Predicting stream N and P concentrations from loads and catchment characteristics at regional scale: A concentration ratio method

Science of the Total Environment

Volumn 409, Issue 24, 2011, Pages 5392-5402

  Oehler, F ^a   Elliott, A H ^a  

^a NATIONAL INSTITUTE OF WATER AND ATMOSPHERIC RESEARCH   (New Zealand)

Author keywords

Boosted Regression Trees; Nitrate; Nitrogen; Phosphorus; SPARROW; Watershed

Indexed keywords

BOOSTED REGRESSION TREES; CATCHMENT CHARACTERISTICS; CONCENTRATION PREDICTION; CONCENTRATION RATIO; DIRECT APPROACH; HYBRID APPROACH; LAND-USE CHANGE; MANAGEMENT SCENARIOS; MEAN RATIO; MODELING APPROACH; MODELING PERFORMANCE; MODELING TECHNIQUE; REGIONAL SCALE; SIMPLE METHOD; SPARROW; SPATIAL INTERPOLATION; STEADY STATE; TEMPORAL DYNAMICS; TOTAL NITROGEN; TOTAL PHOSPHORUS;

BUDGET CONTROL; CATCHMENTS; FORECASTING; LAND USE; PHOSPHORUS; PLANT EXTRACTS; RUNOFF; WATER QUALITY;

CONCENTRATION (PROCESS);

NITRATE; NITROGEN; PHOSPHORUS;

CATCHMENT; NITROGEN; PHOSPHORUS; REGRESSION ANALYSIS; STREAM; WATER QUALITY; WATERSHED;

ARTICLE; CONCENTRATION RATIO METHOD; CONTROLLED STUDY; DYNAMICS; ENVIRONMENTAL PARAMETERS; EQUILIBRIUM CONSTANT; HYBRID; LAND USE; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; STEADY STATE; STREAM (RIVER); WATER QUALITY;

ENVIRONMENT; ENVIRONMENTAL MONITORING; MODELS, CHEMICAL; MODELS, THEORETICAL; NEW ZEALAND; NITRATES; NITROGEN; PHOSPHORUS; RIVERS; SEASONS; TIME FACTORS; WATER MOVEMENTS; WATER POLLUTANTS, CHEMICAL; WATER QUALITY;

EID: 80054738595     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2011.08.025     Document Type: Article

References (69)

