| 25 Apr 2025
Evaluation of a socio-hydrological water resource model for drought management in groundwater-rich areas
Abstract. Groundwater is a drought resilient source of water supply for many water users globally. Managing these highly-used groundwater stores is complicated by the episodic nature of droughts and by our limited understanding of water systems’ response to extreme events. Models are useful tools to simulate a range of prepared drought interventions, however, we need to ensure robust representation of surface water and groundwater storage, their users, and management interventions for drought resilience. A robust modelling approach is therefore essential for decision-making in groundwater management.
In this study, we present a Socio-Hydrological Water Resource (SHOWER) model for drought management in groundwater-rich regions. We evaluate SHOWER using a response-based and a data-based model evaluation in Great Britain which considers the modelling uncertainty, dynamic impact of management and modelling setups available. 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 simulations. Secondly, we tested the accuracy of heavily influenced discharge and groundwater level simulations in three catchments representative of typical hydrogeological conditions and water management practices in Great Britain (data-based evaluation). In the response-based method, we have found consistent simulations for all model setups and identified which parameters were influential to model output at what times. The data-based analysis shows that calibration can be focused on either source-specific or combined model outputs using a ‘best overall’ calibration approach that captures groundwater levels and low flows. The source-specific calibrations result in the highest and narrowest KGE ranges for discharge and groundwater (KGE: 0.75–0.84 and 0.62–0.95 respectively) with larger ranges using a `best overall' approach (KGE: 0.55–0.79 and 0.27–0.91). Integrated water management interventions have significant impact on flows and groundwater beyond parameter uncertainty and show leverage to reduce droughts by minimising shortages in water demand. With the modular and open-access structure of SHOWER we aim to provide a useful new tool for groundwater managers to explore their management interventions further, increasing drought resilience strategies using a robust modelling approach.
This study did a thorough evaluation of the SHOWER model for modeling groundwater responses to water management scenarios in real catchments in groundwater-rich areas. I found the modeling approach to be robust, well-documented and technically sound. I believe it is exemplary work of interest and value for HESS readers. I have two main comments.
My first comment: Clarify the knowledge gap. In the introduction, particularly around page 2, lines 40-75, and in Table S1, a variety of existing modeling approaches and limitations are introduced. At this point, it sounds like the paper simply combines and evaluates a groundwater model, rainfall-runoff model, and water management practices model, with a calibration including of management interventions in real catchments. If other models are already doing these, either individually or in combination, then the specific novel advancement of this work should be clearer. I suggest adding a little more background on specific models or cases that do similar things, potentially including those mentioned in Table S1 or models like SWAT-MODFLOW and ParFlow used in other areas. Then, describe more clearly how the SHOWER model and/or the analyses in this study go beyond the previous work to fill a specific knowledge gap (perhaps something related to water management and droughts in real catchments).
My second comment: Highlight results beyond evaluations. The study is very heavy on the technical aspects, and the results are essentially model evaluations, without highlighting further scientific theories or comparisons tested. This is evident in the title (“Evaluation of…”), as well as the methods and results section headers which only go to calibration and evaluation. I suggest bringing the results beyond model evaluation, uncertainties, and parameter sensitivities more prominence. As an idea for this, you could state and test a scientific hypothesis about the groundwater and water management interactions, like a case study in the real catchments to show a scientific application of the model. The paper is already set up with different management scenarios that are tested to see their hydrologic impacts on droughts compared to baseline (e.g., Figures S9 to S11, lines 17-18 in the abstract, and lines 455-458 in the conclusion), so it is possible that no new analyses need to be done, just reframing. Perhaps you could create a hypothesis in the last introduction paragraph (Third,…) about different management scenarios affecting things like drought duration and deficit in different geologies, and then put all these findings into a 3rd results section with a header clearly beyond evaluations (like “3.3. Management scenarios and drought impacts”). This could help show the theoretical advancements in real catchments to potential users of the model.
Minor comments:
Title: Consider updating the title to clarify the more novel scientific findings. Evaluation of a model in itself does not suggest the scientific advancement of the work to me, and I think it has the potential to be more generally impactful. Perhaps something like “Socio-hydrological model reveals how water resource management affects drought duration and deficit in real groundwater-rich catchments”
Page 2, lines 29-34. Consider condensing this to basically say highly managed groundwater systems are present around the globe, rather than elaborating on the individual regions. Though it’s fine with me if you prefer to keep it as-is.
Page 20, lines 360-370. I’m not sure what the scientific value of showing that the SHOWER model had similar or better discharge performance to other models is, particularly if those models were simulated using different approaches for factors like input data, time periods, observations, or calibrations. I suggest condensing this.
Page 22, lines 424-425. I suggest rewording as something more direct, like “However, this simplification creates the opportunity for modelers with insufficient time and computing resources for more expensive models to be able to calibrate the model and explore results in detail.”
Page 23, lines 455-458. I really like this conclusions statement. It shows the scientific implications and exciting capabilities of the work well.
I wish the authors the best with this manuscript and their future endeavors.