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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A unified ensemble soil moisture dataset across the continental United States

Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Albedo as a modulator of climate response to tropical deforestation

An atmospheric general circulation model with land surface properties represented by the simplified Simple Biosphere model is used to investigate the effects on local climate due to tropical deforestation for the Amazon basin. One control and three anomaly integrations of 4 years' duration are performed. In the anomaly integrations, rain forest in South America is replaced by degraded grassland. The anomaly integrations differ only in the optical properties of the grassland vegetation, with net surface albedos ranging from the same as to 0.09 lighter than that of rain forest. It is found that the change in climate, particularly rainfall, is strongly dependent on the change in surface albedo that accompanies deforestation. Replacement of forest by grass causes a reduction in transpiration and reduces frictional convergence by decreasing surface roughness. However, precipitation averaged over the deforested area is not necessarily reduced. Average precipitation decreases when the increase in albedo is greater than 0.03. If surface albedo is not increased appreciably as a result of deforestation, moisture flux convergence driven by the increase in surface temperature can offset the other effects, and average precipitation increases. As albedo is increased, surface temperature does not change, but surface latent and sensible heat flux decreases due to reduced radiational energy absorbed at the surface, resulting in a reduction in convection and precipitation. A change in the distribution of precipitation due to deforestation that appears to be independent of the albedo is observed.

Dirmeyer, Paul A.↗
NASA NTRS2000

The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales

The three-layer snow model is coupled to the global catchment-based Land Surface Model (LSM) of the NASA Seasonal to Interannual Prediction Project (NSIPP) project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period 1987-1988. The various snow processes included in the three-layer model, such as snow melting and re-freezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale.

Stieglitz, Marc↗
NASA NTRS2017

Multi-Modal Active Perception for Autonomously Selecting Landing Sites on Icy Moons

Selecting suitable landing sites is fundamental to achieving many mission objectives in planetary robotic lander missions. However, due to sensing limitations, landing sites which are both safe and scientifically valuable often cannot be determined reliably from orbit, particularly, in icy moon missions where orbital sensing data is noisy and incomplete. This paper presents an active perception approach to Entry Descent and Landing (EDL) which enables the lander to autonomously plan informative descent trajectories, acquire high quality sensing data during descent and exploit this additional information to select higher utility landing sites. Our approach consists of two components: probabilistic modeling of landing site features and approximate trajectory planning using a sampling based planner. The proposed framework allows the lander to plan long horizons paths and remain robust to noisy data. Results in simulated environments show large performance improvements over alternative approaches and show promise that our approach has strong potential to improve science return of not only icy moon missions but EDL systems in general.

autonomy↗
NASA NTRS2018

The Land Surface Data Toolkit (LDT v7.2) - A Data Fusion Environment for Land Data Assimilation Systems

The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

droughts and floods↗
NASA NTRS1946

Tank Tests of a 1/7-Size Powered Dynamic Model of the Grumman XJR2F-1 Amphibian: Spray Characteristics, Take-Off and Landing Stability in Smooth Water - Langley Tank Model 212, TED No. NACA 2378

Tests of a model of the XJR2F-Y amphibian were made in Langley tank no. to determine the spray characteristics and the take-off and landing stability. At a gross load of 22,000 pounds full size, spray entered the propeller disk only at a very narrow range of speeds. The spray striking the flaps was not excessive and no appreciable wetting of the tail surfaces was noted. The trim limits of stability appeared to be satisfactory and the upper-limit porpoising was not violent. The stable range of center-of-gravity locations with flaps set 20deg was well aft of the desired operating range. However, with flaps up, the forward limit was about 18 percent mean aerodynamic chord and the aft limit about 28.5 percent mean aerodynamic chord at a load of 26,000 pounds and with elevators deflected -10deg. Under these conditions the location of the step is considered satisfactory. Tests showed that the effect of water in the nose-wheel well would be to move the forward limit aft about 2-percent mean aerodynamic chord. Without ventilation of the main step, the model skipped during landing at most trims, but this skipping was not violent. With the ventilation, the model skipped lightly only at trims where the afterbody keel was approximately parallel to the water (around 7.5 deg).

Land, Norman S.↗
NASA NTRSDate not supplied

Efficient and Accurate Terrain Integration & Application to Modeling and Simulation of Planetary Landing Systems

As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.

