Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Temperature extremes: Effect on plant growth and development

AbstractTemperature is a primary factor affecting the rate of plant development. Warmer temperatures expected with climate change and the potential for more extreme temperature events will impact plant productivity. Pollination is one of the most sensitive phenological stages to temperature extremes across all species and during this developmental stage temperature extremes would greatly affect production. Few adaptation strategies are available to cope with temperature extremes at this developmental stage other than to select for plants which shed pollen during the cooler periods of the day or are indeterminate so flowering occurs over a longer period of the growing season. In controlled environment studies, warm temperatures increased the rate of phenological development; however, there was no effect on leaf area or vegetative biomass compared to normal temperatures. The major impact of warmer temperatures was during the reproductive stage of development and in all cases grain yield in maize was significantly reduced by as much as 80−90% from a normal temperature regime. Temperature effects are increased by water deficits and excess soil water demonstrating that understanding the interaction of temperature and water will be needed to develop more effective adaptation strategies to offset the impacts of greater temperature extreme events associated with a changing climate

Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States

Seasonal freeze-thaw (FT) impacts much of the northern hemisphere and is an important control on its water, energy, and carbon cycle. Although FT in natural environments extends south of 45°N, FT studies using the L-band have so far been restricted to boreal or greater latitudes. This study addresses this gap by applying a seasonal threshold algorithm to Soil Moisture Active Passive (SMAP) data (L3_SM_P) to obtain a FT product south of 45°N (‘SMAP FT’), which is then evaluated at SMAP core validation sites (CVS) located in the contiguous United States (CONUS). SMAP landscape FT retrievals are usually in good agreement with 0–5 cm soil temperature at SMAP grids containing CVS stations (\u3e70%). The accuracy could be further improved by taking into account specific overpass time (PM), the grid-specific seasonal scaling factor, the data aggregation method, and the sampling error. Annual SMAP FT extent maps compared to modeled soil temperatures derived from the Goddard Earth Observing System Model Version 5 (GEOS-5) show that seasonal FT in CONUS extends to latitudes of about 35–40°N, and that FT varies substantially in space and by year. In general, spatial and temporal trends between SMAP and modeled FT were similar

Effect of Corn or Soybean Row Position on Soil Water

Crop plants can funnel water to the soil and increase water content more in the row relative to the interrow. Because the row intercepts more soil water after rains and higher root density, the soil may also dry out more between rains than does soil in the interrow. The objectives of this study were to determine if there is a row position difference in soil wetting after rain and drying between rains, and to determine the seasonal nature of these differences. The first experiment examined soil water content 0 to 0.06 m in row, interrow, and quarter corn row positions for eight sites at specific times during a corn (Zea mays L.)-growing season. During the growing season, the second experiment examined automated soil water measurements at one site for two corn years and one soybean (Glycine max [L.] Merr.) year at row and interrow positions to 0.15-m depth. Soil water content changes were significantly greater in the row than the interrow for some mid-season dates. Temporal soil water changes showed that row wetting and drying dominated over interrow soil water changes for mid season. The mean ratio of row/(row + interrow) soil water changes for wetting was 0.76 and 0.77 for corn and 0.64 for soybean and for drying was 0.58 and 0.84 for corn and 0.60 for soybean. Soybean showed the row effect for a shorter time of the season (up to 71 days) compared with corn (up to 159 days)

Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm

The Crop Wind-Energy Experiment (CWEX) provides a platform to investigate the effect of wind turbines and large wind farms on surface fluxes of momentum, heat, moisture, and carbon dioxide (CO2). In 2010 and 2011, eddy covariance flux stations were installed between two lines of turbines at the southwest edge of a large Iowa wind farm from late June to early September. We report changes in fluxes of momentum, sensible heat, latent heat, and CO2 above a corn canopy after surface air had passed through a single line of turbines. In 2010, our flux stations were placed within a field with homogeneous land management practices (same tillage, cultivar, chemical treatments). We stratify the data according to wind direction, diurnal condition, and turbine operational status. Within these categories, the downwind–upwind flux differences quantify turbine influences at the crop surface. Flux differences were negligible in both westerly wind conditions and when the turbines were non operational. When the flow is perpendicular (southerly) or slightly oblique (southwesterly) to the row of turbines during the day, fluxes of CO2 and water (H2O) are enhanced by a factor of five in the lee of the turbines (from three to five turbine diameter distances downwind from the tower) as compared to a west wind. However, we observe a smaller CO2 flux increase of 30–40% for these same wind directions when the turbines are off. In the nighttime, there is strong statistical significance that turbine wakes enhance upward CO2 fluxes and entrain sensible heat toward the crop. The direction of the scalar flux perturbation seems closely associated to the differences in canopy friction velocity. Spectra and co-spectra of momentum components and co-spectra of heat also demonstrate nighttime influence of the wind turbine turbulence at the downwind station

Aglite: A 3-Wavelength Lidar System for Quantitative Assessment of Agricultural Air Quality and Whole Facility Emissions

Ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) are increasingly being used to characterize ambient aerosols due to key advantages (i.e., wide area of regard (10 km2), fast response time (s-1), high spatial resolution (\u3c10 \u3em) and high sensitivity). Scanning lidar allows for 3D imaging of atmospheric motion and aerosol variability, which can be used to quantitatively evaluate particulate matter (PM) concentrations and emissions. Space Dynamics Laboratory, in conjunction with USDA ARS, has developed and successfully deployed a lidar system called Aglite to characterize PM in diverse settings. Aglite is a portable scanning elastic lidar system with three wavelengths (355, 532, and 1064 nm), 6 m long range bins, and an effective range from 0.5 to 15 km. Filter-based PM samplers, optical particle counters, and various meteorological instruments were deployed to provide environmental and PM conditions for use in the lidar retrieval method. The developed retrieval algorithm extracts aerosol optical parameters, which were constrained by the point measurements, and converts return signals to PM concentrations. Once calibrated, the Aglite system can map the spatial distribution and temporal variation of the PM concentrations. Whole facility or operation-based emission rates were calculated from the lidar PM data with a mass balance approach. Concentration comparisons with upwind and downwind point sensors were made to verify data quality; lidar-derived PM levels were usually in good agreement with point sensor measurements. Comparisons of lidar-based emissions with emissions estimated through other methods using point sensor data generally show good agreement