Search Results

You are looking at 1 - 6 of 6 items for

  • Author or Editor: J. L. Hatfield x
  • Refine by Access: All Content x
Clear All Modify Search
T. B. Parkin, T. C. Kaspar, Z. Senwo, J. H. Prueger, and J. L. Hatfield

Abstract

Soil respiration is an important component of the carbon dynamics of terrestrial ecosystems. Many factors exert controls on soil respiration, including temperature, soil water content, organic matter, soil texture, and plant root activity. This study was conducted to quantify soil respiration in the Walnut Creek watershed in central Iowa, and to investigate the factors controlling this process. Six agricultural fields were identified for this investigation: three of the fields were cropped with soybean [Glycine max (L.) Merr.] and three were cropped with corn (Zea mays L.). Within each field, soil respiration was measured at nine locations, with each location corresponding to one of three general landscape positions (summit, side slope, and depression). Soil respiration was measured using a portable vented chamber connected to an infrared gas analyzer. Soil samples were collected at each location for the measurement of soil water content, pH, texture, microbial biomass, and respiration potential. Field respiration rates did not show a significant landscape effect. However, there was a significant crop effect, with respiration from cornfields averaging 37.5 g CO2 m−2 day−1 versus an average respiration of 13.1 g CO2 m−2 day−1 in soybean fields. In contrast, laboratory measurements of soil respiration potential, which did not include plant roots, showed a significant landscape effect and an insignificant cropping system effect. Similar relationships were observed for soil organic C and microbial biomass. Additional analyses indicate that corn roots may be more important than soybean roots in their contribution to surface CO2 flux, and that root respiration masked landscape effects on total soil respiration. Also, the failure to account for soil respiration may lead to biased estimates of net primary production measured by eddy covariance.

Full access
D. W. Meek, J. H. Prueger, W. P. Kustas, and J. L. Hatfield

Abstract

Two eddy covariance instrument comparison studies were conducted before and after the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) field campaign to 1) determine if observations from multiple sensors were equivalent for the measured variables over a uniform surface and to 2) determine a least significant difference (LSD) value for each variable to discriminate between daily and hourly differences in latent and sensible heat and carbon dioxide fluxes, friction velocity, and standard deviation of the vertical wind velocity from eddy covariance instruments placed in different locations within the study area. The studies were conducted in early June over an alfalfa field and in mid-September over a short grass field. Several statistical exploratory, graphical, and multiple-comparison procedures were used to evaluate each daily variable. Daily total or average data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using univariate procedures. There were no significant sensor differences in any of the daily measurements for either intercomparison period. Hourly averaged data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using mixed model procedures. Sensor differences for pre- and post-intercomparisons were minimal for hourly and daily values of CO2, water vapor, sensible heat, friction velocity, and standard deviation for vertical wind velocity. Computed LSD values were used to determine significant daily differences and threshold values for the variables monitored during the SMACEX campaign.

Full access
J. H. Prueger, J. L. Hatfield, T. B. Parkin, W. P. Kustas, L. E. Hipps, C. M. U. Neale, J. I. MacPherson, W. E. Eichinger, and D. I. Cooper

Abstract

A network of eddy covariance (EC) and micrometeorological flux (METFLUX) stations over corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] canopies was established as part of the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in central Iowa during the summer of 2002 to measure fluxes of heat, water vapor, and carbon dioxide (CO2) during the growing season. Additionally, EC measurements of water vapor and CO2 fluxes from an aircraft platform complemented the tower-based measurements. Sensible heat, water vapor, and CO2 fluxes showed the greatest spatial and temporal variability during the early crop growth stage. Differences in all of the energy balance components were detectable between corn and soybean as well as within similar crops throughout the study period. Tower network–averaged fluxes of sensible heat, water vapor, and CO2 were observed to be in good agreement with area-averaged aircraft flux measurements.

Full access
J. H. Mather, T. P. Ackerman, W. E. Clements, F. J. Barnes, M. D. Ivey, L. D. Hatfield, and R. M. Reynolds

The interaction of clouds and radiation is a particularly difficult issue in the study of climate change. Clouds have a large impact on the earth's radiation budget but the range of spatial and temporal scales and the complexity of the physical processes associated with clouds made these interactions difficult to simulate. The Department of Energy's Atmospheric Radiation Measurement (ARM) program was established to improve the understanding of the interaction of radiation with the atmosphere with a particular emphasis on the effects of clouds. To continue its role of providing data for the study of these interactions, the ARM program deployed an Atmospheric Radiation and Cloud Station (ARCS) in the tropical western Pacific. This site began operation in October 1996. The tropical western Pacific is a very important climatic region. It is characterized by strong solar heating, high water vapor concentrations, and active convection. The ARCS is equipped with a comprehensive suite of instruments for measuring surface radiation fluxes and properties of the atmospheric state and is intended to operate for the next 10 years. The ARCS is an integrated unit that includes a data management system, a site monitor and control system, an external communications system, redundant electrical power systems, and containers that provide shelter for the equipment as well as work space for site operators, technicians, and visiting scientists. The dataset the ARCS produces will be invaluable in studying issues related to clouds and radiation in the Tropics. The site is located in Manus Province, Papua New Guinea, at 2.060°S, 147.425°E, 300 km north of the island of New Guinea. Two more ARCS are planned for deployment across the tropical Pacific.

Full access
William B. Willis, William E. Eichinger, John H. Prueger, Cathleen J. Hapeman, Hong Li, Michael D. Buser, Jerry L. Hatfield, John D. Wanjura, Gregory A. Holt, Alba Torrents, Sean J. Plenner, Warren Clarida, Stephen D. Browne, Peter M. Downey, and Qi Yao

Abstract

Pollutant emissions to the atmosphere commonly derive from nonpoint sources that are extended in space. Such sources may contain area, volume, line, or a combination of emission types. Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. Point measurement arrays often lack in spatial and temporal resolution and accuracy. In recent years, lidar has supplemented point measurements in agricultural research by sampling spatial ensembles nearly instantaneously. Here, a methodology using backscatter data from an elastic scanning lidar is presented to estimate emission rates from extended sources. To demonstrate the approach, a known amount of particulate matter was released upwind of a vegetative environmental buffer, a barrier designed to intercept emissions from animal production facilities. The emission rate was estimated downwind of the buffer, and the buffer capture efficiency (percentage of particles captured) was calculated. Efficiencies ranged from 21% to 74% and agree with the ranges previously published. A comprehensive uncertainty analysis of the lidar methodology was performed, revealing an uncertainty of 20% in the emission rate estimate; suggestions for significantly reducing this uncertainty in future studies are made. The methodology introduced here is demonstrated by estimating the efficiency of a vegetative buffer, but it can also be applied to any extended emission source for which point samples are inadequate, such as roads, animal feedlots, and cotton gin operations. It can also be applied to any pollutant for which a lidar system is configured, such as particulate matter, carbon dioxide, and ammonia.

Full access
Shuguang Liu, Ben Bond-Lamberty, Lena R. Boysen, James D. Ford, Andrew Fox, Kevin Gallo, Jerry Hatfield, Geoffrey M. Henebry, Thomas G. Huntington, Zhihua Liu, Thomas R. Loveland, Richard J. Norby, Terry Sohl, Allison L. Steiner, Wenping Yuan, Zhao Zhang, and Shuqing Zhao

Abstract

Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.

Full access