Groundwater modelling in SWAT+: Considerations for a realistic baseflow simulation

Abstract

Hydrological models are valuable tools that support the evaluation and management of water resources. Among catchment scale models, SWAT has been widely used, and a revision of this model, SWAT+, was recently released. Groundwater simulation has been pointed out as one of the main weaknesses of these models, and despite the introduction of some improvements in the new version, an accurate simulation of groundwater flow is still a pending task. One of the most common issues is the inability of the model to maintain baseflow during long dry periods. Baseflow is common in aquifer areas and has great relevance for water quality and ecosystems. Although some authors proposed different solutions to its inaccurate simulation (e.g., editing aquifers configuration, coupling with other models, etc.) this work aimed to determine if a realistic groundwater simulation is possible using SWAT+ and determine the reasons why this is not being achieved. For this purpose, a groundwater dominated catchment was modelled, and different calibration approaches were carried out and compared, including strategies such as hard calibration, soft calibration, or sensitivity analysis. A comprehensive analysis of model outputs was crucial to achieve a model with a realistic simulation of groundwater contribution to the streamflow, both in amount and timing. The properly parameterization of some parameters (perco, latq_co, lat_ttime, and especially alpha) was key. Limitations of hard calibration were evidenced, such as the necessity of complementing this strategy with an evaluation of the simulation of hydrological processes. For those parameters governing streamflow components simulation, a detailed description of its functioning is included in this manuscript, together with some guidelines to achieve a statistically accurate model with a realistic groundwater flow simulation. Follow these recommendations will be useful for SWAT users, and will serve to create more reliable models.