Results for “Environmental changes” — Page 87

NASA NTRS2016

Health and Climate Impacts of Ocean-Going Vessels in East Asia

East Asia has the most rapidly growing shipping emissions of both CO2 and traditional air pollutants, but the least in-depth analysis. Full evaluation of all pollutants is needed to assess the impacts of shipping emissions. Here, using an advanced method based on detailed dynamic ship activity data, we show that shipping emissions in East Asia accounted for 16% of global shipping CO2 in 2013, compared to only 4-7% in 2002-2005. Increased emissions lead to large adverse health impacts, with 14,500-37,500 premature deaths per year. Global mean radiative forcing from East Asian shipping is initially negative, but would become positive after approximately eight years for constant current emissions. As a large fraction of vessels are registered elsewhere, joint efforts are necessary to reduce emissions and mitigate the climate and health impacts of shipping in the region.

climate change impacts↗

NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗

NASA NTRS2018

Evaluation of a High-Resolution Numerical Weather Prediction Model’s Simulated Clouds Using Observations from CloudSat, GOES-13 and in Situ Aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada’s Global Environmental Multiscale (GEM)modelwhen run with the Milbrandt–Yau double-moment cloud microphysical scheme and one-way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16May 2015 over French Guiana.Data fromCloud- Sat’s cloud-profiling radar and GOES-13’s imager were compared to data either simulated directly by GEM or produced by operating on GEM’s cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three-dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada’s National Research Council Convair-580 and the French SAFIRE Falcon-20 – whose flight paths were aligned with CloudSat’s ground-track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES-13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice-water content (IWC) near 11 km altitude (Falcon-20), it produces too much graupel and snow near 7 km (Convair-580). This produced large differences between CloudSat’s and COSP-generated radar reflectivities and two-way attenuations. On the other hand, CloudSat’s inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM’s visible reflectances exceeded GOES-13’s on account of having produced too much low-level liquid cloud. It is expected that GEM’s disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Qu, Zhipeng↗

NASA NTRS2021

Follow-up Water Quality Analysis at the Little Patuxent River in Anne Arundel County, Maryland andImpact to the Health of the Chesapeake Bay

In accordance with the federal Clean Water Act, it is of utmost importance to identify impaired water bodies and subsequently implement Total Maximum Daily Loads (TMDL) as needed to achieve desirable water quality standards. One of these water bodies include the Little Patuxent River (LPR) in the Anne Arundel and Howard Counties in Maryland, which is the subject of this report. In the 1996, 1998, and then 2006 Maryland Integrated Report, the LPR was found to be impaired by nutrients, bacteria, suspended sediments, and cadmium (Cd). As a result, TMDLs were created to limit factors that were contributing to these problems (Maryland Department of the Environment 2011, December). But later in 2008 and 2009, water quality analyses (WQAs) found that the LPR’s condition had improved. From a Category 5 Cd and phosphorus classification in 1996 it was reduced to Category 2 in 2008 for Cd and in 2009 for phosphorus (Maryland Department of the Environment 2011, December). However, it has been more than a decade since the last WQA and the environmental conditions may have changed. This lack of testing could result in untracked, accumulated damages to the overall health of the LPR. We present testing results for the 13 site locations on the LPR to evaluate turbidity, pH, phosphates, nitrates, water temperature, dissolved oxygen (D.O.), coliform bacteria, and cadmium concentrations in sediments. We will also present next steps including remediation strategies.

Aaban Syed↗

NASA NTRS2021

Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

crop model↗

NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗

NASA NTRS2021

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W./sq.m.μm.sr). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Zbyněk Malenovský↗

NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗

NASA NTRSDate not supplied

Global Land Surface Soil Moisture Database Derived from IASI Measurements on Board MetOp Satellites

Global land surface soil moisture has been derived from MetOp IASI ultraspectral infrared measurements over the past 16 years. Monthly and spatially gridded soil moisture is produced to show some phenomena associated with its natural variability. This study has aimed to demonstrate that thermal infrared remote sensing data can be used for monitoring global surface environmental characteristics and associated change through the continuity observations provided by the MetOp series. The time-series of soil moisture can be used to estimate its associated trend.

Remote sensing↗

NASA NTRS2023

Intensified Warming and Aridity Accelerate Terminal Lake Desiccation in the Great Basin of the Western United States

Terminal lakes in the Great Basin (GB) of the western US host critical wildlife habitat and food for migrating birds and can be associated with serious human health and economic consequences when they desiccate. Water levels have declined dramatically in the last 100+ years due to diversion of inflows, drought and climate change. Satellite-derived environmental science data records (ESDRs) from the MODerate-resolution Imaging Spectroradiometer (MODIS) (snow cover, evapotranspiration (ET) and land surface temperature (LST)), enable a unique approach to evaluate the effects of aridification on terminal lakes and to study their individual vulnerabilities. Surface and air temperatures in the GB are rising dramatically, with a sharp rise in the rate of increase observed beginning around 2011, while the number of days of snow cover is declining especially in the western mountainous part of the GB as exemplified in Mono Basin, California. Rising temperatures coincide with fewer days of snow cover, a decrease of inflow to the lakes and greater evaporation of water from the lakes. MODIS ESDRs show strong and statistically significant increasing surface temperature (LST) in the GB, a reduction in the number of days of snow cover, and mixed results in ET. ET declined slightly in the more arid parts of the GB due to greater moisture restrictions to evaporation from extended drought, while ET increased in the more-vegetated, wetter, mountainous northeastern parts as temperatures have risen. Severe and costly ecological, human health and economic consequences are expected if the lakes continue to decline as predicted.

