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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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8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state’s climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

Rachel L Lamb↗
NASA NTRS2023

Evaluation of a Regional Crop Model Implementation for Sub-National Yield Assessments in Kenya

CONTEXT: Cropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVE: This study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODS: The RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONS: The median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ~1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCE: One of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Crop model↗
NASA NTRS2012

Satellite-based Assessment of Climate Controls on US Burned Area

Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate-fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997 2010) and Monitoring Trends in Burn Severity (MTBS, 1984 2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5 resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE and fire activity in the Alaska, while water deficit (precipitation PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6 12 months). Fire season PE in creased from the 1980s 2000s, enhancing climate-driven fire risk in the southern and western US where PE-BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990s 2000s highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climatefire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity.

Morton, D. C.↗
NASA NTRS2013

A City and National Metric measuring Isolation from the Global Market for Food Security Assessment

The World Bank has invested in infrastructure in developing countries for decades. This investment aims to reduce the isolation of markets, reducing both seasonality and variability in food availability and food prices. Here we combine city market price data, global distance to port, and country infrastructure data to create a new Isolation Index for countries and cities around the world. Our index quantifies the isolation of a city from the global market. We demonstrate that an index built at the country level can be applied at a sub-national level to quantify city isolation. In doing so, we offer policy makers with an alternative metric to assess food insecurity. We compare our isolation index with other indices and economic data found in the literature.We show that our Index measures economic isolation regardless of economic stability using correlation and analysis

Brown, Molly E.↗
NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗
NASA NTRS2021

Land-Use Harmonization Datasets for Global Carbon Budget 2019 and Beyond

Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period, and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to the best models in well-organized scientific assessments. The Land-Use Harmonization dataset (LUH2), previously developed and used as input for CMIP6 simulations, has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessment. These annual LUH2-GCB updates and extensions have incorporated annual FAO wood harvest data updates for dataset years after 2015 and HYDE gridded agriculture area data updates (based on annual FAO agricultural area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of one or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use change data relating toagricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, and crop rotationsand pasture managementand are used by both book-keeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. For GCB 2019,a more significant update to LUH2 was produced (LUH2-GCB2019) to correct cropland and grazing area errors in the underlying input datasets for the globally important region of Brazil, as far back as 1950. From 1951-2012 the LUH2-GCB2019 dataset begins to diverge from the LUH2 v2hdataset, with peak differences in Brazil in the year 2000 for grazing land (difference of 100,000 km2) and in the year 2009 for cropland (difference of 77,000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. These LUH2-GCB2019 corrections for Brazil provide the base for future LUH2-GCB updates including the recent LUH2-GCB2020 dataset, and present a starting point for operationalizing the creation of these datasets toreduce time-lags due to the multiple input dataset and model latencies.

L Chini↗
NASA NTRS2022

Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

Climate change↗
NASA NTRS2022

GEDI Launches a New Era of Biomass Inference from Space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO 2 . The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

Ralph Dubayah↗