Terrain↗
DOE OSTI2016

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Storm and Annual Time Scale Hydrological Data for the Russian River Watershed 1996-2022

These files contains observed and simulated hydrological data (discharge, precipitation) that have been aggregated to the storm-event scale and to the annual time scale for the Russian River Watershed (RRW), California. Observed data are obtained from 12 different USGS hydrological stations located throughout the watershed, and discharge is simulated using the GR5H hourly hydrological model. Precipitation data are obtained from the NASA NLDAS NOAH Community Land Surface Model. All simulated and observed discharge are aggregated to the storm and annual time scales. We investigated changes in watershed hydrological conditions in the Russian River Watershed, a Mediterranean, drought prone, wildfire-adapted ecosystem, following eleven wildfires that occurred from 2017-2020. We ask two research questions: 1) How do interacting wildfire events, drought, and atmospheric rivers impact hydrological conditions of the watershed and in particular streamflow?, and 2) What percentage of new and/or cumulative wildfire disturbance is required to initiate hydrological change? We hypothesize that sub-watersheds of the RRW Mediterranean ecoregion have not burned beyond an intrinsic, and still unknown, threshold required to initiate change. Using a series of paired burned/unburned catchments nested within the larger watershed, we examined temporal and spatial patterns of pre-and-post wildfire water hydrology using a rainfall runoff hydrological model compared with data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The impact of multi-sensor land data assimilation on river discharge estimation

River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.

Rui, Hualan↗
NASA NTRS2023

Southern Idaho Ecological Conservation: Investigating the Impact of Targeted Grazing to Improve Wetland Habitat in the Sterling Wildlife Management Area

Wetland ecosystems are vital for biodiversity conservation and ecosystem services. The Sterling Wildlife Management Area in Bingham County, Idaho, has management concerns about decadent and accumulated vegetation growth encroaching on wetland habitat, which presents challenges for wildlife, decreases biodiversity, and limits public access. Targeted grazing has been proposed as a sustainable alternative to chemical herbicides or burning. Land managers introduced targeted cattle grazing in January 2021 to reduce biomass. NASA DEVELOP partnered with the Idaho Department of Fish and Game to determine the impact of grazing using NASA Earth observations from Landsat 8 Operational Land Imager (OLI) in Google Earth Engine (GEE). Images were processed with TerrSet’s Land Change Modeler and ArcGIS Pro’s Change Detection Wizard to understand land changes following grazing. A Normalized Difference Vegetation Index (NDVI) analysis was performed to assess impacts on vegetation productivity and compare variance in biomass before and after grazing. A Normalized Difference Water Index (NDWI) was used to compare changes in the wetland and its vegetation content to evaluate the suitability of the area for migratory birds post-grazing. Results showed a decrease in the vegetation index and an increase in the water index postgrazing. The DEVELOP team’s analysis suggests that grazing helps break down thick, senesced vegetation and increase soil moisture. Providing a workflow model will aid partners in continuing to monitor this management area and other management areas across the state.

change detection↗
NASA NTRS2001

Microwave Remote Sensing and the Cold Land Processes Field Experiment

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Kim, Edward J.↗
NASA NTRS2019

Water Storage Trends in High Mountain Asia

Changes in terrestrial water storage (TWS) in High Mountain Asia (HMA) could have major societal impacts, as the region’s large reservoirs of glaciers, snow, and groundwater provide a freshwater source to more than one billion people. We seek to quantify and close the budget of secular changes in TWS over the span of the GRACE satellite mission (2003–2016). To assess the TWS trend budget we consider a new high-resolution mass trend product determined directly from GRACE L1B data, glacier mass balance derived from Digital Elevation Models (DEMs), groundwater variability determined from confined and unconfined well observations, and terrestrial water budget estimates from a suite of land surface model simulations with the NASA Land Information System (LIS). This effort is successful at closing the aggregated TWS trend budget over the entire HMA region, the glaciated portion of HMA, and the Indus and Ganges basins, where the full-region trends are primarily due to the glacier mass balance and groundwater signals. Additionally, we investigate the closure of TWS trends at individual 1-arc-degree mascons (area ≈12,000 km2); a significant improvement in spatial resolution over previous analyses of GRACE-derived trends. This mascon-level analysis reveals locations where the TWS trends are well-explained by the independent datasets, as well as regions where they are not; identifying specific geographic areas where additional data and model improvements are needed. The accurate characterization of total TWS trends and its components presented here is critical to understanding the complex dynamics of the region, and is a necessary step toward projecting future water mass changes in HMA.

