Advertisement

Environmental Science and Pollution Research

, Volume 25, Issue 21, pp 21070–21085 | Cite as

Developing a non-point source P loss indicator in R and its parameter uncertainty assessment using GLUE: a case study in northern China

  • Jingjun Su
  • Xinzhong Du
  • Xuyong Li
Research Article

Abstract

Uncertainty analysis is an important prerequisite for model application. However, the existing phosphorus (P) loss indexes or indicators were rarely evaluated. This study applied generalized likelihood uncertainty estimation (GLUE) method to assess the uncertainty of parameters and modeling outputs of a non-point source (NPS) P indicator constructed in R language. And the influences of subjective choices of likelihood formulation and acceptability threshold of GLUE on model outputs were also detected. The results indicated the following. (1) Parameters RegR2, RegSDR2, PlossDPfer, PlossDPman, DPDR, and DPR were highly sensitive to overall TP simulation and their value ranges could be reduced by GLUE. (2) Nash efficiency likelihood (L1) seemed to present better ability in accentuating high likelihood value simulations than the exponential function (L2) did. (3) The combined likelihood integrating the criteria of multiple outputs acted better than single likelihood in model uncertainty assessment in terms of reducing the uncertainty band widths and assuring the fitting goodness of whole model outputs. (4) A value of 0.55 appeared to be a modest choice of threshold value to balance the interests between high modeling efficiency and high bracketing efficiency. Results of this study could provide (1) an option to conduct NPS modeling under one single computer platform, (2) important references to the parameter setting for NPS model development in similar regions, (3) useful suggestions for the application of GLUE method in studies with different emphases according to research interests, and (4) important insights into the watershed P management in similar regions.

Keywords

Non-point source P indicator Uncertainty analysis GLUE Likelihood formulation Acceptability threshold 

Notes

Funding information

This study was supported by the National Natural Science Funding (Grant number: 41401590), Project of State Key Laboratory of Urban and Regional Ecology in China (SKLURE2017-1-5), Major Science and Technology Program for Water Pollution Control and Treatment (Grant number: 2015ZX07203-005-01).

