Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application

Abstract

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a distributed catchment model and to estimate prediction limits after conditioning based on observed catchment-wide streamflow. Prediction limits were derived not only for daily streamflow but also for piezometric levels and for extreme events. The latter analysis was carried out considering independent partial duration time series (PDS) obtained from the observed daily streamflow hydrograph. Important data uncertainties were identified. For streamflow the stage-discharge data analysis led to estimate an average data uncertainty of about 3 m3 s − 1. For piezometric levels, data errors were estimated to be in the order of 5 m in average and 10 m at most. The GLUE analysis showed that most of the inspected parameters are insensitive to model performance, except the horizontal and vertical components of the hydraulic conductivity of one of the geological layers that have the most influence on the streamflow model performance in the application catchment. The study revealed a considerable uncertainty attached to the simulation of both high flows and low flows (i.e., in average terms 5 m3 s − 1 before the Bayesian updating of the prediction limits). Similarly, wide prediction intervals were obtained for the piezometric levels in relevant wells, in the order of 3.3 and 1.5 m before and after the Bayesian updating of the prediction limits, respectively. Consequently, the results suggest that, in average terms, the model of the catchment predicts overall outputs within the limitations of the errors in the input variables.

This is a preview of subscription content, log in to check access.

References

  1. Allen GR, Pereira LS, Raes D, Martin S (1998) Crop evapotranspiration—guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Rome

  2. Beven KJ (1989) Changing ideas in hydrology the case of physically based models. J Hydrol 105:157–172

  3. Beven KJ (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16:41–51

  4. Beven KJ (2000) Uniqueness of place and process representations in hydrological modelling. Hydrol Earth Syst Sci 4(2):203–213

  5. Beven KJ (2001a) How far can we go in distributed hydrological modelling? Hydrol Earth Syst Sci 5(1):1–12

  6. Beven KJ (2001b) Rainfall-runoff modelling: the primer. Wiley, Chichester

  7. Beven KJ (2002) Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrol Process 16:189–206

  8. Beven KJ (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36

  9. Beven KJ, Binley AM (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6(3):279–298

  10. Beven KJ, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems. J Hydrol 249:11–29

  11. Beven KJ, Young PC (2003) Comment on Bayesian recursive parameter estimation for hydrologic models by M Thiemann, M Trosset, H Gupta and S Sorooshian. Water Resour Res 39(5):1116. doi:10.1029/2001WR001183

  12. Beven KJ, Smith PJ, Freer J (2008) So just why would a modeller choose to be incoherent? J Hydrol 354:15–32

  13. Binley AM, Beven KJ, Calver A, Watts LG (1991) Changing responses in hydrology: assessing the uncertainty in physically based model predictions. Water Resour Res 27(6):1253–1261

  14. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill International Editions, Singapore, 572 pp

  15. Christiaens K, Feyen J (2002) Constraining soil hydraulic parameter and output uncertainty of the distributed hydrological MIKE SHE model using the GLUE framework. Hydrol Process 16(2):373–391

  16. Coles S, Pauli F (2002) Models and inference for uncertainty in extremal dependence. Biometrika 89(1):183–196

  17. Colin H, Urbach P (1989) Scientific reasoning: the Bayesian approach. Open Court, La Salle, IL, USA, 314 pp

  18. DHI (1998) MIKE-SHE v.5.30 user guide and technical reference manual. Danish Hydraulic Institute, Denmark

  19. Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO Irrigation and Drainage Paper 24, Rome

  20. Engman ET (1986) Roughness coefficients for routing surface runoff. J Irrig Drain Eng 112(1):39–53

  21. Feyen L, Vázquez RF, Christiaens K, Sels O, Feyen J (2000) Application of a distributed physically-based hydrological model to a medium size catchment. Hydrol Earth Syst Sci 4(1):47–63

  22. 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(7):2161–2173

  23. Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and non-commensurable measures of information. Water Resour Res 34(4):751–763

  24. Haan CT, Barfield BJ, Hayes JC (1994) Design hydrology and sedimentology for small catchments. Harcourt Brace & Company, California, USA, 587 pp

  25. Jain SK, Storm B, Bathurst JC, Refsgaard JC, Sing RD (1992) Application of the SHE to catchments in India—Part 2: field experiments and simulation studies on the Kolar subcatchment of the Narmada River. J Hydrol 140:25–47

  26. Jayatilaka CJ, Storm B, Mudgway LB (1998) Simulation of water flow on irrigation bay scale with MIKE SHE. J Hydrol 208:108–130

  27. Klepper O, Scholten H, van de Kamer JPG (1991) Prediction uncertainty in an ecological model of the Oosterschelde estuary. J Forecast 10:191–209

  28. Kristensen KJ, Jensen SE (1975) A model for estimating actual evapotranspiration from potential evapotranspiration. Nord Hydrol 6:170–188

  29. Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J Hydrol 211:69–85

  30. Lamb R, Beven KJ, Myrabø S (1998) Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model. Adv Water Resour 22:305–317