1. Alameddine I., Qian S.S., Reckhow K.H. A bayesian changepoint-threshold model to examine the effect of tmdl implementation on the flow-nitrogen concentration relationship in the neuse river basin. Water Res 2011, 45(1):51-62.
2. Alexander R.B., Smith R.A., Schwarz G.E., Boyer E.W., Nolan J.V., Brakebill J.W. Differences in phosphorus and nitrogen delivery to the gulf of mexico from the mississippi river basin. Environ Sci Technol 2008, 42(3):822-830.
3. Arnold J.G., Fohrer N. Swat 2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 2005, 19(3):563-572.
4. Basset-Mens C., Anibar L., Durand P., van der Werf H.M.G. Spatialised fate factors for nitrate in catchments: modelling approach and implication for LCA results. Sci Total Environ 2006, 367(1):367-382.
5. Biggs B. New Zealand periphyton guideline: detecting monitoring and managing enrichment of streams. Technical Report 2000, Ministry for the Environment, New Zealand.
6. Biggs B.J.F. Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae. J North Am Benthol Soc 2000, 19(1):17-31.
7. Borra S., Di Ciaccio A. Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Comput Stat Data Anal 2010, 54(12):2976-2989.
8. Breiman L., Friedman J., Olshen R., Stone C. Classification and regression trees 1984, Wadsworth International Group, Belmont, CA, USA.
9. Burman P. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 1989, 76(3):503.
10. Collins R., Whitehead P., Butterfield D. Nitrogen leaching from catchments in the middle hills of nepal; an application of the inca model. Sci Total Environ 1999, 228(2-3):259-274.
11. Correll D.L., Jordan T.E., Weller D.E. Effects of precipitation and air temperature on nitrogen discharges from rhode river watersheds. Water Air Soil Pollut 1999, 115(1-4):547-575.
12. Cutler D., Edwards T., Beard K., Cutler A., Hess K., Gibson J., et al. Random forests for classification in ecology. Ecology 2007, 88(11):2783-2792.
13. Efron B. Better bootstrap confidence intervals. J Am Stat Assoc 1987, 82:171-185.
14. Elith J., Leathwick J.R., Hastie T. A working guide to boosted regression trees. J Anim Ecol 2008, 77(4):802-813.
15. Elliott A., Alexander R., Schwarz G., Shankar U., Sukias J., McBride G. Estimation of nutrient sources and transport for New Zealand using the hybrid mechanistic-statistical model sparrow. J Hydrol (NZ) 2005, 44(1):1-27.
16. Elliott A., Shankar U., Hicks D., Woods R., Dymond J. Sparrow regional regression for sediment yields in New Zealand rivers 2008, 242-249. International Association of Hydrological Sciences.
17. Ensign S., Doyle M. Nutrient spiraling in streams and river networks. J Geophys Res 2006, 111(G4):G04009.
18. Friedman J.H. Greedy function approximation: a gradient boosting machine. Ann Stat 2001, 29:1189-1232.
19. Friedman H., Meulman J. Multiple additive regression trees with application in epidemiology. Stat Med 2003, 22(9):1365-1381.
20. Grizzetti B., Bouraoui F., Granlund K., Rekolainen S., Bidoglio G. Modelling diffuse emission and retention of nutrients in the Vantaanjoki watershed (Finland) using the swat model. Ecol Modell 2003, 169(1):25-38.
21. He B., Oki T., Sun F., Komori D., Kanae S., Wang Y., et al. Estimating monthly total nitrogen concentration in streams by using artificial neural network. J Environ Manage 2011, 92(1):172-177.
22. Hecky R.E., Kilham P. Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment. Limnol Oceanogr 1988, 33:796-822.
23. Hoffmann, M. Assessment of leaching loss estimates and gross load of nitrogen from arable land in Sweden. Doctoral thesis; Swedish Univ. of Agric. Sci.; Uppsala; 1999.
24. Isermann K. Share of agriculture in nitrogen and phosphorous emissions into the surface waters of western Europe against the background of their eutrophication. Fert Res 1990, 26:253-269.
25. Justic D., Rabalais N.N., Turner R.E. Stoichiometric nutrient balance and origin of coastal eutrophication. Mar Pollut Bull 1995, 30(1):41-46.
26. Kim J. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Comput Stat Data Anal 2009, 53(11):3735-3745.
27. Kronvang B., Grant R., Larsen S., Svendsen M., Kristensen P. Non-point-source nutrient losses to the aquatic environment in Denmark - impact of agriculture. Mar Freshwater Res 1995, 46:167-177.
28. Kronvang B., Vagstad N., Behrendt H., Bogestrand J., Larsen S.E. Phosphorus losses at the catchment scale within Europe: an overview. Soil Use Manag 2007, 23(s1):104-116.
29. Leathwick J., Julian K., Elith J., Chadderton L., Ferrier S., Snelder T. A biologically-optimised environmental classification of New Zealand rivers and streams. CR NIWA Client Report HAM2007-043 2007, National Institute of Water and Atmospheric Research, Hamilton, New Zealand.
30. Lek S., Guiresse M., Giraudel J.L. Predicting stream nitrogen concentration from watershed features using neural networks. Water Res 1999, 33(16):3469-3478.
31. Martin C., Aquilina L., Gascuel-Odoux C., Molenat J., Faucheux M., Ruiz L. Seasonnal and interannual variations of nitrate and chloride in stream waters related to spatial and temporal patterns of groundwater concentrations in agricultural catchments. Hydrol Process 2004, 18:1237-1254.
32. McMahon G., Alexander R.B., Qian S. Support of total maximum daily load programs using spatially referenced regression models. J Water Resour Plann Manag-Asce 2003, 129(4):315-329.
33. Merot P., Aurousseau P., Gascuel-Odoux C., Durand P. Innovative assessment tools to improve water quality and watershed management in farming areas. Integr Environ Assess Manag 2009, 5(1):158-166.
34. Molinaro A., Simon R., Pfeiffer R. Prediction error estimation: a comparison of resampling methods. Bioinformatics 2005, 21(15):3301.
35. New Zealand Ministry for the Environment New Zealand land cover database 2 user guide. Technical Report 2004.
