Uncertainty estimation in phosphorus models

Modeling Phosphorus in the Environment

Volumn , Issue , 2006, Pages 131-160

  Beven, Keith ^a   Page, Trevor ^a   McGechan, Malcolm ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b SCOTTISH AGRICULTURAL COLLEGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

UNCERTAINTY ESTIMATION;

PHOSPHORUS;

EID: 85056930460     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (56)

1. Beck, M.B. 1987. Water quality modeling: a review of the analysis of uncertainty, Water Resour. Res. 23:1393-1442.
2. Bernado, J. M. and A.F.M. Smith. 1994. Bayesian Theory. Chichester, U.K.: Wiley.
3. Beven, K.J. 1993, Prophecy, reality and uncertainty in distributed hydrological modelling. Advances in Water Resources 16:41-51.
4. Beven, K.J. 2000. Uniqueness of place and process representations in hydrological modeling. Hydrol. Earth Sys. Sci. 4(2):203-213.
5. Beven, K.J. 2001. On hypothesis testing in hydrology. Hydrol. Process. 15:1655-1657.
6. Beven, K.J. 2002a. Towards an alternative blueprint for a physically-based digitally simulated hydrologic response modelling system. Hydrol. Process. 16(2):189-206.
7. Beven, K.J. 2002b. Towards a coherent philosophy for environmental modeling. Proc. Roy. Soc. Lond. A 458:2465-2484.
8. Beven, K.J. 2004a. Does an inter-agency meeting in Washington imply uncertainty? Hydrol. Process. 18:1747-1750.
9. Beven, K.J. 2004b. Response to “emergence of a new kind of relativism in environmental modelling: a commentary.” Proc. Roy. Soc. Lond. A 460:2147-2151.
10. Beven, K.J. 2004c. Surface runoff at the Horton Hydrologic Laboratory (or not?). J. Hydrol. 293:219-234.
11. Beven, K.J. 2005. On the concept of model structural error. Water Sci. Technol. 52(6):165-175.
12. Beven, K.J. 2006a. A manifesto for the equifinality thesis. J. Hydrol., 320:18-36.
13. t=H(SR)A as closure. Hydrol. Earth Sys. Sci., in press.
14. Beven, K.J. and A.M. Binley. 1992. The future of hydrological models-model calibration and uncertainty prediction. Hydrol. Process. 6:279-298.
15. Beven, K.J. and J. Freer. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems. J. Hydrol. 249:11-29.
16. Beven, K.J., A.L. Heathwaite, P.M. Haygarth, D.E. Walling, R. Brazier, and P. Withers. 2005. On the concept of delivery of sediment and nutrients to stream channels, Hydrol. Process. 19(2):551-556.
17. Beven, K.J. and P.C. Young. 2003. Comment on Bayesian recursive parameter estimation for hydrologic models by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian. Water Resour. Res. W01116.doi:10.1029/2001WR001183.
18. Box, G.E.P. and D.R. Cox. 1964. An analysis of transformations. J. Roy. Stat. Soc. B26:211-243.
19. Freer, J., K.J. Beven, and B. Ambroise. 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.
20. Freer, J.E., K.J. Beven, and N.E. Peters. 2002. Multivariate seasonal period model rejection within the generalised likelihood uncertainty estimation procedure. Pp. 69-87 in Calibration of Watershed Models, Q. Duan, H. Gupta, S. Sorooshian, A.N. Rousseau, and R. Turcotte (Eds.). Washington, D.C.: AGU Books.
21. Hall, J.W. 2003. Handling uncertainty in the hydroinformatic process. J. Hydroinform. 5(4):215-232.
22. Hooda, P.S., M. Moynagh, I.F. Svoboda, A.C. Edwards, H.A. Anderson, and G. Sym. 1999. Phosphorus loss in drainflow from intensively managed grassland soils. J. Environ. Qual. 28:1235-1242.
23. Hornberger, G.M. and R.C. Spear. 1981. An approach to the preliminary analysis of environmental systems. J. Environ. Manage. 12:7-18.
24. Howson, C. and P. Urbach. 1993. Scientific Reasoning: The Bayesian Approach, 2nd ed. Chicago: Open Court.
25. Iorgulescu, I., K.J. Beven, and A. Musy. 2005. Data-based modelling of runoff and chemical tracer concentrations in the Haute-Menthue (Switzerland) research catchment. Hydrol. Process. 19:2557-2574.
26. Jarvis, N. 1994. The MACRO model-technical description and sample simulations. Reports and Dissertations 19, Swedish University of Agricultural Sciences, Department of Soil Sciences, Uppsala.
27. Jarvis, N., K.G. Villholth, and B. Ulén. 1999. Modelling particle mobilization and leaching in macroporous soil. Eur. J. Soil Sci. 50:621-632.
28. Kavetski, D., S.W. Franks, and G. Kuczera. 2002. Confronting input uncertainty in environmental modeling. Pp. 49-68 in Calibration of Watershed Models, Q. Duan, H. Gupta, S. Sorooshian, A.N. Rousseau, and R. Turcotte (Eds.). Washington, D.C.: AGU Books.
29. Kennedy, M.C. and A. O’Hagan. 2001. Bayesian calibration of mathematical models. J. Roy. Statist. Soc. D63(3):425-450.
30. Kirchner, J.W., X. Feng, and C. Neal. 2001. Catchment-scale advection and dispersion as a mechanism for fractal scaling in stream tracer concentrations. J. Hydrol. 254:82-101.
