Advertisement

Assessing uncertainty in annual nitrogen, phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset

Abstract

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH4-N), nitrate as nitrogen (NO3-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO3-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO3-N load estimates (relative error 1.5–23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19–96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Alberto, A., St-Hilaire, A., Courtenay, S. C., & van den Heuvel, M. R. (2016). Monitoring stream sediment loads in response to agriculture in Prince Edward Island, Canada. Environmental Monitoring and Assessment, 188(7), 415. https://doi.org/10.1007/s10661-016-5411-3

  2. Appling, A. P., Leon, M. C., & McDowell, W. H. (2015). Reducing bias and quantifying uncertainty in watershed flux estimates: the R package loadflex. Ecosphere, 6(12), art269. https://doi.org/10.1890/ES14-00517.1

  3. Aulenbach, B. T. (2013). Improving regression-model-based streamwater constituent load estimates derived from serially correlated data. Journal of Hydrology, 503, 55–66. https://doi.org/10.1016/j.jhydrol.2013.09.001

  4. Aulenbach, B. T., & Hooper, R. P. (2005). Improving stream solute load estimation by the composite method: a comparative analysis using data from the Panola Mountain research watershed. Proceedings of the 2005 Georgia Water Resources Conference.

  5. Aulenbach, B. T., & Hooper, R. P. (2006). The composite method: an improved method for stream-water solute load estimation. Hydrological Processes, 20(14), 3029–3047. https://doi.org/10.1002/hyp.6147

  6. Aulenbach, B. T., Burns, D. A., Shanley, J. B., Yanai, R. D., Bae, K., Wild, A. D., Yang Y. Yi, D. (2016). Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watersheds. Ecosphere, 7(6). https://doi.org/10.1002/ecs2.1298.

  7. Baker D. B., Richards R. P., Loftus T. T., Kramer J. W. (2004) A new flashiness index: characteristics and applications to midwestern rivers and streams. Journal of the American Water Resources Association, 40(2):503–522

  8. Carpenter, S. R., Booth, E. G., Kucharik, C. J., & Lathrop, R. C. (2014). Extreme daily loads: role in annual phosphorus input to a north temperate lake. Aquatic Sciences, 77(1), 71–79. https://doi.org/10.1007/s00027-014-0364-5

  9. Cochran, W. G. (1977). Sampling techniques. New York: Wiley.

  10. Cohn, T. A. (2005). Estimating contaminant loads in rivers: an application of adjusted maximum likelihood to type 1 censored data. Water Resources Research, 41(7), 1–13. https://doi.org/10.1029/2004WR003833

  11. Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D., & Summers, R. M. (1992). The validity of a simple statistical-model for estimating fluvial constituent loads—an empirical study involving nutrient loads entering Chesapeake Bay. Water Resources Research, 28(9), 2353–2363. https://doi.org/10.1029/92WR01008

  12. Cox, N. J., Warburton, J., Armstrong, A., & Holliday, V. J. (2008). Fitting concentration and load rating curves with generalized linear models. Earth Surface Processes and Landforms, 33(1), 25–39. https://doi.org/10.1002/esp.1523

  13. Defersha, M. B., & Melesse, A. M. (2012). Effect of rainfall intensity, slope and antecedent moisture content on sediment concentration and sediment enrichment ratio. Catena, 90, 47–52. https://doi.org/10.1016/j.catena.2011.11.002

  14. Eskelinen, R., Ronkanen, A. K., Marttila, H., & Klove, B. (2016). Assessment of uncertainty in suspended sediment load at constructed wetland inlet and outlet. Environmental Monitoring and Assessment, 188(6), 188–365. https://doi.org/10.1007/s10661-016-5381-5

  15. Fraterrigo, J. M., & Downing, J. A. (2008). The influence of land use on lake nutrients varies with watershed transport capacity. Ecosystems, 11(7), 1021–1034. https://doi.org/10.1007/s10021-008-9176-6

  16. Gao, P., & Josefson, M. (2012). Temporal variations of suspended sediment transport in Oneida Creek watershed, central New York. Journal of Hydrology, 426–427, 17–27. https://doi.org/10.1016/j.jhydrol.2012.01.012

  17. Glasgow, H. B., & Burkholder, J. M. (2000). Water quality trends and management implications from a five-year study of a eutrophic estuary. Ecological Applications, 10(4), 1024–1046.

  18. Harmel R. D., King K. W., Haggard B. E., Wren D. G., Sheridan J. M. (2006) Practical guidance for discharge and water quality data collection on small watersheds. Transactions of the ASABE, 49(4), 937–948

  19. Hatch, L. K., Mallawatantri, A., Wheeler, D., Gleason, A., Mulla, D., Perry, J., …, Brezonik, P. (2001). Land management at the major watershed—agroecoregion intersection. Journal of Soil and Water Conservation, 56(1), 44–51. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-0035101872&partnerID=tZOtx3y1.

