Forests are the dominant terrestrial ecosystem on Earth. We review the environmental factors controlling their structure and global distribution and evaluate their current and future trajectory. Adaptations of trees to climate and resource gradients, coupled with disturbances and forest dynamics, create complex geographical patterns in forest assemblages and structures. These patterns are increasingly discernible through new satellite and airborne observation systems, improved forest inventories, and global ecosystem models. Forest biomass is a complex property affected by forest distribution, structure, and ecological processes. Since at least 1990, biomass density has consistently increased in global established forests, despite increasing mortality in some regions, suggesting that a global driver such as elevated CO2 may be enhancing biomass gains. Global forests have also apparently become more dynamic. Advanced information about the structure, distribution, and biomass of the world's forests provides...

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The Structure, Distribution, and Biomass of the World's Forests

. We present the application of a global carbon cycle modeling system to the estimation of monthly regional CO 2 fluxes from the column-averaged mole fractions of CO 2 ( X CO 2 ) retrieved from spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to observed atmospheric CO 2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface p CO 2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO 2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from GOSAT FTS SWIR Level 2 X CO 2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO 2 data. Our result indicated that adding the GOSAT X CO 2 retrievals to the GLOBALVIEW data in the flux estimation brings changes to fluxes of tropics and other remote regions where the surface-based data are sparse. The uncertainties of these remote fluxes were reduced by as much as 60% through such addition. Optimized fluxes estimated for many of these regions, were brought closer to the prior fluxes by the addition of the GOSAT retrievals. In most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variabilities estimated with the component models.

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Regional CO 2 flux estimates for 2009–2010 based on GOSAT and ground-based CO 2 observations

Coarse woody debris (CWD, dead wood sections a parts per thousand yen10 cm diameter) represents a large store of carbon in tropical forests; however, estimates of the flux of carbon from CWD in these forests remain poorly constrained. The objective of this study was to resolve the dry/wet season response of respiration in CWD (R (cwd)), and investigate the importance of biotic and abiotic factors for predicting the seasonal change of R (cwd) at the ecosystem level. This study presents a 4-month time series of R (cwd) measurements conducted on 42 dead trees (26 species) at the Paracou Research Station in French Guiana. R (cwd) measurements were repeated 13 times on each CWD sample from July to November 2011, spanning the transition from wet to dry season, and then from dry season to the following wet season. Seasonal drought caused monthly R (cwd) to drop by 20.5 +/- A 5.1% over the wet-dry transition. Changes in woody tissue moisture content explained 41.9% of the measured seasonal variability in R (cwd), but 60% of the seasonal variability in mean forest R (cwd) rates could be modelled using surface soil water content. We estimate that R (cwd) is approximately 5% of annual ecosystem respiration (R (eco)) and that seasonal variations in R (cwd) contribute appreciably to seasonal variations of R (eco), and should be included in functional models simulating the response of tropical rainforest ecosystems to current and future climate.

The Response of Tropical Rainforest Dead Wood Respiration to Seasonal Drought

During the last three decades, intensive campaigns and experiments have been conducted for acquiring micrometeorological data in the Amazonian ecosystems, which has increased our understanding of the variation, especially seasonally, of the total energy available for the atmospheric heating process by the surface, evapotranspiration and carbon exchanges. However, the measurements obtained by such experiments generally cover small areas and are not representative of the spatial variability of these processes. This chapter aims to discuss several algorithms developed to estimate surface energy and carbon fluxes combining satellite data and micrometeorological observations, highlighting the potentialities and limitations of such models for applications in the Amazon region. We show that the use of these models presents an important role in understanding the spatial and temporal patterns of biophysical surface parameters in a region where most of the information is local. Data generated may be used as inputs in earth system surface models allowing the evaluation of the impact, both in regional as well as global scales, caused by land-use and land-cover changes.

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Methods to Evaluate Land-Atmosphere Exchanges in Amazonia Based on Satellite Imagery and Ground Measurements

The Amazon basin is vulnerable to extreme drought events that can severely impact the local economy and ecosystems, with consequences for the global climate and ecology. However, hydrologic droughts have been mostly studied in the context of semi-arid environments and temperate regions and little is known on droughts on tropical, wet environments such as the Amazon. Also, the limited hydrologic data have been an obstacle in advancing the understanding of Amazon hydrologic systems and their response to droughts. This work employs new data analysis and stochastic simulation methods to reveal important features of droughts in the Amazon basin. First, spatial-temporal patterns of hydrologic droughts are identified from existing streamflow data, using principal component analysis and Monte Carlo simulations to account for the uncertainty due to missing data. Second, hydrologic droughts are investigated at the watershed scale. A conceptual model is developed to quantify groundwater and subsurface water storages and fluxes during droughts, using existing hydrologic data at four representative Amazon watersheds. Then, the functioning of the typical deep soil columns and deep-rooted vegetation systems during droughts is further examined at the point scale. A one-dimensional, soil-vegetation hydrologic model is coupled with a stochastic rainfall and evapotranspiration model, calibrated with hourly data from conventional gauges and eddy covariance towers, to compute statistical characteristics of water balance components: evaporation of intercepted water, transpiration, deep percolation, and change in soil water storage. Finally, the dependency of evapotranspiration on cloudiness and soil moisture is analyzed using statistical hypothesis testing and stochastic simulations of rainfall and evapotranspiration, using observations of heat fluxes at the land surface, obtained from eddy covariance towers.Results show distinct modes of spatial-temporal variability of droughts, with marked differences among sub-basins, especially between northern and southern rivers. A significant trend towards more intense droughts is found in the southern sub-basins. Correlations of drought indices with climatic anomalies originating from the Pacific and Atlantic Oceans are significant, but do not explain all the temporal variability of the major patterns of droughts. That suggests that hydrologic processes at smaller scales might play important roles in shaping drought characteristics. At the watershed scale, the propagation of rainfall deficits through the hydrologic systems highlights the importance of Amazon subsurface hydrology in promoting delays of timing, amplification of duration and attenuation of magnitude of deficits in subsurface, groundwater, and river systems. When annual rainfall is below average, the greater release of watershed water storage contributes to streamflow, helping to attenuate deficits. Moreover, propagation of streamflow deficits from upstream to downstream river portions affects drought characteristics in some rivers. At the point scale, the simulations of the soil-vegetation system reveal that, when rainfall is scarce, the drainage out of the bottom of the deepest soil layer is severely reduced and soil water storage decreases, while evapotranspiration is less impacted. The relationship between deep percolation and average soil moisture is found to vary in the wet and dry seasons. Finally, evapotranspiration is found to be more dependent on cloudiness, rather than soil moisture. The association of rainfall deficits with increased evapotranspiration can potentially exacerbate droughts, although the magnitude of that effect is not large at the interannual time scale. Those results highlight the potential of statistical and stochastic simulation methods in the study of the hydrology of data-scarce regions. Furthermore, they identify and quantify important hydrologic processes that shape the magnitude and duration of droughts in wet environments, which helps to better assess the vulnerability of such sensitive systems.

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Analysis of Hydrologic Droughts in the Amazon Basin

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H. Ribeiro da Rocha

Papers

The carbon balance of South America: status, decadal trends and main determinants