31. Klepper, O., H. Scholten, and J.P.G. van de Kamer. 1991. Prediction uncertainty in an ecological model of the Oosterschelde estuary. J. Forecasting 10:191-209.
32. Klir, G.J. 1994. The many faces of uncertainty. Pp. 3-19 in Uncertainty Modelling and Analysis: Theory and Applications, B.M. Ayyub and M.M. Gupta (Eds.). Amsterdam, the Netherlands: North-Holland Elsevier.
33. Krzysztofowicz, R. 2002. Bayesian system for probabilistic river stage forecasting, J. Hydrol. 268:16-40.
34. Laplace, P.-S. 1820. Essai Philosophique Sur Les Probabilitiés. (English translation, 1951, Dover: New York).
35. McGechan M.B. 2002. Effects of timing of slurry spreading on leaching of soluble and particulate inorganic phosphorus explored using the MACRO model. Biosys. Eng. 83:237-252.
36. McGechan M.B., R. Graham, A.J.A. Vinten, J.T. Douglas, and P.S. Hooda. 1997. Parameter selection and testing the soil water model SOIL. J. Hydrol. 195:312-334.
37. McGechan, M.B., N.J. Jarvis, P.S. Hooda, and A.J. Vinten. 2001. Parameterisation of the MACRO model to represent leaching of colloidal attached inorganic phosphorus following slurry spreading. Soil Use Manage. 18:61-67.
38. O’Hagan, A. 2004. Bayesian tools for analysing and reducing uncertainty in process modeling. Presented at the NERC Workshop on Uncertainty, Complexity, and Predictive Reliability of Environmental/Biological Models, April 14-16, Nottingham University, UK.
39. Osidele, O.O., W. Zeng, and M.B. Beck. 2005. A random search methodology for examining parametric uncertainty in water quality models. Water Sci. Technol. 53(1): 33-40.
40. Page, T., K.J. Beven, and J. Freer. 2006. Modelling the chloride signal at the Plynlimon Catchments, Wales, using a modified dynamic TOPMODEL. Hydrol. Process. in press.
41. Richards, L.A. 1931. Capillary conduction of liquids in porous mediums. Physics 1:318-333.
42. Romanowicz, R., K.J. Beven, and J. Tawn. 1998. Evaluation of predictive uncertainty in non-linear hydrological models using a Bayesian approach. Pp. 297-317 in Statistics for the Environment II: Water Related Issues, V. Barnett and K.F. Turkman (Eds.). Chichester, UK: Wiley.
43. Romanowicz, R.J., P.C. Young, and K.J. Beven. 2004. Data assimilation in the identification of flood inundation models: derivation of on-line multi-step ahead predictions of water levels. Pp. 348-353 in Hydrology: Science and Practice for the 21st Century, Vol. 1, B. Webb, N. Arnell, C. Onof, N. McIntyre, R. Gurney, and C. Kirby (Eds.). Wallingford, UK: British Hydrological Society.
44. Rose, K.A., E.P. Smith, R.H. Gardner, A.L. Brenkert, and S.M. Bartell. 1991. Parameter sensitivities, Monte Carlo filtering and model forecasting under uncertainty. J. Forecasting 10:117-134.
45. Spear, R.C., T.M. Grieb, and N. Shang. 1994. Parameter uncertainty and interaction in complex environmental models. Water Resour. Res. 30:3159-3170.
46. Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian. 2001. Bayesian recursive parameter estimation for hydrologic models. Water Resourc. Res. 37(10):2521-2535.
47. Van Straten, G. and K.J. Keesman. 1991. Uncertainty propagation and speculation in projective forecasts of environmental change. J. Forecasting 10:163-90.
48. Vazquez, R. 2003. Assessment of the performance of physically based distributed codes simulating medium size hydrological systems. Ph.D. dissertation, Katolieke Universiteit Leuven, Belgium.
49. Villholth, K.G., N.J. Jarvis, O.H. Jacobsen, and H. de Jonge. 2000. Field investigations and modelling of particle-facilitated pesticide transport in macroporous soil. J. Environ. Qual. 29:1298-1309.
50. Vrugt J.A., W. Bouten, H.V. Gupta, and S. Sorooshian. 2002. Toward improved identifiability of hydrologic model parameters: the information content of experimental data. Water Resour. Res. doi:10.1029/2001WR001118, WR001642, pp. 48-1 to 48-13.
51. Vrugt, J. A., H.V. Gupta, W. Bouten, and S. Sorooshian. 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. doi:10.1029/2002WR001642, WR001201, pp. SWC1-1 to SWC1-15.
52. Ye, M., S.P. Neuman, and P.D. Meyer. 2004. Maximum likelihood Bayesian averaging of spatial variability models in unfractured tuff. Water Resour. Res. doi:10.1029/2003WR002.099, WR05104, pp. 1-19.
53. Young, P.C. 1983. The validity and credibility of models for badly-defined systems. Pp. 69-98 in Uncertainty and Forecasting of Water Quality, M.B. Beck and G. van Straten (Eds.). Berlin: Springer-Verlag.
54. Young, P.C. 2001. Data-based mechanistic modelling and validation of rainfall-flow processes. Pp. 117-161 in Model Validation: Perspectives in Hydrological Science, M.G. Anderson and P.D. Bates (Eds.). Chichester, U.K.: Wiley.
55. Young, P.C. 2002. Advances in real time forecasting. Phil. Trans. Roy. Soc. Lond. A360:1430-1450.
56. Young, P.C. 2003. Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale. Hydrol. Process. 17:2195-2217.