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

Environmental Science and Pollution Research volume 25, pages 21070–21085 (2018)Cite this 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.

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References

  • Aksoy H, Kavvas ML (2005) A review of hillslope and watershed scale erosion and sediment transport models. Catena 64:247–271

    Article  Google Scholar 

  • 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 16:1321–1334

    Article  Google Scholar 

  • Beven K (2016) Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrolog Sci J 61:1652–1665

    Article  Google Scholar 

  • Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298

    Article  Google Scholar 

  • Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Process 28:5897–5918

    Article  Google Scholar 

  • 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):11–29

    Article  Google Scholar 

  • 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 

  • Bivand R, Keitt T, Rowlingson B (2016) rgdal: bindings for the geospatial data abstraction library [online]. R package version 1:1–10

    Google Scholar 

  • 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 31:630–648

    Article  Google Scholar 

  • Bolster CH, Vadas PA (2013) Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model. J Environ Qual 42:1109–1118

    Article  CAS  Google Scholar 

  • Bolster CH, Vadas PA, Boykin D (2016) Model parameter uncertainty analysis for an annual field-scale P loss model. J.Hydro. 539:27–37

    Article  CAS  Google Scholar 

  • Buczko U, Kuchenbuch RO (2007) Phosphorus indices as risk-assessment tools in the USA and Europe—a review. J Plant Nutr Soil Sc 170:445–460

    Article  CAS  Google Scholar 

  • 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 23:991–1010

    Article  Google Scholar 

  • 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 69:2533–2540

    Article  CAS  Google Scholar 

  • Enright P, Madramootoo CA (2004) Phosphorus losses in surface runoff and subsurface drainage waters on two agricultural fields in Quebec. Mater Constr 29:35–44

    Google Scholar 

  • 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 32:2161–2173

    Article  Google Scholar 

  • Freni G, Mannina G, Viviani G (2008) Uncertainty in urban stormwater quality modelling: the effect of acceptability threshold in the GLUE methodology. Water Res 42:2061–2072

    Article  CAS  Google Scholar 

  • 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 408:138–145

    Article  CAS  Google Scholar 

  • 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 304:151–165

    Article  CAS  Google Scholar 

  • 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 142:246–255

    Article  Google Scholar 

  • Haith DA, Shoenaker LL (1987) Generalized watershed loading functions for stream flow nutrients. JAWRA J Am Water Res Assoc 23:471–478

    Article  Google Scholar 

  • Heathwaite A, Dils R (2000) Characterising phosphorus loss in surface and subsurface hydrological pathways. Sci Total Environ 251:523–538

    Article  Google Scholar 

  • 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. 19:1–11

    Article  Google Scholar 

  • Hijmans RJ, van Etten J (2014): raster: geographic data analysis and modeling. R package version 2, 15

  • Kleinman PJ, Sharpley AN, Moyer BG, Elwinger GF (2002) Effect of mineral and manure phosphorus sources on runoff phosphorus. J Environ Qual 31:2026–2033

    Article  CAS  Google Scholar 

  • 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 84:215–227

    Article  Google Scholar 

  • 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 109:174–183

    Article  Google Scholar 

  • 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 390:210–221

    Article  Google Scholar 

  • Little JL, Nolan SC, Casson JP, Olson BM (2007) Relationships between soil and runoff phosphorus in small Alberta watersheds. J Environ Qual 36:1289–1300

    Article  CAS  Google Scholar 

  • 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 344:211–223

    Article  CAS  Google Scholar 

  • Liu BY, Bi XG, Fu SH (2010) Beijing soil loss equation. Science Publisher, Beijing

    Google Scholar 

  • 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 37:741–752

    Article  CAS  Google Scholar 

  • 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 23:587–591

    Google Scholar 

  • 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: 486–492

  • 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 141:417–425

    Article  CAS  Google Scholar 

  • MOA (2013): The report on the fertilizer utilization efficiency on three major crops in China. In: China MoA (Hrsg.), Beijing

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydro 10:282–290

    Article  Google Scholar 

  • Nathan RJ, McMahon TA (1990) Evaluation of automated techniques for base flow and recession analyses. Water Res Res 26:1465–1473

    Article  Google Scholar 

  • 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. 243–250

  • 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 

  • Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R news 5:9–13

    Google Scholar 

  • Radcliffe DE, Freer J, Schoumans O (2009) Diffuse phosphorus models in the United States and Europe: their usages, scales, and uncertainties. J Environ Qual 38:1956–1967

    Article  CAS  Google Scholar 

  • 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 44:614–628

    Article  CAS  Google Scholar 

  • RCoreTeam (2014) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, p 2013

    Google Scholar 

  • 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):1187–1191

    Article  Google Scholar 

  • 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 29:175–186

    Article  Google Scholar 

  • Sharpley A (1980) The enrichment of soil phosphorus in runoff sediments. J Environ Qual 9:521–526

    Article  CAS  Google Scholar 

  • Sharpley A, Kleinman P, McDowell R, Gitau M, Bryant R (2002) Modeling phosphorus transport in agricultural watersheds: processes and possibilities. J Soil Water Conserv 57:425–439

    Google Scholar 

  • 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 41:1711–1719

    Article  CAS  Google Scholar 

  • 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 16:121–132

    Article  Google Scholar 

  • 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 12:739–746

    Article  Google Scholar 

  • Soetaert K, Petzoldt T (2016): FME: a flexible modelling environment for inverse modelling, sensitivity, identifiability, Monte Carlo analysis. R package version 1.3.5

  • 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. 523:739–757

    Article  Google Scholar 

  • Spear R, Hornberger G (1980) Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis. Water Res 14:43–49

    Article  CAS  Google Scholar 

  • 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 63:374–385

    Article  CAS  Google Scholar 

  • 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. 534:19–30

    Article  CAS  Google Scholar 

  • Turner BL, Leytem AB (2004) Phosphorus compounds in sequential extracts of animal manures: chemical speciation and a novel fractionation procedure. Environ Sci Technol 38:6101–6108

    Article  CAS  Google Scholar 

  • Vadas P, Kleinman P, Sharpley A, Turner B (2005) Relating soil phosphorus to dissolved phosphorus in runoff. J Environ Qual 34:572–580

    Article  CAS  Google Scholar 

  • 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 38:1645–1653

    Article  CAS  Google Scholar 

  • 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: 393–415

  • Williams J, Renard K, Dyke P (1983) EPIC: a new method for assessing erosion’s effect on soil productivity. J Soil Water Conserv 38:381–383

    Google Scholar 

  • 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 168:170–176

    Article  CAS  Google Scholar 

  • 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 54:211–221

    Article  Google Scholar 

  • 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:W08407

    Google Scholar 

  • 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. 113–159

  • 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, Beijing

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Funding

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).

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Affiliations

  1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Room 302, No. 18 Shuangqing Road, Haidian District, Beijing, 100085, China

    Jingjun Su & Xuyong Li

  2. Athabasca River Basin Research Institute (ARBRI), Athabasca University, 10011 109 St. NW, Edmonton, AB, T5J 3S8, Canada

    Xinzhong Du

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  1. Jingjun Su
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  2. Xinzhong Du
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  3. Xuyong Li
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Correspondence to Xuyong Li.

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Responsible editor: Marcus Schulz

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Su, J., Du, X. & Li, X. Developing a non-point source P loss indicator in R and its parameter uncertainty assessment using GLUE: a case study in northern China. Environ Sci Pollut Res 25, 21070–21085 (2018). https://doi.org/10.1007/s11356-018-2113-0

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  • DOI: https://doi.org/10.1007/s11356-018-2113-0

Keywords

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