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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Groundwater Recharge Estimated by Land Surface Models: An Evaluation in the Conterminous U.S.

Estimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

snowmelt↗
NASA NTRS2006

March 2003 EOS Aqua AMSR-E Arctic Sea Ice Field Campaign

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.

Cavalieri, Donald J.↗
NASA NTRS1999

Land-Atmosphere Interactions: Successes, Problems and Prospects

After two decades of active research, a much better understanding of the broader role of biospheric processes on the local climate has emerged. A surface-albedo increase, particularly in desert border regions of the subtropics (as well as the deforested tropical regions), leads to a net surface energy deficit, which in turn leads to a relative sinking and reduced rainfall. On the other hand, studies of the influence of altered ratios of evapotranspiration and sensible fluxes, in situations where the net solar income is unchanged, show that evapotranspiration is a more desirable flux for increased precipitation and vitality of the biosphere. Besides providing water vapor and convective available potential energy (CAPE) to the lower troposphere, evapotranspiration helps in building larger CAPE before "turning on" the moist-convection. Larger CAPE in the lower troposphere enables convection to reach into the deeper atmosphere thereby heating the upper troposphere; indeed, moist-convection is also accompanied by the evaporation of falling precipitation that cools and moistens the lower atmosphere. While convective, as opposed to stratiform, precipitation reduces the fractional cloud cover; it also allows more solar radiation to reach the surface thereby invigorating surface fluxes. These, together with moist convection and associated downdrafts help to maintain the characteristic upper temperature limit(s) of the moist-land as well as oceanic regions. Regardless of the above understanding, several important problems continue to hinder the accurate simulation of a realistic land atmosphere interaction in a numerical model (both GCM and/or Meso-scale models). Among the unsolved problems are parameterization of sub-grid scale land processes that include small-scale variability of soil moisture, snow-cover and snow-physics, the biodiversity of the biosphere, orography, local drainage characteristics under natural conditions, and surface flow over the natural terrain. A well-known non-linear response of surface fluxes to these variations makes the problem of parameterizing land-atmosphere interaction processes hard-to-address and simulate, particularly in a GCM. In our presentation, we will discuss how orographic, snow-cover, and water table interactions can be included into a Simple Biosphere Model such as SiB/SSiB. Figure I shows how, in the Russian region, spring snowmelt affects the soil moisture profile. Corresponding figure 2 shows how interaction with the water table decreases the natural evapotranspiration in the Sahel region simulation. While these simulations need better validation with data, the simulations reveal that surface processes are sensitive to these parameterizations. With these developments, we continue to advance our understanding of the interaction of land with the atmosphere aloft, but the intrinsic variability of the newer parameters, e. g., hydraulic properties of the soil, diminish the positive influences of these advances on the improved climate simulation with GCMs.

Sud, Y. C.↗
NASA NTRS2020

Over-Winter Persistence of Supraglacial Lakes on the Greenland Ice Sheet: Results and Insights From a New Model

We present a newly developed 1-D numerical energy-balance and phase transition supraglacial lake model: Glacier Lake. Glacier Lake incorporates snowfall, in situ snow and ice melt, incoming water from the surrounding catchment, ice lid formation, basal freeze-up and thermal stratification. Snow cover and temperature are varied to test lake development through winter and the maximum lid thickness is recorded. Average wintertime temperatures of −2 to −30◦C and total snowfall of 0 to 3.45 m lead to a range of the maximum lid thickness from 1.2 to 2.8 m after ~250 days, with snow cover exerting the dominant control. An initial ice temperature of −15◦C with simulated advection of cold ice from upstream results in 0.6 m of basal freeze-up. This suggests that lakes with water depths above 1.3 to 3.4 m (dependent on winter snowfall and temperature) upon lid formation will persist through winter. These buried lakes can provide a sizeable water store at the start of the melt season, expedite future lake formation and warm underlying ice even in winter.

glacier hydrology↗
NASA NTRS1992

A snow wetness retrieval algorithm for SAR

The objectives of this study are: (1) to evaluate the backscattering signals response to snow wetness; and (2) to develop an algorithm for snow wetness measurement using C-band polarimetric synthetic aperture radar (SAR). In hydrological investigations, modeling and forecasting of snowmelt runoff requires information about snowpack properties and their spatial variability. In particular, timely measurement of snow parameters is needed for operational hydrology. The liquid water content of snowpack is one of the important parameters. Active microwave sensors are highly sensitive to liquid water in the snowpack because of the large dielectric contrast between ice and water in the microwave spectrum. They are not affected by weather and have a spatial resolution compatible with the topographic variation in alpine regions. However, a quantitative algorithm for retrieval snow wetness has not yet been developed.

