Section 1: Publication
Larabi Samah, Schnorbus Markus A.
A process-based sensitivity guided calibration of the VIC model
Samah Larabi, Markus A. Schnorbus (2022). A process-based sensitivity guided calibration of the VIC model. Proceedings of the GWF Annual Open Science Meeting, May 16-18, 2022.
Land surface models have large numbers of parameters the sensitivities of which can vary spatially over large spatial domains. In past applications of these models over large domains, calibration has often relied on adjustment of few select parameters that are assumed to have consistent sensitivity regardless of the presence of large hydro-climatic gradients. This approach limits the flexibility of the model to operate effectively under varying hydro-climatic conditions (i.e., model agility). In this study, we explore a sensitivity guided process-based calibration applied to the Variable Infiltration Capacity Model (VIC) over five basins with dissimilar hydroclimatic conditions. A global sensitivity analysis was used to evaluate the sensitivity of 44 VIC parameters to streamflow, evapotranspiration, and snow cover area. This study shows that expanding parameter calibration beyond the traditionally selected parameters improves model performance to simulate internal hydrologic processes. Regardless of the hydroclimatic conditions, fifteen parameters are consistently recommended for calibration which includes eleven soil parameters and four vegetation parameters (root fractions, spring LAI and minimum stomatal resistance). The recommended soil parameters are the thickness of the three soil layers, maximum velocity of baseflow i.e., dsmax, the fraction of dsmax where non-linear baseflow begins, variable infiltration curve parameter, fraction of maximum soil moisture where non-linear baseflow occurs, bulk density, fractional soil moisture content at the critical point and residual moisture. Additional parameters should also be considered depending on the dominance of hydrological processes, such as snow parameters in snow-dominated regions. Overall, this study shows that calibration guided by a multi-objective sensitivity analysis improves model agility and accuracy.
Plain Language Summary
Section 2: Additional Information
GWF: Global Water Futures
GWF-CORE: Core Modelling and Forecasting
Pacific Climate Impacts Consortium, University of Victoria
Hydrology and Terrestrial Ecosystems
AOSM2022 Core Modelling and Forecastin Team First Author: Samah Larabi, Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada Additional Authors: Markus A. Schnorbus, Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada