the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Apr 2025
Physics-based simulation of hydrological processes in a high-elevation glaciated environment focusing on groundwater
Abstract. Understanding the role of groundwater is crucial to improving the quantification of the hydrological response to climate change in high-elevation glaciated environments. However, few studies have been conducted due to the lack of in-situ hydroclimatic observations, the complex topography, and the difficulty of characterizing surface-subsurface water exchange processes in these terrains. In this study, we adopt a fully-distributed, physics-based hydrological model, WaSiM, with an integrated 2-dimensional groundwater module to quantify the observed streamflow variations and their interactions with groundwater in a high-elevation glaciated catchment (Martell Valley) in the central European Alps since the 2000s. Extensive field observations (meteorology, vegetation, glacier mass balance, soil properties, groundwater levels, river discharge) are collected to analyze hydrological processes and to constrain the model parameters. We observe that shallow alpine groundwater levels respond nearly as quickly as streamflow to snowmelt and heavy rainfall inputs, as their measured hydrographs show. Because hydrological models rarely simulate this quick groundwater response, this highlights the need for improved subsurface parametrization in hydrological modeling. Surprisingly, subsurface lateral flow plays a minor role in river discharge generation at the study site, providing new insights into the hydrological processes in such an environment. Lastly, our results underline the challenges of integrating point-scale groundwater observations into a distributed hydrological model, with important implications for future piezometer installation in the field. This study sheds new light on surface-subsurface hydrological processes in high-elevation glaciated environments. It highlights the importance of improving subsurface representation in hydrological modeling.
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Status: open (until 21 May 2025)
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CC1: 'Comment on egusphere-2025-1500', Nima Zafarmomen, 11 Apr 2025
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- The reliance on manual calibration for a complex, fully-distributed model with numerous parameters is a significant limitation. While the authors justify this choice due to computational constraints and data availability, the manuscript would benefit from a more thorough discussion of the implications of manual calibration on parameter uncertainty and model robustness. For instance, how sensitive are the key findings (e.g., minor role of subsurface lateral flow) to parameter choices? A sensitivity analysis, even if limited, could strengthen the credibility of the results.
- The multi-signal calibration approach is a strength, but the sequential calibration process (surface to subsurface) may introduce biases. The authors should discuss potential dependencies between modules (e.g., snow module influencing groundwater recharge) and how these were addressed to ensure consistency across the calibration steps.- The finding that subsurface lateral flow plays a minor role in streamflow generation is intriguing but requires further scrutiny. The assumption of homogeneous subsurface properties (e.g., hydraulic conductivity, storage coefficient) across the catchment may oversimplify the complex geology of the Martell Valley, which is noted to be heterogeneous (Section 2). This assumption could bias the model toward underestimating lateral flow. The authors should explore whether spatially variable subsurface parameters, informed by available geological data, could alter this conclusion.
- The model’s inability to reproduce the strong winter recession at borehole ID 4478 (Section 5.3) suggests limitations in capturing preferential flow paths or other subsurface processes. The manuscript would benefit from a deeper discussion of alternative mechanisms (e.g., macropore flow, fractured bedrock) that could explain this discrepancy, potentially supported by literature or additional field observations.- The challenges of integrating point-scale groundwater observations into a distributed model are well-articulated, but the proposed solutions (e.g., using TWI to guide piezometer placement, comparing neighboring cells) need more rigorous evaluation. For instance, how representative are the TWI-based recommendations for other high-alpine catchments with different topographic or geologic characteristics? A sensitivity analysis of TWI resolution or comparison with other topographic indices could enhance the generalizability of these recommendations.
- The manuscript highlights the mismatch between observed and modeled river networks due to DEM uncertainties (Section 5.6.3). This issue could significantly affect groundwater-surface water interactions, yet it is only briefly addressed. A quantitative assessment of DEM uncertainty (e.g., comparing simulations with different DEM resolutions) would strengthen the discussion and provide more concrete guidance for future studies.- The authors note that the Martell Valley is relatively dry compared to other Alpine catchments (Section 5.6.2), which may limit the applicability of findings to wetter environments. Similarly, the lithology (crystalline bedrocks, shallow soils) may not be representative of other high-alpine settings. The discussion should more explicitly address the conditions under which the key findings (e.g., minor role of lateral flow, rapid groundwater response) are likely to hold, potentially by comparing with studies in contrasting catchments.
- The manuscript claims that the rapid groundwater response is rarely simulated by hydrological models (Section 6), but this statement requires more substantiation. A brief review of other physics-based models (e.g., HydroGeoSphere, ParFlow) and their ability to capture such dynamics would contextualize the novelty of WaSiM’s performance and clarify the need for improved subsurface parameterization.- The underestimation of winter baseflow (Sections 5.4, 6) is attributed to shallow river channels and homogeneous subsurface parameterization, but observational uncertainties in low-flow measurements (e.g., sensor limitations in freezing conditions) are also significant (Section 5.6.4). The manuscript should more clearly disentangle model limitations from observational uncertainties, possibly by discussing the reliability of winter discharge data or exploring alternative data sources (e.g., tracer studies) to validate baseflow contributions.
- The claim that baseflow contributes significantly to winter streamflow (up to 40% in some subcatchments, Section 5.4) is compelling but relies on model simulations rather than direct observations. Additional evidence, such as isotopic or chemical tracers, could corroborate this finding and enhance confidence in the model’s representation of baseflow dynamics.
- The introduction is comprehensive but lengthy, with some repetition (e.g., challenges of alpine hydrology are mentioned multiple times). Streamlining the introduction to focus on key gaps and the study’s objectives would improve readability.
- Section 5.6 is titled “Challenges and opportunities for modeling high-alpine glaciated environment,” but it primarily discusses challenges. Explicitly addressing opportunities (e.g., leveraging remote sensing, integrating machine learning for parameter estimation) would balance the narrative and highlight future research directions.
- The use of abbreviations (e.g., PEQ, TWI, DTW) is frequent, and a glossary or table defining these terms would aid readers unfamiliar with the terminology.- Figure 8 is visually rich but overwhelming due to the number of panels. Consider splitting it into two figures (e.g., one for percolation/recharge, another for groundwater level/exfiltration/infiltration) or using a subset of months to improve clarity.
- Table 3 and Table 4 list calibrated parameters but lack units for some parameters (e.g., “Scaling for max.deposition” in Table 3). Ensuring consistency in units and providing brief explanations for less intuitive parameters would enhance accessibility.
- The caption for Figure 5 could clarify that panels (c-d) show simulations for multiple grid cells, as this is not immediately obvious from the figure alone.I highly recommend to discuss the SWAT-MODFLOW papers which integrates SW-GW and cite below paper:
Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districtsCitation: https://doi.org/10.5194/egusphere-2025-1500-CC1
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