Water Resources Management

, Volume 23, Issue 7, pp 1325–1349 | Cite as

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

Article

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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, RomeGoogle Scholar
  2. Beven KJ (1989) Changing ideas in hydrology the case of physically based models. J Hydrol 105:157–172CrossRefGoogle Scholar
  3. Beven KJ (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16:41–51CrossRefGoogle Scholar
  4. Beven KJ (2000) Uniqueness of place and process representations in hydrological modelling. Hydrol Earth Syst Sci 4(2):203–213Google Scholar
  5. Beven KJ (2001a) How far can we go in distributed hydrological modelling? Hydrol Earth Syst Sci 5(1):1–12Google Scholar
  6. Beven KJ (2001b) Rainfall-runoff modelling: the primer. Wiley, ChichesterGoogle Scholar
  7. Beven KJ (2002) Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrol Process 16:189–206CrossRefGoogle Scholar
  8. Beven KJ (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36CrossRefGoogle Scholar
  9. Beven KJ, Binley AM (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6(3):279–298CrossRefGoogle Scholar
  10. Beven KJ, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems. J Hydrol 249:11–29CrossRefGoogle Scholar
  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 CrossRefGoogle Scholar
  12. Beven KJ, Smith PJ, Freer J (2008) So just why would a modeller choose to be incoherent? J Hydrol 354:15–32CrossRefGoogle Scholar
  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):1253–1261CrossRefGoogle Scholar
  14. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill International Editions, Singapore, 572 ppGoogle Scholar
  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):373–391CrossRefGoogle Scholar
  16. Coles S, Pauli F (2002) Models and inference for uncertainty in extremal dependence. Biometrika 89(1):183–196CrossRefGoogle Scholar
  17. Colin H, Urbach P (1989) Scientific reasoning: the Bayesian approach. Open Court, La Salle, IL, USA, 314 ppGoogle Scholar
  18. DHI (1998) MIKE-SHE v.5.30 user guide and technical reference manual. Danish Hydraulic Institute, DenmarkGoogle Scholar
  19. Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO Irrigation and Drainage Paper 24, RomeGoogle Scholar
  20. Engman ET (1986) Roughness coefficients for routing surface runoff. J Irrig Drain Eng 112(1):39–53CrossRefGoogle Scholar
  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):47–63CrossRefGoogle Scholar
  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):2161–2173CrossRefGoogle Scholar
  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):751–763CrossRefGoogle Scholar
  24. Haan CT, Barfield BJ, Hayes JC (1994) Design hydrology and sedimentology for small catchments. Harcourt Brace & Company, California, USA, 587 ppGoogle Scholar
  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 140:25–47CrossRefGoogle Scholar
  26. Jayatilaka CJ, Storm B, Mudgway LB (1998) Simulation of water flow on irrigation bay scale with MIKE SHE. J Hydrol 208:108–130CrossRefGoogle Scholar
  27. Klepper O, Scholten H, van de Kamer JPG (1991) Prediction uncertainty in an ecological model of the Oosterschelde estuary. J Forecast 10:191–209CrossRefGoogle Scholar
  28. Kristensen KJ, Jensen SE (1975) A model for estimating actual evapotranspiration from potential evapotranspiration. Nord Hydrol 6:170–188Google Scholar
  29. Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J Hydrol 211:69–85CrossRefGoogle Scholar
  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 22:305–317CrossRefGoogle Scholar
  31. Law AM, Kelton WD (1991) Simulation modeling and analysis. McGraw-Hill International Editions, UKGoogle Scholar
  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):233–241CrossRefGoogle Scholar
  33. Madsen H (2003) Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Res 26(2):205–216CrossRefGoogle Scholar
  34. Madsen H, Wilson G, Ammentorp HC (2002) Comparison of different automated strategies for calibration of rainfall-runoff models. J Hydrol 261:48–59CrossRefGoogle Scholar
  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):663–677CrossRefGoogle Scholar
  36. National Academy Press (1990) Ground water models: scientific and regulatory applications. National Research Council, Washington, 303 ppGoogle Scholar
  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 14:27–43Google Scholar
  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 175:595–613CrossRefGoogle Scholar
  39. Pickand J (1975) Statistical inference using extreme order statistics. Ann Stat 3:119–131CrossRefGoogle Scholar
  40. Refsgaard JC (1997) Parameterisation, calibration and validation of distributed hydrological models. J Hydrol 198:69–97CrossRefGoogle Scholar
  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, BelgiumGoogle Scholar
  42. Refsgaard JC, Storm B (1995) MIKE SHE. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, USA, pp 809–846Google Scholar
  43. Rosbjerg D, Madsen H, Rasmussen PF (1992) Prediction in partial duration series with generalized Pareto-distributed exceedances. Water Resour Res 28(11):3001–3010CrossRefGoogle Scholar
  44. Spear RC, Hornberger GM (1980) Eutrophication in Peel Inlet, II: identification of critical uncertainties via generalised sensitivity analysis. Water Res 14:43–49CrossRefGoogle Scholar
  45. Troch PA, Paniconi Cl, McLaughlinc D (2003) Catchment-scale hydrological modeling and data assimilation. Adv Water Res 26(2):131–135CrossRefGoogle Scholar
  46. van Genuchten MTh (1980) A closed form equation for predicting the hydraulic conductivity in unsaturated soils. Soil Sci Soc Am J 44:892–898Google Scholar
  47. Vázquez RF (2003) Assessment of the performance of physically based distributed codes simulating medium size hydrological systems. PhD dissertation, Katholieke Universiteit LeuvenGoogle Scholar
  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):329–350CrossRefGoogle Scholar
  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):309–327CrossRefGoogle Scholar
  50. Vázquez RF, Feyen J (2004) Potential evapotranspiration for the distributed modelling of Belgian basins. J Irrig Drain Eng 130(1):1–8CrossRefGoogle Scholar
  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 334:73–87CrossRefGoogle Scholar
  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):355–372CrossRefGoogle Scholar
  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):2159–2179CrossRefGoogle Scholar
  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 ppGoogle Scholar
  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 15–25Google Scholar
  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 11:219–239CrossRefGoogle Scholar
  57. Yu PSh, Yang TCh, Chen ShJ (2001) Comparison of uncertainty analysis methods for a distributed rainfall-runoff model. J Hydrol 244:43–59CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Unidad HidráulicaTécnica y Proyectos S.A. (TYPSA)BarcelonaSpain
  2. 2.Centre for Research on Environmental Systems and Statistics, Institute for Environmental and Biological SciencesLancaster UniversityLancasterUK
  3. 3.Department Landbeheer-en Economie, Division Soil and Water ManagementK.U. LeuvenHeverleeBelgium

Personalised recommendations