Large-Scale Biosphere-Atmosphere Experiment Research Articles

DOWNWARD LONGWAVE RADIATION ESTIMATES FOR CLEAR AND ALL-SKY CONDITIONS IN CENTRAL AMAZONIA

The downward longwave radiation (L↓), really is difficult of the net radiation the measured values component. Therefore, different parameterizations have been proposed to estimate it. Thus, in this study we evaluated the performance of various parameterizations for the L↓ flows estimated and their interaction with other variables measured as well as the air temperature and water vapour pressure interactions with L↓. In this study evaluates the performance of the downward longwave radiation parameters for clear and cloudy sky days applied in Central Amazonia region. The datasets used in this study was measured from micrometeorological tower controlled by Large Scale Biosphere Atmosphere experiment in Amazonia (LBA) from Manaus city, Amazonas. It was also found that precipitation and water vapour pressure exert enough on L↓ flows influence, especially in the wet season. The models for clear-sky conditions proposed, using their original coefficients trend to underestimate the L↓ flows measured. Best results for clear-sky were obtained with Idso & Jackson (1969), Brutsaert (1975) and Prata (1996) .

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests

Evapotranspiration (ET) is considered a key variable in the understanding of the Amazonian tropical forests and their response to climate change. Remote-Sensing (RS) based evapotranspiration models are presented as a feasible means in order to provide accurate spatially-distributed ET estimates over this region. In this work, the performance of four commonly used ET RS models was evaluated over Amazonia using Moderate Resolution Imaging Spectroradiometer (MODIS) data. RS models included i) Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), ii) Penman-Monteith MODIS operative parametrization (PM-Mu), iii) Surface Energy Balance System (SEBS), and iv) Satellite Application Facility on Land Surface Analysis (LSASAF). These models were forced using two ancillary meteorological data sources: i) in-situ data extracted from Large-Scale Biosphere-Atmosphere Experiment (LBA) stations (scenario I), and ii) three reanalysis datasets (scenario II), including Modern-Era Retrospective analysis for Research and Application (MERRA-2), European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim), and Global Land Assimilation System (GLDAS-2). Performance of algorithms under the two scenarios was validated using in-situ eddy-covariance measurements. For scenario I, PT-JPL provided the best agreement with in-situ ET observations (RMSE = 0.55 mm/day, R = 0.88). Neglecting water canopy evaporation resulted in an underestimation of ET measurements for LSASAF. SEBS performance was similar to that of PT-JPL, nevertheless SEBS estimates were limited by the continuous cloud cover of the region. A physically-based ET gap-filling method was used in order to alleviate this issue. PM-Mu tended to overestimate in-situ ET observations. For scenario II, quality assessment of reanalysis input data demonstrated that MERRA-2, ERA-Interim and GLDAS-2 contain biases that impact model performance. In particular, biases in radiation inputs were found the main responsible of the observed biases in ET estimates. For the region, MERRA-2 tends to overestimate daily net radiation and incoming solar radiation. ERA-Interim tends to underestimate both variables, and GLDAS tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these reanalysis inputs generally explain the observed discrepancies in model spatial and temporal patterns.

Energy balance closure in the Southwest Amazon forest site—a statistical approach

Energy balance closure is the main indicator of the quality of energy flux measurements obtained by the eddy covariance technique. Many researchers use a simple linear regression model between energy balance components to evaluate closure. However, these studies typically fail to verify the appropriateness of the statistical assumptions of regression analysis, which can lead to erroneous conclusions if the model is not satisfactory. Thus, the aim of this study was to calibrate and validate simple and robust with bootstrap and cross-validation linear regression models to verify the efficiency of energy balance closure in the Amazon ecosystem. Measurements of net radiation and latent, sensible, and ground heat fluxes were made from January to December 2008 in a tropical rain forest area in South West Amazonia in an experimental site belonging to the network of flux towers of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The results demonstrated that simple linear regression models are not appropriate for analyzing energy balance closure. However, robust linear regression models with bootstrap and cross-validation improved the fit to the data. Despite the better fit, there was an increase in energy balance closure residuals suggesting that the eddy covariance technique is underestimating the values of energy fluxes in Amazon forest areas more than what were reported in previous researches.

