Assessing the performance of a semi‐distributed hydrological model under various watershed discretization schemes. Issue 18 (14th July 2015) - Haghnegahdar, Amin; Tolson, Bryan A.; Craig, James R.; Paya, Karol T.; Quinton, William; Peters, Daniel L.
Abstract Physically based distributed and semi‐distributed hydrological models have become some of the primary tools for water resources studies and management over the past decades owing to increased computational capabilities and advances in data measurements. Representation of the existing heterogeneity in nature still remains one of the main challenges in these models and is accomplished primarily via watershed discretization, subdividing watersheds into hydrologically similar land parcels. Discretization decisions in distributed modelling studies are often ad hoc and determined with little or no quantitative analysis to support these decisions. In this work, we present a quantitative methodology for assessing alternative watershed discretization schemes in terms of their corresponding model performance in ungauged basins. The effect of the computational time spent for calibrating each scheme (calibration budget) is considered as part of the assessment. Here, these schemes differ in how they represent landscape attributes and range from a simple lumped scheme to more complex ones by adding spatial land cover and then soil information. The methodology is demonstrated using the Modélisation Environmentale–Surface et Hydrologie (MESH) model as applied to the Nottawasaga River basin in Ontario, Canada. Results reveal that model performance in ungauged basins depends upon the location of the validation sub‐basin (i.e. upstream or downstream) with respect to the calibration sub‐basins. Also, using a more complex scheme did not necessarily lead to improved performance in validation, when constrained by calibration budget. Therefore, the calibration budget also should be considered as a factor in the assessment process. This methodology was also implemented using a shorter sub‐period for calibration, which leads to substantial computational saving. Results of the sub‐period test were promising and consistent particularly when sufficient budget is spent to calibrate the model. Other strategies utilized for reducing the computational burden of the proposed analyses are also discussed in this study. Copyright © 2015 John Wiley & Sons, Ltd.