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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Turbulent Kinetic Energy Budgets over Gentle Topography Covered by Forests

Large-eddy simulations of flow over a “horizontally” uniform model forest are used to investigate the effects of gentle topography on the turbulent kinetic energy (TKE) budget within the canopy roughness sublayer. Despite significant differences between simulations using idealized sinusoidal topography and real topography of the Amazon forest, results indicate that the effects of topography are located predominantly in the upper canopy and above, and are mostly caused by mean advection of TKE. The “horizontally” averaged TKE budget from idealized and real gentle topographies are almost identical to that for flat terrain, including a clear inertial layer above the roughness sublayer in which shear production is balanced by local dissipation. At topography crests, where observational towers are usually located, mean vertical advection of TKE can be as important as horizontal advection. Here, we propose the use on an approximate TKE balance equation to estimate mean advection from single tower measurements, and introduce a new advection index that can be used as a proxy to quantify the importance of the topography on the TKE budget.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗
NASA NTRS2011

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Ni o Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Chen, Yang↗
NASA NTRS2022

Diverse Responses of Multiple Satellite-Derived Vegetation Greenup Onsets to Dry Periods in the Amazon

Satellite-derived vegetation greenness and seasonal dynamics in the Amazon have generated considerable academic debate over the past two decades. Despite this, the phenological timing of Amazon forests and, in particular their responses to dry periods, remain poorly understood. Here we explicitly identify the diverse timing of vegetation canopy greenup onsets from 10-min geostationary satellite observations, and compute the timing of both the start and end of dry periods from daily precipitation data. We, for the first time, reveal that the Amazon vegetation canopy regularly experiences two cycles of greenup onsets during a year. The occurrence of greenup onset varies diversely from the start to end of the dry periods, but demonstrates regular shifts in local areas, although irregular shifts across the region. The multiple greenup onsets show complex spatial shifts, which closely follow the spatial movement of dry periods. The results provide a new insight into our understanding of the complexity of Amazonian vegetation canopy dynamics during dry periods, which could significantly improve the simulation of carbon and water cycles.

Satellite-Derived Vegetation↗
NASA NTRS2011

Predicting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. Here we investigated the relationship between year-to-year changes in satellite-derived estimates of fire activity in South America and sea surface temperature (SST) anomalies. We found that the Oceanic Ni o Index (ONI) was correlated with interannual fire activity in the eastern Amazon whereas the Atlantic Multidecadal Oscillation (AMO) index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model that predicted regional annual fire season severity (FSS) with 3-5 month lead times. Our approach provides the foundation for an early warning system for forecasting the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for mitigation of greenhouse gas and air pollutant emissions.

Chen, Yang↗
NASA NTRS2014

Far-Reaching Impacts of African Dust- A Calipso Perspective

African dust can transport across the tropical Atlantic and reach the Amazon basin, exerting far-reaching impacts on climate in downwind regions. The transported dust influences the surface-atmosphere interactions and cloud and precipitation processes through perturbing the surface radiative budget and atmospheric radiative heating and acting as cloud condensation nuclei and ice nuclei. Dust also influences biogeochemical cycle and climate through providing nutrients vital to the productivity of ocean biomass and Amazon forests. Assessing these climate impacts relies on an accurate quantification of dust transport and deposition. Currently model simulations show extremely large diversity, which calls for a need of observational constraints. Kaufman et al. (2005) estimated from MODIS aerosol measurements that about 144 Tg of dust is deposited into the tropical Atlantic and 50 Tg of dust into the Amazon in 2001. This estimated dust import to Amazon is a factor of 3-4 higher than other observations and models. However, several studies have argued that the oversimplified characterization of dust vertical profile in the study would have introduced large uncertainty and very likely a high bias. In this study we quantify the trans-Atlantic dust transport and deposition by using 7 years (2007-2013) observations from CALIPSO lidar. CALIPSO acquires high-resolution aerosol extinction and depolarization profiles in both cloud-free and above-cloud conditions. The unique CALIPSO capability of profiling aerosols above clouds offers an unprecedented opportunity of examining uncertainties associated with the use of MODIS clear-sky data. Dust is separated from other types of aerosols using the depolarization measurements. We estimated that on the basis of 7-year average, 118142 Tg of dust is deposited into the tropical Atlantic and 3860 Tg of dust into the Amazon basin. Substantial interannual variations are observed during the period, with the maximum to minimum ratio of about 1.6 and 2.5 for the deposition to the tropical Atlantic and Amazon, respectively. The MODIS-based estimates appear to fall within the range of CALIPSO-based estimates; and the difference between MODIS and CALIPSO estimates can be largely attributed to the interannual variability, which is corroborated by long-term surface dust concentration observations in the tropical Atlantic. Considering that CALIPSO generally tends to underestimate the aerosol loading, our estimate is likely to represent a low bound for the dust transport and deposition estimate. The finding suggests that models have substantial biases and considerable effort is needed to improve model simulations of dust cycle.