Shi, Jian-Cheng↗
NASA NTRS2016

Annual Greenland Accumulation Rates (2009-2012) from Airborne Snow Radar

Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 gigahertz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semi-automated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 percent. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR - Modele Atmospherique Regional for Greenland and vicinity) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

surface melt↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗
NASA NTRS1984

Microwave emissions from snow

The radiation emitted from dry and wet snowpack in the microwave region (1 to 100 GHz) is discussed and related to ground observations. Results from theoretical model calculations match the brightness temperatures obtained by truck mounted, airborne and spaceborne microwave sensor systems. Snow wetness and internal layer structure complicate the snow parameter retrieval algorithm. Further understanding of electromagnetic interaction with snowpack may eventually provide a technique to probe the internal snow properties

Chang, A. T. C.↗
DOE OSTI2024

Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (~7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Synthesis of passive microwave and radar altimeter data for estimating accumulation rates of polar snow

In this paper, we compare dry-snow extinction coefficients derived from radar altimeter data with brightness temperature data from passive microwave measurements over a portion of the East Antarctic plateau. The comparison between the extinction coefficients and the brightness temperatures shows a strong negative correlation, where the correlation coefficients ranged from -0.87 to -0.95. The extinction coefficient of the dry polar snow decreases with increasing surface elevation, while the average brightness temperature increases with surface elevation. Our analysis shows that the observed trends are related to geographic variations in scattering coefficient of snow, which in turn are controlled by variations in surface temperature and snow accumulation rate. By combining information present in the extinction coefficient and brightness temperature data sets, we develop a model that can be used to obtain quantitative estimates of the accumulation rate of dry polar snow.

Davis, Curt H.↗
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 NTRS2020

Impact of Surface Albedo Assimilation on Snow Estimation

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

surface albedo↗
NASA NTRS2023

Shoshone River Water Resources II​: Quantifying Sediment Input in the Shoshone River in Wyoming using the Soil and Water Assessment Tool for Enhanced Water Quality Monitoring

The Willwood Dam, an irrigation diversion dam located on the Shoshone River (Wyoming, USA), has faced ongoing issues with sediment accumulation and needs frequent sediment flushing to remain operable. However, high suspended sediment levels during flushing events have negatively impacted downstream aquatic ecology and recreational opportunities. To address these problems, DEVELOP partnered with the Wyoming Department of Environmental Quality, Shoshone River Partners, and United States Geologic Survey. During term one, the team developed a workflow to map turbidity using PlanetScope imagery, analyze time series precipitation data, and create landcover maps. For this term, we focused on three methods to gain a better understanding of sediment sources: 1) improving remote sensing of turbidity, 2) modeling sediment transport within the watershed using the Soil and Water Assessment Tool (SWAT), and 3) conducting a snow cover time series analysis using Suomi NPP VIIRS imagery. Through remote sensing, we found Dry Creek/Homesteader Creek and Penney Gulch/Rough Gulch had the highest concentration sediment plumes. The SWAT+ model created a high-resolution grid model of the watershed, identifying high sedimentation in the western and southern subbasins but displayed low-correlated calibration and validation results due to limited observed data. We analyzed the snow cover extent alongside other hydrologic variables and found that snow melt events correlated with increases in suspended sediment concentration and turbidity values. Coupling remote sensing with hydrological modeling will give watershed managers a new perspective on high-priority regions for implementation of remediation to reduce the sediment build-up at the Willwood Dam.

