Preprints
https://doi.org/10.5194/hess-2023-143
https://doi.org/10.5194/hess-2023-143
11 Aug 2023
 | 11 Aug 2023
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

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, Mélanie Trudel, and Robert Leconte

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-143', Anonymous Referee #1, 01 Oct 2023
    • AC1: 'Reply on RC1', Dipti Tiwari, 28 Nov 2023
  • RC2: 'Comment on hess-2023-143', Anonymous Referee #2, 30 Oct 2023
    • AC2: 'Reply on RC2', Dipti Tiwari, 28 Nov 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-143', Anonymous Referee #1, 01 Oct 2023
    • AC1: 'Reply on RC1', Dipti Tiwari, 28 Nov 2023
  • RC2: 'Comment on hess-2023-143', Anonymous Referee #2, 30 Oct 2023
    • AC2: 'Reply on RC2', Dipti Tiwari, 28 Nov 2023
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Dipti Tiwari, Mélanie Trudel, and Robert Leconte

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Latest update: 22 Feb 2024
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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.