Skip to main content
SpringerLink
Log in

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

  • Published:
Water Resources Management Aims and scope Submit manuscript

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 via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

    Article  Google Scholar 

  • 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

    Article  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(7):2161–2173

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Unidad Hidráulica, Técnica y Proyectos S.A. (TYPSA), Mallorca 272-276 3°A, 08037, Barcelona, Spain

    R. F. Vázquez

  2. Centre for Research on Environmental Systems and Statistics, Institute for Environmental and Biological Sciences, Lancaster University, Lancaster, LA1 4YQ, UK

    K. Beven

  3. Department Landbeheer-en Economie, Division Soil and Water Management, K.U. Leuven, Celestijnenlaan 200 E, 3001, Heverlee, Belgium

    J. Feyen

Authors
  1. R. F. Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Beven
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Feyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

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

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-008-9329-6

Keywords

Search

Navigation