the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Enhancing physically based and distributed hydrological model calibration through internal state variable constraints
Abstract. This study investigates the effectiveness of various calibration approaches within the Water Balance Simulation Model (WaSiM) to enhance the representation of hydrological variables. We assess the impact of three distinct configurations: Baseline (BL), Physical Groundwater Model (GW), and Physical Groundwater with Recharge Calibration (GW-RC) on the representation of hydrological variables. The analysis demonstrates that while traditional calibration primarily enhances streamflow prediction, integrating recharge and groundwater dynamics significantly refines the model’s ability to depict subsurface processes. The GW-RC configuration, with minimal emphasis on recharge in the objective function, shows a marked improvement in representing both the spatial and seasonal variability of groundwater recharge, suggesting that even small and targeted calibration adjustments can significantly enhance the accuracy and realism of model outputs. Although this approach may reduce the model’s flexibility in mirroring observed streamflow, it enhances the precision with which other hydrological processes are represented, providing a more accurate reflection of watershed dynamics. Our findings underscore the importance of multi-variable calibration frameworks, which incorporate both streamflow and internal hydrological variables, in developing robust models capable of adapting to anticipated hydrological shifts due to climate change. This approach provides a more accurate reflection of watershed dynamics and offers valuable insights for calibration strategies in hydrological modelling, water resource management and climate adaptation strategies.
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Status: open (until 30 Dec 2024)
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CC1: 'Comment on egusphere-2024-3353', Nima Zafarmomen, 23 Nov 2024
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The integration of internal hydrological state variables, particularly groundwater recharge, into model calibration is commendable and addresses key limitations in traditional hydrological modeling.
The chosen weights for the constrained Kling-Gupta efficiency (e.g., 70% KGE, 20% recharge standard deviation) appear somewhat arbitrary. A sensitivity analysis to justify these weights would enhance the study’s robustness.
While the paper briefly mentions equifinality, a more in-depth exploration of how incorporating internal state variables addresses this challenge would strengthen the theoretical contribution.
The high computational demands of the GW-RC configuration are not discussed in detail. Including a section on computational trade-offs would provide valuable insights for practitioners.
Data assimilation is a powerful technique widely used to integrate observations into hydrological models, improving predictions by dynamically updating model states. In this study, the authors propose an innovative calibration approach focusing on internal state variables, which aligns well with the goals of improved process representation. However, the absence of a discussion or application of data assimilation leaves an unexplored opportunity to further enhance the model's performance. then I strongly suggest to cite below papers:
"assimilation of Sentinel-based leaf area index for surface-groundwater interaction modeling in irrigation districts"
'Multivariate Assimilation of Satellite-based Leaf Area Index and Ground-based River Streamflow for Hydrological Modeling of Irrigated Watersheds using SWAT+'
Citation: https://doi.org/10.5194/egusphere-2024-3353-CC1
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