Abstract
Phosphorus (P) load apportionment models (LAMs), requiring only spatially and temporally paired P and flow (Q) measurements, provide outputs of variable accuracy using long-term monthly datasets. Using a novel approach to investigate the impact of catchment characteristics on accuracy variation, 91 watercourses’ Q-P datasets were applied to two LAMs, BM and GM, and bootstrapped to ascertain standard errors (SEs). Random forest and regression analysis on data pertaining to catchments’ land use, steepness, size, base flow and sinuosity were used to identify the individual relative importance of a variable on SE. For BM, increasing urban cover was influential on raising SEs, accounting for c.19% of observed variation, whilst analysis for GM found no individually important catchment characteristic. Assessment of model fit evidenced BM consistently outperformed GM, modelling P values to ±10% of actual P values in 85.7% of datasets, as opposed to 17.6% by GM. Further catchment characteristics are needed to account for SE variation within both models, whilst interaction between variables may also be present. Future research should focus on quantifying these possible interactions and should expand catchment characteristics included within the random forest. Both LAMs must also be tested on a wide range of high temporal resolution datasets to ascertain if they can adequately model storm events in catchments with diverse characteristics.
Similar content being viewed by others
References
Andrianaki, M., Shrestha, J., Kobierska, F., Nikolaidis, N. P., & Bernasconi, S. M. (2019). Assessment of SWAT spatial and temporal transferability for a high-altitude glacierized catchment. Hydrology and Earth System Sciences, 23(8), 3219–3232. https://doi.org/10.5194/hess-23-3219-2019.
Antoniadis, V., Koliniati, R., Efstratiou, E., Golia, E., & Petropoulos, S. (2016). Effect of soils with varying degree of weathering and pH values on phosphorus sorption. CATENA, 139, 214–219. https://doi.org/10.1016/j.catena.2016.01.008.
Bergström, L., Kirchmann, H., Djodjic, F., Kyllmar, K., Ulen, B., Liu, J., Andersson, H., Aronsson, H., Börjesson, G., Kynkäänniemi, P., Svanbäck, A., & Villa, A. (2015). Turnover and losses of phosphorus in Swedish agricultural soils: Long-term changes, leaching trends, and mitigation measures. Journal of Environmental Quality, 44(2), 512–523. https://doi.org/10.2134/jeq2014.04.0165.
Bieroza, M. Z., & Heathwaite, A. L. (2015). Seasonal variation in phosphorus concentration-discharge hysteresis inferred from high frequency in situ monitoring. Journal of Hydrology, 524, 333–347. https://doi.org/10.1016/j.jhydrol.2015.02.036.
Bong, C. H. J., Lau, T. L., & Ghani, A. A. (2016). Potential of tipping flush gate for sedimentation management in open stormwater sewer. Urban Water Journal, 13(5), 486–498. https://doi.org/10.1080/1573062X.2014.994002.
Bowes. M. J., Smith, J. T., Jarvie, H. P, & Neal, C. (2008). Modelling of phosphorus inputs to rivers and diffuse point sources. Science of the Total Environment, 395(2–3), 125–138. https://doi.org/10.1016/j.scitotenv.2008.01.054.
Bowes, M. J., Smith, J. T., Jarvie, H. P., Neal, C., & Barden, R. (2009). Changes in point and diffuse source phosphorus inputs to the river Frome (Dorest, UK) from 1966 to 2006. Science of the Total Environment, 407, 1954–1966. https://doi.org/10.1016/j.scitotenv.2008.11.026.
Bowes, M. J., Neal, C., Jarvie, H. P., Smith, J. T., & Davies, H. N. (2010). Predicting phosphorus concentrations in British rivers resulting from the introduction of improved phosphorus removal from sewage effluent. Science of the Total Environment, 408(19), 4239–4250. https://doi.org/10.1016/j.scitotenv.2010.05.016.
