Abstract
ABSTRACT Hydrological models can benefit from soft calibration, a process by which the proper simulation of hydrological variables is proved while or before addressing hard calibration. Soft calibration reduces the probability of obtaining a statistically accurate but unrealistic model. However, it requires soft data, which is often hard to acquire or unavailable. This work presents HydRoVars, an R tool developed to facilitate the estimation of data which can be implemented in a soft calibration procedure. It allows us to estimate two key hydrological indices (the runoff coefficient and baseflow index) and weather-related variables at the catchment scale for one or numerous basins. The runoff coefficient is calculated automatically from precipitation and streamflow datasets. Groundwater contribution is estimated through a semi-automatic process based on a baseflow filter which considers hydrogeological properties. Modellers would benefit from incorporating soft calibration in their calibration procedures, and this tool might help to estimate these relevant hydrological variables in their modelled area. The tool has been tested in 19 subbasins of the Tagus River basin (Spain) located in different geological regions. In the test cases, we demonstrate the usefulness of this tool to improve the model representation and gain an understanding of the catchments' hydrology.
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