dust↗
DOE OSTI2022

Amazonian terrestrial water balance inferred from satellite-observed water vapor isotopes

Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region’s water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). However, large and poorly characterized uncertainties in both fluxes, and in their difference, make it challenging to evaluate spatiotemporal variations of water balance and its dependence on ET or P. Here, we show that satellite observations of the HDO/H 2 O ratio of water vapor are sensitive to spatiotemporal variations of ET-P over the Amazon. When calibrated by basin-scale and mass-balance estimates of ET-P derived from terrestrial water storage and river discharge measurements, the isotopic data demonstrate that rainfall controls wet Amazon water balance variability, but ET becomes important in regulating water balance and its variability in the dry Amazon. Changes in the drivers of ET, such as above ground biomass, could therefore have a larger impact on soil moisture and humidity in the dry (southern and eastern) Amazon relative to the wet Amazon.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

global warming↗
DOE OSTI2022

Decoupling of greenness and gross primary productivity as aridity decreases

Ecosystem primary productivity is a key ecological process influencing many ecosystem services, including carbon storage. Thus, clarifying how primary productivity in terrestrial ecosystems responds to climatic variability can reveal key mechanisms that will drive future changes in the global carbon budget. Satellite products of canopy greenness are widely used as proxies for vegetation productivity to evaluate how ecosystems respond to climate variability. However, to what degree inter-annual variations in productivity are consistent with greenness and how this relationship varies spatially remains unclear. Here we investigated the strength of the coupling between inter-annual variations in leaf area index (LAI, a measure of greenness) and ecosystem gross primary productivity (GPP) derived from eddy covariance towers, i.e., the r2 of the LAI-GPP relationship. Overall, inter-annual GPP and LAI were highly coupled (i.e., high r2) in arid grasslands, but were fully decoupled in mesic evergreen broadleaf forests, indicating that this relationship varies strongly along aridity gradients. A possible mechanism of the spatial variation in the LAI-GPP relationship is that the tradeoff between ecosystem structure (LAI) and physiology (photosynthesis per unit leaf area) becomes stronger in more humid climates. Land models overestimated the r2 of LAI-GPP correlation for most ecosystem types and failed to capture the spatial pattern along aridity gradients. We conclude that relying on greenness products for evaluating inter-annual changes in vegetation productivity may bias assessments, especially in tropical rainforest ecosystems. Our findings may also reconcile observed disparities between responses in greenness and GPP during drought in Amazon forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advancing Molecular Level Understanding of Aerosol Processes in the Amazon and Integration with Modeling (Final Report)

The Amazon forest is being converted to urban and agricultural uses through land clearing including large scale burning. It is also the dominant source of biogenic hydrocarbons globally, which can chemically transform in the atmosphere to form secondary organic aerosol (SOA). Most aerosols (solid or liquid particles suspended in air) are organic and formed through secondary chemical processes. This means that SOA chemical composition and their physical properties can impact cloud formation, the hydrologic cycle, and radiative balance in this region. The 2014 GoAmazon field campaign at the DOE/ARM facility at T3 afforded study of chemical transformations in the region downwind of Manaus. Local biogenic hydrocarbons emissions are high, and their chemical oxidation can be studied with varying degrees of influence by the urban plume. We collected aerosol filter samples and made time-resolved molecular level measurements by deploying a sequential filter sampler and a Semi-Volatile Thermal desorption Aerosol Gas Chromatograph (SV-TAG) during Jan-Mar 2014 (wet season) and Aug-Oct 2014 (dry season).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hot season gets hotter due to rainfall delay over tropical land in a warming climate

