Are temporary stream observations useful for calibrating a lumped hydrological model?

Abstract

Multi-criteria model calibration can lead to a better representation of hydrological processes and reduce parameter uncertainty compared to calibration on streamflow data alone. However, the additional data may be difficult to collect or aggregate into a representative catchment average value that can be used to calibrate a lumped model. Temporary streams are highly dynamic, and their flow state can be observed visually. However, data on the state of temporary streams are still uncommon and rarely used in hydrological catchment modelling. In this study, we used a unique dataset with discrete flow state observations for temporary streams in France and evaluated how informative these data are for calibrating a lumped, bucket-type hydrological model. We calibrated the HBV model for 92 catchments using discharge or stream-level data at different temporal resolutions (daily, one daily value per month, or one daily value per season) and used the observed flow states of temporary streams as a proxy of groundwater storage. Temporary stream data generally did not result in a better overall discharge simulation for the validation period. For catchments for which the model performance based on the calibration on only discharge or stream-level data was poor, it was more likely to lead to an improvement in model performance. The use of temporary stream data in combination with discharge data reduced the uncertainties in the low-flow simulations for up to half of the catchments. This improvement was caused by a better-constrained storage coefficient for the slowest groundwater reservoir and the elimination of parameter sets that led to substantial variations in groundwater storage. However, the improvements in low-flow simulations or parameter uncertainty due to the inclusion of temporary stream data in model calibration were not related to catchment characteristics. Thus, it remains unclear for which catchments temporary stream data can help to improve low-flow simulations and reduce parameter uncertainty.