05 Mar 2024
 | 05 Mar 2024

Equifinality Contaminates the Sensitivity Analysis of Process-Based Snow Models

Tek Kshetri, Amir Khatibi, Yiwen Mok, Shahabul Alam, Hongli Liu, and Martyn P. Clark

Abstract. This study assesses the impact of different flux parameterizations and model parameters on simulations of snow depth. Through a sensitivity analysis in a process-based snow model based on the SUMMA framework, various options for parametrizing snow processes and adjusting parameter values were evaluated to identify optimal modeling approaches, understand sources of uncertainty, and determine reasons for model weaknesses. The study focused on model parameterizations of precipitation partitioning, liquid water flow, snow albedo, atmospheric stability, and thermal conductivity. In this study, sensitivity analysis (SA) is performed using the one-at-a-time (OAT) SA method as well as the Morris Method to estimate Elementary Effects, aiming to further explore the magnitudes and patterns of sensitivities. The sensitivity analyses in this study are used to evaluate process parameterizations, model parameter values, and model configurations. Performance metrics such as the Nash-Sutcliffe Efficiency (NSE), the Kling-Gupta Efficiency (KGE), the root mean squared log error (RMSLE), and mean are used to assess the similarity between simulated and observed data. Bootstrapping is employed to estimate the variability of mean Elementary Effects and establish confidence bounds. The key findings of this research indicate that sensitivity analysis of snow modelling parameters plays a crucial role in understanding their impact on decision outcomes. The study identified the most sensitive parameters, such as critical temperature and thermal conductivity of snow, as well as liquid water drainage parameters. It was observed that water balance fluxes exhibited higher sensitivity than energy balance fluxes in simulating snow processes. The analysis also highlighted the importance of accurately representing water balance processes in snow models for improved accuracy and reliability. A key finding in this study is that the sensitivity of performance metrics to model parameters is contaminated by equifinality (i.e., parameter perturbations lead to similar performance metrics for quite different snow depth time series), and hence many published parameter sensitivity studies may provide misleading results. These findings have implications for snow hydrology research and water resource management, providing valuable insights for optimizing snow modelling and enhancing decision-making.

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Tek Kshetri, Amir Khatibi, Yiwen Mok, Shahabul Alam, Hongli Liu, and Martyn P. Clark

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-3049', Steven Markstrom, 13 May 2024
    • AC2: 'Reply on RC1', Tek Kshetri, 02 Jul 2024
  • RC2: 'Comment on egusphere-2023-3049', Francesca Pianosi, 05 Jun 2024
    • AC1: 'Reply on RC2', Tek Kshetri, 02 Jul 2024
Tek Kshetri, Amir Khatibi, Yiwen Mok, Shahabul Alam, Hongli Liu, and Martyn P. Clark
Tek Kshetri, Amir Khatibi, Yiwen Mok, Shahabul Alam, Hongli Liu, and Martyn P. Clark


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Short summary
This study reveals a crucial discovery: when tweaking model parameters, similar performance metrics might mislead—different parameter settings can yield comparable results in snow depth predictions. This "equifinality" challenges past studies, suggesting that evaluating model tweaks based on performance alone might not reflect actual variations in snow depth forecasts.