Avaliação dos produtos de evapotranspiração baseados em sensoriamento remoto MOD16 e GLEAM em sítios de fluxos turbulentos do Programa LBA

This study aims to evaluate the performance of MOD16 and GLEAM evapotranspiration (ET) datasets in nine eddy covariance monitoring sites. Data from both ET products were downloaded and its daily means calculated. Evapotranspiration estimations were then compared to the observed ET in the eddy covariance monitoring sites from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA). We performed a statistical analysis using the correlation coefficient (R), the root mean square error (RMSE) and BIAS. Results indicate that, in general, both products can represent the observed ET in the eddy covariance flux towers. MOD16 and GLEAM showed similar values to the calculated statistics when ET estimates were compared to observed ET. Model estimates and eddy covariance flux towers are subject to uncertainties that influence the analysis of remotely-sensed ET products.

Fluxos de CO2 em uma área de floresta tropical úmida na Amazônia Ocidental em um ano de El Niño

The objective this paper was to quantify the hourly variability of CO2 fluxes in the year of the El Niño, in 2015, in a tropical rain forest in the Western Amazon. Data of CO2 fluxes collected at 63.5 m height in a tower of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia in the Jaru Biological Reserve, composed of the Open Ombrophilous forest, were analyzed. Data from the dry-wet period were used, which by convention, the negative and positive values indicate absorption and emission of CO2, respectively. The CO2 uptake in the forest had higher levels between 9:30 a.m. and 4:00 p.m., with mean values between -19 and -13 μmol m-2 s-1, however the peak absorption occurs at 1:00 p.m. with -30 μmol m-2 s-1, considering that at this time the sun's rays are most intense. In average, the Rebio Jaru absorbs more than the areas of the transition Amazonia-Cerrado, indicating that the physiological activities in Ombrophilous forest require a greater amount of carbon. The average daily behavior of CO2 fluxes showed higher concentrations of absorption during the day than emissions overnight, behaving as a carbon sink, corroborating the importance of the forest in assimilation of the atmospheric carbon.

Evaluation of MODIS-based estimates of water-use efficiency in Amazonia

ABSTRACTThis study aimed to assess the spatial-temporal patterns of water-use efficiency (WUE) obtained through MODIS gross primary productivity (GPP) and evapotranspiration (ET) products (MOD17 for GPP and MOD16 for ET) in the Upper Tapajos and Curua-Una River basins, located in the oriental flank of the Amazon region, and to validate the results with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. The spatial variation of WUE was primarily related to the larger presence of forested areas in the Upper Tapajos River basin (western part) compared with the Curua-Una River basin (eastern part), which is situated within the so-called arc of deforestation. Temporally, WUE showed a pronounced seasonal pattern, varying with the dry and wet seasons in the region. A decrease of ~3% in WUE was observed during the dry season, which was related to the low water availability and increased vapour pressure deficit during the dry period, which induces stomatal closure, leading to a decline in the photosynthetic rate. Comparison between the WUE estimates obtained by MODIS data and observations from the LBA towers showed an average error of 17%, varying between ~12% and ~28% for the different sites. MODIS WUE depends on the accuracy of both GPP and ET estimation. In this sense, we highlight that improvements in both MODIS GPP and ET products are necessary and should focus on reducing the uncertainties related to the biophysical vegetation parameters and meteorological data that serve as input information in the algorithms.

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin

Abstract. Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

VARIÁVEIS METEOROLÓGICAS EM ÁREAS DE FLORESTA E PASTAGEM NA AMAZÔNIA OCIDENTAL EM ANOS DE EVENTOS EXTREMOS

Deforestation in the Amazon comes from historical contexts which date from the colonization of the region. Several studies have identified climate change from the conversion of forests into agricultural systems. So, this study aimed to identify possible differences in the behavior of the specific humidity, air temperature and net radiation due to conversion of forest to pasture, using data corresponding to extreme event years, once these have occurred in short period of time. Therefore, it was used measures of the variables of a tropical rain forest area and a pasture area, where there are two towers belonging to the Large Scale Biosphere-Atmosphere Experiment in Amazonia - LBA. The measurements correspond to the years of 2005 and 2010, which occurred drought events, and 2009, a year that occurred a flood period. For each variable were calculated hourly means of each site, separated by year and by period. It was found that the years with dry events showed greater variability in all variables, especially in the dry period of the mentioned years, being the pasture the most affected area. Understanding this dynamic enables better planning in agricultural activities in the region.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