Bowes, M. J., Jarvie, H. P., Naden, P. S., Old, G. H., Scarlett, P. M., Roberts, C., Armstrong, L. K., Harman, S. A., Wickham, H. D., & Collins, A. L. (2014). Identifying priorities for nutrient mitigation using river concentration-flow relationships: The Thames basin, UK. Journal of Hydrology, 517, 01–12. https://doi.org/10.1016/j.jhydrol.2014.03.063.
Breiman, L. (2001). Random forests. Machine Learning, 45, 05–32. https://doi.org/10.1023/A:1010933404324.
Bridge, J. S., & Demicco, R. V. (2008). Earth surface processes, landforms and sediment deposits. New York: Cambridge University Press.
Cassidy, R., & Jordan, P. (2011). Limitations of instantaneous water quality sampling in surface-water catchments: Comparison with near-continuous phosphorus time-series data. Journal of Hydrology, 405(1–2), 182–193. https://doi.org/10.1016/j.jhydrol.2011.05.020.
Charlton, M. B., Bowes, M. J., Hutchins, M. G., Orr, H. G., Soley, R., & Davison, P. (2018). Mapping eutrophication risk from climate change: Future phosphorus concentrations in English rivers. Science of the Total Environment, 613, 1510–1526. https://doi.org/10.1016/j.scitotenv.2017.07.218.
Chen, D., Dahlgren, R. A., & Lu, J. (2013). A modified load apportionment model for identifying point and diffuse source nutrient inputs to rivers from stream monitoring data. Journal of Hydrology, 501, 25–34. https://doi.org/10.1016/j.jhydrol.2013.07.034.
Crochmore, L., Rafael, P., Luis, P., Abdulghani, H., Ilias, P., Kristina, I., Jafet, A., & Berit, A. (2018). Understanding and evaluating catchment memory from a global hydrological model: Paper presented at the 20th EGU general assembly conference 04–13 April 2018 Vienna, Austria. Germany: European Geosciences Union.
Crockford, L., O’Riordain, O., Taylor, D., Melland, A. R., Shortle, G., & Jordan, P. (2017). The application of high temporal resolution data in river catchment modelling and management strategies. Environmental Monitoring and Assessment, 189(9), 461. https://doi.org/10.1007/s10661-017-6174-1.
Cutler, D. R., Edwards, T. C., Beard, K. H, Cutler, A., Hess, K. T., Gibson, J. & Lawler, J. J. (2007) Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1.
Daryanto, S., Wang, L., & Jacinthe, P. A. (2017). Meta-analysis of phosphorus loss from no-till soils. Journal of Environmental Quality, 46(5), 1028–1037. https://doi.org/10.2134/jeq2017.03.0121.
Deckers, D., Booij, M. J., Rientjes, T. M., & Krol, M. S. (2010). Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model. Water Resources Management, 24(14), 3961–3985. https://doi.org/10.1007/s11269-010-9642-8.
EA (Environment Agency). not dated. Download open water quality archive datasets. environment.data.gov.uk/water-quality/view/download.
Efron, B. (1979). Bootstrap methods: Another look at the Jacknife. The Annals of Statistics, 1, 01–26. https://doi.org/10.1007/978-1-4612-4380-9_41.
Ekstrøm, C. T. (2016). The R primer. Boca Raton: CRC Press.
Elwan, A., Singh, R., Patterson, M., Roygard, J., Horne, D., Clothier, B., & Jones, G. (2018). Influence of sampling frequency and load calculation methods on quantification of annual river nutrient and suspended solids loads. Environmental Monitoring and Assessment, 190(2), 78. https://doi.org/10.1007/s10661-017-6444-y.
ESRI (Environmental Systems Research Institute). (2019). ArcMap. desktop.arcgis.com/en/arcmap/.
Fletcher, D., MacKenzie, D., & Villouta, E. (2005). Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression. Environmental and Ecological Statistics, 12, 45–54. https://doi.org/10.1007/s10651-005-6817-1.
