Hydrodynamic models with rain-on-the-grid capabilities are usually
computationally expensive. This makes the use of automatic calibration
algorithms hard to apply due to the large number of model runs. However, with
the recent advances in parallel processing, computational resources, and
increasing high-resolution climatologic and GIS data, high-resolution
hydrodynamic models can be used for optimization-based calibration. This paper
presents a global optimization-based algorithm to calibrate a fully distributed
hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed
data (i.e., discharge, or pollutant concentration) as input. The algorithm can
find a near-optimal set of parameters to explain observed gauged data. The
modeling framework presented here, although applied in a poorly-gauged
catchment, can be adapted for catchments with more detailed observations. We
applied the algorithm in different cases of the V-Tilted Catchment, the
Wooden-Board catchment, and in an existing urban catchment with heterogeneous
data. The results of automatic calibration indicate NSE=0.99 for
the V-Tilted catchment, RMSE=830 mgL−1 for salt
concentration pollutographs (i.e., 8.3% of the event mean concentration), and
NSE=0.89 for the urban catchment case study. This paper also
explores the issue of equifinality in modeling calibration (EqMC). Equifinality
is defined as the set of different parameter combinations that can provide
equally good or accepted results, within the physical parameter ranges. EqMC
decreases with the number of events and increases with the choice of partially
or nonproducing runoff ones. Furthermore, results indicate that providing more
accurate parameter ranges based on a priori knowledge of the catchment is
fundamental to reduce the chances of finding a set of parameters with
equifinality.Comment: Preprint submitted to Journal of Hydrolog