Environmental Modeling & Assessment

, Volume 21, Issue 6, pp 681–690 | Cite as

Statistically Extracted Fundamental Watershed Variables for Estimating the Loads of Total Nitrogen in Small Streams

  • Scott C. Kronholm
  • Paul D. Capel
  • Silvia Terziotti
Article

Abstract

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.

Keywords

Fundamental variables Random forest regression Recursive partitioning Nitrogen National model 

Supplementary material

10666_2016_9525_MOESM1_ESM.docx (1.4 mb)
ESM 1(DOCX 1.40 mb)
10666_2016_9525_MOESM2_ESM.xlsx (72 kb)
ESM 2(XLSX 71.6 kb)

References

  1. 1.
    Howarth, R. W., Billen, G., Swaney, D., Townsend, A., Jaworski, N., Lajtha, K., Downing, J. A., Elmgren, R., Caraco, N., Jordan, T., Berendse, F., Freney, J., Kudeyarov, V., Murdoch, P., & Zhao-Liang, Z. (1996). Regional nitrogen budgets and riverine N and P fluxes for the drainages to the North Atlantic Ocean: Natural and human influences. In Nitrogen cycling in the North Atlantic Ocean and its watersheds (pp. 75–139). Netherlands: Springer. doi:10.1007/978-94-009-1776-7_3.CrossRefGoogle Scholar
  2. 2.
    Vitousek, P. M., Aber, J. D., Howarth, R. W., Likens, G. E., Matson, P. A., Schindler, D. W., Schlesinger, W. H., & Tilman, D. G. (1997). Human alteration of the global nitrogen cycle: sources and consequences. Ecological Applications, 7(3), 737–750. doi:10.1890/1051-0761(1997)007[0737:HAOTGN]2.0.CO;2.Google Scholar
  3. 3.
    Finlay, J. C., Small, G. E., & Sterner, R. W. (2013). Human influences on nitrogen removal in lakes. Science, 342(6155), 247–250. doi:10.1126/science.1242575.CrossRefGoogle Scholar
  4. 4.
    Passeport, E., Vidon, P., Forshay, K. J., Harris, L., Kaushal, S. S., Kellogg, D. Q., & Stander, E. K. (2013). Ecological engineering practices for the reduction of excess nitrogen in human-influenced landscapes: a guide for watershed managers. Environmental Management, 51(2), 392–413. doi:10.1007/s00267-012-9970-y.CrossRefGoogle Scholar
  5. 5.
    Dubrovsky, N. M., Burow, K. R., Clark, G. M., Gronberg, J. M., Hamilton, P. A., Hitt, K. J., Mueller, D. K., Munn, M. D., Nolan, B. T., Puckett, L. J., Rupert, M. G., Short, T. M., Spahr, N. E., Sprague, L. A., & Wilber, W. G. (2010). The quality of our Nation’s waters—nutrients in the Nation’s streams and groundwater, 1992–2004. U.S. Geological Survey Circular, 1350, 174.Google Scholar
  6. 6.
    Ribaudoa, M. O., Heimlich, R., & Peters, M. (2005). Nitrogen sources and Gulf hypoxia: potential for environmental credit trading. Ecological Economics, 52(2), 159–168. doi:10.1016/j.ecolecon.2004.07.021.CrossRefGoogle Scholar
  7. 7.
    Alexander, R. B., Smith, R. A., & Schwarz, G. E. (2000). Effect of stream channel size on the delivery of nitrogen to the Gulf of Mexico. Nature, 403(6771), 758–761. doi:10.1038/35001562.CrossRefGoogle Scholar
  8. 8.
    Johnes, P. J. (1996). Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modeling approach. Journal of Hydrology, 183(3), 323–349. doi:10.1016/0022-1694(95)02951-6.CrossRefGoogle Scholar
  9. 9.
    Billy, C., Birgand, F., Ansart, P., Peschard, J., Sebilo, M., & Tournebize, J. (2013). Factors controlling nitrate concentrations in surface waters of an artificially drained agricultural watershed. Landscape Ecology, 28(4), 665–684. doi:10.1007/s10980-013-9872-2.CrossRefGoogle Scholar
  10. 10.
    Peterson, E. W., & Benning, C. (2013). Factors influencing nitrate within a low-gradient agricultural stream. Environmental Earth Sciences, 68(5), 1233–1245. doi:10.1007/s12665-012-1821-x.CrossRefGoogle Scholar
  11. 11.
    Onderka, M., Wrede, S., Rodný, M., Pfister, L., Hoffmann, L., & Krein, A. (2012). Hydrogeologic and landscape controls of dissolved inorganic nitrogen (DIN) and dissolved silica (DSi) fluxes in heterogeneous catchments. Journal of Hydrology, 450, 36–47. doi:10.1016/j.jhydrol.2012.05.035.CrossRefGoogle Scholar
  12. 12.
    Hively, W. D., Hapeman, C. J., McConnell, L. L., Fisher, T. R., Rice, C. P., McCarty, G. W., Ali, M. S., Whitall, D. R., Downey, P. M., Niño de Guzmán, G. T., Bialek-Kalinski, K., Lang, M. W., Gustafson, A. B., Sutton, A. J., Sefton, K. A., & Fetcho, J. A. H. (2011). Relating nutrient and herbicide fate with landscape features and characteristics of 15 subwatersheds in the Choptank River watershed. Science of the total environment, 409(19), 3866–3878. doi:10.1016/j.scitotenv.2011.05.024.CrossRefGoogle Scholar
