Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Soil heat flux”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Enhancement of surface-atmosphere fluxes by desert-fringe vegetation through reduction of surface albedo and of soil heat flux

Under the arid conditions prevailing at the end of the dry season in the western Negev/northern Sinai region, vegetation causes a sharp increase relative to bare soil in the daytime sensible heat flux from the surface to the atmosphere. Two mechanisms are involved: the increase in the surface absorptivity and a decrease in the surface heat flux. By increasing the sensible heat flux to the atmosphere through the albedo and the soil heat flux reductions, the desert-fringe vegetation increases the daytime convection and the growth of the planetary boundary layer. Removal of vegetation by overgrazing, by reducing the sensible heat flux, tends to reduce daytime convective precipitation, producing higher probabilities of drought conditions. This assessment of overgrazing is based on observations in the Sinai/Negev, where the soil albedo is high and where overgrazing produces an essential bare soil. Even if the assessment for the Sinai/Negev does not quantitatively apply throughout Africa, the current practice in many African countries of maintaining a large population of grazing animals, can contribute through the mesoscale mechanisms described to reduce daytime convective precipitation, perpetuating higher probabilities of drought. Time-of-day analysis of precipitation in Africa appears worthwhile, to better assess the role of the surface conditions in contributing to drought.

Otterman, J.↗
NASA NTRS1992

Area-averaged surface fluxes and their time-space variability over the FIFE experimental domain

The underlying mean and variance properties of surface net radiation, sensible-latent heat fluxes and soil heat flux are studied over the densely instrumented grassland region encompassing FIFE. Flux variability is discussed together with the problem of scaling up to area-averaged fluxes. Results are compared and contrasted for cloudy and clear situations and examined for the influence of surface-induced biophysical controls (burn and grazing treatments) and topographic controls (aspect ratios and slope factors).

Smith, E. A.↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Use of ground-based remotely sensed data for surface energy balance calculations during Monsoon '90

Surface energy balance was evaluated at a semiarid watershed using direct and indirect measurements of the turbulent fluxes, a remote technique based on measurements of surface reflectance and temperature, and conventional meteorological information. Comparison of remote estimates of net radiant flux and soil heat flux densities with measured values showed errors on the order of +/-40 W/sq m. To account for the effects of sparse vegetation, semi-empirical adjustments to aerodynamic resistance were required for evaluation of sensible heat flux density (H). However, a significant scatter in estimated versus measured latent heat flux density (LE) was still observed, +/-75 W/sq m over a range from 100-400 W/sq m. The errors of H and LE estimates were reduced to +/-50 W/sq m when observations were restricted to clear sky conditions.

Moran, M. S.↗
NASA NTRS1992

Measuring surface fluxes in CAPE

Two stations (site 1612 and site 2008) were operated by the University of Georgia group from 6 July 1991 to 18 August 1991. The following data were collected continuously: surface energy fluxes (i.e., net radiation, soil heat fluxes, sensible heat flux and latent heat flux), air temperature, vapor pressure, soil temperature (at 1 cm depth), and precipitation. Canopy reflectance and light interception data were taken three times at each site between 6 July and 18 August. Soil moisture content was measured twice at each site.

Kanemasu, E. T.↗
NASA NTRS1998

Assimilation of Surface Temperature in Land Surface Models

Hydrological models have been calibrated and validated using catchment streamflows. However, using a point measurement does not guarantee correct spatial distribution of model computed heat fluxes, soil moisture and surface temperatures. With the advent of satellites in the late 70s, surface temperature is being measured two to four times a day from various satellite sensors and different platforms. The purpose of this paper is to demonstrate use of satellite surface temperature in (a) validation of model computed surface temperatures and (b) assimilation of satellite surface temperatures into a hydrological model in order to improve the prediction accuracy of soil moistures and heat fluxes. The assimilation is carried out by comparing the satellite and the model produced surface temperatures and setting the "true"temperature midway between the two values. Based on this "true" surface temperature, the physical relationships of water and energy balance are used to reset the other variables. This is a case of nudging the water and energy balance variables so that they are consistent with each other and the true" surface temperature. The potential of this assimilation scheme is demonstrated in the form of various experiments that highlight the various aspects. This study is carried over the Red-Arkansas basin in the southern United States (a 5 deg X 10 deg area) over a time period of a year (August 1987 - July 1988). The land surface hydrological model is run on an hourly time step. The results show that satellite surface temperature assimilation improves the accuracy of the computed surface soil moisture remarkably.