Tropical land generally experiences the hottest period (spring) in a year just before the onset of wet season. Previous studies suggested that in a warming climate, the wet season would come later, but its origin is debated and its impact on temperature remains unknown. Here, we find that the warming of hot season would be amplified under global warming, and refer to it as “hot-season-gets-hotter” phenomenon. The amplified hot season warming is closely tied to the amplified warming of hot temperature percentiles. The hot-season-gets-hotter phenomenon is mainly due to the rainfall delay and most evident in the Amazon, where spring is warming by almost 1?K more than the annual mean and the 99th percentile temperatures are warming ~30% more than the mean by the end of 21st century in a high emission scenario. Comparing experiments with and without land-atmosphere coupling, it is further found that the rainfall delay is initially driven by the enhanced effective atmospheric heat capacity and then substantially amplified by positive soil moisture-atmosphere feedback. In the satellite period, observations consistently show that the hot-season-gets-hotter phenomenon has already emerged along with the rainfall delay in the Amazon. Intensified hot and dry spring climate can enhance risks of drought, heatwaves and wildfires, threatening the Amazon forest and habitats in the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mixing states of Amazon basin aerosol particles transported over long distances using transmission electron microscopy

The Amazon basin is important for understanding the global climate because of its carbon cycle and as a laboratory for obtaining basic knowledge of the continental background atmosphere. Aerosol particles play an important role in the climate and weather, and knowledge of their compositions and mixing states is necessary to understand their influence on the climate. For this study, we collected aerosol particles from the Amazon basin during the Green Ocean Amazon (GoAmazon2014/5) campaign (February to March 2014) at the T3 site, which is located about 70 km from Manaus, and analyzed them using transmission electron microscopy (TEM). TEM has better spatial resolution than other instruments, which enables us to analyze the occurrences of components that attach to or are embedded within other particles. Based on the TEM results of more than 10 000 particles from several transport events, this study shows the occurrences of individual particles including compositions, size distributions, number fractions, and possible sources of materials that mix with other particles. Aerosol particles during the wet season were from both natural sources such as the Amazon forest, Saharan desert, Atlantic Ocean, and African biomass burning and anthropogenic sources such as Manaus and local emissions. These particles mix together at an individual particle scale. The number fractions of mineral dust and sea-salt particles increased almost 3-fold when long-range transport (LRT) from the African continent occurred. Nearly 20 % of mineral dust and primary biological aerosol particles had attached sea salts on their surfaces. Sulfates were also internally mixed with sea-salt and mineral dust particles. The TEM element mapping images showed that several components with sizes of hundreds of nanometers from different sources commonly occur within individual LRT aerosol particles. We conclude that many aerosol particles from natural sources change their compositions by mixing during transport. The compositions and mixing states of these particles after emission result in changes in their hygroscopic and optical properties and should be considered when assessing their effects on climate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Effect of land use change on the carbon cycle in Amazon soils

The overall goal of this study was to provide a quantitative understanding of the cycling of carbon in the soils associated with deep-rooting Amazon forests. In particular, we wished to apply the understanding gained by answering two questions: (1) what changes will accompany the major land use change in this region, the conversion of forest to pasture? and (2) what is the role of carbon stored deeper than one meter in depth in these soils? To construct carbon budgets for pasture and forest soils we combined the following: measurements of carbon stocks in above-ground vegetation, root biomass, detritus, and soil organic matter; rates of carbon inputs to soil and detrital layers using litterfall collection and sequential coring to estimate fine root turnover; C-14 analyses of fractionated SOM and soil CO2 to estimate residence times; C-13 analyses to estimate C inputs to pasture soils from C-4 grasses; soil pCO2, volumetric water content, and radon gradients to estimate CO2 production as a function of soil depth; soil respiration to estimate total C outputs; and a model of soil C dynamics that defines SOM fractions cycling on annual, decadal, and millennial time scales.

Trumbore, Susan E.↗
NASA NTRS2003

Estimation of the Anthropogenic Influence in the Sahel by Terra and Aqua

Saharan dust impacts the Earth radiation balance. It was suggested that anywhere between 20-50% of the dust is anthropogenic, generated from human activity in the Sahel. Recent study By Prospero et al noticed that most of the sources should be north of Latitude of 15 deg. where population density is small. Here we use Aqua and Terra data to study the dust sources and their possible anthropogenic origin. Dust packages emitted from the Bodele depression and other sources in the southern part of the Sahara, are transported in the winter by a Northern East winds over the Sahel to the ocean. We are tracking these packages with MODIS from their sources to the ocean, showing that most of the dust reaching the Ocean from the Sahelian shore is generated in the Sahara, suggesting that the anthropogenic influence in the area is negligible. Saharan dust was suggested to be the main source of nutrients to the Amazon forest. Here we are also showing that these South Saharan sources around the Bodele depression are very active in the winter and rather passive in the summer, suggesting that the type of dust reaching Brazil, and its mineral content from these sources may be different from the dust that arrives to North America and Europe emitted in the summer from different sources.

