal University of Viçosa, Av. P. H. Rolfs, s/n, Viçosa – MG, 36570-000, Brazil, hewlley@vicosa.ufv.br ID-09 ABSTRACT It is well known that fluxes of water, energy and carbon between tropical rainforests and the atmosphere vary considerably from site to site. This variation may be the result of climate variations only, or could be the result of a combination of different climate, vegetation and soil characteristics at each site. In this work, we propose a modeling experiment to evaluate what are the sources of variations of fluxes across tropical rainforest sites. The methodology consists of calibrating an ecosystem model (IBIS) using data collected at five micrometeorological sites in areas of primary forest. The model is initially calibrated using data from each individual site, and then re-calibrated using data from all sites. By comparing the biophysical parameters obtained in the individual calibration with the parameters obtained in the multi-site calibration, we can determine whether vegetation biophysical parameters are significantly different among sites, and to what extent their intra-biome variability is responsible for the variations on the fluxes between the surface and the atmosphere. In addition, these results will allow an estimate of which errors are expected when a vast biome like the Amazon tropical rainforest is represented by a single set of parameters in large-scale dynamical ecosystem models or in the context of climate models. OBJECTIVES  Evaluate what are the sources of variation of fluxes across tropical rainforest sites through a modeling experiment, comparing the model calibrated at each site individually with the multi-site calibration;  Determine whether the biophysical parameters of the vegetation are significantly different inside a same biome, and to what extent the intra-biome variability of those parameters is responsible for the variations in the fluxes between the surface and the atmosphere that happens at different sites in the same vegetation type;  Determine which errors are expected if we use a single set of parameters in all sites and, by extension, which errors are expected if we use a single set of parameters in Amazonia as a whole;  Evaluate whether the variability in fluxes across sites are the result of climate variations only, or if they are due to variations of climate and and vegetation characteristics at each site;  Provide an initial answer about the convenience of continuing to use a generic calibration for all of the cells coverred by a single biome in a climate model, and decide whether it is necessary to introduce modifications in the way models work, in order to consider eventual spatial variations in the model parameters. METHODOLOGY DESCRIPTION OF THE SITES The data used of calibrating an ecosystem model IBIS were collected at five micrometeorological sites in areas of primary forest of the project LBA: Reserves Jaru, Caxiuanã, Cuieiras, Tapajós km 67 and Tapajós km 83. The Biological Reserve of Jaru (10º46'S, 61º56'W) it is located approximately 80 km to the north of Ji-Paraná-RO, with canopy medium height of 30 m (Alvalá, et al., 2002). In this reserve there are available data of short wave, long wave and PAR radiation, relative humidity, atmospheric pressure, temperature of the air and of the surface, precipitation, horizontal wind speed and direction and fluxes of CO 2, for the period of March of 1999 to December of 2000. The Reserve of Caxiuanã (01º42'30"S, 51º31'45"W) is located in the Melgaço-PA, about 350 km to the west of Belém- PA. The extension of the forest is of 33000 hectares, with a dense plain of firm earth, with annual precipitation average of 2500 mm, height of the canopy of approximately 35 m and leaf area index varying between five and six (Carswell et al., 2002). In this reserve there are data on air temperature and relative humidity, fluxes of CO 2, sensible heat, latent heat and heat in the soil, horizontal wind speed, incident short wave radiation, net long wave radiation, albedo of the canopy, precipitation and atmospheric pressure, all measured above the canopy (Santos and Costa, 2003), for the years 2000 and 2001. The Reserve of Cuieiras (02º36'33"S, 60º12'34"W) is located 60 km to the north of Manaus-AM. The site is located on an extense plateau with a tropical forest with canopy height of 30 m and a LAI that varies between approximately five in the dry season to about six in the wet season. This site has vegetation and topography typical of the central Amazonia (Malhi et al., 2002). The available data in this site are fluxes of CO2, sensible heat, latent heat, soil heat, horizontal wind speed, incident short wave radiation, net long wave radiation, incident PAR, precipitation, air temperature, soil moisture and atmospheric pressure for the period of June of 1999 to November of 2001. The National Forest of Tapajós is located at the margin of the highway Santarém-Cuiabá (BR-163) and it has experimental sites close to the entrances of km 67 (02º51'25"S, 54º58'15"W) and km 83 (03º03'01"S, 54º56'32"W) of this highway (Goulden et al., 2003). The vegetation is a dense tropical rainforest with emerging trees, with a canopy height of approximately 40 m. The forest extends 5 km to the east, 8 km to the south and 40 km to the north before reaching the pasture (Saleska et al., 2003). In these sites, there are available data of incident solar radiation, net radiation, PAR, relative humidity, atmospheric pressure, air and soil temperature, precipitation, horizontal wind speed and fluxes of CO2, sensible heat, latent heat and soil heat for the interval between April 2001 to May 2002 for the km 67 site, and from June 2000 to June 2001 for the km 83 site. DESCRIPTION OF THE DATA Temperature and specific humidity of the air, precipitation, speed of the wind, atmospheric pressure, fluxes of radiation of short wave and long wave are the variables used to force the model IBIS. Yet the fluxes of sensible heat, latent heat, soil heat, net radiation and net ecosystem exchange will be used to validate the model results. Figure 1 displays the gaps of meteorological and flux data in each site, for four of the five sites used. CALIBRATION OF THE MODEL IN INDIVIDUAL SITES The calibration process involves a reasonably large number of simulations selecting, in each one of them, pre-selected values for specific model parameters. In each simulation, the results of the model output are compared against observed data, looking to minimize the absolute value of the average error and root mean square error at the same time (RMSE). In this work, we intend to introduce and to test the concept of hierarchical calibration of Land Surface Models. This concept tries to mirror the hierarchy of the processes that determine the functioning of an ecosystem.  