Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices

Due to the significant costs associated with stream health monitoring, hydrological modeling is widely used to calculate ecologically-relevant hydrologic indices to better understand the overall condition of streams within large and diverse watersheds. However, hydrologic modeling's ability to replicate these indices is limited, especially when calibrating models by optimizing a single objective function or when selecting a single optimal solution. Hence, this study evaluates the performance of multi-objective model calibration in representing 167 hydrologic indices of ecological interest using the median values of different Pareto-optimal solution sets. For this purpose, two strategies based on three Nash-Sutcliffe Efficiencies (NSE), and root-mean-squared error (RMSE) with explicit hydrograph partitioning, were implemented. Additionally, the k-means clustering technique was employed to define subsets of Pareto-optimal solutions representing high, medium, and low flow conditions. The Soil and Water Assessment Tool (SWAT) was set up for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA, and was calibrated using a single streamflow gauging station and the recently proposed multi-objective optimization algorithm - Non-dominated Sorting Genetic Algorithm III (NSGA-III). The results show that the NSE- and RMSE-based calibration strategies can represent 128 and 123 indices within a range of ±30% relative errors, respectively. In addition, the results of this study demonstrated that using different set of solutions, instead of a single optimal solution, introduces more flexibility in the predictability of different hydrologic indices of ecological interest. Moreover, it is suggested that a multi-objective calibration approach allows the systematic identification of groups of poorly represented and closely related indices. Knowing these poorly represented indices would facilitate the formulation of additional objective functions intended to improve model performance or to detect structural inadequacies.