Forber, K. J., Withers, P. J. A., Ockenden, M. C., & Haygarth, P. M. (2018). The phosphorus transfer continuum: A framework for exploring effects of climate change. Ag Environ Let, 3, 180036. https://doi.org/10.2134/ael2018.06.0036.
Fox, J. (2015). Applied regression analysis and generalized linear models (Third ed.). Thousand Oaks: SAGE Publications, Inc..
Gotelli, N. J. (2001). Research frontiers in null model analysis. Global Ecology and Biogeography, 10, 337–343. https://doi.org/10.1046/j.1466-822X.2001.00249.x.
GOV.UK. (2018). Climate change means more frequent flooding, warns Environment Agency. www.gov.uk/government/news/climate-change-means-more-frequent-flooding-warns-environment-agency.
Greene, S., Taylor, D., McElarney, Y. R., & Jordan, P. (2011). An evaluation of catchment-scale phosphorus mitigation using load apportionment modelling. Science of the Total Environment, 409(11), 2211–2221. https://doi.org/10.1016/j.scitotenv.2011.02.016.
He, S., Wang, D., Chang, S., Fang, Y., & Lan, H. (2018). Effects of morphology of sediment-transporting channels on the erosion and deposition of debris flows. Environment and Earth Science, 77(14). https://doi.org/10.1007/s12665-018-7721-y.
Holloway, M. J., Beven, K. J., Benskin, C. McW. H., Cllins, A.L., Evans, R., Falloon, P.D., Forber, K.J., Hiscock, K.M., Kahana, R., Macleod, C. J. A., Ockenden, M. C., Villamizar, M. L., Wearing, C., Withers, P. J. A., Zhou, J. G., Barber, N. J. & Haygarth, P. M. (2018). The challenges of modelling phosphorus in a headwater catchment: Applying a ‘limits of acceptability’ uncertainty framework to a water quality model. Journal of Hydrology, 558, 607–624. https://doi.org/10.1016/j.jhydrol.2018.01.063.
Hung, C. J. (2018). Catchment hydrology in the Anthropocene: Impacts of land-use and climate change on stormwater runoff. South Carolina: University of South Carolina.
Jacobson, C. R. (2011). Identification and quantification of the hydrological impacts of imperviousness in urban catchments: A review. Journal of Environmental Management, 6, 1438–1448. https://doi.org/10.1016/j.jenvman.2011.01.018.
Jarvie, H. P., Sharpley, A. N., Scott, J. T., Haggard, B. E., Bowes, M. J., & Massey, L. B. (2012). Within-river phosphorus retention: Accounting for a missing piece in the watershed phosphorus puzzle. Environmental Science & Technology, 46(24), 13284–13292. https://doi.org/10.1021/es303562y.
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, 241–258. https://doi.org/10.1016/j.jhydrol.2006.07.006.
Jung, H., Senf, C., Jordan, P., & Krueger, T. (2020). Benchmarking inference methods for water quality monitoring and status classification. Environmental Monitoring and Assessment, 192, 261. https://doi.org/10.1007/s10061-020-8223-4.
Koenker, R. (2019). Quantreg: Quantile Regression. R package version 5.40. CRAN.R-project.org/package=quantreg.
Leaf, S. (2018). Taking the P out of pollution: An English perspective on phosphorus stewardship and the water framework directive. Water Environment Journal, 32, 04–08. https://doi.org/10.1111/wej.12268.
Li, X., Wong, W., Lamoureux, E. L., & Wong, T. Y. (2012). Are linear regression techniques appropriate for analysis when the dependent (outcome) variable is not normally distributed? Investigative Opthalmology and Visual Science, 53, 3082–3083. https://doi.org/10.1167/iovs.12-9967.
Li, Z., Tang, H., Xiao, Y., Zhao, H., Li, Q., & Ji, F. (2016). Factors influencing phosphorus adsorption onto sediment in a dynamic environment. Journal of Hydro-environment Research, 10, 01–11. https://doi.org/10.1016/j.jher.2015.06.002.
