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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Mechanisms and Consequences of Intra-Crystalline Enrichment of Ancient Radiogenic Pb in Detrital Hadean Zircons from the Jack Hills, Western Australia

The recent discovery of Pb* (radiogenic lead) -enriched domains (PEDs) in zircon using high-resolution scanning ion imaging, atom probe tomography and transmission electron microscopy indicates that the U-Pb isotopic system in zircon can be affected not only by Pb* loss but also by intra-crystalline Pb* enrichment. However, the formation mechanism of PEDs and the consequences for in-situ U-Pb dating remain elusive. To further understand these issues, scanning ion imaging and U-Pb dating were carried out on 51 Hadean detrital zircon grains from the Jack Hills, Western Australia. Of these, 8 grains were found to contain micrometer-scale PEDs with elevated Pb-207 and Pb-206 in the ion images. Isotope profiles across these PEDs confirm that they represent unsupported Pb* resulting from intra-grain mobilization and local enrichment of Pb* during ancient overprinting events. The PEDs are rare in most grains, with only 1 – 3 Pb* hotspots present in any ~70 μm × 70 μm imaged area, and are located either in dark-CL, high-U growth zones, or associated with bright-CL, low-Th recrystallized domains, suggesting that they were likely formed by local-scale processes. The PEDs located in dark-CL, high-U growth zones likely resulted from Pb* trapped in local, interconnected pathways produced by radiation damage during thermal overprinting events. Those associated with bright-CL, low-Th domains are interpreted as Pb* concentrated in nanoscale pores formed during fluid-present recrystallization of non-metamict zircon. Our interpretations therefore imply that intra-crystalline enrichment of Pb* in zircon can occur at various scales and through diverse mechanisms. Age calculations based on the ion images demonstrate that incorporation of PEDs in conventional in-situ SIMS spots produces spuriously old ages that are either reversely discordant or concordant, depending on the time of Pb* mobilization and the analytical precision. The implication is that careful investigation of intra-grain distribution of U-Th-Pb isotopes is required in order to better understand the U-Pb isotopic system and to obtain reliable crystallization ages for ancient zircons affected by complex Pb* mobilization and redistribution.

Unsupported radiogenic lead↗
NASA NTRS2020

Sub-Zero Alteration in an Isotopically Heavy Brine Preserved in a Pristine H Chondrite Xenolith

Introduction: Brecciated H chondrites host a variety of xenoliths, including unshocked, phyllo- silicate-rich carbonaceous chondrites (CCs). The brecciated H chondrite Zag (H3-6) is one of two chondrites to host macroscopic (1 - 5mm), xenolithic crystals of halite (NaCl) with aqueous fluid inclusions and organics. A ~1cm CC xenolith in Zag (Zag clast) has mineralogy similar to CI chondrites, but it has a unique bulk oxygen isotopic composition among all meteorites (Δ(exp 17)O = 1.49 ± 0.04 ‰, δ(exp 18)O = 22.38 ± 0.17 ‰). The Zag clast encloses halite in its matrix, linking the coarse, matrix halite and the xenolith to the same parent object, suggested to be hydrovolcanically active. Its bulk C and N contents are the highest among chondrites and bulk δ(exp 15)N is similar to CR chondrites and Bells. Insoluble organic material (IOM) in the Zag clast has δD and δ(exp 15)N “hotspots”, also similar to CR chondrites and Bells (C2-ung.). We provide further isotopic characterization of the Zag clast to constrain the formation temperature and origin of its primary and secondary components.

Frank, D. R.↗
NASA NTRS2017

Chemical Complexity Induced by Efficient Ice Evaporation in the Barnard 5 Molecular Cloud

Cold gas-phase water has recently been detected in a cold dark cloud, Barnard 5 located in the Perseus complex, by targeting methanol peaks as signposts for ice mantle evaporation. Observed morphology and abundances of methanol and water are consistent with a transient non-thermal evaporation process only affecting the outermost ice mantle layers, possibly triggering a more complex chemistry. Here we present the detection of the complex organic molecules (COMs) acetaldehyde (CH3CHO) and methyl formate (CH3OCHO), as well as formic acid (HCOOH) and ketene (CH2CO), and the tentative detection of di-methyl ether (CH3OCH3) towards the ‘methanol hotspot’ of Barnard 5 located between two dense cores using the single dish OSO 20m, IRAM 30m, and NRO 45m telescopes. The high energy cis- conformer of formic acid is detected, suggesting that formic acid is mostly formed at the surface of interstellar grains and then evaporated. The detection of multiple transitions for each species allows us to constrain their abundances through LTE and non-LTE methods. All the considered COMs show similar abundances between ∼ 1 and ∼ 10 % relative to methanol depending on the assumed excitation temperature. The non-detection of glycolaldehyde, an isomer of methyl formate, with a [glycolaldehyde]/[methyl formate] abundance ratio lower than 6 %, favours gas phase formation pathways triggered by methanol evaporation. According to their excitation temperatures derived in massive hot cores, formic acid, ketene, and acetaldehyde have been designated as ‘lukewarm’ COMs whereas methyl formate and di-methyl ether were defined as ‘warm’ species. Comparison with previous observations of other types of sources confirms that lukewarm and warm COMs show similar abundances in low-density cold gas whereas the warm COMs tend to be more abundant than the lukewarm species in warm protostellar cores. This abundance evolution suggests either that warm COMs are indeed mostly formed in protostellar environments and/or that lukewarm COMs are efficiently depleted by increased hydrogenation efficiency around protostars.