Balance of solar radiation, source of photosynthesis and heating of the entire system;  Net Radiation, that includes the solar radiation and the infrared radiation;  The partition of the net radiation in sensible heat flux, latent heat flux and soil heat flux, in addition to the daytime carbon balance;  The night-time carbon flux, representing the ecosystem respiration;  The seasonal processes, like allocation of carbon in leaves and stems. This methodology also recognizes that the slower processes are an integration of the faster processes, because the model will be calibrated initially for the instantaneous processes (net radiation), followed by the processes on the time scale from minutes to hours (fluxes of energy and mass), followed by the daily time-scale processes (daytime/nighttime exchange of CO 2 ), and finally by the seasonal processes (allocation of carbon). As the processes are interconnected (for example, the partition of the net radiation between sensible and latent heat affects the temperature of the canopy, which affects the infrared radiation emission), not all the times a satisfactory calibration will be reached in the first attempt. This procedure is then repeated, in an iterative process, until a satisfactory calibration is reached. The quality of the calibration will be evaluated according to the mean error, the root mean square error (RMSE) and an analysis of the cumulative behavior of the model results with respect to the data. We believe that, using an iterative procedure that follows the hierarchical sequence, we will obtain a robust calibration and parameters consistent with the way an ecosystem operates. MULTI-SITE CALIBRATION It is well known that fluxes of water, energy and carbon between tropical rainforests and the atmosphere vary considerably from site to site. However, it still is not known if those variations are due only to the variations of the climate among sites, or if there are variations in the vegetation and soils that also contribute to the differences in the fluxes. If there are variations in the way the ecosystem functions, it is also not known which important parameters to the functioning of the ecosystem present spatial variability. To determine the spatial variability, the procedure described in the section above it will be repeated for the five sites. The parameters obtained at each site will be compared, when it will be possible to know how the ecosystem parameterization affects the surface fluxes (energy, water and carbon) in different sites. If the calibrated parameters are similar among themselves in all sites, this will indicate that the difference observed among sites is due only to variations in the climate from site to site; on the other hand, if the calibrated parameters are considerably different among themselves, the difference observed in the behavior of the sites is probably due to both variations in climate as well as in the ecosystem. In addition, the model IBIS will be calibrated using the same procedure of the section above, however looking for a single calibration (generic) for all sites. The difference between the simulated fluxes using the generic calibration and each individual calibration will indicate which errors are expected if we use a single group of parameters in all sites. Depending on the obtained results, we will decide if it is convenient to continue to use a generic calibration for all of the cells with tropical rainforest, or if it is necessary to includes spatial variability in the biophysical parameters of the model. REFERENCES Alvalá, R. C. S., Gielow, H. R., Freitas, H. C., Lopes, J. M., Manzi, A. O., von Randow, C., Dias M. A. F. S., Cabral, O. M. R., e Waterloo, M. J., Intradiurnal and seasonal variability of soil temperature, heat flux, soil moisture content, and thermal properties under forest and pasture in Rondônia. J. Geophysical Research, v.107: doi: 10.1029/2001JD000599, 2002. Carswell, F. E., Costa, A. L., Palheta, M., Malhi, Y., Meir, P., Costa, P. R., Ruivo, M. de L., Leal, L. do S. M., Costa, J. M. N., Clement, R. J., e Grace, J., Seasonality in CO 2 and H 2 O flux at in Amazonian rain forest. J. Geophysical Research, v.107: 8076 doi: 10.1029/2000JD000284, 2002. Goulden, M. L., Miller, S. D., Rocha, H. R., Menton, M. C., Freitas, H. C., Figueira, A. M. S. e Sousa, C. A. D., Diel and seasonal patterns of tropical forest CO 2 exchange. Ecological Applications, no prelo. Malhi, Y., Pegoraro, E., Nobre, A. D., Pereira, M. G. P., Grace, J., Culf, A. D., e Clement R., Energy and water dynamics of a central Amazonian rain forest. J. Geophysical Research, v.107, NO. D20, 8061, doi: 10.1029/2001JD000623, 2002. Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, S. C. Wofsy, Rocha, H. R., Camargo, P. B., Crill P., Daube, H. C. de Freitas, Hutyra L., Keller, M., Kichhoff V., Menton M., Munger J. W., Pyle E. H., Rice A. H., e Silva H., Carbon in Amazon forests: Unexpected seasonal fluxes and disturbance – induced losses. Science, v.302: 1554-1557, 2003. Santos, S.N.M. e Costa, M. H., Simulações de fluxo de carbono em um ecossistema de Floresta Tropical. Revista Brasileira de Meteorologia, v.18: 87- 96, 2003. Pitman, A. J., The evolution of, and revolution in, land surface schemes designed for Climate Models. J. Climatology, v. 23: 479-510, 2003. ATMOSPHERE (prescribed atmospheric datasets) canopy physics energy balance water balance aero - dynamics soil physics energy balance water balance plant physiology photosynthesis & leaf respiration stomatal conductance canopy nitrogen allocation LAND SURFACE MODULE gross photosynthesis foliage respiration, & optimal C/N ratios for each plant functional type MODULE gross primary production total respiration net primary production allocation turnover growth of leaves stems & roots mortality & disturbance BELOW GROUND CARBON & NITROGEN CYCLING MODULE PHENOLOGY MODULE carbon cycling decomposition of litter & soil organic matter soil respiration nitrogen cycling nitrogen mineralization nitrificationdenitrificationbudburstsenescencedormancy Framework of the IBIS terrestrial ecosystem model. The characteristic time scales of the processes are indicated at the bottom of the figure. vegetation structure and biomass litter fall nitrogen supply t ~ weeks to years daily LAI Temperature, photosynthesis t ~ minutes to hours