Structural calibration of an semi-distributed hydrological model of the Liard River basin

• Main Authors: Brown, Genevieve, Craig, James R.
• Format: Dataset
• Language: unknown
• Published: Taylor & Francis 2020
• Subjects: 59999 Environmental Sciences not elsewhere classified; FOS Earth and related environmental sciences; Ecology; FOS Biological sciences; 69999 Biological Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; FOS Computer and information sciences; 19999 Mathematical Sciences not elsewhere classified; FOS Mathematics; Plant Biology; Canada; Liard; Liard River
• Online Access: https://dx.doi.org/10.6084/m9.figshare.12865675; https://tandf.figshare.com/articles/dataset/Structural_calibration_of_an_semi-distributed_hydrological_model_of_the_Liard_River_basin/12865675

The development of hydrological models that produce practically useful and physically defensible results is an ongoing challenge in hydrology. This challenge is further compounded in large, spatially variable basins with sparse data, where a detailed understanding of a basin’s hydrological response may be limited. This study presents an iterative and stepwise calibration strategy for model structure and parameters for a hydrological model of the 275,000 km 2 Liard River basin in northern Canada. The calibration procedure was optimized to exploit and represent available data at 29 stream gauges and included the use of multiple data sources to constrain model calibration and improve model function. A flexible modelling framework was used to allow the explicit inclusion of locally varied model structure within the calibration procedure. The final model exhibits strong performance in both calibration and validation, and represents significantly different hydrological responses in different portions of the basin well. The calibration procedure helped to identify differences in hydrological processes within the basin which have not been considered by other models of the Liard. The ability to modify model structure in order to account for different hydrological regimes in different parts of the basin is demonstrated to improve model performance locally and globally.