Preprints
https://doi.org/10.5194/hess-2023-21
https://doi.org/10.5194/hess-2023-21
 
24 Jan 2023
24 Jan 2023
Status: this preprint is currently under review for the journal HESS.

Towards reducing the high cost of parameter sensitivity analysis in hydrologic modelling: a regional parameter sensitivity analysis approach

Samah Larabi1, Juliane Mai2, Markus Schnorbus1, Bryan Tolson2, and Francis Zwiers1 Samah Larabi et al.
  • 1Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada
  • 2University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, Ontario, Canada

Abstract. Land surface models have many parameters that have a spatially variable impact on model outputs. In applying these models, sensitivity analysis (SA) is sometimes performed as an initial step to select calibration parameters. As these models are applied on large domains, performing sensitivity analysis across the domain is computationally prohibitive. Here, using a VIC deployment to a large domain as an example, we show that watershed classification based on climatic attributes and vegetation land cover helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. We evaluate the sensitivity of 44 VIC model parameters with regard to streamflow, evapotranspiration and snow water equivalent over 25 basins with a median size of 5078 km2. Basins are clustered based on their climatic and land cover attributes. Performance of transferring parameter sensitivity between basins of the same cluster is evaluated by the F1 score. Results show that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a target basin, with F1 scores ranging between 0.66 (evapotranspiration) to 1 (snow water equivalent). While climatic attributes are sufficient to identify sensitive parameters for streamflow and evapotranspiration, including vegetation class significantly improves skill in identifying sensitive parameters for snow water equivalent. This work reveals that there is opportunity to leverage climate and land cover attributes to greatly increase the efficiency of parameter sensitivity analysis and facilitate more rapid deployment of land surface models over large spatial domains.

Samah Larabi et al.

Status: open (until 21 Mar 2023)

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Samah Larabi et al.

Samah Larabi et al.

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Short summary
The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modelling domains. Here, using a large-scale VIC deployment, we show that watershed classification helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.