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United States Department of Agriculture

Agricultural Research Service

Title: Sensitivity of the Riparian Ecosystem Management Mode (Remm) to Perturbations in Vegetation Parameters: Evaluating the Effectiveness of Specific Conservation Practices

Authors
item Graff, Carrie
item Sadeghi, Ali
item Lowrance, Robert
item Williams, Randall

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: September 1, 2004
Publication Date: October 31, 2004
Citation: Graff, C.D., Sadeghi, A.M., Lowrance, R.R., Williams, R.G. 2004. Sensitivity of the Riparian Ecosystem Management Mode (REMM) to perturbations in vegetation parameters: evaluating the effectiveness of specific conservation practices [abstract]. ASA-CSSA-SSSA Annual Meeting. 2004 CDROM.

Technical Abstract: The Riparian Ecosystem Management Model (REMM) can be used as a tool for assessing the effectiveness of riparian buffers to reduce nutrient and sediment loads into ground and surface waters. Knowledge of the sensitivity of output concentrations to variations in buffer characteristics can aid an assessment by focusing resources on those parameters contributing most to the outputs of interest. A single-variable, quantitative sensitivity analysis of N, P and sediment output to changes in vegetation characteristics such as plant height, LAI and rooting depth was performed. Additionally, perturbations in precipitation amount and duration, slope of the buffer zone, Manning's N and surface condition values were also evaluated. Results indicate that surface water concentrations of N, P and sediment are not sensitive to changes in vegetation parameters but are highly sensitive to perturbations in precipitation, slope and Manning's N. Because surface water routing is limited to upland runoff (no additional runoff is generated within the buffer) and infiltration is a function of soil moisture, the effect of vegetation parameters on surface water outputs is limited to their contribution to surface soil moisture. Asymmetric sensitivity plots indicate there is a threshold value for some variables below or above which the output is less sensitive.

Last Modified: 9/23/2014
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