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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

How Satellite Soil Moisture Data Can Help to Monitor the Impacts of Climate Change: SMAP Case Studies

Socially and economically costly extreme weather events have become more prevalent in the last decade. Monitoring and early warning systems could help mitigate the impact of such events by allowing people to better prepare themselves to manage their responses to these events. One significant element of an effective warning system is soil moisture because it is a key determinant of the exchange of water and heat energy between the land and atmosphere, the partitioning of precipitation between infiltration and runoff, and therefore has an influence on weather patterns and streamflow. In addition, soil moisture governs plant water availability - the key to crop yield forecasting. For these reasons, a wide range of organizations use soil moisture information to better predict and monitor climate and weather phenomena such as floods and droughts. By improving soil moisture estimates, it may be possible to improve the monitoring and early warning systems upon which these organizations rely, and hence better mitigate the impacts of extreme weather events. Through case studies, this article discusses several uses of soil moisture data products from NASA's Soil Moisture Active Passive (SMAP) mission to help improve soil moisture-related monitoring and early warning systems.

agriculture↗
NASA NTRS2021

Delaware Ecological Forecasting: Assessing Land Cover and Soil to Identify Suitable Sites for Tidal Marsh Migration in Delaware

Tidal wetlands provide vital resources for the state of Delaware, crucial not only for maintaining important ecosystem functions, but also for providing human populations with substantial services. Healthy wetland networks offer protection from severe weather, reduce flooding, improve water quality, and provide opportunities for education and recreation. However, human activities in combination with natural events, continue to cause substantial loss of wetland cover and damage wetland health. Over the last thirty years, the state of Delaware has experienced a net loss of roughly 5,000 acres of wetland. In collaboration with the Delaware Department of Natural Resources and Environmental Control (DNREC), the team used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to develop a methodology to monitor recent changes in wetland cover and forecast landward marsh migration due to sea-level rise, changes to climate, and human development. Trend analysis of current and past climate conditions in precipitation and temperature revealed an overall increase in both metrics. Using Land Change Modeler in TerrSet and Suitability Modeler in ArcGIS Pro, the team visualized landcover shifts over the last 20 years, indicating a general pattern of net wetland loss and identified locations where marsh migration could potentially occur in the future. These observations will enable better planning for restoration activities and in form decision-making to preserve wetland health and ecosystem functions.

McKenna Brahler↗
NASA NTRS1978

International Symposium on Remote Sensing of Environment, 12th, Manila, Philippines, April 20-26, 1978, Proceedings. Volumes 1, 2 & 3

The papers outline the remote sensing activities being carried out in a number of countries throughout the world, and present details on a variety of individual projects. Topics studied include a worldwide approach to remote sensing and mineral exploration, a land use information system based on statistical inference, acoustic radar and remote sensing in the boundary layer, procedure for land-use analysis in developing countries, an airborne geochemical system, remote sensing of snowpack with microwave radiometers for hydrologic applications, wheat production forecasts based on Landsat data, airborne lidar aerosol measurements over the U.S. and Europe, Landsat inventory of agricultural and forest resources in Bangladesh, application of satellite imagery to flood plain mapping in Thailand, and vegetation mapping of Nigeria from radar.

Source record↗
NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗
NASA NTRS2014

Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa Currently interact/collaborate with operational partners on evaluation of soil moisture products Drought/fire Extreme heat Convective initiation Flood and water borne diseases Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

Zavodsky, Bradley↗
NASA NTRS2011

Responding to the Consequences of Climate Change

The talk addresses the scientific consensus concerning climate change, and outlines the many paths that are open to mitigate climate change and its effects on human activities. Diverse aspects of the changing water cycle on Earth are used to illustrate the reality climate change. These include melting snowpack, glaciers, and sea ice; changes in runoff; rising sea level; moving ecosystems, an more. Human forcing of climate change is then explained, including: greenhouse gasses, atmospheric aerosols, and changes in land use. Natural forcing effects are briefly discussed, including volcanoes and changes in the solar cycle. Returning to Earth's water cycle, the effects of climate-induced changes in water resources is presented. Examples include wildfires, floods and droughts, changes in the production and availability of food, and human social reactions to these effects. The ~lk then passes to a discussion of common human reactions to these forecasts of climate change effects, with a summary of recent research on the subject, plus several recent historical examples of large-scale changes in human behavior that affect the climate and ecosystems. Finally, in the face for needed action on climate, the many options for mitigation of climate change and adaptation to its effects are presented, with examples of the ability to take affordable, and profitable action at most all levels, from the local, through national.

Hildebrand, Peter H.↗
NASA NTRS2020

Large-Scale Influences on Atmospheric River–Induced Extreme Precipitation Events Along the Coast of Washington State

Transient, narrow plumes of strong water vapor transport, referred to as Atmospheric Rivers (ARs) are responsible for much of the precipitation along the west coast of the United States. Along the coast of Oregon and Washington, the most intense cool season precipitation events are almost always induced by an AR and can result in detrimental impacts on society due to mudslides and flooding. It is therefore important to understand the large scale influence on extreme AR events so that they can be accurately predicted on timescales ranging from numerical weather prediction to seasonal forecasts. Here, characteristics of ARs that result in observed extreme precipitation events are compared to typical ARs on the coast of Washington State using data from the Modern Era Retrospective analysis for Research and Applications, Version 2. In addition to more intense water vapor transport, notable differences in the synoptic scale forcing are present during extreme precipitation events that are not present during typical AR events. In particular, an anomalously deep low pressure system is stationed to the west in the Gulf of Alaska, alongside a jet streak overhead. Attention will also be given to subseasonal and seasonal teleconnection patterns that are known to influence the weather in the Pacific Northwest of the United States. While little influence can be seen from the phase of the El Nino Southern Oscillation, Pacific Decadal Oscillation, and Pacific North American Pattern, the Madden Julian Oscillation (MJO) can play a role in determining the strength of precipitation associated with in AR on the Washington Coast. Lastly, interactions between the MJO and other teleconnection patterns will be explored to determine key features that should be investigated when making subseasonal predictions for AR activity and the associated precipitation in the Pacific Northwest.