Bryant D Loomis↗
NASA NTRS1993

An initial comparison of CFD with experiment for a geometrically simplified STOVL model

The transition flight performance of a representative Short Take-Off or Vertical Landing (STOVL) model is investigated using side-by-side experimental and numerical simulations. The model consists of a 60 deg cropped delta wing planform; a simple fuselage shape blended to the wing; and tandem, circular, high-pressure-air lift jets that exit perpendicular to the flat lower surface. The configuration was chosen to minimize the geometric modeling complexity while retaining the important flow physics of the lift-jet/aerodynamic surface interaction. Three-dimensional, turbulent Navier-Stokes computations are made using a multiple, overset grid scheme. Results are presented and compared with the measured forces and pressures for the model at a freestream Mach number of 0.14 and a 10 deg angle-of-attack without lift jets operating. Computed surface flow patterns and particle traces show that the simulation predicts primary and secondary wing leading edge vortices for these conditions.

Roth, Karlin R.↗
DOE OSTI2015

Assessing the relative influence of surface soil moisture and ENSO SST on precipitation predictability over the contiguous United States

This study assesses the relative influence of soil moisture memory and tropical sea surface temperature (SST) in seasonal rainfall over the contiguous United States. Using observed precipitation, the NINO3.4 index and soil moisture and evapotranspiration simulated by a land surface model for 61 years, analysis was performed using partial correlations to evaluate to what extent land surface and SST anomaly of El Niño and Southern Oscillation (ENSO) can affect seasonal precipitation over different regions and seasons. Results show that antecedent soil moisture is as important as concurrent ENSO condition in controlling rainfall anomalies over the U.S., but they generally dominate in different seasons with SST providing more predictability during winter while soil moisture, through its linkages to evapotranspiration and snow water, has larger influence in spring and early summer. The proposed methodology is applicable to climate model outputs to evaluate the intensity of land-atmosphere coupling and its relative importance.

Yoon, Jin-Ho↗
NASA NTRS2007

Aerosol Radiative Effects on Deep Convective Clouds and Associated Radiative Forcing

The aerosol radiative effects (ARE) on the deep convective clouds are investigated by using a spectral-bin cloud-resolving model (CRM) coupled with a radiation scheme and an explicit land surface model. The sensitivity of cloud properties and the associated radiative forcing to aerosol single-scattering albedo (SSA) are examined. The ARE on cloud properties is pronounced for mid-visible SSA of 0.85. Relative to the case excluding the ARE, cloud fraction and optical depth decrease by about 18% and 20%, respectively. Cloud droplet and ice particle number concentrations, liquid water path (LWP), ice water path (IWP), and droplet size decrease significantly when the ARE is introduced. The ARE causes a surface cooling of about 0.35 K and significantly high heating rates in the lower troposphere (about 0.6K/day higher at 2 km), both of which lead to a more stable atmosphere and hence weaker convection. The weaker convection and the more desiccation of cloud layers explain the less cloudiness, lower cloud optical depth, LWP and IWP, smaller droplet size, and less precipitation. The daytime-mean direct forcing induced by black carbon is about 2.2 W/sq m at the top of atmosphere (TOA) and -17.4 W/sq m at the surface for SSA of 0.85. The semi-direct forcing is positive, about 10 and 11.2 W/sq m at the TOA and surface, respectively. Both the TOA and surface total radiative forcing values are strongly negative for the deep convective clouds, attributed mostly to aerosol indirect forcing. Aerosol direct and semi-direct effects are very sensitive to SSA. Because the positive semi-direct forcing compensates the negative direct forcing at the surface, the surface temperature and heat fluxes decrease less significantly with the increase of aerosol absorption (decreasing SSA). The cloud fraction, optical depth, convective strength, and precipitation decrease with the increase of absorption, resulting from a more stable and dryer atmosphere due to enhanced surface cooling and atmospheric heating.

Fan, J.↗
NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