References

  1. Aksoy H, Kavvas ML (2005) A review of hillslope and watershed scale erosion and sediment transport models. Catena 64:247–271CrossRefGoogle Scholar
  2. Alatorre LC, Beguería S, Lana-Renault N, Navas A, García-Ruiz JM (2012) Soil erosion and sediment delivery in a mountain catchment under scenarios of land use change using a spatially distributed numerical model. Hydrol Earth Syst Sc 16:1321–1334CrossRefGoogle Scholar
  3. Beven K (2016) Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrolog Sci J 61:1652–1665CrossRefGoogle Scholar
  4. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298CrossRefGoogle Scholar
  5. Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Process 28:5897–5918CrossRefGoogle Scholar
  6. Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydro 249(1–4):11–29CrossRefGoogle Scholar
  7. Bi X, Duan S, Li Y, Liu B, Fu S, Ye Z, Yuan A, Lu B (2006) Study on soil loss equation in Beijing. Sci. Soil Water Conserv 4:6–13 (In Chinese)Google Scholar
  8. Bivand R, Keitt T, Rowlingson B (2016) rgdal: bindings for the geospatial data abstraction library [online]. R package version 1:1–10Google Scholar
  9. Blasone R-S, Vrugt JA, Madsen H, Rosbjerg D, Robinson BA, Zyvoloski GA (2008) Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling. Adv Water Res 31:630–648CrossRefGoogle Scholar
  10. Bolster CH, Vadas PA (2013) Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model. J Environ Qual 42:1109–1118CrossRefGoogle Scholar
  11. Bolster CH, Vadas PA, Boykin D (2016) Model parameter uncertainty analysis for an annual field-scale P loss model. J.Hydro. 539:27–37CrossRefGoogle Scholar
  12. Buczko U, Kuchenbuch RO (2007) Phosphorus indices as risk-assessment tools in the USA and Europe—a review. J Plant Nutr Soil Sc 170:445–460CrossRefGoogle Scholar
  13. Dean S, Freer J, Beven K, Wade AJ, Butterfield D (2009) Uncertainty assessment of a process-based integrated catchment model of phosphorus. Stoch Env Res Risk A 23:991–1010CrossRefGoogle Scholar
  14. Du X, Li X, Hao S, Wang H, Shen X (2014) Contrasting patterns of nutrient dynamics during different storm events in a semi-arid catchment of northern China. Water Sci Technol 69:2533–2540CrossRefGoogle Scholar
  15. Enright P, Madramootoo CA (2004) Phosphorus losses in surface runoff and subsurface drainage waters on two agricultural fields in Quebec. Mater Constr 29:35–44Google Scholar
  16. Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 32:2161–2173CrossRefGoogle Scholar
  17. Freni G, Mannina G, Viviani G (2008) Uncertainty in urban stormwater quality modelling: the effect of acceptability threshold in the GLUE methodology. Water Res 42:2061–2072CrossRefGoogle Scholar
  18. Freni G, Mannina G, Viviani G (2009) Uncertainty in urban stormwater quality modelling: the influence of likelihood measure formulation in the GLUE methodology. Sci Total Environ 408:138–145CrossRefGoogle Scholar
  19. Gelbrecht J, Lengsfeld H, Pöthig R, Opitz D (2005) Temporal and spatial variation of phosphorus input, retention and loss in a small catchment of NE Germany. J Hydro 304:151–165CrossRefGoogle Scholar
  20. Gong Y, Shen Z, Hong Q, Liu R, Liao Q (2011) Parameter uncertainty analysis in watershed total phosphorus modeling using the GLUE methodology. Agric Ecosyst Environ 142:246–255CrossRefGoogle Scholar
  21. Haith DA, Shoenaker LL (1987) Generalized watershed loading functions for stream flow nutrients. JAWRA J Am Water Res Assoc 23:471–478CrossRefGoogle Scholar
  22. Heathwaite A, Dils R (2000) Characterising phosphorus loss in surface and subsurface hydrological pathways. Sci Total Environ 251:523–538CrossRefGoogle Scholar
  23. Heathwaite A, Fraser A, Johnes P, Hutchins M, Lord E, Butterfield D (2003) The Phosphorus Indicators Tool: a simple model of diffuse P loss from agricultural land to water. Soil Use Manage. 19:1–11CrossRefGoogle Scholar
  24. Hijmans RJ, van Etten J (2014): raster: geographic data analysis and modeling. R package version 2, 15Google Scholar
