Parameter uncertainty and identifiability of a conceptual semi-distributed model to simulate hydrological processes in a small headwater catchment in Northwest China

作     者：Shuai Ouyang Heike Puhlmann Shunli Wang Klaus von Wilpert Osbert Jianxin Sun 

作者机构：Institute of Forestry and Climate Change ResearchBeijing Forestry University35 Qinghua East RoadHaidian DistrictBeijing 100083China Chair of HydrologyUniversity of FreiburgFahnenbergplatz79098 Freiburg i.Br.Germany Academy of Water Resource Conservation Forests in Qilian MountainsZhangye 734000GansuProvinceChina Forest Research Institute of Baden-WürttembergDepartment of Soils and EnvironmentWonnhaldestr.479100 Freiburg i.Br.Germany 

出 版 物：《Ecological Processes》 (生态过程（英文）)

年 卷 期：2014年第3卷第1期

页      面：142-158页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This research was jointly funded by Robert Bosch Foundation and Beijing Municipal Commission of Education(Key Laboratory for Silviculture and Conservation) 

主　　题：Dynamic identifiability analysis HBV-light model Hydrological modeling Sensitivity analysis Uncertainty analysis 

摘      要：Introduction:Conceptual hydrological models are useful tools to support catchment water ***,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult ***,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 ***: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 *** 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 ***,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 ***:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable *** parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity *** 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 *** results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.