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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Isolating the effect of biomass burning aerosol emissions on 20th century hydroclimate in South America and Southeast Asia

Abstract Biomass burning is a significant source of aerosol emissions in some regions and has a considerable impact on regional climate. Earth system model simulations indicate that increased biomass burning aerosol emissions contributed to statistically significant decreases in tropical precipitation over the 20th century. In this study, we use the Community Earth System Model version 1 Large Ensemble (CESM1-LENS) experiment to evaluate the mechanisms by which biomass burning aerosol contributed to decreased tropical precipitation, with a focus on South America and Southeast Asia. We analyze the all-but-one forcing simulations in which biomass burning aerosol emissions are held constant while other forcings (e.g., greenhouse gas concentrations) vary throughout the 20th century. This allows us to isolate the influence of biomass burning aerosol on processes that contribute to decreasing precipitation, including cloud microphysics, the radiative effects of absorbing aerosol particles, and alterations in regional circulation. We also show that the 20th century reduction in precipitation identified in the CESM1-LENS historical and biomass burning experiments is consistent across Coupled Model Intercomparison Project Phase 5 models with interactive aerosol schemes and the CESM2 single-forcing experiment. Our results demonstrate that higher concentrations of biomass burning aerosol increases the quantity of cloud condensation nuclei and cloud droplets, limiting cloud droplet size and precipitation formation. Additionally, absorbing aerosols (e.g., black carbon) contribute to a warmer cloud layer, which promotes cloud evaporation, increases atmospheric stability, and alters regional circulation patterns. Corresponding convectively coupled circulation responses, particularly over the tropical Andes, contribute to further reducing the flow of moisture and moisture convergence over tropical land. These results elucidate the processes that affect the water cycle in regions prone to biomass burning and inform our understanding of how future changes in aerosol emissions may impact tropical precipitation over the 21st century.

Environmental Sciences & Ecology↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

Beaudoing, Hiroko Kato↗
NASA NTRS2019

Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

Dandridge, Chelsea↗
NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗
NASA NTRS2022

Climate Change-Induced Peatland Drying in Southeast Asia

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

VIIRS↗
DOE OSTI2022

Quantifying Impacts of Renewable Electricity Deployment on Air Quality and Human Health in Southeast Asia Based on AIMS III Scenarios

This study augments the ASEAN Interconnection Masterplan Study III (AIMS III) by quantifying changes to air quality and human health that result from its renewable integration and transmission interconnection scenarios. Performing this analysis requires translation of the changes in projected generation from different power sector fuel sources in the AIMS III scenarios to changes in air pollutant emissions, developing what is known as an emissions inventory for each scenario and year evaluated. An emissions inventory represents who emits air pollutants, from where the pollutants are emitted, when, and how much of which air pollutants are emitted. With the assistance of the ASEAN Centre for Energy and leveraging the best available in-region public data sources, the National Renewable Energy Laboratory (NREL) team compiled a detailed inventory of power plants in the ASEAN region, mapping their PM 2.5 , sulfur oxides (SOx), and nitrogen oxides (NOx) emissions. A new, first-of-its-kind, and user-friendly global air quality model, Global InMAP (Thakrar et al. 2022), is then used to transform the inventory of changes in emissions to changes in concentration of fine particulate matter. Global InMAP then maps the location of human populations in ASEAN countries to calculate humans' exposure to PM 2.5 and estimate excess PM 2.5 -caused mortality attributable to thermal generation sources, as modeled in the AIMS III scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Online Monitoring Applications Enabled by Phasor Measurement Units: Technical Assistance to the Power Sectors of Southeast Asia

In this report, an overview of several online applications enabled by PMU measurements is provided. Besides a brief technical background for each application, the report also discusses control room displays and alarming methodologies used by North American organizations, and applicable standards set by the North American Electric Reliability Corporation (NERC). The five applications discussed herein are inertia monitoring, linear state estimation, voltage stability monitoring, small-signal stability monitoring, and forced oscillation monitoring.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Offline Power Systems Applications Enabled by Phasor Measurement Units: Technical Assistance to the Power Sectors of Southeast Asia

