Skip to main content

Advertisement

SpringerLink
Log in

On the choice of calibration periods and objective functions: A practical guide to model parameter identification

  • Research Article
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

Despite the development of new measuring techniques, monitoring systems and advances in computer technology, rainfall-flow modelling is still a challenge. The reasons are multiple and fairly well known. They include the distributed, heterogeneous nature of the environmental variables affecting flow from the catchment. These are precipitation, evapotranspiration and in some seasons and catchments in Poland, snow melt also. This paper presents a review of work done on the calibration and validation of rainfall-runoff modelling, with a focus on the conceptual HBV model. We give a synthesis of the problems and propose a practical guide to the calibration and validation of rainfall-runoff models.

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

  • Abbott, M.B., J.C. Bathurst, J.A. Cunge, P.E. O’Connell, and J. Rasmussen (1986a), An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”. 1: History and philosophy of a physicallybased, distributed modelling system, J. Hydrol. 87,1–2, 45–59, DOI: 10.1016/0022-1694(86)90114-9.

    Article  Google Scholar 

  • Abbott, M.B., J.C. Bathurst, J.A. Cunge, P.E. O’Connell, and J. Rasmussen (1986b), An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”. 2: Structure of a physically-based, distributed modelling system, J. Hydrol. 87,1–2, 61–77, DOI: 10.1016/0022-1694(86)90115-0.

    Article  Google Scholar 

  • Abebe, N.A., F.L. Ogden, and N.R. Pradhan (2010), Sensitivity and uncertainty analysis of the conceptual HBV rainfall-runoff model: Implications for parameter estimation, J. Hydrol. 389,3–4, 301–310, DOI: 10.1016/j.jhydrol.2010.06.007.

    Article  Google Scholar 

  • Akhtar, M., N. Ahmad, and M.J. Booij (2009), Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region, Hydrol. Earth Syst. Sci. 13,7, 1075–1089, DOI: 10.5194/hess-13-1075-2009.

    Article  Google Scholar 

  • Andréasson, J., S. Bergström, B. Carlsson, L.P. Graham, and G. Lindström (2004), Hydrological change — climate change impact simulations for Sweden, Ambio 33,4, 228–234, DOI: 10.1579/0044-7447-33.4.228.

    Google Scholar 

  • Arnold, J.G., R. Srinivasan, R.S. Muttiah, and J.R. Williams (1998), Large area hydrologic modeling and assessment. Part I: Model development, J. Am. Water Resour. Assoc. 34,1, 73–89, DOI: 10.1111/j.1752-1688.1998.tb05961.x.

    Article  Google Scholar 

  • Aronica, G., B. Hankin, and K. Beven (1998), Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data, Adv. Water Resour. 22,4, 349–365, DOI: 10.1016/S0309-1708(98)00017-7.

    Article  Google Scholar 

  • Bergström, S. (1976), Development and application of a conceptual runoff model for Scandinavian catchments, RH07, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden.

    Google Scholar 

  • Bergström, S., B. Carlsson, M. Gardelin, G. Lindström, A. Pettersson, and M. Rummukainen (2001), Climate change impacts on runoff in Sweden — assessments by global climate models, dynamical downscaling and hydrological modelling, Clim. Res. 16,2, 101–112, DOI: 10.3354/cr016101.

    Article  Google Scholar 

  • Beven, K. (2006), A manifesto for the equifinality thesis, J. Hydrol. 320,1–2, 18–36, DOI: 10.1016/j.jhydrol.2005.07.007.

    Article  Google Scholar 

  • Beven, K., and A. Binley (1992), The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process. 6,3, 279–298, DOI: 10.1002/hyp.3360060305.

    Article  Google Scholar 

  • Blasone, R.S., J.A. Vrugt, H. Madsen, D. Rosbjerg, B.A. Robinson, and G.A. Zyvoloski (2008), Generalized likelihood uncertainty estimation(GLUE) using adaptive Markov Chain Monte Carlo sampling, Adv. Water Resour. 31,4, 630–648, DOI: 10.1016/j.advwatres.2007.12.003.

    Article  Google Scholar 

  • Booij, M.J., and M.S. Krol (2010), Balance between calibration objectives in a conceptual hydrological model, Hydrol. Sci. J. 55,6, 1017–1032, DOI: 10.1080/02626667.2010.505892.

    Article  Google Scholar 

  • Box, G.E.P., and G.C. Tiao (1992), Bayesian Inference in Statistical Analysis, John Wiley and Sons Inc., New York.

    Book  Google Scholar 

  • Boyle, D. (2000), Multicriteria calibration of hydrological models, Ph.D. Thesis, University of Arizona, Tucson.

    Google Scholar 

  • Boyle, D.P., H.V. Gupta, S. Sorooshian, V. Koren, Z. Zhang, and M. Smith (2001), Toward improved streamflow forecasts: value of semidistributed modeling, Water Resour. Res. 37,11, 2749–2759, DOI: 10.1029/2000WR000207.