land surface temperature↗

NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

W T Wallace↗

NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

William T. Wallace↗

NASA NTRS2023

Uncertainty in Land-Use Adaptation Persists Despite Crop Model Projections Showing Lower Impacts Under High Warming

Climate change is expected to impact crop yields and alter resource availability. However, the understanding of the potential of agricultural land-use adaptation and its costs under climate warming is limited. Here, we use a global land system model to assess land-use-based adaptation and its cost under a set of crop model projections, including CO2 fertilization, based on climate model outputs. In our simulations of a low-emissions scenario, the land system responds through slight changes in cropland area in 2100, with costs close to zero. For a high emissions scenario and impacts uncertainty, the response tends toward cropland area changes and investments in technology, with average adaptation costs between −1.5 and +19 US$05 per ton of dry matter per year. Land-use adaptation can reduce adverse climate effects and use favorable changes, like local gains in crop yields. However, variance among high-emissions impact projections creates challenges for effective adaptation planning.

Agriculture↗

NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗

NASA NTRSDate not supplied

Effective Engagement for Community Action with NASA’s Indigenous Peoples Initiative

NASA’s Indigenous Peoples Initiative (IPI) seeks to support and cultivate efforts within Indigenous communities and NASA to increase the use of Earth Observations (EO) to inform decisions, policies, and actions. IPI is dedicated to building lasting relationships with Indigenous communities by creating a trusted, reliable, and Indigenous-centric geospatial community with a focus on environmental justice and climate issues on Indigenous lands and territories. Indigenous communities through conservation, restoration, and adaptation have the opportunity to further empower their landscape work through the potential use of EO and other NASA data and tools. We will outline our guiding principles, multiple approaches, and lessons learned for working alongside Indigenous communities in a respectful and reciprocal manner. These include the recognition of multiple ways of knowing, co-development of trainings, acknowledgement of all sovereignty, maintaining healthy relationships with tribal natural resource managers and building bridges for their engagement with NASA researchers and other federal agency partners, and making space for reflection and adaptation of our practices as we grow. Future activities, such as dialogue sessions with Indigenous communities around EO needs, aim to enhance these practices and ultimately benefit the ability for Indigenous communities to maintain and protect their lands, people, and practices in the face of environmental injustices and climate change.

Effective↗

NASA NTRSDate not supplied

Effects of Long Duration Space Flight on Low Frequency Hearing

Pure tone hearing threshold data has been collected on astronauts since the early days of spaceflight. Between 1981 and 2001 (STS-1 thru 108) it was found that of the 608 shuttle astronauts, 17% returned home with a significant hearing threshold shift, and of that 6% had permanent changes in the high frequencies. Believed to be due to prolonged noise exposure on the space crafts. (Hart, 2006) As NASA aimed for longer duration missions the uncertainty about the effects of month-long noise exposure needed to be addressed. To avoid constraining crewmembers to constant earplug usage, and satisfying budget demands, a focused effort on overcoming the issues began. Noise monitoring systems were introduced onto the International Space Station (ISS) and astronauts were given education on the noise environment aboard the ISS, the hazards of noise induced hearing loss, hearing conservation principles, and countermeasure strategies (earplugs, noise cancellation headsets). As well as being trained on a new countermeasure effort that had been in trial testing on prior shuttle missions (STS-6 thru 8), On-Orbit Hearing Assessment (OOHA). (Hart, 2006) OOHA was implemented to routinely monitor the hearing thresholds of crewmembers who are on orbit for longer than 30 days. Flight Surgeons on ground monitor these test for significant hearing changes while aboard. Between 2000 and 2021 the ISS OOHA system (EarQ) recorded changes in the low frequency hearing thresholds. Low frequency hearing changes are not typically caused by noise exposure, and it was initially believed that these changes were due to environmental noise interference. In 2021 an ISS software upgrade (KUDUwave) allowed for a more comprehensive hearing assessment. The new testing system allowed for additional tests that helped describe the type of hearing loss being experienced and determine the influence of ambient noise or other environmental factors. OOHA Data indicates a low frequency hearing shift in 40% of all tests and 60% of all crewmembers. Since 2021, OOHA results (using the KUDUwave) suggest that these shifts are sensorineural and are not the result of ambient noise. While hearing shifts are seen at both 250 and 500 Hz, metrics tracking these shifts have only used 500 Hz data. The lack of 250 Hz has prevented accurate reporting of low frequency hearing shift recovery. The aim of this study is to capture 250 Hz data and accurately present low frequency hearing loss recovery.

hearing↗

DOE OSTI2015

Life Cycle Greenhouse Gas Emissions: Natural Gas and Power Production

This presentation was given at the 2015 Energy Information Administration (EIA) Energy Conference. It discusses the importance of understanding greenhouse gas (GHG) emissions from the power and natural gas sectors, and then goes into more detail on the life cycle GHG emissions from natural gas and from power generation in general.

Climate Change↗

DOE OSTI2016

Life Cycle Greenhouse Gas Emissions: Natural Gas and Power Production

This presentation was given at the EPRI Winter Meeting, Natural Gas Interest Group in February 2016 and provided the technical details behind and results from the life cycle greenhouse gas analysis of natural gas extraction across the United States and its use in power production relative to coal-fired power.

CH4↗