An evaluation of impacts of DEM resolution and parameter correlation on TOPMODEL modeling uncertainty

► Uncertainty should be due solely to DEM resolution changes by fixing the parameter values. ► The Copula-based analysis of the correlation structures of the model parameters. ► Evaluation of uncertainty of parameters with more behavioral parameter sets. ► Uncertainty due to parameter correlation can be considerably removed. Hydrological modeling uncertainties are the results of many factors such as input error, calibration accuracy, parameter uncertainty, model structure, and so on. Wherein, input errors and parameter uncertainties are the two of the major factors influencing the uncertainties of hydrological modeling. TOPMODEL is a rainfall–runoff model that bases its distributed predictions on analysis of watershed topography, which is widely used in hydrological modeling practices. In this study, the effects of DEM resolution and parameter correlation on TOPMODEL modeling uncertainties are evaluated by using GLUE technique. The uncertainty evaluation is performed by modeling the rainfall–runoff processes of three tributaries in the Hanjiang River, one of the major tributaries of the Yangtze River, China. The results show no evident effects of the DEM resolution on the uncertainty intervals of the TOPMODEL simulation. This can be attributed to the fact that the modeling uncertainty is due solely to changes of DEM resolution by fixing the parameter values to avoid the artifacts resulted from interactions between ln( a/tan( B)) and the parameters. In addition, the copula functions are used to produce more behavioral parameter sets for the same sample time intervals when the model parameters are in good correlation, and which can benefit thorough evaluation of effects of parameter correlation on the hydrological modeling uncertainty. With the same number of the behavioral parameter sets, after putting the parameter correlation under consideration, the simulated runoff series by the TOPMODEL with the behavioral parameter sets can fit reasonably better the observed runoff series. Thus, the uncertainty due to parameter correlation of the TOPMODEL modeling can be considerably removed. This study is of great theoretical and practical merits in sound understanding of the modeling behaviors of the TOPMODEL under the influences of inputs and parameter correlation.