An empirical‐analytical model of the vertical wind speed profile above and within an Amazon forest site

ABSTRACTThe present study considers the mean wind velocity profiles measured from a 60 m high tower in the Rebio‐Jarú forest (10 ° 04.7 ′ S, 61 ° 52.0 ′ W), located in the Brazilian northwestern state of Rondônia. The data were collected during the wet season as part of an intensive campaign of the large scale biosphere–atmosphere experiment in Amazonia. Nine cup anemometers were vertically placed to provide a good estimate of the inflection point height of the mean wind velocity profile. The resulting data were used to formulate a mean vertical wind speed profile, ū(z), based on key parameters such as the inflection point height and the leaf area index. The modified hyperbolic tangent function was used to provide a more flexible fit to the experimental data. An exponential term was also added to the ū(z) function, so that it can assume the appropriate ‘s’ shape near the ground. Thus, some parameters were incorporated into the analytical profile function to enable more flexibility. The presented results demonstrate that the profile is a good fit to the experimental data measured above and within the Amazon forest canopy.

The influence of soils on heterotrophic respiration exerts a strong control on net ecosystem productivity in seasonally dry Amazonian forests

Abstract. Net ecosystem productivity of carbon (NEP) in seasonally dry forests of the Amazon varies greatly between sites with similar precipitation patterns. Correctly modeling the NEP seasonality with terrestrial ecosystem models has proven difficult. Previous modelling studies have mostly advocated for incorporating processes that act to reduce water stress on gross primary productivity (GPP) during the dry season, such as deep soils and roots, plant-mediated hydraulic redistribution of soil moisture, and increased dry season leaf litter generation which reduces leaf age and thus increases photosynthetic capacity. Recent observations, however, indicate that seasonality in heterotrophic respiration also contributes to the observed seasonal cycle of NEP. Here, we use the dynamic vegetation model CLASS-CTEM (Canadian Land Surface Scheme–Canadian Terrestrial Ecosystem Model) – without deep soils or roots, hydraulic redistribution of soil moisture, or increased dry season litter generation – at two Large-Scale Biosphere–Atmosphere Experiment (LBA) sites (Tapajós km 83 and Jarú Reserve). These LBA sites exhibit opposite seasonal NEP cycles despite reasonably similar meteorological conditions. Our simulations are able to reproduce the observed NEP seasonality at both sites. Simulated GPP, heterotrophic respiration, latent and sensible heat fluxes, litter fall rate, soil moisture and temperature, and basic vegetation state are also compared with available observation-based estimates which provide confidence that overall the model behaves realistically at the two sites. Our results indicate that representing the effect of soil moisture on heterotrophic respiration in terms of soil matric potential and constraining heterotrophic respiration when absolute soil matric potential is both low (wetter soils) and high (drier soils), with optimum conditions in between, allows %appropriately representing the influence of soil texture and depth, %through soil moisture, on seasonal patterns of GPP and, especially, % heterotrophic respiration is important to correctly simulate NEP seasonality.