Liaw, A. (2018). randomForest v4.6–14. cran.r-project.org/web/packages/randomForest/index.html.
Ligges, U. (2015). nortest function. cran.r-project.org/web/packages/nortest/index.html.
Locatelli, L., Mark, O., Mikkelsen, P. S., Arnbjerg,-Nielsen, K., Deletic, A., Roldin, M. & Binning, P. J. (2017). Hydrologic impact of urbanization with extensive stormwater infiltration. Journal of Hydrology, 544, 524–537. https://doi.org/10.1016/j.jhydrol.2016.11.030.
Lou, H., Zhao, C., Yang, S., Shi, L., Wang, L., Ren, X. & Bai, J. (2018). Quantitative evaluation of legacy phosphorus and its spatial distribution. Journal of Environmental Management, 211, 296–305. https://doi.org/10.1016/j.jenvman.2018.01.062.
MacDonald, G. K., Bennet, E. M., & Taranu, Z. E. (2012). The influence of time, soil characteristics, and land-use history on soil phosphorus legacies: A global meta-analysis. Global Change Biology, 18(6), 1904–1917. https://doi.org/10.1111/j.1365-2486.2012.02653.x.
Maxwell, R. M., & Condon, l. E., Kollet, S. J., Maher, K., Haggerty, R. & Forrester, M. M. (2016). The imprint of climate and geology on the residence times of groundwater. Geophysical Research Letters, 43, 701–708. https://doi.org/10.1002/2015GL066916.
McDowell, R. W., Elkin, K. R., & Kleinman, P. J. A. (2017). Temperature and nitrogen effects on phosphorus uptake by agricultural stream- bed sediments. Journal of Environmental Quality, 46, 295–301. https://doi.org/10.2134/jeq2016.09.0352.
Neave, M. & Rayburg, S. (2016). Designing urban rivers to maximise their geomorphic and ecologic diversity. International Journal of GEOMATEGeotechnique, Construction Materials and Environment, 11(25), 2468–2473. http://www.geomatejournal.com/sites/default/files/articles/2468-2473-5164-Neave-Sept-2016-c1.pdf.
NRFA (National River Flow Archive) (2019a). Derived flow statistics. https://nrfa.ceh.ac.uk/derived-flow-statistics.
NRFA (National River Flow Archive). (2019b). FEH catchment statistics. https://nrfa.ceh.ac.uk/feh-catchment-descriptors.
Omari, H., Dehbi, A., Lammini, A., & Abdallaoui, A. (2019). Study of phosphorus adsorption on the sediments. Journal of Chemistry, 1–10. https://doi.org/10.1155/2019/2760204.
O’Riordain, S. & Crockford, L. (2014). Phoslam package in R. https://github.com/seanpor/phoslam.
OS (Ordnance Survey). (2019). OS open rivers shapefile download. https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-rivers.html.
Osbourne, J.W. & Overbay, A. (2004). The power of outliers (and why researchers should always check for them). Practical Assessment, Research and Evaluation, (6), 01–12. scholarworks.umass.edu/pare/vol9/iss1/6/.
Pallant, J. (2016). SPSS survival manual (6th ed.). Berkshire: Open University Press.
Pumo, D., Arnone, E., Francipane, A., Caracciolo, D., & Noto, L. V. (2017). Potential implication of climate change and urbanization on watershed hydrology. Journal of Hydrology, 554, 80–99. https://doi.org/10.1016/j.jhydrol.2017.09.002.
R Core Team. (2019). A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga, M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L., Horn, R., Kiss, A., Kohnova, S., Leitinger, G., Lennartz, B., Parajka, J., Perdigao, R., Peth, S., Plavcova, L., Quinton, J. N., Robinson, M., Salinas, J. L., Santoro, A., Szolgay, J., Tron, S., Akker, J. J. H, Viglione, A. & Bloschl, G. (2017). Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research. Water Resources Research, 53, 5209–5219. https://doi.org/10.1002/2017WR020723.