  25. Kleinman PJ, Sharpley AN, Moyer BG, Elwinger GF (2002) Effect of mineral and manure phosphorus sources on runoff phosphorus. J Environ Qual 31:2026–2033CrossRefGoogle Scholar
  26. Kleinman PJ, Sharpley AN, Saporito LS, Buda AR, Bryant RB (2009) Application of manure to no-till soils: phosphorus losses by sub-surface and surface pathways. Nutr Cycl Agroecosys 84:215–227CrossRefGoogle Scholar
  27. Lesschen JP, Schoorl JM, Cammeraat LH (2009) Modelling runoff and erosion for a semi-arid catchment using a multi-scale approach based on hydrological connectivity. Geomorphology 109:174–183CrossRefGoogle Scholar
  28. Li L, Xia J, Xu C-Y, Singh VP (2010) Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models. J Hydro 390:210–221CrossRefGoogle Scholar
  29. Little JL, Nolan SC, Casson JP, Olson BM (2007) Relationships between soil and runoff phosphorus in small Alberta watersheds. J Environ Qual 36:1289–1300CrossRefGoogle Scholar
  30. Liu S, Brazier R, Heathwaite L (2005) An investigation into the inputs controlling predictions from a diffuse phosphorus loss model for the UK; the Phosphorus Indicators Tool (PIT). Sci Total Environ 344:211–223CrossRefGoogle Scholar
  31. Liu BY, Bi XG, Fu SH (2010) Beijing soil loss equation. Science Publisher, BeijingGoogle Scholar
  32. McDowell R, Dou Z, Toth J, Cade-Menun B, Kleinman P, Soder K, Saporito L (2008) A comparison of phosphorus speciation and potential bioavailability in feed and feces of different dairy herds using P nuclear magnetic resonance spectroscopy. J Environ Qual 37:741–752CrossRefGoogle Scholar
  33. Men M, Chen J, Yu Z, Xu H (2007) Assessment of soil erosion based on SOTER in Hebei Province (in Chinese). Chinese Agr Sci Bull 23:587–591Google Scholar
  34. Menzel, R. (1980). Enrichment ratios for water quality modeling in CREAMS: a field-scale model for chemicals, runoff, and erosion from agricultural management systems. USDA-SEA Conservation Research Report. Washington, DC, USDA-SEA 3: 486–492Google Scholar
  35. Mittelstet AR, Heeren DM, Fox GA, Storm DE, White MJ, Miller RB (2011) Comparison of subsurface and surface runoff phosphorus transport rates in alluvial floodplains. Agric Ecosyst Environ 141:417–425CrossRefGoogle Scholar
  36. MOA (2013): The report on the fertilizer utilization efficiency on three major crops in China. In: China MoA (Hrsg.), BeijingGoogle Scholar
  37. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydro 10:282–290CrossRefGoogle Scholar
  38. Nathan RJ, McMahon TA (1990) Evaluation of automated techniques for base flow and recession analyses. Water Res Res 26:1465–1473CrossRefGoogle Scholar
  39. National Standards Compilation Group of People’s Republic of China. (1989). Water quality-determination of total phosphorus-ammonium molybdate spectrophotometric method (GB/T 11893-1989). China Environmental Science Press, Beijing, pp. 243–250Google Scholar
  40. NEP (2002) National survey on pollution of livestock and poultry industries and its countermeasures. Ecology Conservation Department of National Environmental Protection Bureau, Beijing (in Chinese)Google Scholar
  41. Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R news 5:9–13Google Scholar
  42. Radcliffe DE, Freer J, Schoumans O (2009) Diffuse phosphorus models in the United States and Europe: their usages, scales, and uncertainties. J Environ Qual 38:1956–1967CrossRefGoogle Scholar
  43. Radcliffe DE, Reid DK, Blombäck K, Bolster CH, Collick AS, Easton ZM, Francesconi W, Fuka DR, Johnsson H, King K, Larsbo M, Youssef MA, Mulkey AS, Nelson NO, Persson K, Ramirez-Avila JJ, Schmieder F, Smith DR (2015) Applicability of models to predict phosphorus losses in drained fields: a review. J Environ Qual 44:614–628CrossRefGoogle Scholar
  44. RCoreTeam (2014) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, p 2013Google Scholar
  45. Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors. Water Resour Res 46(5):1187–1191CrossRefGoogle Scholar
  46. Scholefield P, Heathwaite AL, Brazier RE, Page T, Schärer M, Beven K, Hodgkinson R, Withers P, Walling D, Haygarth PM (2013) Estimating phosphorus delivery from land to water in headwater catchments using a fuzzy decision tree approach. Soil Use Manage 29:175–186CrossRefGoogle Scholar