The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon

Abstract. Carbon cycling in the Amazon is closely linked to atmospheric processes and climate in the region as a consequence of the strong coupling between the atmosphere and biosphere. This work examines the effects of changes in net radiation due to atmospheric aerosol particles and clouds on the net ecosystem exchange (NEE) of CO2 in the Amazon region. Some of the major environmental factors affecting the photosynthetic activity of plants, such as air temperature and relative humidity, were also examined. An algorithm for clear-sky irradiance was developed and used to determine the relative irradiance, f, which quantifies the percentage of solar radiation absorbed and scattered due to atmospheric aerosol particles and clouds. Aerosol optical depth (AOD) was calculated from irradiances measured with the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, onboard the Terra and Aqua satellites, and was validated with ground-based AOD measurements from AERONET (Aerosol Robotic Network) sun photometers. Carbon fluxes were measured using eddy covariance technique at the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) flux towers. Two sites were studied: the Jaru Biological Reserve (RBJ), located in Rondonia, and the Cuieiras Biological Reserve at the K34 LBA tower (located in a preserved region in the central Amazon). Analysis was performed continuously from 1999 to 2009 at K34 and from 1999 to 2002 at RBJ, and includes wet, dry and transition seasons. In the Jaru Biological Reserve, a 29% increase in carbon uptake (NEE) was observed when the AOD ranged from 0.10 to 1.5 at 550 nm. In the Cuieiras Biological Reserve, the aerosol effect on NEE was smaller, accounting for an approximate 20% increase in NEE. High aerosol loading (AOD above 3 at 550 nm) or high cloud cover leads to reductions in solar flux and strong decreases in photosynthesis up to the point where NEE approaches zero. The observed increase in NEE is attributed to an enhancement (~50%) in the diffuse fraction of photosynthetic active radiation (PAR). The enhancement in diffuse PAR can be done through increases in aerosols and/or clouds. In the present study, it was not possible to separate these two components. Significant changes in air temperature and relative humidity resulting from changes in solar radiation fluxes under high aerosol loading were also observed at both sites. Considering the long-range transport of aerosols in the Amazon, the observed changes in NEE for these two sites may occur over large areas in the Amazon, significantly altering the carbon balance in the largest rainforest in the world.

What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network

We investigated the seasonal patterns of Amazonian forest photosynthetic activity, and the effects thereon of variations in climate and land-use, by integrating data from a network of ground-based eddy flux towers in Brazil established as part of the ‘Large-Scale Biosphere Atmosphere Experiment in Amazonia’ project. We found that degree of water limitation, as indicated by the seasonality of the ratio of sensible to latent heat flux (Bowen ratio) predicts seasonal patterns of photosynthesis. In equatorial Amazonian forests (5° N–5° S), water limitation is absent, and photosynthetic fluxes (or gross ecosystem productivity, GEP) exhibit high or increasing levels of photosynthetic activity as the dry season progresses, likely a consequence of allocation to growth of new leaves. In contrast, forests along the southern flank of the Amazon, pastures converted from forest, and mixed forest-grass savanna, exhibit dry-season declines in GEP, consistent with increasing degrees of water limitation. Although previous work showed tropical ecosystem evapotranspiration (ET) is driven by incoming radiation, GEP observations reported here surprisingly show no or negative relationships with photosynthetically active radiation (PAR). Instead, GEP fluxes largely followed the phenology of canopy photosynthetic capacity (Pc), with only deviations from this primary pattern driven by variations in PAR. Estimates of leaf flush at three non-water limited equatorial forest sites peak in the dry season, in correlation with high dry season light levels. The higher photosynthetic capacity that follows persists into the wet season, driving high GEP that is out of phase with sunlight, explaining the negative observed relationship with sunlight. Overall, these patterns suggest that at sites where water is not limiting, light interacts with adaptive mechanisms to determine photosynthetic capacity indirectly through leaf flush and litterfall seasonality. These mechanisms are poorly represented in ecosystem models, and represent an important challenge to efforts to predict tropical forest responses to climatic variations.

Overview of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia Data Model Intercomparison Project (LBA-DMIP)

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere–Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.

Carbon dynamics in the Amazonian Basin: Integration of eddy covariance and ecophysiological data with a land surface model

