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Model development and data collection for whole turfgrass systems

Posted on:2001-10-14Degree:Ph.DType:Dissertation
University:Texas A&M UniversityCandidate:King, Kevin WayneFull Text:PDF
GTID:1468390014952492Subject:Engineering
Abstract/Summary:
Turfgrass systems are one of the most intensively managed land uses in the U.S. To investigate the water quality and quantity impact, both monitoring and modeling can be used. Water quantity and quality data were collected and analyzed from 20-Mar-98 to 30-Apr-99 for a municipal golf course in Austin, TX Nutrient concentrations in runoff exiting the course exhibited a statistically significant increase in NO3+NO2-N compared to runoff entering the course, a significant decrease in NH4-N, but no difference in PO4-P. Baseflow concentration showed a significant increase in NO3+NO2-N (+0.85 mg L-1), a significant reduction in NH4-N (–0.14 mg L-1), and no change in PO4-P.; Using documented slow release fertilizer data, a first order decay equation was fit with reasonable accuracy for both surface 0.63) and incorporated 0.70) applications. Temperature and 7-day dissolution amount were determined as best descriptive parameters for the surface model while soil moisture and temperature were used for the incorporated model. Each model was validated with a limited amount of data.; Likewise, a theoretical thatch growth routine was developed based on the sigmoid transition function. The routine allows thatch buildup during the growing season to a user supplied management level. Provisions were also included for thatch control (topdressing, coring, etc.) and dormant season decline. The inclusion of the thatch layer significantly affected simulated average annual values of evapotranspiration and irrigation. Due to the lack of quantitative data, no comparison of simulated thatch depth to measured data could be made.; After incorporation of the developed routines, the Soil and Water Assessment Tool (SWAT) was applied on the 13 months of measured data. Simulated hydrology and NO3-N were compared to measured values. Runoff from 17 events was reproduced with an efficiency of 0.08 for curve number and 0.24 for Green-Ampt. When two questionable events were omitted from the analysis, the efficiencies increased to 0.51 (curve number) and 0.57 (Green-Ampt). NO3-N in the surface runoff was simulated with efficiencies of 0.02 (curve number) and 0.01 (Green-Ampt). These low efficiencies were attributed to assumptions made in transport algorithms compounded by errors in simulated runoff.
Keywords/Search Tags:Data, Model, Simulated, Runoff
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