Collow, Allison↗
NASA NTRS2010

Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

Kumar, Sujay V.↗
NASA NTRS2014

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

Planetary Boundary Layer↗
NASA NTRSDate not supplied

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. The team used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent (DSWE) derived from Landsat 8 Operational Land Imager. The team successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
NASA NTRS2022

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. We used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent(DSWE) derived from Landsat 8 Operational Land Imager. We successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding Subseasonal Moisture Recycling from Extreme MCSs Using a Land–Atmosphere Coupled Water Tracer Model

Extreme precipitation events often produce severe flooding and pose significant threats to our society. However, their subseasonal impacts on the terrestrial and atmospheric systems are not well understood. Here, we use a new land–atmosphere coupled water tracer tool embedded in the Weather Research and Forecasting (WRF) Model to “tag” precipitation produced by a series of extreme mesoscale convective systems (MCSs) in May 2015 over the southern Great Plains to reveal their contributions to different terrestrial water storages and moisture recycling during May–July. During May, we find that precipitation from earlier MCSs can contribute to 20%–50% of total evapotranspiration (ET) in later May and contribute ∼10% of local precipitation for later MCSs. After May, the water “tagged” to preceding May MCS events has the most active contribution to moisture recycling during the first 5 days in June, but this contribution diminishes quickly afterward. Both periods indicate a quick turnover of tagged precipitation, which may be representative of the southern Great Plains region. The quick turnover is closely associated with the direct evaporation from the soil surface due to the predominant shrubland and grassland coverage over this region. Over some forested areas, however, the tracer-ET flux is dominated by transpiration that stably supplies a small fraction (<1%) of moisture to the atmosphere throughout June and July. In conclusion, the tracer-contributed plume (through evaporation and transpiration) in atmospheric moisture can extend 500–700 km downstream between May and July, highlighting the far-reaching contribution to moisture recycling and precipitation from the tagged MCS events in the southern Great Plains.

Atmosphere-land interaction↗
NASA NTRS2009

Monitoring Coastal Marshes for Persistent Saltwater Intrusion

Primary goal: Provide resource managers with remote sensing products that support ecosystem forecasting models requiring salinity and inundation data. Work supports the habitat-switching modules in the Coastal Louisiana Ecosystem Assessment and Restoration (CLEAR) model, which provides scientific evaluation for restoration management (Visser et al., 2008). Ongoing work to validate flooding with radar (NWRC/USGS) and enhance persistence estimates through "fusion" of MODIS and Landsat time series (ROSES A.28 Gulf of Mexico). Additional work will also investigate relationship between saltwater dielectric constant and radar returns (Radarsat) (ROSES A.28 Gulf of Mexico).

Kalcic, Maria↗
NASA NTRS2023

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information with Lower-latency NASA Data

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated datasets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast (GEOS-CF), daily historical (1980-present) concentrations of speciated PM2.5 based on the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (1) describing temporal and spatial trends in disease and potential environmental exposures, (2) identifying populations most affected, (3) generating hypotheses about associations between health and environmental exposures, and (4) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

air quality↗
NASA NTRS2009

An Operational Assessment of the MODIS False Color Composite with the Great Falls, Montana National Weather Service

The close and productive collaborations between the NWS Warning and Forecast Office (WFO) in Great Falls, MT and the Short Term Prediction and Research Transition (SPORT) Center at NASA/Marshall Space Flight Center have provided a unique opportunity for science sharing and technology transfer. In particular, SPoRT has provided a false color composite product derived from MODIS data, which is part of NASA's Earth Observing System. This product is designed to delineate snow and ice covered ground, bare ground and clouds. The Great Falls WFO has been a test bed of the MODIS false color composite as a tool in operations to monitor the development and dissipation of snow cover In particular, preliminary applications have shown that the product can be used to monitor snow cover in remote locations as well as ice in rivers. This information can lead to improved assessments of flooding potential during post event conditions where rapid melting and runoff are anticipated. The potential of this product on future geostationary satellites may substantially contribute to the NWS mission by providing enhanced situational awareness. The operational use of this product has been transitioned at WFO Great Falls through a process of product implementation, discussions with the service hydrologist and forecasters, and post event analysis. A concentrated assessment period from January to March, 2008 was initiated to investigate the impact of the MODIS false color product on WFO Great Falls' operations. This presentation will emphasize the impact the MODIS false color product had in the WFO's situational awareness and how best this information can be used to influence operational decisions.

Loss, GIna↗
NASA NTRS2018

Forecasting and Monitoring Intense Thunderstorms in the Hindu Kush Himalayan Region: Spring 2018 Forecasting Experiment

Some of the most intense thunderstorms on the planet occur in the Hindu Kush Himalayan (HKH) region of South Asia - where many organizations lack the capacity needed to predict, observe and/or effectively respond to the threats associated with high-impact convective weather. Among the hazards include tornadoes, damaging straight-line winds (known as Nor'westers in the HKH region), large hail, and flash flooding, which typically peak in the pre-wet-monsoon season. Previous studies have documented a disproportionately large number of casualties associated with intense thunderstorms in this region; therefore, the goal of this project is to increase situational awareness of these hazards through short-term modeling and satellite assessment tools.

remote sensing↗