11 Aug 2023
 | 11 Aug 2023
Status: this preprint is currently under review for the journal HESS.

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Dipti Tiwari, Mélanie Trudel, and Robert Leconte

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow parameters such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The future Terrestrial Snow Mass Mission (TSMM) aims to provide high-resolution spatially distributed SWE information; thus, spatial SWE calibration should be considered along with conventional streamflow calibration for model optimization since the overall water balance is often a key objective in the hydrological modelling. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration approach of hydrological models and attempts to determine whether raw SNODAS data can be utilized for hydrological model calibration. The SPAtial Efficiency (SPAEF) metric is explored for spatially calibrating SWE. The HYDROTEL hydrological model is applied to the Au Saumon River Watershed (∽1120 km2) in Eastern Canada using MSWEP precipitation data and ERA-5 land reanalysis temperature data as input to generate high-resolution SWE and streamflow. Different calibration experiments are performed combining Nash-Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE), and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Multi-Objective Optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance. Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling-Gupta Efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model.

Dipti Tiwari et al.

Status: open (until 23 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Dipti Tiwari et al.

Dipti Tiwari et al.


Total article views: 450 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
342 98 10 450 17 5 7
  • HTML: 342
  • PDF: 98
  • XML: 10
  • Total: 450
  • Supplement: 17
  • BibTeX: 5
  • EndNote: 7
Views and downloads (calculated since 11 Aug 2023)
Cumulative views and downloads (calculated since 11 Aug 2023)

Viewed (geographical distribution)

Total article views: 425 (including HTML, PDF, and XML) Thereof 425 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 26 Sep 2023
Short summary
Calibrating hydrological models with multiple objective functions enhances model robustness. By integrating spatially distributed snow information into the calibration process, the overall performance of the model can be enhanced without compromising the model outputs. In this study, HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (SPAtial Efficiency metric) alongside NSE and RMSE, with the aim of identifying the optimal calibration strategy.