astrochemistry – ISM: abundances – ISM: clouds – I↗
NASA NTRS2020

Twenty‐First Century Drought Projections in the CMIP6 Forcing Scenarios

There is strong evidence climate change will increase drought risk and severity, but these conclusions depend on the regions, seasons, and drought metrics being considered. We analyze changes in drought across the hydrologic cycle (precipitation, soil moisture, and runoff) in projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6). The multi‐model ensemble shows robust drying in the mean state across many regions and metrics by the end of the 21st century, even following the more aggressive mitigation pathways (SSP1‐2.6 and SSP2‐4.5). Regional hotspots with strong drying include western North America, Central America, Europe and the Mediterranean, the Amazon, southern Africa, China, Southeast Asia, and Australia. Compared to SSP3‐7.0 and SSP5‐8.5, however, the severity of drying in the lower warming scenarios is substantially reduced and further precipitation declines in many regions are avoided. Along with drying in the mean state, the risk of the historically most extreme drought events also increases with warming, by 200–300% in some regions. Soil moisture and runoff drying in CMIP6 is more robust, spatially extensive, and severe than precipitation, indicating an important role for other temperature‐sensitive drought processes, including evapotranspiration and snow. Given the similarity in drought responses between CMIP5 and CMIP6, we speculate both generations of models are subject to similar uncertainties, including vegetation processes, model representations of precipitation, and the degree to which model responses to warming are consistent with observations. These topics should be further explored to evaluate whether CMIP6 models offer reasons to have increased confidence in drought projections.

drought↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors Mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRS2020

Global Declines in Human-Driven Mangrove Loss

Global mangrove loss has been attributed primarily to human activity. Anthropogenic loss hotspots across Southeast Asia and around the world have characterized the ecosystem as highly threatened, though natural processes such as erosion can also play a significant role in forest vulnerability. However, the extent of human and natural threats has not been fully quantified at the global scale. Here, using a Random Forest-based analysis of over one million Landsat images, we present the first 30-meter resolution global maps of the drivers of mangrove loss from 2000-2016, capturing both human-driven and natural stressors. We estimate that 62% of global losses between 2000-2016 resulted from land-use change, primarily through conversion to aquaculture and agriculture. Up to 80% of these human-driven losses occurred within six Southeast Asian nations, reflecting the regional emphasis on enhancing aquaculture for export to support economic development. Both anthropogenic and natural losses declined between 2000-2016, though slower declines in natural loss caused an increase in their relative contribution to total global loss area. We attribute the decline in anthropogenic losses to the regionally-dependent combination of increased emphasis on conservation efforts and a lack of remaining mangroves viable for conversion. While efforts to restore and protect mangroves appear to be effective over decadal time scales, the emergence of natural drivers of loss presents an immediate challenge for coastal adaptation. We anticipate that our results will inform decision making within conservation and restoration initiatives by providing a locally-relevant understanding of the causes of mangrove loss.

Coastal wetlands↗
NASA NTRS2020

Studying the 2019-2020 Australian Bushfires Using NASA Data

The 2019-2020 season has been one of the worst fire seasons on record. Australia has seen unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. NASA's satellites not only tracked the event in real time, using resources such as the Global Actives Fires and Hotspots Dashboard you see below, but also collected large volumes of rich data that scientists and researchers can use to study the event and the regional and global effects of the disaster. In this Esri StoryMap, we will guide you through the factors leading up to the 2019-2020 Australian bushfires disaster, the effect this event has had on air quality and global atmospheric composition, and the science behind researching the tie between disasters and public health. This story map will use data from ASDC-supported NASA missions such as the Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), Stratospheric Aerosol and Gas Experiment (SAGE III) on the International Space Station (ISS), and Multi-angle Imaging SpectroRadiometer (MISR).

Sanjana Paul↗
NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗
NASA NTRS2021

Tree-Ring Reconstruction of the Atmospheric Ridging Feature that Causes Flash Drought in the Central United States Since 1500

Rapid drought intensification, or flash droughts, is often driven by anomalous atmospheric ridging and can cause severe and complex impacts on water availability and agriculture, but the full range of variability of such events in terms of intensity and frequency is unknown. New tree-ring reconstructions of May–July mid-tropospheric ridging and soil moisture anomalies back to 1500 CE in the central United States — a hotspot for flash drought — suggest that over the last five centuries, anomalies in these two variables combined to indicate flash-drought conditions in ~17% of years and exceptionally severe flash drought in ~4% of years, similar to frequencies in recent decades. However, over one-third of all inferred exceptional flash droughts occurred since 1900, suggesting the 20th century was highly flash-drought prone. These results may guide future work to diagnose the roles of external, oceanic, and land-surface forcing of warm-season atmospheric circulation and hydroclimate over North America.

