Abstract
The quest for good practice in modelling merits thorough and sustained attention since good practice increases the credibility and impact of the information, and insight that modelling seeks to generate. This paper presents the findings of an evaluation whose goal was to understand the uncertainty in applying a distributed hydrological model to the Grote Nete catchment in Flanders, Belgium. Uncertainties were selected for investigation depending on how significantly they affected the model's decision variables. A Fault Tree was used to determine various combinations of inputs, mathematical code, and human error failures that could result in a specified risk. A combination of forward and backward approaches was used in developing the Fault Tree. Eleven events were identified as contributing to the top event. A total of 7 gates were used to describe the Fault Tree. A critical path analysis was carried out for the events and established their rank or order of significance. Three measures of importance were applied, namely the F-Vesely, the Birnbaum, and the B-Proschan importance measures. Model development of distributed models involves considerable uncertainty. Many of these dependencies arise naturally and their correct evaluation is crucial to the accurate analysis of the modelling system reliability.
Highlights
The quest for good practice in modelling merits thorough and sustained attention since good practice increases the credibility and impact of the information, and insight that modelling seeks to generate (Jakeman et al, 2006)
This paper presents the findings of an evaluation whose goal was to understand the uncertainty in applying a distributed hydrological model to the Grote Nete catchment in Flanders, Belgium
This paper presents the findings of an evaluation whose goal was to understand the uncertainty in applying a distributed hydrological model to the Grote Nete catchment in Belgium, which could be otherwise stated as the assessment of whether the model can do what is reasonably expected of it in representing the distributed hydrology of this catchment
Summary
The quest for good practice in modelling merits thorough and sustained attention since good practice increases the credibility and impact of the information, and insight that modelling seeks to generate (Jakeman et al, 2006). The evaluation was conducted as a first step in model development by a trial and error process whose aims included learning about the hydrologic characteristics of the study area, the information available for modeling this area, and MIKE SHE as the modeling tool used in distributed modeling of the study area. This was a site-specific evaluation, but whose outcome can be used in similar circumstances. This paper presents a case of the solution to the problem of building distributed models with the quality characteristics necessary for representation of the complex hydrology of a natural catchment
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