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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Agrivoltaic Grazing Systems for a Sustainable Future: A Multi‐Disciplinary Review & Gap Analysis

Solar photovoltaics (PV) is the fastest growing source of electricity in the world, however, its expansion has raised concerns about the displacement of agriculture and the degradation of rangeland ecosystems. This has prompted research and development of multi-land-use systems that co-prioritize energy production alongside agriculture, commonly referred to as agrivoltaics. Currently, agrivoltaic grazing (also called solar grazing) is one of the most common forms of these systems. However, peer-reviewed research on these systems is limited. This review synthesizes research on agrivoltaic grazing systems, drawing upon literature addressing agrivoltaics broadly and considering relevant literature in adjacent fields of study. Based on this review, we identify six key gaps and priority directions for future research. These include foundational fieldwork to characterize the layered ecological impacts of solar PV and grazing and understand PV-livestock integration from both an animal welfare and a PV infrastructure perspective. This will facilitate the development of biogeochemical and economic models that improve our predictive capacity and ability to compare system designs. We also identify gaps in understanding the human aspects of these systems and emphasize the importance of utilizing collaborative research methods and increasing research on the social dimensions of agrivoltaic grazing systems.

14 SOLAR ENERGY↗
DOE OSTI2025

Mapping Nitrogen Pathways in Organic Manure through GCAM

As the world has become more and more globalized, a need for a comprehensive and extensive modelling system that maps out the vast majority of processes and items around the world and can display how interdependent they are. Furthermore, such a model should be able to predict future processes and values such that it can guide scientific thinking and policy. This is where the importance of GCAM becomes so apparent. GCAM, the Global Change Analysis Model, that incorporates many different fields and can predict such a wide variety of processes. As the model expands and encompasses more fields and goes into greater detail, there is a need for previously incorporated processes to be further fleshed out. My project is dedicated to creating the organic nitrogen fertilizer pathway, such that there are two nitrogen fertilizer pathways, synthetic and organic (manure). Previously, only synthetic fertilizer was included, with all organic fertilizer (manure) being omitted from the model. Developing the synthetic path is relatively easy, as the process for creating synthetic fertilizer is an industrial process that has been perfected over decades, and thus has a very precise input/output coefficient of nitrogen, but determining that same coefficient for organic manure is much harder, because, well, digestion is not an industrial process, especially considering the many different types of animals have different processes, and a good amount of the manure they produce won’t be applied to the soil. I obtained data from the UN’s FAO on manure nitrogen applied to soil, and filled in the gaps and created many different visualizations to help comprehend the data. These findings will foster a greater understanding of nitrogen flows, which is crucial to fields like industrial agriculture, polluting runoff, and nitrifying bacteria and archaea in the soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
DOE OSTI2020

Minnesota Solar Pathways: Illuminating Pathways to 10% Solar (Final Technical Report)

The Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, was a three-year project designed to explore least-risk, best-value strategies for meeting the State of Minnesota’s solar goals. As part of this aim, the Pathways Team modeled renewable generation costs, examined ways to streamline interconnection, and evaluated technologies that can increase solar hosting capacity on the distribution grid. Midway through the project period, the Pathways Team expanded the scope of study using the same modeling framework from the Minnesota Solar Potential Analysis to estimate and compare generation costs for high renewables penetration scenarios across the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. In addition, the analysis of individual MISO subregions compared with the whole region highlights the opportunities of enabling market potential compared with impact of ongoing transmission constraints. Beyond the analysis of future scenarios, the partnerships initiated and supported through this project underscored the ongoing need for work with stakeholders to address key issues, particularly around siting, and leverage symbiotic strategies to meet state goals.

14 SOLAR ENERGY↗
DOE OSTI2019

Combining anaerobic digestion and hydrothermal liquefaction in the conversion of dairy waste into energy: A techno economic model for New York state

There is increasing global interest and policies being enacted to lower greenhouse gas emissions, especially from the agricultural sector. In the U.S. for example, states with large dairy operations may combine proven manure valorization technologies, such as anaerobic digestion and hydrothermal liquefaction. Sustainable manure treatment would increase the recovery of energy and other useful co-products, namely biogas, biocrude oil and hydro-char as well as lower the environmental impacts. In this study, the economic feasibility of implementing a centralized bioenergy system in New York State was investigated. Here, the feasibility of this transformation depends on many factors, including capital costs, discount rates, and other financing arrangements, electricity selling prices, incentives and farm sizes and locations. For a large-scale implementation in New York State accounting for nearly 50% of the state’s dairy farms, our model of a distributed, hybrid anaerobic/hydrothermal system was shown to treat 590 million liters of wet manure per day, producing 607 million kWh of electricity, 162,000 L of biocrude oil and 117,000 kg of hydro-char per day. Electricity selling price is a critical factor. Increasing the electricity selling price from wholesale ($\$0.06$ /kWh) to retail ($\$0.18$ kWh) increased the net present value from $\$395$ million to $1.5 billion (considering a 40-year project lifetime).

