A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model.

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Assessment of the effects of human activity and natural condition on the outflow of Syr Darya River: A stepwise-cluster factorial analysis method

Environmental Research ◽

10.1016/j.envres.2020.110634 ◽

2021 ◽

Vol 194 ◽

pp. 110634

Author(s):

X.B. Zhai ◽

Y.P. Li ◽

Y.R. Liu ◽

G.H. Huang

Keyword(s):

Human Activity ◽

Natural Condition ◽

Factorial Analysis ◽

Analysis Method ◽

Syr Darya

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Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2007.05.017 ◽

2008 ◽

Vol 47 (11-12) ◽

pp. 1134-1149 ◽

Cited By ~ 87

Author(s):

Kurt K. Benke ◽

Kim E. Lowell ◽

Andrew J. Hamilton

Keyword(s):

Sensitivity Analysis ◽

Water Balance ◽

Parameter Uncertainty ◽

Prediction Error ◽

Hydrological Model

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Sensitivity-guided evaluation of the HBV hydrological model parameterization

Journal of Hydroinformatics ◽

10.2166/hydro.2012.011 ◽

2012 ◽

Vol 15 (3) ◽

pp. 967-990 ◽

Cited By ~ 8

Author(s):

M. B. Zelelew ◽

K. Alfredsen

Keyword(s):

Sensitivity Analysis ◽

Hydrological Model ◽

River Basin Management ◽

Low Flow ◽

Model Parameters ◽

Sensitivity Index ◽

Analysis Method ◽

Flow Conditions ◽

Model Parameterization ◽

Sensitivity Analysis Method

Applying hydrological models for river basin management depends on the availability of the relevant data information to constrain the model residuals. The estimation of reliable parameter values for parameterized models is not guaranteed. Identification of influential model parameters controlling the model response variations either by main or interaction effects is therefore critical for minimizing model parametric dimensions and limiting prediction uncertainty. In this study, the Sobol variance-based sensitivity analysis method was applied to quantify the importance of the HBV conceptual hydrological model parameterization. The analysis was also supplemented by the generalized sensitivity analysis method to assess relative model parameter sensitivities in cases of negative Sobol sensitivity index computations. The study was applied to simulate runoff responses at twelve catchments varying in size. The result showed that varying up to a minimum of four to six influential model parameters for high flow conditions, and up to a minimum of six influential model parameters for low flow conditions can sufficiently capture the catchments' responses characteristics. To the contrary, varying more than nine out of 15 model parameters will not make substantial model performance changes on any of the case studies.

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Uncertainty in hydrological modelling of climate change impacts in four Norwegian catchments

Hydrology Research ◽

10.2166/nh.2011.010 ◽

2011 ◽

Vol 42 (6) ◽

pp. 457-471 ◽

Cited By ~ 27

Author(s):

Deborah Lawrence ◽

Ingjerd Haddeland

Keyword(s):

Climate Change ◽

Parameter Uncertainty ◽

Hydrological Model ◽

Climate Model ◽

Annual Maximum ◽

Climate Scenarios ◽

Local Conditions ◽

Practical Applications ◽

Maximum Flows ◽

Hydrological Parameter

Projections for the hydrological impacts of climate change are necessarily reliant on a chain of models for which numerous alternative models and approaches are available. Many of these alternatives produce dissimilar results which can undermine their use in practical applications due to these differences. A methodology for developing climate change impact projections and for representing the range of model outcomes is demonstrated based on the application of a hydrological model with input data from six regional climate scenarios, which have been further adjusted to match local conditions. Multiple best-fit hydrological model parameter sets are also used so that hydrological parameter uncertainty is included in the analysis. The methodology is applied to consider projected changes in the average annual maximum daily mean runoff in four catchments (Flaksvatn, Viksvatn, Masi and Nybergsund) which are characterised by regional differences in seasonal flow regimes. For catchments where rainfall makes the predominant contribution to annual maximum flows, hydrological parameter uncertainty is significant relative to other uncertainty sources. Parameter uncertainty is less important in catchments where spring snowmelt dominates the generation of maximum flows. In this case, differences between climate scenarios and methods for adjusting climate model output to local conditions dominate uncertainty.

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