Tuesday, 7 October 2008: 3:00 PM

George R. Brown Convention Center, 362DE

Jay Ham¹, Jesse Nippert¹, John Blair¹, Kira Arnold¹ and Nate A. Brunsell², (1)Dept. of Agronomy, Kansas State University, Throckmorton Hall, Manhattan, KS 66506
(2)University of Kansas, Department of Geography, Lawrence, KS 66045

Variations in topography, soil type, vegetative cover, and species composition may cause spatial variability in the surface energy balance across the landscape. Quantifying this variation is necessary to estimate carbon and water balances at the same areal scale used to make land management decisions (i.e., field or watershed scale). Furthermore, measures of spatial variability will aid in the study of ecosystem processes, improve hydrologic modeling, and enhance the interpretation of data from remote sensing platforms and flux towers. A 10-station sensor network was developed to measure the spatial variation in the surface energy balance in a tallgrass prairie ecosystem near Manhattan, Kansas. The geomorphology of the landscape inherently created significant spatial variability in the above- and below-ground environment. Stations in the sensor network were distributed at 30- to 50-m intervals between two eddy covariance towers in an annually burned, ungrazed watershed. One tower was located in an upland and lowland topographic position, respectively. Measurements at each station included: air and soil temperature, relative humidity, wind speed, surface temperature, soil heat flux, and soil water content. Data were accessible real-time using a wireless network. Ancillary bi-weekly measurements included canopy reflectance, canopy size, and measurements of soil and leaf gas exchange using handheld instruments. Data from the sensor network and flux data from the eddy covariance towers were coupled with a numerical modeling technique to approximate latent heat and sensible heat fluxes at each station in the network. Results document the spatial scale and temporal stability of the surface energy balance within the watershed and illustrate the need to account for the spatial variability present across a relatively homogenous landscape.

See more of: Symposium --Integrating Instrumentation, Modeling, and Remote Sensing in Honor of John Norman
See more of: A03 Agroclimatology & Agronomic Modeling

2008 Joint Annual Meeting (5-9 Oct. 2008): Sensor Networks for Measuring Spatial Variability in the Landscape Energy Balance.

618-15 Sensor Networks for Measuring Spatial Variability in the Landscape Energy Balance.