  20. Hirsch, R. M. (2014). Large biases in regression-based constituent flux estimates: causes and diagnostic tools. Journal of the American Water Resources Association, 50(6), 1401–1424. https://doi.org/10.1111/jawr.12195

  21. Hiscock, J. G., Thourot, C. S., & Zhang, J. (2003). Phosphorus budget—land use relationships for the northern Lake Okeechobee watershed, Florida. Ecological Engineering, 21(1), 63–74. https://doi.org/10.1016/j.ecoleng.2003.09.005

  22. Jiang, R., Woli, K. P., Kuramochi, K., Hayakawa, A., Shimizu, M., & Hatano, R. (2010). Hydrological process controls on nitrogen export during storm events in an agricultural watershed. Soil Science and Plant Nutrition, 56(1), 72–85. https://doi.org/10.1111/j.1747-0765.2010.00456.x

  23. Johnes, P. J. (2007). Uncertainties in annual riverine phosphorus load estimation: impact of load estimation methodology, sampling frequency, baseflow index and catchment population density. Journal of Hydrology, 332(1–2), 241–258. https://doi.org/10.1016/j.jhydrol.2006.07.006

  24. Knoll, L. B., Vanni, M. J., Renwick, W. H., Dittman, E. K., & Gephart, J. A. (2013). Temperate reservoirs are large carbon sinks and small CO2 sources: results from high-resolution carbon budgets. Global Biogeochemical Cycles, 27(1), 52–64. https://doi.org/10.1002/gbc.20020

  25. Lee, C. J., Hirsch, R. M., Schwarz, G. E., Holtschlag, D. J., Preston, S. D., Crawford, C. G., & Vecchi, A. V. (2016). An evaluation of methods for estimating decadal stream loads. Journal of Hydrology, 542, 185–203. https://doi.org/10.1016/j.jhydrol.2016.08.059

  26. Liang, X., Schilling, K., Zhang, Y. K., & Jones, C. (2016). Co-kriging estimation of nitrate-nitrogen loads in an agricultural river. Water Resources Management, 30(5), 1771–1784. https://doi.org/10.1007/s11269-016-1250-9

  27. Likens, G. E., Bormann, F. H., Pierce R. S., Eaton, J. S., & Johnson N. M. (1977). Biogeochemistry of a forested ecosystem (3rd ed.). New York: Springer-Verlag.

  28. Moatar, F., & Meybeck, M. (2004). Compared performance of different algorithms for estimating annual nutrient loads discharged by the eutrophic river Loire. Hydrological Processes, 19(2), 429–444. https://doi.org/10.1002/hyp.5541

  29. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—a discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6

  30. Parry, R. (1998). Agricultural phosphorus and water quality: a U.S. Environmental Protection Agency perspective. Journal of Environment Quality, 27(2), 258. https://doi.org/10.2134/jeq1998.00472425002700020003x

  31. Pektas, A. O. (2015). Determining the essential parameters of bed load and suspended sediment load. International Journal of Global Warming, 8(3), 335–359. https://doi.org/10.1504/IJGW.2015.072656

  32. Quilbé, R., Rousseau, A. N., Duchemin, M., Poulin, A., Gangbazo, G., & Villeneuve, J. P. (2006). Selecting a calculation method to estimate sediment and nutrient loads in streams: application to the Beaurivage River (Quebec, Canada). Journal of Hydrology, 326(1-4), 295–310. https://doi.org/10.1016/j.jhydrol.2005.11.008

  33. R Core Team. (2015). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing https://www.R-project.org/

  34. Renwick, W. H., Vanni, M. J., Zhang, Q., & Patton, J. (2006). Water quality trends and changing agricultural practices in a midwest U.S. watershed, 1994–2006. Journal of Environmental Quality, 37(5), 1862–1874. https://doi.org/10.2134/jeq2007.0401

  35. Richards, R. P., & Baker, D. B. (2002). Trends in water quality in LEASEQ rivers and streams (northwestern Ohio), 1975–1995. Journal of Environmental Quality, 31(1), 90–96. https://doi.org/10.2134/jeq2002.9000

  36. Royer, T. V., David, M. B., & Gentry, L. E. (2006). Timing of riverine export of nitrate and phosphorus from agricultural watersheds in Illinois: implications for reducing nutrient loading to the Mississippi River. Environmental Science and Technology, 40(13), 4126–4131. https://doi.org/10.1021/es052573n

  37. Sherriff, S. C., Rowan, J. S., Fenton, O., Jordan, P., Melland, A. R., Mellander, P. E., & Huallacháin, D. (2016). Storm event suspended sediment-discharge hysteresis and controls in agricultural watersheds: implications for watershed scale sediment management. Environmental Science and Technology, 50(4), 1769–1778. https://doi.org/10.1021/acs.est.5b04573

  38. Singh, A., Imtiyaz, M., Isaac, R. K., & Denis, D. M. (2012). Comparison of soil and water assessment tool (SWAT) and multilayer perception (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India. Agricultural Water Management, 104, 113–120. https://doi.org/10.1016/j.agwat.2011.12.005

  39. Srivastava, P., McNair, J. N., & Johnson, T. E. (2006). Comparison of process-based and artificial neural network approaches for streamflow modeling in an agricultural watershed. Journal of the American Water Resources Association, 42(3), 545–563. https://doi.org/10.1111/j.1752-1688.2006.tb04475.x

  40. Stackpoole, S. M., Stets, E. G., & Striegl, R. G. (2014). The impact of climate and reservoirs on longitudinal riverine carbon fluxes from two major watersheds in the Central and Intermontane West. Biogeosciences, 119(5), 848–863. https://doi.org/10.1002/2013JG002496

  41. Stenback, G. A., Crumpton, W. G., Schilling, K. E., & Helmers, M. J. (2011). Rating curve estimation of nutrient loads in Iowa rivers. Journal of Hydrology, 396(1–2), 158–169. https://doi.org/10.1016/j.jhydrol.2010.11.006

  42. Tonderski, A., Grimvall, A., Dojlido, J. R., & Vandijk, G. M. (1995). Monitoring nutrient transport in large rivers. Environmental Monitoring and Assessment, 34(3), 245–269. https://doi.org/10.1007/BF00554797

  43. Ullrich, A., & Volk, M. (2009). Application of the Soil and Water Assessment Tool (SWAT) to predict the impact of alternative management practices on water quality and quantity. Agricultural Water Management, 96(8), 1207–1217. https://doi.org/10.1016/j.agwat.2009.03.010

  44. USDA (United States Department of Agriculture). (1992). Watershed plan and environmental assessment for four mile creek watershed, Ohio and Indiana. Washington: USDA.

  45. Vanni, M. J., Renwick, W. H., Headworth, J. L., Auch, J. D., & Schaus, M. H. (2001). Dissolved and particulate nutrient flux from three adjacent agricultural watersheds: a five-year study. Biogeochemistry, 54(1), 85–114. https://doi.org/10.1023/A:1010681229460

  46. Vanni, M. J., Arend, K. K., Bregman, M. T., Bunnell, D. B., Garvey, J. E., Gonzalez, M. J., et al. (2005). Linking landscapes and food webs: effects of omnivorous fish and watersheds on reservoir ecosystems. Bioscience, 55(2), 155–167.

Download references

Acknowledgements

We are especially grateful for the assistance from Hueston Woods State Park personnel for allowing us to install and operate gauging stations within the park and for their cooperation throughout the study period. We also thank the many students and research associates at Miami University who participated in data collection and analysis over the 21 years, especially Annie Bowling, Alan Christian, Janelle Duncan, Jenifer Headworth, Lesley Knoll, Elizabeth Mette, Peter Levi, and Tera Ratliff. We thank M. Gonzalez, T. Willimson, A. Rock, T. Fisher, and M. Barrett for their helpful comments, and two anonymous reviewers for improvements to this manuscript. Concentration and discharge data for Four Mile Creek, Little Four Mile Creek, and Marshall’s Branch for 1994–2008 are publicly available in Ecological Archives E094-085-D1. Concentration and discharge data for 2009–2014 are available at request of the corresponding author.

Funding information

This research was supported mainly by National Science Foundation awards 9318452, 9726877, 0235755, 0743192, and 1255159 (the latter three awards are from the Long-term Research in Environmental Biology (LTREB) program). Additional support was provided by the Ohio Department of Natural Resources (Division of Parks and Division of Wildlife) and the Miami Valley Resource Conservation and Development District.

Author information

Correspondence to Patrick T. Kelly.

Electronic supplementary material

ESM 1

(DOCX 47916 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kelly, P.T., Vanni, M.J. & Renwick, W.H. Assessing uncertainty in annual nitrogen, phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset. Environ Monit Assess 190, 91 (2018). https://doi.org/10.1007/s10661-018-6470-4

Download citation

Keywords

  • Stream load
  • Loadflex
  • Uncertainty
  • Composite method
  • Nitrate
  • Suspended sediments