Abstract
In this chapter the identifiability of a conceptual rainfall-runoff HBV model parameters in several tributaries of the Middle River Vistula is addressed. For this purpose, two approaches have been used. In the first, the application of a global sensitivity analysis by Sobol resulted in estimates of the influence of HBV model parameters on the goodness of fit of the model output. An alternative approach is based on an analysis of the posterior distribution of model parameters estimated with the help of an SCEM-UA algorithm. The results of the two approaches are similar. In both cases the parameters of the soil moisture store from the HBV model are better defined than the rest of parameters, as their influence is the highest and the relative standard deviation of parameter posterior distribution is the smallest. Small differences in the results are probably due to interactions between the parameters.
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References
Abebe NA, Ogden FL, Pradhan NR (2010) Sensitivity and uncertainty analysis of the conceptual HBV rainfall-runoff model: implications for parameter estimation. J Hydrol 389:301–310
Andréassian V, Le Moine N, Perrin C, Ramos M-H, Oudin L, Mathevet T, Lerat J, Berthet L (2012) All that glitters is not gold: the case of calibrating hydrological models. Hydrol Process 26(14):2206–2210. doi:10.1002/hyp.9264
Archer G, Saltelli A, Sobol I (1997) Sensitivity measures, ANOVA-like techniques and the use of bootstrap. J Stat Comput Simul 58:99–120
Bergström S (1995) The HBV model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, pp 443–476
Bergström S, Forsman A (1973a) Development of a conceptual deterministic rainfall–runoff model. Nord Hydrol 4:147–170
Bergström S, Forsman A (1973b) Development of a conceptual deterministic rainfall-runoff model. Nord Hydrol 4(3):147–170
Booij MJ (2005) Impact of climate change on river flooding assessed with different spatial model resolutions. J Hydrol 303:176–198
Booij MJ, Krol MS (2010) Balance between calibration objectives in a conceptual hydrological model. Hydrol Sci J 55:1017–1032
Box GEP, Cox DR (1964) An analysis of transformations. J Roy Stat Soc B 26:211–252
Cannavó F (2012) Sensitivity analysis for volcanic source modeling quality assessment and model selection. Comp Geosci 44:52–59
Deckers DLEH, Booij MJ, Rientjes THM, Krol MS (2010) Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model. Water Resour Manage 24:3961–3985
Hamon WR (1961) Estimation potential evapotranspiration. J Hydraul Div Proc Amer Soc Civil Eng 107–120
Herman JD, Kollat JB, Reed PM, Wagener T (2013) Technical note: method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrol Earth Syst Sci 17(7):2893–2903. doi:10.5194/hess-17-2893-2013
Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52(1):1–17
HWSD, 2012. FAO/IIASA/ISRIC/ISSCAS/JRC (2012) Harmonized world soil database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria
Kochanek K, Karamuz E, Osuch M (2015) Distributed modelling of flow in the middle reach of the River Vistula, this issue
Lindström G (1997) A simple automatic calibration routine for the HBV model. Nord Hydrol 28(3):153–168
Lindström G, Johansson B, Persson M, Gardelin M, Bergström S (1997) Development and test of the distributed HBV-96 hydrological model. J Hydrol 201(1–4):272–288
Nita J, Małolepszy Z, Chybiorz R (2007) A digital terrain model in visualization and interpretation of geological and geomorphological settings. Prz Geol 55:511–520
Osuch M, Romanowicz RJ, Booij M (2015) The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics. Hydrol Sci J. doi:10.1080/02626667.2014.967694
Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M, Kundzewicz ZW (2015) Are so called modern metaheuristics successful in calibrating simple conceptual rainfall-runoff models?. Hydrol Sci J, submitted
Romanowicz RJ, Osuch M (2015) Semi-distributed flood forecasting system for the River Vistula reach, Chapter 9, this book
Romanowicz RJ, Osuch M, Grabowiecka M (2013) On the choice of calibration periods and objective functions: a practical guide to model parameter identification. Acta Geophys 61(6):1477–1503
Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Wiley, New York
Seibert J (1997) Estimation of parameter uncertainty in the HBV model. Nord Hydrol 28(4/5):247–262
Seibert J (1999) Regionalisation of parameters for a conceptual rainfall runoff model. Agric For Meteorol 98–99:279–293
Seibert J (2003) Reliability of model predictions outside calibration conditions. Nord Hydrol 34:477–492
Shin MJ, Guillaume JHA, Croke BFW, Anthony J, Jakeman AJ (2015a) A review of foundational methods for checking the structural identifiability of models: results for rainfall-runoff. J Hydrol 520:1–16
Shin M-J, Guillaume JHA, Croke BFW, Jakeman AJ (2015b) A review of foundational methods for checking the structural identifiability of models: results for rainfall-runoff. J Hydrol 520:1–16
Sorooshian S, Duan Q, Gupta VK (1993) Calibration of rainfall-runoff models: application of global optimization to the Sacramento soil moisture accounting model. Water Resour Res 29(4):1185–1194. doi:10.1029/92WR02617
Tokarczyk T (2013) Classification of low flow and hydrological drought for a river basin. Acta Geophys 61(2):404–421
Uhlenbrook S, Seibert J, Leibundgut C, Rodhe A (1999) Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure. Hydrol Sci J 44(5):779–797
van Werkhoven K, Wagener T, Reed P, Tang Y (2009) Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Adv Water Resour 32(8):1154–1169. doi:10.1016/j.advwatres.2009.03.002
Vrugt JA, Gupta HV, Bouten W, Sorooshian S (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
Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk A 19(6):378–387. doi:10.1007/s00477-005-0006-5
Zelelew MB, Alfredsen K (2013) Sensitivity-guided evaluation of the HBV hydrological model parametrization. J Hydroinf 15(3):967–990
Zhan C, Song X, Xia J, Tong C (2013) An efficient integrated approach for global sensitivity analysis of hydrological model parameters. Environ Model Softw 41:39–52
Acknowledgments
This study was supported by the project “Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)” carried by the Institute of Geophysics, Polish Academy of Sciences, on the order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The hydro-meteorological data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.
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Osuch, M. (2015). Sensitivity and Uncertainty Analysis of Precipitation-Runoff Models for the Middle Vistula Basin. In: Romanowicz, R., Osuch, M. (eds) Stochastic Flood Forecasting System. GeoPlanet: Earth and Planetary Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-18854-6_5
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