  31. Law AM, Kelton WD (1991) Simulation modeling and analysis. McGraw-Hill International Editions, UK

  32. Legates DR, McCabe GJ (1999) Evaluating the use of ‘goodness-of-fit’ measures in hydrological and hydroclimatic model validation. Water Resour Res 35(1):233–241

  33. Madsen H (2003) Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Res 26(2):205–216

  34. Madsen H, Wilson G, Ammentorp HC (2002) Comparison of different automated strategies for calibration of rainfall-runoff models. J Hydrol 261:48–59

  35. Naff RL, Haley DF, Sudicky EA (1998) High-resolution Monte Carlo simulation of flow and conservative transport in heterogeneous porous media. Water Resour Res 34(4):663–677

  36. National Academy Press (1990) Ground water models: scientific and regulatory applications. National Research Council, Washington, 303 pp

  37. Pandey MD, van Gelder PHAJM, Vrijling JK (2003) Bootstrap simulations for evaluating the uncertainty associated with peaks-over-threshold estimates of extreme wind velocity. Environment 14:27–43

  38. Parkin G, O’Donnell G, Ewen J, Bathurst JC, O’Connell PE, Lavabre J (1996) Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment. J Hydrol 175:595–613

  39. Pickand J (1975) Statistical inference using extreme order statistics. Ann Stat 3:119–131

  40. Refsgaard JC (1997) Parameterisation, calibration and validation of distributed hydrological models. J Hydrol 198:69–97

  41. Refsgaard JC, Butts MB (1999) Determination of grid scale parameters in catchment modelling by upscaling local scale parameters. Proceedings of the International Workshop of EurAgEng’s Field of Interest on Soil and Water “Modelling of transport processes in soils at various scales in time and space”. Leuven, Belgium

  42. Refsgaard JC, Storm B (1995) MIKE SHE. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, USA, pp 809–846

  43. Rosbjerg D, Madsen H, Rasmussen PF (1992) Prediction in partial duration series with generalized Pareto-distributed exceedances. Water Resour Res 28(11):3001–3010

  44. Spear RC, Hornberger GM (1980) Eutrophication in Peel Inlet, II: identification of critical uncertainties via generalised sensitivity analysis. Water Res 14:43–49

  45. Troch PA, Paniconi Cl, McLaughlinc D (2003) Catchment-scale hydrological modeling and data assimilation. Adv Water Res 26(2):131–135

  46. van Genuchten MTh (1980) A closed form equation for predicting the hydraulic conductivity in unsaturated soils. Soil Sci Soc Am J 44:892–898

  47. Vázquez RF (2003) Assessment of the performance of physically based distributed codes simulating medium size hydrological systems. PhD dissertation, Katholieke Universiteit Leuven

  48. Vázquez RF, Feyen J (2002) Assessment of the performance of a distributed code in relation to the ETp estimates. Water Resour Manag 16(4):329–350

  49. Vázquez RF, Feyen J (2003) Effect of potential evapotranspiration estimates on effective parameters and performance of the MIKE SHE-code applied to a medium-size catchment. J Hydrol 270(4):309–327

  50. Vázquez RF, Feyen J (2004) Potential evapotranspiration for the distributed modelling of Belgian basins. J Irrig Drain Eng 130(1):1–8

  51. Vázquez RF, Feyen J (2007) Assessment of the effects of DEM gridding on the predictions of basin runoff using MIKE SHE and a modelling resolution of 600 m. J Hydrol 334:73–87

  52. Vázquez RF, Feyen L, Feyen J, Refsgaard JC (2002) Effect of grid-size on effective parameters and model performance of the MIKE SHE code applied to a medium sized catchment. Hydrol Process 16(2):355–372

  53. Vázquez RF, Willems P, Feyen J (2008) Improving the predictions of a MIKE SHE catchment-scale application by using a multi-criteria approach. Hydrol Process 22(13):2159–2179

  54. Vereecken H (1988) Pedotransferfunctions for the generation of hydraulic properties for Belgian soils. Faculty of Agricultural and Applied Biological Sciences, Katholieke Universiteit Leuven (K.U. Leuven), Leuven, Belgium, 254 pp

  55. Willems P (1998) Hydrological applications of extreme value analysis. In: Wheater H, Kirby C (eds) Hydrology in a changing environment, vol III. Wiley, USA, pp 15–25

  56. Xevi E, Christiaens K, Espino A, Sewnandan W, Mallants D, Sorensen H, Feyen J (1997) Calibration, validation and sensitivity analysis of the MIKE-SHE model using the Neuenkirchen catchment as case study. Water Resour Manag 11:219–239

  57. Yu PSh, Yang TCh, Chen ShJ (2001) Comparison of uncertainty analysis methods for a distributed rainfall-runoff model. J Hydrol 244:43–59

Download references

Author information

Correspondence to R. F. Vázquez.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Vázquez, R.F., Beven, K. & Feyen, J. GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application. Water Resour Manage 23, 1325–1349 (2009). https://doi.org/10.1007/s11269-008-9329-6

Download citation

Keywords

  • MIKE SHE
  • Likelihood
  • Uncertainty
  • Prediction limits
  • Monte Carlo simulations
  • GLUE
  • Partial duration time series
  • Extreme value analysis
  • Piezometers