Lakshmi, Venkataraman↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
NASA NTRS1997

On the Potential Impact of Daytime Surface Sensible Heat Flux on the Dissipation of Martian Cold Air Outbreaks

The Martian daytime soil surface temperature is governed primarily by the net irradiance balance and surface soil heat flux. Thus the outbreak of a cold air mass generates increased sensible heat flux that is conducive to daytime dissipation of the cold air mass thermal characteristics. Conceptual and scaling evaluations of this dissipation are provided while comparison is made with similar situations on Earth. It is estimated that sensible heat flux contribution to the dissipation of the original thermal structure of the cold air could be three times larger than the corresponding situation on Earth. Illustrative numerical model simulations provide scaling of the potential impact on the dissipation of cold air masses for various combinations of background wind speed and latitudes.

Segal, M.↗
NASA NTRS1988

Evaluation of surface energy and radiation balance systems for FIFE

The energy balance and radiation balance components were determined at six sites during the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) conducted south of Manhattan, Kansas during the summer of 1987. The objectives were: to determine the effect of slope and aspect, throughout a growing season, on the magnitude of the surface energy balance fluxes as determined by the Energy Balance Method (EBM); to investigate the calculation of the soil heat flux density at the surface as calculated from the heat capacity and the thermal conductivity equations; and to evaluate the performance of the Surface Energy and Radiation Balance System (SERBS). A total of 17 variables were monitored at each site. They included net, solar (up and down), total hemispherical (up and down), and diffuse radiation, soil temperature and heat flux density, air and wet bulb temperature gradients, wind speed and direction, and precipitation. A preliminary analysis of the data, for the season, indicate that variables including net radiation, air temperature, vapor pressure, and wind speed were quite similar at the sites even though the sites were as much as 16 km apart and represented four cardinal slopes and the top of a ridge.

Fritschen, Leo J.↗
NASA NTRS1982

Delineation of cold-prone areas using nighttime SMS/GOES thermal data Effects of soils and water

Infrared digital data from geostationary satellites were used to demonstrate the usefulness of remotely sensed surface temperature data to delineate microscale and mesoscale climates. Nocturnal winter data (December-February) from 1976-77 to 1978-79 over Florida revealed noticeable contrasts in surface temperature patterns. Colder areas were associated with low soil moisture content in the upper layers of excessively drained and well drained sandy soils, whereas warmer areas were associated with bodies of water, wetlands, or poorly drained soils. An unexpected surface temperature pattern for one night where the north-central Florida climatic zone was colder than the north Florida climatic zone was found to be caused by differences in antecedent frontal rainfall. Differences in surface radiant energy fluxes over these two areas at 0200 EST 20 December 1977, based on average satellite-sensed surface temperatures, were compared with differences in soil heat fluxes that were computed from 1.5 m climatological temperatures and soil thermal properties by use of a simplified surface energy balance equation.

Chen, E.↗
NASA NTRS1992

Surface energy fluxes on four slope sites during FIFE 1988

Four slopes (facing north, south, east, and west) in the Konza Prairie Research Natural Area were selected to study the effect of topography on surface energy balance and other micrometeorological variables. Energy fluxes, air temperature, and vapor pressure were measured on the slopes throughout the 1988 growing season. Net radiation was highest on the south facing slope and lowest on the north facing slope, and the difference was more than 150 W/sq m (20-30 percent) at solar noon. For daily averages the difference was about 25 W/sq m (15 percent) early in the season and increased to about 60 W/sq m (30-50 percent) in September. Soil heat fluxes were similar for all the slopes. The absolute values of sensible heat flux were consistently lower on the north facing slope compared with other slopes. The south facing slope had the greatest day-to-day fluctuation in latent heat flux as a result of the interaction of net radiation, soil moisture, and green leaf area. Differences were found in the partitioning of the available energy among the slopes, and the north facing slope had a higher percentage of energy dissipated into latent heat flux. The north facing slope had higher air temperatures during the day and higher vapor pressures both during the day and at night when the wind was from the south.