36. Oehler F., Durand P., Bordenave P., Saadi Z., Salmon-Monviola J. Modelling denitrification at the catchment scale. Sci Total Environ 2009, 407(5):1726-1737.
37. Oehler F., Rutherford J.C., Coco G. The use of machine learning algorithms to design a generalized simplified denitrification model. Biogeosciences 2010, 7(10):3311-3332.
38. Ohte N., Tokuchi N., Katsuyama M., Hobara S., Asano Y., Koba K. Episodic increases in nitrate concentrations in streamwater due to the partial dieback of a pine forest in Japan: runoff generation processes control seasonality. Hydrol Process 2003, 17:237-249.
39. Paerl H.W. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters. Limnol Oceanogr 1988, 33:823-847.
40. Palmer A. Dimensional analysis and intelligent experimentation 2008, World Scientific, Singapore.
41. Pasti R., de Castro L., Coelho G., Von Zuben F. Neural network ensembles: immune-inspired approaches to the diversity of components. Nat Comput 2010, 9:625-653.
42. Perrone M.P., Cooper L.N. When networks disagree: ensemble methods for hybrid neural networks 1993, 126-142. Chapman and Hall.
43. Petry J., Soulsby C., Malcolm I.A., Youngson A.F. Hydrological controls on nutrient concentrations and fluxes in agricultural catchments. Sci Total Environ 2002, 294(1-3):95-110.
44. Pinkerton M.H., Smith A.N., Raymond B., Hosie G.W., Sharp B., Leathwick J.R., et al. Spatial and seasonal distribution of adult oithona similis in the southern ocean: predictions using boosted regression trees. Deep Sea Res Part I 2010, 57(4):469-485.
45. Poor C.J., McDonnell J.J. The effects of land use on stream nitrate dynamics. J Hydrol 2007, 332(1-2):54-68.
46. Quinton J., Catt J., Hess T. The selective removal of phosphorus from soil: is event size important?. J Environ Qual 2001, 30(2):538-545.
47. R Development Core Team R: a language and environment for statistical computing 2010, R Foundation for Statistical Computing, Vienna, Austria.
48. Radcliffe D.E., Freer J., Schoumans O. Diffuse phosphorus models in the United States and Europe: their usages, scales, and uncertainties. J Environ Qual 2009, 38(5):1956-1967.
49. Reckhow K., Coffey S., Henning M., Smith K., Banting R. EUTROMOD: technical guidance and spreadsheet models for nutrient loading and lake eutrophication. Technical Report 1992, North American Lake Management Society, 95pp.
50. Ridgeway G. gbm: generalized boosted regression models 2007.
51. Roe B., Yang H., Zhu J., Liu Y., Stancu I., Mcgregor G. Boosted decision trees as an alternative to artificial neural networks for particle identification. Nucl Instrum Methods Phys Res Sect A 2005, 543(2-3):577-584.
52. Rothwell J., Dise N., Taylor K., Allott T., Scholefield P., Davies H., et al. Predicting river water quality across North West England using catchment characteristics. J Hydrol 2010, 395:153-162.
53. Ruiz L., Abiven S., Durand P., Martin C., Vertes F., Beaujouan V. Effect on nitrate concentration in stream water of agricultural practices in small catchments in Brittany: I. annual nitrogen budgets. Hydrol Earth Syst Sci 2002, 6(3):497-505.
54. Ruiz L., Abiven S., Martin C., Durand P., Beaujouan V., Molenat J. Effect on nitrate concentration in stream water of agricultural practices in small catchments in Brittany: II. Temporal variations and mixing processes. Hydrol Earth Syst Sci 2002, 6(3):507-513.
55. Schapire R. The boosting approach to machine learning - an overview. MSRI Workshop on Nonlinear Estimation and Classification, 2002 2003, Springer, New York.
56. Semadeni-Davies, A., Elliott, S., Shankar, U. CLUES: Linking land use to water quality. 10th-11th February, 2010, Massey University, Palmerston North; Palmerston North, New Zealand: Fertilizer and Lime Research Centre, Massey University. p. 274-288.
57. Skeffington R. European nitrogen policies, nitrate in rivers and the use of the INCA model. Hydrol Earth Syst Sci 2002, 6(3):315-324.
58. Smith D., Maasdam R. New Zealand's national river water quality network 1. Design and physico-chemical characterisation. NZ J Mar Freshwater Res 1994, 28(1):19-35.
59. Smith R.A., Schwarz G.E., Alexander R.B. Regional interpretation of water-quality monitoring data. Water Resour Res 1997, 33(12):2781-2798.
60. Smith V.H., Tilman G.D., Nekola J.C. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ Pollut 1999, 100(1-3):179-196.
61. Smith R.A., Alexander R.B., Schwarz G.E. Natural background concentrations of nutrients in streams and rivers of the conterminous united states. Environ Sci Technol 2003, 37(14):3039-3047.
62. Snelder T., Biggs B. Multiscale river environment classification for water resources management. J Astron Soc Western Australia 2002, 38:1225-1239.
63. Snelder T.H., Lamouroux N., Leathwick J.R., Pella H., Sauquet E., Shankar U. Predictive mapping of the natural flow regimes of France. J Hydrol 2009, 373(1-2):57-67.
64. Thomas S.M., Ledgard S.F., Francis G.S. Improving estimates of nitrate leaching for quantifying New Zealand's indirect nitrous oxide emissions. Nutr Cycling Agroecosyst 2005, 73(2-3):213-226.
65. Tibshirani R., Tibshirani R. A bias correction for the minimum error rate in cross-validation. Ann Appl Stat 2009, 3(2):822-829.
66. Unwin M., Snelder T., Booker D., Ballantine D., Lessard J. Predicting water quality in New Zealand rivers from catchment-scale physical, hydrological and land use descriptors using random forest models. Technical Report 2010, Ministry for the Environment, New Zealand.
67. Wang P.F., Martin J., Morrison G. Water quality and eutrophication in Tampa bay, Florida. Estuarine Coastal Shelf Sci 1999, 49(1):1-20.
68. Woods R., Hendrikx J., Henderson R., Tait A. Estimating mean flow of New Zealand rivers. J Hydrol NZ 2006, 45:95-110.
69. Worrall F., Burt T.P. Inter-annual controls on nitrate export from an agricultural catchment - how much land-use change is safe?. J Hydrol 2001, 243(3-4):228-241.