Information contained in eddy covariance flux tower data has multiple uses for the development and application of global land surface models, such as evaluation/validation, calibration and process parameterization for carbon stocks and fluxes. In this study, we combine Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) project data collected from a network of eddy covariance flux towers deployed across the Amazonian basin to improve the carbon stocks and flux estimated by a land surface model, the Integrated Science Assessment Model (ISAM). We evaluate the key model parameters for carbon allocation factors, maintenance respiration as well as the autotrophic respiration using the LBA site measurements. Our model results show that the total biomass ranges from 0.7kgCm−2yr−1 for the pasture site to 20.8kgCm−2yr−1 for the forest site. The ISAM estimates for GPP and NPP are well within the uncertainty range of the site measurement data. Also, the results revealed that all, but one forest site have lower net primary productivity (NPP) to gross primary productivity (GPP) ratio (NPP/GPP=0.4 compared to the savanna and the pasture sites (NPP/GPP=0.5). This is because savanna and the pasture sites experienced the longest dry season and plants growing in such environmental conditions have stronger efficiency to store carbon compared to forests. The forest evergreen site (Km67) has a higher measured NPP/GPP ratio (0.5), because of higher carbon accumulation. Soil carbon is lowest for the pasture site (Km77) (7.2kgCm−2yr−1) and highest for the forest site (Km34) (12.8kgCm−2yr−1). The model results suggest that all the forest sites are a net sink for atmospheric CO2, while the savanna (PDG) and pasture (FNS) sites are neutral and another pasture site (Km77) is net source for atmospheric CO2. Meanwhile, the model results highlight the importance of the LBA site data to improve the model performance for the tropical Amazon region. The study also suggest the need for a network of long-term monitoring plans to measure changes in the vegetation and soil carbon biomass at the local and regional levels. Such programs will be necessary to make reliable global carbon emissions estimates.

Limits and Limitlessness: Exploring Time in Scientific Practice

This article explores some of the ways that time figures in the scientific practices of instrumental micrometeorology and climatic and weather modeling. It draws on ethnographic work done with the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), an international scientific project that aims to assess the role of the Amazon forest in the global carbon cycle and to provide sustainable techniques for the future management of the region. An examination of the knowl- edge practices that have emerged from this ethnography (such as cali- bration and prediction) provides an opportunity to rethink the relation between ‘natural time’ and ‘social time(s)’. This allows for a discussion of the roles that certainty, uncertainty, finiteness, and limitlessness play in both scientific and ethnographic practice.

The Contributions of Regional Knowledge Networks Researching Environmental Changes in Latin America and Africa: a Synthesis of what they can do and why they can be policy relevant

We provide a synthesis of what regional scientific research networks in less developed regions of the world can do and why they might be relevant for societal decisions and practice. We do so through a focus on three regional science network initiatives that aim to enhance understanding of the multiscalar dynamics of global environmental change (GEC) regionally and globally, namely the Southern Africa Regional Science Initiative (SAFARI 2000), the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), and the Inter-American Institute for Global Change (IAI). With a view to aiding future efforts at regional research network formation, we assess whether and how these three networks enhanced regional science, and the extent to which they sought and managed to bridge the science-policy gap that challenges GEC science as a whole. Identifying key decisions and attributes bearing on their successes, the analysis attends specifically to how the three networks sought to build capacity, how differences and similarities between them affected their level of autonomy from governments, and how this and other factors influenced their functioning and achievements.

Open Environmental Data in Developing Countries: Who Benefits?

Open Environmental Data in Developing Countries: Who Benefits?

Aboveground Biomass Dynamics in the Amazonian Rainforest under Influence of Reduction in Rainfall

Exchange of biomass between the ecosystem and the atmosphere plays an important role in regional and global carbon cycles that have a major impact on biodiversity. This study evaluated the effect of reduction in rainfall on the aboveground biomass in an Amazonian rainforest. Data for this study were obtained from the “Long-term drought impact on water and carbon dioxide fluxes in Amazonian Tropical Rainforest Experiment (ESECAFLOR)” which was a subproject of Large Scale Biosphere Atmosphere Experiment in Amazon forest (LBA), carried out in terra firma rainforest in Caxiuana National Forest, Para, Brazil. The experimental design entailed two experimental sites each with one hectare of natural forest: control TFE (a simulated soil drought or ‘throughfall exclusion’ experiment) under normal conditions of climate and treatment TFE with rainfall exclusion of about 50%. The tree growth parameters employed in the study were based on monthly data from the experimental period from January 2005 to May 2009. Results indicated that a decrease in rainfall significantly affected the tree growth parameters, resulting in a decrease of biomass (21.1 t ha-1 year-1) and basal area (1.04 m2 ha-1 year-1). The Amazonian rainforest may become increasingly vulnerable to higher background tree mortality rates in response to drought events, such as El Nino.