Skip to page content
Return to Top

TIAER and Tarleton State University published in JSWC

The works of The Texas Institute for Applied Environmental Research’s (TIAER), Dr. Narayanan Kannan, Research Scientist, and Dr. Ali Saleh, Associate Director, Tarleton State University’s College of Agricultural and Consumer Sciences Assistant Professor, Dr. Edward Osei, alongside Dr. Yang Cao, Research Scientist for the Institute of Renewable Natural Resources have been published in the Journal of Soil and Water Conservation.  The group’s work, entitled Estimating Sediment and Nutrient Delivery Ratios in the Big Sunflower Watershed Using a Multiple Linear Regression Model, describes the identification of dominant pollutant delivery mechanisms in the watershed, estimation of instream pollutant delivery ratios (DR) from subbasins to watershed outlets, and development of a tool to estimate changes in instream pollutant DR for “what-if” scenarios. This study is part of an effort to analyze the nutrient load reductions obtained from the best management practices (BMPs) implementations in the Big Sunflower Watershed (BSW) (Figure 1). A 45% nutrient reduction goal was set for the watershed based on the US Environmental Protection Agency Science Advisory Board’s Gulf of Mexico hypoxia report.

The Big Sunflower Watershed is a 7,800 km2 intensively cultivated agricultural watershed in the State of Mississippi. Modeled results from the Comprehensive Environmental and Economic Optimization Tool (CEEOT) modeling system, consisting of the Soil and Water Assessment Tool (SWAT) and Agricultural Policy and Environmental Extender (APEX) mod­els, were used to develop multiple regression equations to estimate the sediment and nutrient DRs for this watershed.

big sunflower watershed

This study attempted to estimate instream sediment, nitrogen (N), and phosphorus (P) DRs using geomor­phological watershed attributes and flow and pollutant loads from subwatersheds as explanatory variables in a multiple linear regression (MLR) framework. The explanatory variables were chosen based on their strength of correlations and type of relationship with DRs.

Based on the results obtained, it can be concluded that the multiple linear regression model is one of the appropriate ways for esti­mating pollutant DRs for the study area. This was evident from the analysis of variance (ANOVA) results and strength of regression relationships.

Although the instream DRs estimated in this study are developed for BSW, the approach could be easily adopted to other similar watersheds. The Microsoft Excel based spreadsheet tool developed to analyze what-if scenarios on pollutant DRs appears to be a useful tool that would be of interest to watershed managers.

This study was funded by the World Resource Institute.   

For more information about the study, contact TIAER at http://tiaer.tarleton.edu/contact.html  or (254) 968- 9567.  To view the publication in JSWC, visit: http://www.jswconline.org/content/72/5/438.full.pdf+html?sid=17b90440-5e97-4f1d-a783-7ed01d78ddb4