Assessing best management practices for the remediation of selenium in surface water in an irrigated agricultural river valley: sampling, modeling, and multi-criteria decision analysis - Heesemann, Brent E., author; Gates, Timothy K., advisor; Bailey, Ryan T., advisor; Hoag, Dana L., committee member (Colorado State University. Libraries, 2016)
The ecological impacts of selenium have been studied for decades and regulatory standards established in an effort to mitigate them. Agricultural activities in regions with high levels of alluvial selenium can lead to in-stream levels that far exceed regulatory limits. Agricultural best management practices (BMPs) are being considered to reduce in-stream selenium concentrations, but exploring the potential effectiveness of these BMPs can only be done after gaining an understanding of the in-stream processes that govern the speciation and transport of selenium in response to loading from irrigation return flows. This study uses extensive field data enhanced by numerical modeling to achieve this. In-stream water and sediment selenium samples, collected over a period of eight years in a region of Colorado’s Lower Arkansas River Valley, were analyzed. A sensitivity analysis (SA) was performed on a two part steady-state water quality / solute transport numerical model capable of simulating in-stream selenium processes. The combination of field data and SA was then used to calibrate an unsteady flow version of the model representative of the region to which it was applied. Dissolved and precipitated selenium species concentrations were accurately predicted by the calibrated model. Model simulations indicated that reduced fertilization is the BMP most effective at reducing in-stream SeO4 and NO3 concentrations out of the four BMPs examined. Reduced irrigation, land fallowing, and canal sealing indicated increases in in-stream SeO4 concentrations, likely caused by a concentration of SeO4 in the adjacent aquifer. Model results also indicated that the tributaries are impacted more by surface runoff as compared to lateral groundwater flows, while the opposite is true for the River. Although reasonable results were obtained from the model, further investigation into the computational processes and calibrated parameter values is required as part of future work. This study also examines the socio-economic feasibility of various BMPs, through the issuing survey to stakeholders in the study region and its evaluation using analytic hierarchy process multi-criteria decision analysis (MCDA). Reduced irrigation was determined to be the most feasible BMP based on the MCDA, with stakeholders showing a clear preference for economic concerns and placing a higher importance on salinity over SeO4 or NO3 concentrations. With model results indicating the effectiveness of various BMPs, and MCDA survey results providing insight into which of the BMPs are most likely to be accepted by stakeholders, it was possible to assess which BMPs are most appropriate for implementation in this study region. In considering both the results from the modeling study and the MCDA, it was determined that reduced fertilization is likely the single best BMP. To date there have been few if any studies utilizing both field data, numerical modeling, and MCDA to so comprehensively describe in-stream selenium processes and the future prospects for selenium remediation in an agricultural region in the western United States.
Keywords: modeling; sampling; surface water; multi-criteria decision analysis; best management practices; selenium