Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to...Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.展开更多
基金This research was jointly funded by Robert Bosch Foundation and Beijing Municipal Commission of Education(Key Laboratory for Silviculture and Conservation).
文摘Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.
