SHOWER: A tool for groundwater drought management

Preparing for drought conditions is complicated by the episodic nature of droughts and by our limited understanding of water systems’ response to extreme events. In this, models are useful tools to simulate a range of plausible to low likelihood drought conditions. Water managers may use these simulations to make plans and consider consequences for both normal and extreme drought events. However, critical in this is the representation of water system resilience to drought conditions and simulated management decisions to in/decrease drought resilience. Decision-making in groundwater management could herein benefit from a robust modelling approach that considers the complexity and uncertainty in water availability, dynamic impact of management and modelling setups available.

In this study, we have converted a lumped conceptual socio-hydrological model to an operational tool to support groundwater management in Great Britain by applying a response-based and a data-based model evaluation.  In the response-based evaluation, we first examined the model consistency with our understanding of the system functioning, and the influence of modelled management scenarios on model predictions. In the data-based evaluation, we tested the accuracy of heavily influenced discharge and groundwater level predictions in three catchments representative of typical hydrogeological conditions and water management practices in Great Britain.

Results show consistent simulations across catchments and identified pointers for influential model parameters in drought conditions. Modelled water management interventions have varying influence on simulated model output. Most effective drought management scenarios have (elements of) integrated water storage use, which minimises shortages in water demand. The data-based analysis shows that calibration can be focused on either low flows or groundwater storage, with reasonable results for both model outputs. We provide a source-specific and ‘best overall’ calibration approach that capture groundwater levels and low flows, which also indicates how model parameters (dis)agree with open-source data and our model perception of the modelled water system.