This report provides a brief overview of several offline (non-real-time) applications facilitated by high-resolution time-synchronized measurements recorded by phasor measurement units (PMUs). The high reporting rate and time-synchronization of PMU records provide a detailed view of power system dynamics, enabling electric utilities to obtain a better understanding of their systems. In this report, the following applications have been reviewed: Power plant model validation, System model validation, Ringdown oscillation analysis, Frequency response analysis, Postmortem analysis of disturbance events Along with a brief technical background of the applications above, applicable North American Electric Reliability Corporation (NERC) standards have been discussed, and examples of implementation in North American organizations have been provided. Implementing several of the discussed applications may need a preliminary stage of data gathering from multiple entities, and several frameworks and process flows have been formulated by organizations around the world for this purpose. However, the data-gathering stage has not been considered in the scope of the present report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1971

Plate convergence, transcurrent faults and internal deformation adjacent to Southeast Asia and the western Pacific

A model for oblique convergence between plates of lithosphere is proposed in which at least a fraction of slip parallel to the plate margin results in transcurrent movements on a nearly vertical fault which is located on the continental side of a zone of plate consumption. In an extreme case of complete decoupling only the component of slip normal to the plate margin can be inferred from underthrusting. Recent movements in the western Sunda region provide the most convincing evidence for decoupling of slip, which in this region is thought to be oblique to the plate margin. A speculative model for convergence along the margins of the Philippine Sea is constructed from an inferred direction of oblique slip in the Philippine region. This model requires that the triple point formed by the junction of the Japanese and Izu-Bonin trenches and the Nankai trough migrate along the Sagami trough.

Fitch, T. J.↗
NASA NTRS1991

The effects of global climate change on Southeast Asia: A survey of likely impacts and problems of adaptation

Study results indicate the likelihood of significant net damages from climate change, in particular damages from sea-level rise and higher temperatures that seem unlikely to be offset by favorable shifts in precipitation and carbon dioxide. Also indicated was the importance of better climate models, in particular models that can calculate climate change on a regional scale appropriate to policy-making. In spite of this potential for damage, there seems to be a low level of awareness and concern, probably caused by the higher priority given to economic growth and reinforced by the great uncertainty in the forecasts. The common property nature of global environment systems also leads to a feeling of helplessness on the part of country governments.

Njoto, Sukrisno↗
NASA NTRS2017

Managing Fire Risk During Drought: The Influence of Certification and El Nino on Fire-Driven Forest Conversion for Oil Palm in Southeast Asia

Indonesia and Malaysia have emerged as leading producers of palm oil in the past several decades, expanding production through the conversion of tropical forests to industrial plantations. Efforts to produce "sustainable" palm oil, including certification by the Roundtable on Sustainable Palm Oil (RSPO), include guidelines designed to reduce the environmental impact of palm oil production. Fire-driven deforestation is prohibited by law in both countries and a stipulation of RSPO certification, yet the degree of environmental compliance isunclear, especially during El Niño events when drought conditions increase fire risk. Here, we used time series of satellite data to estimate the spatial and temporal patterns of fire-driven deforestation on and around oil palm plantations. In Indonesia, fire-driven deforestation accounted for one-quarter of total forest losses on both certified and noncertified plantations. After the first plantations in Indonesia received RSPO certification in 2009,forest loss and fire-driven deforestation declined on certified plantations but did not stop altogether. Oil palm expansion in Malaysia rarely involved fire; only 5 % of forest loss on certified plantations had coincident activefire detections. Interannual variability in fire detections was strongly influenced by El Nino and the timing of certification. Fire activity during the 2002, 2004, and 2006 El Nino events was similar among oil palm plantations in Indonesia that would later become certified, noncertified plantations, and surrounding areas. However, total fire activity was 75% and 66% lower on certified plantations than noncertified plantations during the 2009 and 2015 El Nino events, respectively. The decline in fire activity on certified plantations, including during drought periods, highlights the potential for RSPO certification to safeguard carbon stocks in peatlands and remaining forests in accordance with legislation banning fires. However, aligning certification standards with satellite monitoring capabilities will be critical to realize sustainable palm oil production and meet industry commitments to zero forestation.

Noojipady, Praveen↗