  13. 13.
    Oehler, F., & Elliott, A. H. (2011). Predicting stream N and P concentrations from loads and catchment characteristics at regional scale: a concentration ratio method. Science of the Total Environment, 409(24), 5392–5402. doi:10.1016/j.scitotenv.2011.08.025.CrossRefGoogle Scholar
  14. 14.
    Preston, S. D., Alexander, R. B., Schwarz, G. E., & Crawford, C. G. (2011). Factors affecting stream nutrient loads: a synthesis of regional SPARROW model results for the continental United States. JAWRA Journal of the American Water Resources Association, 47(5), 891–915. doi:10.1111/j.1752-1688.2011.00577.x.CrossRefGoogle Scholar
  15. 15.
    Stålnacke, P., Pengerud, A., Bechmann, M., Garnier, J., Humborg, C., & Novotny, V. (2009). Nitrogen driving force and pressure relationships at contrasting scales: implications for catchment management. International Journal of River Basin Management, 7(3), 221–232. doi:10.1080/15715124.2009.9635385.CrossRefGoogle Scholar
  16. 16.
    Schilling, K. E., & Libra, R. D. (2000). The relationship of nitrate concentrations in streams to row crop land use in Iowa. Journal of Environmental Quality, 29(6), 1846–1851. doi:10.2134/jeq2000.00472425002900060016x.CrossRefGoogle Scholar
  17. 17.
    Mueller, D. K., Ruddy, B. C., & Battaglin, W. A. (1997). Logistic model of nitrate in streams of the upper-midwestern United States. Journal of Environmental Quality, 26(5), 1223–1230. doi:10.2134/jeq1997.00472425002600050005x.CrossRefGoogle Scholar
  18. 18.
    Reynolds, B., & Edwards, A. (1995). Factors influencing dissolved nitrogen concentrations and loadings in upland streams of the UK. Agricultural Water Management, 27(3), 181–202. doi:10.1016/0378-3774(95)01146-A.CrossRefGoogle Scholar
  19. 19.
    Horizon Systems Corporation (2013). National Hydrography Dataset Plus Version 2. Available at http://www.horizon-systems.com/nhdplus/NHDPlusV2_home.php.
  20. 20.
    Brown, J. B., Sprague, L. A., & Dupree, J. A. (2011). Nutrient sources and transport in the Missouri River Basin, with emphasis on the effects of irrigation and reservoirs. JAWRA Journal of the American Water Resources Association, 47(5), 1034–1060. doi:10.1111/j.1752-1688.2011.00584.x.CrossRefGoogle Scholar
  21. 21.
    Moore, R. B., Johnston, C. M., Smith, R. A., & Milstead, B. (2011). Source and delivery of nutrients to receiving waters in the Northeastern and Mid-Atlantic regions of the United States. JAWRA Journal of the American Water Resources Association, 47(5), 965–990. doi:10.1111/j.1752-1688.2011.00582.x.CrossRefGoogle Scholar
  22. 22.
    Rebich, R. A., Houston, N. A., Mize, S. V., Pearson, D. K., Ging, P. B., & Hornig, C. E. (2011). Sources and delivery of nutrients to the northwestern Gulf of Mexico from streams in the south-central United States. JAWRA Journal of the American Water Resources Association, 47(5), 1061–1086. doi:10.1111/j.1752-1688.2011.00583.x.CrossRefGoogle Scholar
  23. 23.
    Robertson, D. M., & Saad, D. A. (2011). Nutrient inputs to the Laurentian Great Lakes by source and watershed estimated using SPARROW watershed models. JAWRA Journal of the American Water Resources Association, 47(5), 1011–1033. doi:10.1111/j.1752-1688.2011.00574.x.CrossRefGoogle Scholar
  24. 24.
    Saleh, D., & Domagalski, J. L. (2012). Using SPARROW to model total nitrogen sources, and transport in rivers and streams of California and adjacent states, USA. In AGU Fall Meeting Abstracts. (Vol. 1, p. L06).Google Scholar
  25. 25.
    Wise, D. R., & Johnson, H. M. (2011). Surface-water nutrient conditions and sources in the United States Pacific Northwest. JAWRA Journal of the American Water Resources Association, 47(5), 1110–1135. doi:10.1111/j.1752-1688.2011.00580.x.CrossRefGoogle Scholar
  26. 26.
    Hoos, A. B., & McMahon, G. (2009). Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks. Hydrological Processes, 23(16), 2275–2294. doi:10.1002/hyp.7323.CrossRefGoogle Scholar
  27. 27.