Suspended Sediment↗
NASA NTRS2008

POLSCAT Ku-Band Radar Remote Sensing of Terrestrial Snow Cover

Characteristics of the POLSCAT data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15 to 0.5 dB increases in backscatter for every 1 cm of snow water equivalent (SWE) accumulation for areas with short vegetation. Based on a simplified radiative transfer model, the change detection technique is used to convert the temporal change of radar backscatter into SWE accumulation for dry snow conditions. The resulting SWE accumulation estimates are consistent with the in-situ SWE measurements, with about 2 to 3 cm Root-Mean-Square (RMS) difference for regions with sagebrush or pasture.

radar↗
NASA NTRS1993

C-band radar backscatter of sea ice in the Weddell Sea, Antarctica during the austral winter of 1992

A C-band ship-based scatterometer was used to measure the backscatter coefficient of sea ice in the Weddell Sea during June and July 1992. These are the first microwave scatterometer data ever to be collected in the Antarctic sea ice cover during the austral winter. The instrument was a frequency-modulated continuous-wave (FM-CW) radar altimeter modified by the University of Kansas Radar Systems and Remote Sensing Laboratory to perform backscatter measurements. Measurements were taken as part of a Jet Propulsion Laboratory experiment aboard the German ice research vessel F.S. Polarstern. Backscatter measurements were performed at incidence angles ranging from 17 to 65 degrees with VV and HV polarization as the Polarstern travelled from east to west across the central Weddell Sea. Backscatter measurements were made of several different types of ice sea including pancake, dark nilas, white nilas, grey, first-year and second-year ice. Periodic external calibrations were performed with the aid of a Luneberg Lens to enable absolute values of backscatter to be derived from the data. At each radar measurement location, in-situ measurements were made of snow and sea ice. Physical and chemical analyses of ice core and snow samples, together with high magnification photography of snow crystallography provide important information with which to develop physical models of the scattering systems. Meteorological information and oceanographic conditions were also recorded throughout the experiment. Many of the stations were chosen to coincide with periods of near-simultaneous or coincident imaging by the ERS-1 satellite Synthetic Aperture Radar (SAR). This enabled spaceborne imaging by the C-band SAR of areas of sea ice in which backscatter measurements were taken. This provides a valuable tool for interpretation of satellite SAR imagery from Antarctic sea ice in terms of the physical properties of the sea ice and snow. Preliminary results of the backscatter from the various ice types and their relation to the physical properties of sea ice are presented.

Hosseinmostafa, R.↗
DOE OSTI2026

Watershed response of the Feather River Basin, California, United States of America to future climate changes

Increasing mean annual temperatures under climate change are expected to reduce seasonal snowpack, increase evapotranspiration (ET), and alter summer baseflow in headwater watersheds worldwide. Strong regional variability in hydrologic responses highlights the need for catchment-scale, physically based models to assess future flood risk and water availability. This study examines climate-driven changes in the hydrologic response of the Upper Feather River watershed in the Sierra Nevada Mountains, California. Four priority climate models and two representative concentration pathways (RCP4.5 and RCP8.5) are used to evaluate future hydrologic responses of the watershed. Hydrologic processes are simulated using an objectively calibrated Soil and Water Assessment Tool Plus model for a historical baseline (1986–2005) and a future period 2070–2099), driven by observed and projected precipitation and temperature. Results indicate a declining contribution of snowfall to annual precipitation, with peak snowfall and water yield shifting 1–3 months earlier. Long-term annual maximum flows are projected to increase considerably, whereas low-flow responses are mixed, with both increases and decreases projected by the end of the century. These findings highlight the need for adaptive watershed management to enhance flood protection, water storage, and drought resilience. Future water resource planning should also account for one-to-three-month shifts in peak water yield and surface runoff due to changes in snowmelt timing and a lower snowfall-to-rainfall ratio under climate change. The modeling framework and insights are transferable to other snow-dominated headwater watersheds experiencing climate-driven change.

Tigabu, T [UC Davis]↗
NASA NTRS1977

An investigation of rotor noise generation by aerodynamic disturbance

An open rotor was considered as a process for converting an unsteady velocity inflow into sound radiation. With the aid of crude assumptions, aero-acoustic transfer functions were defined theoretically for both discrete frequency and broad band noise. A study of the validity of these transfer functions yielded results which show good agreement at discrete frequencies though slightly less good for broad band noise. Agreement in both cases holds over three or more decades of the relevant parameters. A rotating hot wire anemometry system consisting of a single hot wire probe mounted in the nose cone of the rotor was used to quantify fluctuations in the airflow onto a single rotor blade for the transfer function results. Further theoretical analysis revealed that the sound field can be expressed in terms of blade-to-blade correlations in the airflow, and results from two probes rotating simultaneously were modelled mathematically and inserted in the theory. Preliminary results snow encouraging agreement with experimental data.

Whitfield, C. E.↗
NASA NTRS1984

Snowcover influence on backscattering from terrain

The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g/cu cm). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.

Ulaby, F. T.↗