Flash Drought↗
NASA NTRS2019

Negative extreme events in gross primary productivity and their drivers in China during the past three decades

Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC/year) in TRENDY models and 2.3% (i.e. 151 TgC/year) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC/sq.m year) in TRENDY models and Northeast China (−42 gC/sq.m year) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.

Climate change↗
NASA NTRS2020

Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence

Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C3orC4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIF total). We find that the relationships between observed canopy-leavingSIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIF total and GPP converge around two unique models, one for C3and one for C4plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 ± 6.54 PgC/year for the 2015–2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected ‘hotspots’ of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satelliteSIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.

scale satellite SIF observations to terrestrial GP↗
NASA NTRS2021

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Michael Notaro↗
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 NTRSDate not supplied

Assessing Land Cover Change Dynamics in the Peruvian Amazon to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention

Dengue fever and leishmaniasis are two tropical diseases prevalent in Madre de Dios Region of Peru and have been associated with urbanization and road construction. Rapid land use changes such as mining, timber harvesting, and hydroelectric dam development lead to denser human presence in previously sparsely-populated areas which increases the proximity of human settlements to zoonotic disease vectors. In partnership with the Peruvian Ministries of Health (MINSA), the Environment (MINAM), and other in-country collaborators, a NASA DEVELOP team examined Land Use Land Cover (LULC) changes and reported dengue and leishmaniasis incidence in the Madre de Dios region. This sought to help MINSA and MINAM understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine (GEE) with Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. The team then compared the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate disease incidence and key land cover changes across Madre de Dios’s 11 districts. Follow on work will use these products to develop more detailed outbreak risk maps. These products will allow MINSA, MINAM, and other partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision makers and environmental policy.

Nataly Chacon-Buitrago↗
NASA NTRS2021

Compound High Temperature and Low Chlorophyll Extremes in the Ocean Over the Satellite Period

Ocean extreme events severely impact marine organisms and ecosystems. Of particular concern are compoundevents, i.e., when conditions are extreme for multiple potential ocean ecosystem stressors such as temperature and chlorophyll.Yet, little is known about the occurrence, intensity and duration of such compound high temperature (aka marine heatwaves -MHWs) and low chlorophyll (LChl) extreme events, whether their distributions have changed in the past decades and what thepotential drivers are. Here we use satellite-based sea surface temperature and chlorophyll concentration estimates to provide a5first assessment of such compound extreme events. We reveal hotspots of compound MHW and LChl events in the equatorialPacific, along the boundaries of the subtropical gyres, in the northern Indian Ocean, and around Antarctica. In these regions,compound events that typically last one week occur three to seven times more often than expected under the assumption ofindependence between MHWs and LChl events. The occurrence of compound MHW and LChl events varies on seasonalto interannual timescales. At the seasonal timescale, most compound events occur in summer in both hemispheres. At the10interannual timescale, the frequency of compound MHW and LChl events is strongly modulated by large-scale modes of naturalclimate variability such as the El Niño-Southern Oscillation, whose positive phase is associated with increased compoundevent occurrence in the eastern equatorial Pacific and in the Indian Ocean by a factor of up to four. Our results provide a firstunderstanding of where, when and why compound MHW and LChl events occur. Further studies are needed to identify theexact physical and biological drivers of these potentially harmful events in the ocean and their evolution under global warming.

High Temperature↗
NASA NTRS2022

Peru Health and Air Quality II: Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention

Peru's Madre de Dios region is a hotspot for dengue fever and leishmaniasis due to its tropical Amazonian climate. Though treatable, these zoonotic diseases are debilitating for under-resourced communities whose already close proximity to mosquito and sandfly vectors continues to increase via rapid urbanization and deforestation. Peru’s Ministries of Health (MINSA) and Environment (MINAM) are working to better understand the environmental factors amplifying the risk of dengue fever and leishmaniasis transmission. The first term of this project classified the land use and land cover of Madre de Dios’ 11 districts for 2010, 2015, and 2020 and identified a correlation between both diseases and urbanization. Our team expanded this analysis by creating urban-forest edge maps and incorporating climatic and topographic variables with data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), and the Shuttle Radar Topography Mission (SRTM). We determined these variables’ impacts on disease incidence by assessing existing literature and running regression models. Dengue fever correlated with urban-forest edge, urban area, slope, temperature, and precipitation. Leishmaniasis primarily correlated with forest-edge area and elevation, but lacking additional statistical significance prevented further work, a decision supported by the literature. Thus, the risk matrix and risk map which we scripted in R to visualize the risk of disease posed to districts alongside health post locations addresses only dengue fever. The results and products will inform MINSA and MINAM in public health interventions, resource distribution, and policy initiatives.

Jennifer Rogers↗
NASA NTRS2021

Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC/yr for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC/yr). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

land use and land cover change↗