    Article  Google Scholar 

  • Bruen, M., and J. Yang (2006), Combined hydraulic and black-box models for flood forecasting in urban drainage systems, J. Hydrol. Eng. 11,6, 589–596, DOI: 10.1061/(ASCE)1084-0699(2006)11:6(589).

    Article  Google Scholar 

  • Das, S., A. Abraham, U.K. Chakraborty, and A. Konar (2009), Differential evolution using a neighbourhood-based mutation operator, IEEE Trans. Evolut. Comput. 13,3, 526–553, DOI: 10.1109/TEVC.2008.2009457.

    Article  Google Scholar 

  • Deckers, D.L.E.H., M.J. Booij, T.H.M. Rientjes, and M.S. Krol (2010), Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model, Water Resour. Manag. 24,14, 3961–3985, DOI: 10.1007/s11269-010-9642-8.

    Article  Google Scholar 

  • Doherty, J. (2004), PEST: Model-independent parameter estimation. User’s manual. 5th ed., Watermark Numerical Computing, Brisbane, Australia.

    Google Scholar 

  • Efstratiadis, A., and D. Koutsoyiannis (2010), One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J. 55,1, 58–78, DOI: 10.1080/02626660903526292.

    Article  Google Scholar 

  • Fenicia, F., D.P. Solomatine, H.H.G. Savenije, and P. Matgen (2007), Soft combination of local models in a multi-objective framework, Hydrol. Earth Syst. Sci. 11,6, 1797–1809, DOI: 10.5194/hess-11-1797-2007.

    Article  Google Scholar 

  • Gassman, P.W., M.R. Reyes, C.H. Green, and J.G. Arnold (2007), The soil and water assessment tool: historical development, applications, and future research directions, Trans. Am. Soc. Agricult. Biol. Eng. 50,4, 1211–1250.

    Google Scholar 

  • Graham, L.P., J. Andréasson, and B. Carlsson (2007), Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods — a case study on the Lule River basin, Climatic Change 81,1, Suppl., 293–307, DOI: 10.1007/s10584-006-9215-2.

    Article  Google Scholar 

  • Gupta, H.V., S. Sorooshian, and P.O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res. 34,4, 751–763, DOI: 10.1029/97WR03495.

    Article  Google Scholar 

  • Gupta, H.V., H. Kling, K.K. Yilmaz, and G.F. Martinez (2009), Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol. 377,1–2, 80–91, DOI: 10.1016/j.jhydrol.2009.08.003.

    Article  Google Scholar 

  • Hastie, T.J., and R.J. Tibshirani (1990), Generalised Additive Models, Chapman and Hall, New York, 335 pp.

    Google Scholar 

  • Kavetski, D., F. Fenicia, and M.P. Clark (2011), Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment, Water Resour. Res. 47,5, W05501, DOI: 10.1029/2010WR009525.

    Article  Google Scholar 

  • Lawrence, D., and I. Haddeland (2011), Uncertainty in hydrological modelling of climate change impacts in four Norwegian catchments, Hydrol. Res. 42,6, 457–471, DOI: 10.2166/nh.2011.010.

    Article  Google Scholar 

  • Legates, D.R., and G.J. McCabe Jr. (1999), Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res. 35,1, 233–241, DOI: 10.1029/1998WR900018.

    Article  Google Scholar 

  • Lidén, R., and J. Harlin (2000), Analysis of conceptual rainfall-runoff modeling performance in different climates, J. Hydrol. 238,3–4, 231–247, DOI: 10.1016/S0022-1694(00)00330-9.

    Article  Google Scholar 

  • Lindström, G. (1997), A simple automatic calibration routine for the HBV model, Nord. Hydrol. 28,3, 153–168, DOI: 10.2166/nh.1997.009.

    Google Scholar 

  • Lindström, G., B. Johansson, M. Persson, M. Gardelin, and S. Bergström (1997), Development and test of the distributed HBV-96 hydrological model, J Hydrol. 201,1–4, 272–288, DOI: 10.1016/S0022-1694(97)00041-3.

    Article  Google Scholar 

  • Luks, B., M. Osuch, and R.J. Romanowicz (2011), The relationship between snowpack dynamics and NAO/AO indices in SW Spitsbergen, Phys. Chem. Earth 36,13, 646–654, DOI: 10.1016/j.pce.2011.06.004.

    Article  Google Scholar 

  • Nash, J.E., and J.V. Sutcliffe (1970), River flow forecasting through conceptual models. Part I — A discussion of principles, J. Hydrol. 10,3, 282–290, DOI: 10.1016/0022-1694(70)90255-6.

    Article  Google Scholar 

  • Piotrowski, A.P., and J.J. Napiórkowski (2012), Product-Units neural networks for catchment runoff forecasting, Adv. Water Resour. 49, 97–113, DOI: 10.1016/j.advwatres.2012.05.016.

    Article  Google Scholar 

  • Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery (2002), Numerical Recipes in C++, Cambridge University Press, Cambridge.