Koren, I.↗
NASA NTRS2010

Biomass Burning Aerosol Absorption Measurements with MODIS Using the Critical Reflectance Method

This research uses the critical reflectance technique, a space-based remote sensing method, to measure the spatial distribution of aerosol absorption properties over land. Choosing two regions dominated by biomass burning aerosols, a series of sensitivity studies were undertaken to analyze the potential limitations of this method for the type of aerosol to be encountered in the selected study areas, and to show that the retrieved results are relatively insensitive to uncertainties in the assumptions used in the retrieval of smoke aerosol. The critical reflectance technique is then applied to Moderate Resolution Imaging Spectrometer (MODIS) data to retrieve the spectral aerosol single scattering albedo (SSA) in South African and South American 35 biomass burning events. The retrieved results were validated with collocated Aerosol Robotic Network (AERONET) retrievals. One standard deviation of mean MODIS retrievals match AERONET products to within 0.03, the magnitude of the AERONET uncertainty. The overlap of the two retrievals increases to 88%, allowing for measurement variance in the MODIS retrievals as well. The ensemble average of MODIS-derived SSA for the Amazon forest station is 0.92 at 670 nm, and 0.84-0.89 for the southern African savanna stations. The critical reflectance technique allows evaluation of the spatial variability of SSA, and shows that SSA in South America exhibits higher spatial variation than in South Africa. The accuracy of the retrieved aerosol SSA from MODIS data indicates that this product can help to better understand 44 how aerosols affect the regional and global climate.

Zhu, Li↗
NASA NTRS2009

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondonia and the northern portions of the state of Par a. These areas were not significantly impacted by the 2002-2003 El Nino event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranhao and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.

Potter, C.↗
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
NASA NTRS2023

Improving the MODIS LAI Compositing Using Prior Time-Series Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) long-term leaf area index (LAI) products have significantly contributed to global energy fluxes, climate change, and biogeochemistry research. However, the maximum fraction of photosynthetically active radiation absorbed by vegetation (Max-FPAR) compositing strategy of the Collection 6 (C6) products dictates that the main or backup algorithm is always triggered by observations of different quality, which indirectly causes the observed instability in the LAI time-series. Based on MODIS daily LAI retrievals, this study develops a prior knowledge time-series compositing algorithm (PKA) using a linear kernel driven (LKD) model. Our results show that the newly proposed PKA can significantly improve the LAI composites compared to the Max-FPAR strategy using ground-based observations for validation. We found that the PKA performs better than Max-FPAR in various aspects (different sites, seasons, and retrieval index (RI) ranges), with R2 increasing from 0.69 to 0.76 and root means square error (RMSE) decreasing from 1.01 to 0.84 compared to GBOV ground truth. The same improvement was shown for the ground truth LAIs measured at the Honghe and Hailun sites in northeastern China, with R2 increasing from 0.23 to 0.41 and RMSE decreasing from 1.27 to 1.25. In addition, three newly proposed temporal uncertainty metrics (time-series stability, TSS and time-series anomaly, TSA and reconstruction error metric, RE (the proximity to the main RT-based retrievals)) were applied to compare the stability of LAI time-series before and after PKA implementation. We found that the time series stability of PKA LAI was improved, the time series anomalies were reduced, and the retrieval rates of the main algorithm were also greatly enhanced compared to Max-FPAR LAI. A case intercomparison for Max-FPAR-MODIS, Max-FPAR-VIIRS (Visible Infrared Imager Radiometer Suite), and PKA-MODIS LAIs in the Amazon Forest region showed that the PKA is also effective in improving the LAI retrieval over large regions with few qualified observations due to poor atmospheric conditions (RE decreased from 2.37/2.35 (Max-FPAR-MODIS/Max-FPAR-VIIRS) to 2.25 (PKA-MODIS) and RI increased from 61.94%/59.62% to 66.88%). The same improvement was seen in the BELMANIP 2.1 sites for almost all biomes except deciduous broadleaf forest, where the RE decreased from 1.85/2.13 to 1.15 overall. We note that the PKA has the potential to be easily implemented in the operational algorithms of subsequent MODIS and MODIS-like LAI Collections.

MODIS↗