Submitted to: International Symposium on Preferential Flow
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/30/2000
Publication Date: 1/3/2001
Citation: N/A Interpretive Summary: Field studies indicate that preferential flow is the mechanism responsible for surface-applied agricultural chemicals being transported much deeper in the soils than can be described by present modeling schemes. Unfortunately, the spatial variability associated with preferential flow processes has made data interpretation and theoretical predictions nearly meaningless. This study compares several modeling approaches in describing the preferential flow process. One approach considers water and chemical transport as a bimodal process instead of using a single water velocity value, while a third category utilizes a distribution of parameters (stochastic modeling). Results indicated that most applied models provided reasonable predictions of pesticide transport in top 30 cm, but failed to describe deep leaching. Bimodal models provided reliable results in the top meter where macropores dominate. However, the stochastic method was the most effective approach in modeling both surface and subsurface chemical transport behavior. This study has the potential to aid State and Federal agencies in improving their predictions of field scale chemical transport.
Technical Abstract: Preferential flow plays a dominant role in water and chemical transport. However, the main challenge remains to be its inclusion in the water quality models. Tension infiltrometer data indicated a pronounced macropore flow under both conventional and no-till systems in a sandy loam soil in Maryland. Models used to perform the simulations of atrazine transport included a management model, GLEAMS, a mechanistic model, MACRO, and a stochastic model. Results indicate that GLEAMS model provided reasonable prediction of the atrazine in top 30 cm, but failed to trace it down to the deeper depths. Macro model's two-domain component provided reliable results in the upper soil profile where macropores dominate. The stochastic approach was able to predict peak arrival times at 150 cm with a great accuracy for fields with slow release compound regardless of tillage. This study concluded that stochastic approach captures the field heterogeneity better than the deterministic models.