    Google Scholar 

  • Pushpalatha, R., C. Perrin, N. Le Moine, and V. Andréassian (2012), A review of efficiency criteria suitable for evaluating low-flow simulations, J. Hydrol. 420–421, 171–182, DOI: 10.1016/j.jhydrol.2011.11.055.

    Article  Google Scholar 

  • Ratto, M., P.C. Young, R. Romanowicz, F. Pappenberger, A. Saltelli, and A. Pagano (2007), Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology, Hydrol. Earth Syst. Sci. 11,4, 1249–1266, DOI: 10.5194/hess-11-1249-2007.

    Article  Google Scholar 

  • Rientjes, T.H.M., A.T. Haile, E. Kebede, C.M.M. Mannaerts, E. Habib, and T.S. Steenhuis (2011), Changes in land cover, rainfall and stream flow in Upper Gilgel Abbay catchment, Blue Nile basin — Ethiopia, Hydrol. Earth Syst. Sci. 15,6, 1979–1989, DOI: 10.5194/hess-15-1979-2011.

    Article  Google Scholar 

  • Romanowicz, R.J., and K.J. Beven (2006), Comments on generalised likelihood uncertainty estimation, Reliab. Eng. Syst. Safe. 91,10–11, 1315–1321, DOI: 10.1016/j.ress.2005.11.030.

    Article  Google Scholar 

  • Romanowicz, R.J., and R. Macdonald (2005), Modelling uncertainty and variability in environmental systems, Acta Geophys. Pol. 53,4, 401–417.

    Google Scholar 

  • Romanowicz, R.J., K.J. Beven, and J. Tawn (1996), Bayesian calibration of flood inundation models. In: M.G. Anderson, D.E. Walling, and P.D. Bates (eds.), Floodplain Processes, John Wiley and Sons Inc., Chichester, 333–360.

    Google Scholar 

  • Romanowicz, R.J., A. Kiczko, and J.J. Napiórkowski (2010), Stochastic transfer function model applied to combined reservoir management and flow routing, Hydrol. Sci. J. 55,1, 27–40, DOI: 10.1080/02626660903526029.

    Article  Google Scholar 

  • Romanowicz, R.J., A. Kulasová, J. Ředinová, and S. Blazková (2012), Influence of afforestation on water regime in Jizera catchments, Czech Republic, Acta Geophys. 60,4, 1120–1142, DOI: 10.2478/s11600-012-0046-4.

    Article  Google Scholar 

  • Smith, P., K.J. Beven, and J.A. Tawn (2008), Informal likelihood measures in model assessment: Theoretic development and investigation, Adv. Water Resour. 31,8, 1087–1100, DOI: 10.1016/j.advwatres.2008.04.012.

    Article  Google Scholar 

  • Sorooshian, S., and J.A. Dracup (1980), Stochastic parameter estimation procedures for hydrologie rainfall-runoff models: Correlated and heteroscedastic error cases, Water Resour. Res. 16,2, 430–442, DOI: 10.1029/ WR016i002p00430.

    Article  Google Scholar 

  • Sorooshian, S., and V.K. Gupta (1983), Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness, Water Resour. Res. 19,1, 260–268, DOI: 10.1029/WR019i001p00260.

    Article  Google Scholar 

  • Tarantola, A. (1987), Inverse Problems Theory. Methods for Data Fitting and Model Parameter Estimation, Elsevier, Amsterdam.

    Google Scholar 

  • Van den Tillaart, S.P.M., M.J. Booij, and M.S. Krol (2013), Impact of uncertainties in discharge determination on the parameter estimation and performance of a hydrological model, Hydrol. Res. 44,3, 454–466, DOI: 10.2166/nh.2012.147.

    Article  Google Scholar 

  • 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. 39,8, 1201, DOI: 10.1029/2002WR001642.

    Google Scholar 

  • Wagener, T., N. McIntyre, M.J. Lees, H.S. Wheater, and H.V. Gupta (2003), Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis, Hydrol. Process. 17,2, 455–476, DOI: 10.1002/hyp.1135.

    Article  Google Scholar 

  • Wilby, R.L. (2005), Uncertainty in water resource model parameters used for climate change impact assessment, Hydrol. Process. 19,16, 3201–3219, DOI: 10.1002/hyp.5819.

    Article  Google Scholar 

  • Young, P. (2003), Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale, Hydrol. Process. 17,11, 2195–2217, DOI: 10.1002/hyp.1328.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland

    Renata J. Romanowicz, Marzena Osuch & Magdalena Grabowiecka

Authors
  1. Renata J. Romanowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzena Osuch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Magdalena Grabowiecka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renata J. Romanowicz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Romanowicz, R.J., Osuch, M. & Grabowiecka, M. On the choice of calibration periods and objective functions: A practical guide to model parameter identification. Acta Geophys. 61, 1477–1503 (2013). https://doi.org/10.2478/s11600-013-0157-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11600-013-0157-6

Key words

Search

Navigation