Ruhlman, M., Vandelay, A., & Roper, C. (2016). Cooperative planning for source water protection: Targeting sediment in the upper Saluda river watershed. In Presented at the South Carolina water resources conference, 17–18 October 2016. South: Carolina.
San Diego University. (2017). Random Forests. https://dinsdalelab.sdsu.edu/metag.stats/code/randomforest.html.
Seibert, J., Vis, M. J. P., Lewis, E., & van Meerveld, H. J. (2018). Upper and lower benchmarks in hydrological modelling. Hydrological Processes, 32(8), 1120–1125. https://doi.org/10.1002/hyp.11476.
Sharpley, A. (2016). Managing agricultural phosphorus to minimize water quality impacts. Science in Agriculture, 73, 01–08. https://doi.org/10.1590/0103-9016-2015-0107.
Trudeau, M. P., & Richardson, M. (2016). Empirical assessment of effects of urbanization on event flow hydrology in watersheds of Canada’s great lakes-St Lawrence basin. Journal of Hydrology, 541, 1456–1474. https://doi.org/10.1016/j.jhydrol.2016.08.051.
Williams, M. R., King, K. W., Macrae, M. L., Ford, W., Esbroeck, C., Brunke, R. I., English, M. C., & Schiff, S. L. (2015). Uncertainty in nutrient loads from tile-drained landscapes: Effect of sampling frequency, calculation algorithm, and compositing strategy. Journal of Hydrology, 530, 306–316. https://doi.org/10.1016/j.jhydrol.2015.09.060.
Xiao, C., Chen, J., Chen, D., & Chen, R. (2019). Effects of river sinuosity on the self-purification capacity of the Shiwuli River, China. Water Supply, 19(4), 1152–1159. https://doi.org/10.2166/ws.2018.166.
Yaeger, M., Coopersmith, E., Ye, S., Cheng, L., Viglione, A., & Sivapalan, M. (2012). Exploring the physical controls of regional patterns of flow duration curves - part 4: A synthesis of empirical analysis, process modeling and catchment classification. Hydrology and Earth System Sciences, 16(11), 4483–4498. https://doi.org/10.5194/hess-16-4483-2012.
Yu, P. W. C. (2017). Submarine landslides, canyons, and morphological evolution of the east Australian continental margin: A thesis submitted for the degree of doctor of philosophy. Sydney: The University of Sydney.
Zambrano-Bigiarini, M. (2017). HydroGoF function. cran.r-project.org/web/packages/hydroGOF/index.html.
Zhou, J., Zhao, X., & Sun, L. (2013). A new inference approach for joint models of longitudinal data with informative observation and censoring times. Statistica Sinica, 23, 571–593 https://www.jstor.org/stable/24310353.
Availability of data and material
Data were sourced from the National River Flow Archive and the EA historical river water quality. These were obtained under licence and are unavailable for distribution without the express permission of NRFA or the EA.
Funding
No funding was provided for this study aside from supervisory support through Harper Adams University.
Author information
Authors and Affiliations
Contributions
The primary author completed the majority of analyses of data, interpretation of findings and production of manuscript. Second author provided expertise in model and method development. Third author provided expertise in data analysis and development of methods. Fourth author provided supervisory support, aid in interpretation of data, and final manuscript preparation for submission.
Corresponding author
Ethics declarations
Conflicts of interest/competing interests
NA
Code availability
R packages used for data analysis were nortest, quantreg, phlosam, randomForest, hydroGOF and base. The code for this is available on request.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
ESM 1
(XLSX 30 kb)
Rights and permissions
About this article
Cite this article
Stevenson, J.L., O’Riordain, S., Harris, W.E. et al. An investigation into the impact of nine catchment characteristics on the accuracy of two phosphorus load apportionment models. Environ Monit Assess 193, 142 (2021). https://doi.org/10.1007/s10661-021-08875-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-021-08875-9