  47. Sharpley A (1980) The enrichment of soil phosphorus in runoff sediments. J Environ Qual 9:521–526CrossRefGoogle Scholar
  48. Sharpley A, Kleinman P, McDowell R, Gitau M, Bryant R (2002) Modeling phosphorus transport in agricultural watersheds: processes and possibilities. J Soil Water Conserv 57:425–439Google Scholar
  49. Sharpley A, Beegle D, Bolster C, Good L, Joern B, Ketterings Q, Lory J, Mikkelsen R, Osmond D, Vadas P (2012) Phosphorus indices: why we need to take stock of how we are doing. J Environ Qual 41:1711–1719CrossRefGoogle Scholar
  50. Shen ZY, Chen L, Chen T (2012) Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China. Hydrol Earth Syst Sci 16:121–132CrossRefGoogle Scholar
  51. Shen ZY, Xie H, Chen L, Qiu J, Zhong Y (2015) Uncertainty analysis for nonpoint source pollution modeling: implications for watershed models. Int J Environ Sci Te 12:739–746CrossRefGoogle Scholar
  52. Soetaert K, Petzoldt T (2016): FME: a flexible modelling environment for inverse modelling, sensitivity, identifiability, Monte Carlo analysis. R package version 1.3.5Google Scholar
  53. Song XM, Zhang JY, Zhan CS, Xuan YQ, Ye M, Xu CG (2015) Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J.Hydro. 523:739–757CrossRefGoogle Scholar
  54. Spear R, Hornberger G (1980) Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis. Water Res 14:43–49CrossRefGoogle Scholar
  55. Su J, Du X, Li X, Wang X, Li W, Zhang W, Wang H, Wu Z, Zheng B (2016) Development and application of watershed-scale indicator to quantify non-point source P losses in semi-humid and semi-arid watershed, China. Ecol Indic 63:374–385CrossRefGoogle Scholar
  56. Sun M, Zhang X, Huo Z, Feng S, Huang G, Mao X (2016) Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method. J.Hydro. 534:19–30CrossRefGoogle Scholar
  57. Turner BL, Leytem AB (2004) Phosphorus compounds in sequential extracts of animal manures: chemical speciation and a novel fractionation procedure. Environ Sci Technol 38:6101–6108CrossRefGoogle Scholar
  58. Vadas P, Kleinman P, Sharpley A, Turner B (2005) Relating soil phosphorus to dissolved phosphorus in runoff. J Environ Qual 34:572–580CrossRefGoogle Scholar
  59. Vadas P, Good L, Moore P, Widman N (2009) Estimating phosphorus loss in runoff from manure and fertilizer for a phosphorus loss quantification tool. J Environ Qual 38:1645–1653CrossRefGoogle Scholar
  60. de Vente J, Poesen J, Verstraeten G, Van Rompaey A, Govers G.(2008).Spatially distributed modelling of soil erosion and sediment yield at regional scales in Spain. Glob Planet Chang 2008; 60: 393–415Google Scholar
  61. Williams J, Renard K, Dyke P (1983) EPIC: a new method for assessing erosion’s effect on soil productivity. J Soil Water Conserv 38:381–383Google Scholar
  62. Yang Y, Chen Y, Zhang X, Ongley E, Zhao L (2012) Methodology for agricultural and rural NPS pollution in a typical county of the North China Plain. Environ Pollut 168:170–176CrossRefGoogle Scholar
  63. Yen H, Wang X, Fontane DG, Harmel RD, Arabi M (2014) A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling. Environ Model Softw 54:211–221CrossRefGoogle Scholar
  64. Zheng Y, Keller AA (2007) Uncertainty assessment in watershed-scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach. Water Resour Res 43:W08407Google Scholar
  65. Zheng Y, Han F, Tian Y, Wu B, Lin Z (2014): Chapter 5—addressing the uncertainty in modeling watershed nonpoint source pollution. In: Sven Erik Jørgensen XF-LE (Editor), Developments in environmental modelling. Ecological Modelling and Engineering of Lakes and Wetlands. Elsevier, pp. 113–159Google Scholar
  66. Zhu, M. (2011) Study on agricultural NPS loads of Haihe Basin and assessment on its environmental impact (in Chinese). PhD Thesis, Chinese Academy of Agricultural Sciences, BeijingGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina
  2. 2.Athabasca River Basin Research Institute (ARBRI)Athabasca UniversityEdmontonCanada

Personalised recommendations