The Calibration and Uncertainty Evaluation of Spatially Distributed Hydrological

In the last decade, spatially distributed hydrological models have rapidly advanced with the widespread availability of remotely sensed and geomatics information. Particularly, the areas of calibration and evaluation of spatially distributed hydrological models have been attempted in order to reduce the differences between models and improve realism through various techniques. Despite steady efforts, the study of calibrations and evaluations for spatially distributed hydrological models is still a largely unexplored field, in that there is no research in terms of the interactions of snow and water balance components with the traditional measurement methods as error functions. As one of the factors related to runoff, melting snow is important, especially in mountainous regions with heavy snowfall; however, no study considering both snow and water components simultaneously has investigated the procedures of calibration and evaluation for spatially distributed models. Additionally, novel approaches of error functions would be needed to reflect the characteristics of spatially distributed hydrological models in the comparison between simulated and observed values. Lastly, the shift from lumped model calibration to distributed model calibration has raised the model complexity. The number of unknown parameters can rapidly increase, depending on the degree of distribution. Therefore, a strategy is required to determine the optimal degree of model distributions for a study basin. In this study, we will attempt to address the issues raised above. This study utilizes the Research Distributed Hydrological Model (HL-RDHM) developed by Hydrologic Development Office of the National Weather Service (OHD-NWS). This model simultaneously simulates both snow and water balance components. It consists largely of two different modules, i.e., the Snow 17 as a snow component and the Sacramento Soil Moisture Accounting (SAC-SMA) as a water component, and is applied over the Durango River basin in Colorado, which is an area driven primarily by snow. As its main contribution, this research develops and tests various methods to calibrate and evaluate spatially distributed hydrological models with different, non-commensurate, variables and measurements. Additionally, this research provides guidance on the way to decide an appropriate degree of model distribution (resolution) for a specific water catchment