Source: Water Resources Research
The Earth is a dynamic system made up of interconnected components including the atmosphere, hydrosphere, lithosphere and biosphere. Due to the inherent complexity of this system, computer models with tens to hundreds of parameters are needed to unravel the interactions that occur between these domains and to explore potential future changes.
As Earth system models become more sophisticated, there is a growing need for increasingly efficient methods to perform sensitivity analyses; these tools are used to assess how each input factor affects the output of the model.
But in a new study, Gupta and Razavi argue that conventional methods based on performance measures currently used to assess model sensitivity are based on flawed reasoning. The team revisited the theoretical basis of sensitivity analysis for dynamic Earth system models; after reviewing the fundamentals, they developed a new theoretical approach that offers an accurate assessment of parameter significance for individual timesteps and cumulative runs of the model.
The team then demonstrated the potential of this new approach with a case study that analyzes the sensitivity of a conceptual hydrological model’s response to changes in its 10 parameters when applied to the Oldman Basin in the Canadian Rockies. Their results indicate that conventional approaches based on performance metrics and new approaches without metrics differ significantly when ranking parameters that strongly influence model output.
These results highlight the importance of re-examining the basis of sensitivity analyzes to improve our understanding of the Earth system, as well as the models used to represent it. Because this new approach is computationally efficient, the authors argue that it is widely applicable, with historical conditions as well as predictive scenarios. (Water resources research, https://doi.org/10.1029/2018WR022668, 2018)
—Terri Cook, freelance writer
Cook, T. (2019), Reframing Sensitivity Analysis in Earth System Models, Eos, 100, https://doi.org/10.1029/2019EO116217. Published February 21, 2019.
Text © 2019. The authors. CC BY-NC-ND 3.0
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