42 ENGINEERING↗
NASA NTRS2019

The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

global fire atlas↗
DOE OSTI2024

The impact of regional resources and technology availability on carbon dioxide removal potential in the United States

To achieve net zero carbon emissions by mid-century, the United States may need to rely on carbon dioxide removal (CDR) to offset emissions from difficult-to-decarbonize sectors and/or shortfalls in near-term mitigation efforts. CDR can be delivered using many approaches with different requirements for land, water, geologic carbon storage capacity, energy, and other resources. The availability of these resources varies by region in the U.S. suggesting that CDR deployment will be uneven across the country. Using the global change analysis model for the United States (GCAM-USA), we modeled six classes of CDR and explored their potential using four scenarios: a scenario where all the CDR pathways are available (Full Portfolio), a scenario with restricted carbon capture and storage (Low CCS), a scenario where the availability of bio-based CDR options is limited (Low Bio), and a scenario with constraints on enhanced rock weathering (ERW) capabilities (Low ERW). We find that by employing a diverse set of CDR approaches, the U.S. could remove between 1 and 1.9 GtCO 2 /yr by midcentury. In the Full Portfolio scenario, direct air carbon capture and storage (DACCS) predominates, delivering approximately 50% of CO 2 removal, with bioenergy with carbon capture and storage contributing 25%, and ERW delivering 11.5%. Texas and the agricultural Midwest lead in CDR deployment due to their abundant agricultural land and geological storage availability. In the Low CCS scenario, reliance on DACCS decreases, easing pressure on energy systems but increasing pressure on the land. In all cases CDR deployment was found to drive important impacts on energy, land, or materials supply chains (to supply ERW, for example) and these effects were generally more pronounced when fewer CDR technologies were available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2021

Molecular level characterization of wildfire aerosol induced changes to plant health and value

The potential impacts of wildfire smoke on plant health and value are of increasing concern, due both to increasing awareness, and more frequent wildfire events. Wildfires produce substantial amounts of atmospheric pollution in smoke and particulate matter which can travel thousands of miles. These smoke events can blanket entire agricultural regions and cause impacts to plant development. One notable example of this is ‘smoke taint’ in wine, where vines, exposed to smoke, absorb many small volatile phenolic (VP) compounds. These VPs can be metabolized (glycosylated) and transported throughout the plant. Later, during downstream processing (fermentation of the grape musts), these glycosylated VPs can be hydrolyzed to re-release and volatilize the aroma-active phenolic compounds. The final product, thus, smells of smoke. Despite the obvious commercial and general research interests here, the nature of wildfire impacts on plant health are incompletely understood. Using established metabolomics and organic matter (aerosol) characterization methods, the overall aim of this proposal is thus to study wildfire smoke impacts on plant health and value, in the context of a model system (V. vinifera). This project closed early, and this report reflects some of the limited work conducted prior to conclusions. Specifically, this report discusses some sample preparation and initial MALDI-mass spectrometry analysis.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

The Impact of Urbanization on the Precipitation Component of the Water Cycle: A New Perspective