Nie, D.↗
NASA NTRS1990

A study of the surface energy balance on slopes in a tallgrass prairie

Four slopes (north, south, east, and west) were selected on the Konza Prairie Research Natural Area to study the effect of topography on surface energy balance and other micrometeorological variables. Energy fluxes, air temperature, and vapor pressure were measured on the sloped throughout the 1988 growing season. Net radiation was the highest on the south-facing slope and lowest on the north-facing slope, and the difference was more than 150 W/sq m (20 to 30 percent) at solar noon. For daily averages, the difference was 25 W/sq m (15 percent) early in the season and increased to 60 W/sq m (30 to 50 percent) in September. The east-facing and west-facing slopes had the same daily average net radiation, but the time of day when maximum net radiation occurred was one hour earlier for the east-facing slope and one hour later for the west-facing slope relative to solar noon. Soil heat fluxes were similar for all the slopes. The absolute values of sensible heat flux (h) was consistently lower on the north-facing slope compared with other slopes. Typical difference in the values of H between the north-facing and the south-facing slopes was 15 to 30 W/sq m. The south-facing slope had the greatest day to day fluctuation in latent heat flux as a result of interaction of net radiation, soil moisture, and green leaf area. The north-facing slope had higher air temperatures during the day and higher vapor pressures both during the day and at night when the wind was from the south.

Nie, D.↗
NASA NTRS1990

An increase of early rains in southern Israel following land-use change?

Rains at the onset of the October-April rainy season in southern Israel have steeply increased in the last 25 years relative to the previous 20 years, and are accompanied by an appreciable general increase of rainy-season rainfall. This increase in precipitation is specifically attributable to an intensification of the convection and advection processes due to afforestation and increased cultivation-induced enhancement of the daytime sensible heat flux from the generally dry surface; the enhancement proceeds from both the reduced surface albedo and the reduced soil heat flux in October, when insolation is strong. Greater daytime convection can lead to penetration of inversions capping the planetary boundary layer, while strengthened advection can furnish moist air from the Mediterranean.

Otterman, J.↗
NASA NTRS2013

Impacts of Soil-aquifer Heat and Water Fluxes on Simulated Global Climate

Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and in landsurface temperature, and decrease the soil moisture memory of the land surface on seasonal to annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

soil↗
NASA NTRS1975

Evaluation of thermal X/5-detector Skylab S-192 data for estimating evapotranspiration and thermal properties of soils for irrigation management

An energy budget approach to evaluating the SKYLAB X/5-detector S-192 data for prediction of soil moisture and evapotranspiration rate was pursued. A test site which included both irrigated and dryland agriculture in Southern Texas was selected for the SL-4 SKYLAB mission. Both vegetated and fallow fields were included. Data for a multistage analysis including ground, NC-130B aircraft, RB-57F aircraft, and SKYLAB altitudes were collected. The ground data included such measurements as gravimetric soil moisture, percent of the ground covered by green vegetation, soil texture, net radiation, soil temperature gradients, surface emittance, soil heat flux, air temperature and humidity gradients, and cultural practices. Ground data were used to characterize energy budgets and to evaluate the utility of an energy budget approach for determining soil moisture differences among twelve specific agricultural fields.

Moore, D. G.↗
NASA NTRS1992

Turbulent transports over tundra

An extensive period of eddy correlation surface flux measurements was conducted at a site distant from the coast on the western Alaskan tundra. The surface exchange of heat and moisture over tundra during the summer was limited by a strong resistance to transfer from the upper soil layer through the ground cover, with canopy resistances to evaporation observed to be approximately 200 s/m. Though July 1988 was anomalously warm and dry in the region and August was close to normal temperature and rainfall, there was no appreciable difference in the canopy resistance between the periods. During the dry sunny period at the end of July, the observed evaporation rate was 2 mm/d. High canopy resistance led to an approximate equipartition of net radiation between latent and sensible heat, each accounting for 40 percent of the available energy, with heat balance apparently going into soil heat flux.

Fitzjarrald, David R.↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