A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments

Abstract

The simultaneous incorporation of streamflow and evaporation data into sensitivity analysis and calibration approaches has great potential to improve the representation of hydrologic processes in modelling frameworks. This work aims to investigate the capabilities of the Variable Infiltration Capacity (VIC) model in a large-sample application focused on the joint integration of streamflow and evaporation data for 189 headwater catchments located in Spain. The study has been articulated into three parts: (1) a regional sensitivity analysis for a total of 20 soil, routing, and vegetation parameters to select the most important parameters conducive to an adequate representation of the streamflow and evaporation dynamics; (2) a two-fold calibration approach against daily streamflow and monthly evaporation data based on the previous parameter selection for VIC; and (3) an evaluation of model performance based on a benchmark comparison against a well-established hydrologic model for the Spanish domain and a cross-validation test using multiple meteorological datasets to assess the generalizability of the calibrated parameters. The regional sensitivity analysis revealed that only two vegetation parameters – namely, the leaf area index and the minimum stomatal resistance – were sufficient to improve the performance of VIC for evaporation. These parameters were added to the soil and routing parameter during the calibration stage. Results from the two calibration experiments suggested that, while the streamflow performance remained close in both cases, the evaporation performance was highly improved if the objectives for streamflow and evaporation were combined into a single composite function during optimization. The VIC model outperformed the reference benchmark, and the independent meteorological datasets yielded a slight to moderate loss in model performance depending on the calibration experiment considered. Results from this investigation provide valuable insights into VIC parameter sensitivities, with a particular focus on large-sample applications, and highlight the importance of integrating multiple datasets into model calibration as a measure to reduce model equifinality.