    Preston, S. D., Alexander, R. B., Woodside, M. D., Hamilton, P. A. (2009). SPARROW MODELING—enhancing understanding of the nation’s water quality (pp. 6). U.S. Geological Survey Fact Sheet 2009–3019. Reston: Geological Survey.Google Scholar
  28. 28.
    Schwarz, G. E., Hoos, A. B., Alexander, R. B., Smith, R. A. (2006). The SPARROW surface water-quality model: theory, application, and user documentation. U.S. Geological Survey Techniques and Methods Report, Book, 6, Chapter B3.Google Scholar
  29. 29.
    Homer, C. H., Fry, J. A., & Barnes, C. A. (2012). The National Land Cover Database. U.S. Geological Survey Fact Sheet, 3020, 4.Google Scholar
  30. 30.
    Therneau, T. M., Atkinson, B., & Ripley, B. (2013). rpart: recursive partitioning. R package version, 4, 1–3.Google Scholar
  31. 31.
    Liaw, A., & Wiener, M. (2012). randomForest: Breiman and Cutler’s random forests for classification and regression. R package version, 4, 6–7.Google Scholar
  32. 32.
    Core Team, R. (2014). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.Google Scholar
  33. 33.
    Bulmer, M. G. (1979). Principles of statistics. New York: Dover Publications.Google Scholar
  34. 34.
    Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. doi:10.1186/1471-2105-9-307.CrossRefGoogle Scholar
  35. 35.
    Rogerson, P. (2001). Statistical methods for geography. Los Angeles: Sage Publications.Google Scholar
  36. 36.
    Escurra, J. J., Vazquez, V., Cestti, R., De Nys, E., & Srinivasan, R. (2014). Climate change impact on countrywide water balance in Bolivia. Regional Environmental Change, 14(2), 727–742. doi:10.1007/s10113-013-0534-3.CrossRefGoogle Scholar
  37. 37.
    Asner, G. P., Scurlock, J. M., & Hicke, J. A. (2003). Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Global Ecology and Biogeography, 12(3), 191–205. doi:10.1046/j.1466-822X.2003.00026.x.CrossRefGoogle Scholar
  38. 38.
    Reich, P. B., Peterson, D. W., Wedin, D. A., & Wrage, K. (2001). Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum. Ecology, 82(6), 1703–1719. doi:10.1890/0012-9658(2001)082[1703:FAVEOP]2.0.CO;2.Google Scholar
  39. 39.
    Craig, L. S., Palmer, M. A., Richardson, D. C., Filoso, S., Bernhardt, E. S., Bledsoe, B. P., Doyle, M. W., Groffman, P. M., Hassett, B. A., Kaushal, S. S., Mayer, P. M., Smith, S. M., & Wilcock, P. R. (2008). Stream restoration strategies for reducing river nitrogen loads. Frontiers in Ecology and the Environment, 6(10), 529–538. doi:10.1890/070080.CrossRefGoogle Scholar
  40. 40.
    Lindsey, B. D., Breen, K. J., Bilger, M. D., & Brightbill, R. A. (1998). Water quality in the lower Susquehanna River basin, Pennsylvania and Maryland, 1992–95 (Vol. Circular 1168). Washington, DC: US Geological Survey.Google Scholar
  41. 41.
    Stets, E. G., Kelly, V. J., & Crawford, C. G. (2015). Regional and temporal differences in nitrate trends discerned from long-term water quality monitoring data. JAWRA Journal of the American Water Resources Association, 51(5), 1394–1407. doi:10.1111/1752-1688.12321.CrossRefGoogle Scholar
  42. 42.
    Kelly, V., Stets, E. G., & Crawford, C. (2015). Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams. Journal of Hydrology, 525, 559–571. doi:10.1016/j.jhydrol.2015.03.062.CrossRefGoogle Scholar
  43. 43.
    Smith, R. A., Alexander, R. B., & Schwarz, G. E. (2003). Natural background concentrations of nutrients in streams and rivers of the conterminous United States. Environmental Science & Technology, 37(14), 3039–3047. doi:10.1021/es020663b.CrossRefGoogle Scholar
  44. 44.
    United States Geological Survey (USGS). (2014). National Hydrography Dataset (NHD). Reston, VA: United States Geological Survey.Google Scholar
  45. 45.
    Wahl, K. L., & Wahl, T. L. (1995). Determining the flow of Comal Springs at New Braunfels, Texas, Texas Water ’95 (pp. 77–86). San Antonio, Texas: American Society of Civil Engineers.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Scott C. Kronholm
    • 1
  • Paul D. Capel
    • 2
  • Silvia Terziotti
    • 3
  1. 1.Water Resources ScienceUniversity of MinnesotaSt. PaulUSA
  2. 2.U.S. Geological SurveyUniversity of MinnesotaMinneapolisUSA
  3. 3.U.S. Geological SurveySouth Atlantic Water Science CenterRaleighUSA

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