It is estimated that by the year 2025, 60% of the world s population will live in cities (UNFP, 1999). As cities continue to grow, urban sprawl (e.g., the expansion of urban surfaces outward into rural surroundings) creates unique problems related to land use, transportation, agriculture, housing, pollution, and development. Urban expansion also has measurable impacts on environmental processes. Urban areas modify boundary layer processes through the creation of an urban heat island (UHI). The literature indicates that the signature of the urban heat island effect may be resolvable in rainfall patterns over and downwind of metropolitan areas. However, a recent U.S. Weather Research Program panel concluded that more observational and modeling research is needed in this area (Dabberdt et al. 2000). NASA and other agencies initiated programs such as the Atlanta Land-use Analysis: Temperature and Air Quality Project (ATLANTA) (Quattrochi et al. 1998) which aimed to identify and understand how urban heat islands impact the environment. However, a comprehensive assessment of the role of urban-induced rainfall in the global water and energy cycle (GWEC) and cycling of freshwater was not a primary focus of these efforts. NASA's Earth Science Enterprise (ESE) seeks to develop a scientific understanding of the Earth system and its response to natural or human-induced changes to enable improved prediction capability for climate, weather, and natural hazards (NASA, 2000). Within this mission, the ESE has three basic thrusts: science research to increase Earth system knowledge; an applications program to transfer science knowledge to practical use in society; and a technology program to enable new, better, and cheaper capabilities for observing the earth. Within this framework, a research program is underway to further address the co-relationship between land cover use and change (e.g. urban development) and its impact on key components of the GWEC (e.g., precipitation). This presentation discusses the feasibility of using the TRMM or GPM satellite to identify precipitation anomalies likely caused by urbanization (Shepherd et al. 2002). Recent results from analyses of TRMM data around several major U.S. cities (e.g. Dallas, Atlanta, Houston) will be discussed. The presentation also summarizes a NASA-funded research effort to investigate the phenomenon of urban-induced precipitation anomalies using TRMM (future GPM) satellite-based remote sensing, an intensive ground observation/validation effort near Atlanta, and coupled atmosphere-land numerical modeling techniques.

Shephard, J. Marshal↗
NASA NTRS2000

Generating Accurate Urban Area Maps from Nighttime Satellite (DMSP/OLS) Data

There has been an increasing interest by the international research community to use the nighttime acquired "city-lights" data sets collected by the US Defense Meteorological Satellite Program's Operational Linescan system to study issues relative to urbanization. Many researchers are interested in using these data to estimate human demographic parameters over large areas and then characterize the interactions between urban development , natural ecosystems, and other aspects of the human enterprise. Many of these attempts rely on an ability to accurately identify urbanized area. However, beyond the simple determination of the loci of human activity, using these data to generate accurate estimates of urbanized area can be problematic. Sensor blooming and registration error can cause large overestimates of urban land based on a simple measure of lit area from the raw data. We discuss these issues, show results of an attempt to do a historical urban growth model in Egypt, and then describe a few basic processing techniques that use geo-spatial analysis to threshold the DMSP data to accurately estimate urbanized areas. Algorithm results are shown for the United States and an application to use the data to estimate the impact of urban sprawl on sustainable agriculture in the US and China is described.

Imhoff, Marc↗
NASA NTRS2003

Long-Term Climatic Variations in the Almati Oblast in Central Asian Kazakhstan: Correlations between National Centers for Environmental Prediction (NCEP) Reanalysis II Results and Oblast Meteorological Station Data from 1949 to the Present

As part of a larger analysis of country systems described elsewhere, named a Crop Country Inventory, CCI, large variations in annual crop yield for selected climate sensitive agricultural regions or sub-regions within a country have been studied over extended periods in decades. These climate sensitive regions, principally responsible for large annual variations in an entire country s crop production, generally are characterized by distinctive patterns of atmospheric circulation and synoptic processes that result in large seasonal fluctuations in temperature, precipitation and soil moisture as well as other climate properties. The immediate region of interest is drought prone Kazakhstan in Central Asia, part of the Former Soviet Union, FSU. As a partial validation test in a dry southern region of Kazakhstan, the Almati Oblast was chosen. The Almati Oblast, a sub-region of Kazakhstan located in its southeast corner, is one of 14 oblasts within the Republic of Kazahstan. The climate data set used to characterize this region was taken from the results of the current maturely developed Global Climate Model, GCM. In this paper, the GCM results have been compared to the meteorological station data at the station locations, over various periods. If the empirical correlation of the data sets from both the GCM and station data is sufficiently significant, this would validate the use of the superior GCM profile mapping and integration for the climatic characterization of a sub-region. Precipitation values interpolated from NCEP Reanalysis II data, a global climate database spanning over 5 decades since 1949, have been statistically correlated with monthly-averaged station data from 1949 through 1993, and with daily station data from April through August, 1990 for the Almati Oblast in Kazakhstan. The resultant correlation is significant, which implies that the methodology may be extended to different regions globally for Crop Country Inventory studies.

Welker, Jean E.↗
NASA NTRS2018

Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Reichle, Rolf↗
NASA NTRS2018

Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

SMAP↗
NASA NTRS2022

A Central Asia Hydrologic Monitoring Dataset for Food and Water Security Applications in Afghanistan

From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56°N, 30–100°E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Amy McNally↗