the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Oct 2025
Exploring groundwater-surface water interactions and recharge in fractured mountain systems: an integrated approach
Abstract. This study presents an integrated approach to map groundwater-surface water (GW-SW) interactions in a scarcely anthropized Mediterranean mountain catchment (Ussita) characterized by fractured limestone rocks with complex spatial-temporal patterns of hydrological processes. Understanding GW contributions to streams like the Ussita is crucial for addressing environmental challenges, including water resources management and evaluating ecological flows to protect aquatic ecosystems. The use of traditional hydrological techniques, such as discharge measurements along various stream stretches, combined with hydrochemical-isotopic analyses and innovative thermal drone investigations, allowed us to quantify the specific contributions of different limestone aquifers in sustaining streamflow. Integrating satellite-based meteorological datasets with in-situ observations further helped to constrain the water budget and assess the extent of the recharge area. Hydrogeochemical data analyses also revealed that the contribution of snow melt to aquifer recharge is about 20%, which is an important issue to consider for GW availability in case of future spatial-temporal changes in snow patterns. These findings can support further studies in other catchments by guiding and optimizing field campaigns to identify site-specific conditions responsible for GW inflow, from the point source to the stream stretch. Moreover, the results can help optimize resource management, mitigate climate-related risks, and support the long-term sustainability of both upstream and downstream socio-ecological systems.
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RC1: 'Comment on egusphere-2025-4368', Anonymous Referee #1, 25 Oct 2025
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AC1: 'Reply on RC1', Lucio Di Matteo, 06 Nov 2025
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Response to Reviewer#1
We appreciate the Reviewer's valuable feedback and insights, which will help improve some aspects of the manuscript in the revised version.
General comments: The work of Ortenzi et al. presents an interesting dataset for studying mountain catchments in the Mediterranean area. I personally found the introduction and methodology sections rather long, and some basic concepts could easily be moved to the Supporting Information for less experienced readers. Conversely, the description of the monitoring and measurement concepts should be expanded, focusing on important aspects such as the use of the data in the study and their associated uncertainty. In particular, it should be explained why discrete monitoring was performed on those specific dates, why only one thermal image was taken, and whether the temporal and spatial resolution are sufficient to derive robust conclusions. This should be a key point in explaining why other researchers should follow the proposed approach when conducting similar investigations in other catchments. The presentation of the results and the discussion could benefit from more detailed analyses and a more critical assessment of the authors’ conclusions. In my view, the data analysis is somewhat questionable: on one hand, I appreciate its simplicity, but on the other, it should not be oversimplified.
Answer: We agree with the Reviewer that some basic concepts should be moved to the Supporting Information. In the revised manuscript, we will follow up on the suggestion. Moreover, we will enhance the manuscript by incorporating considerations about measurement and estimation uncertainties. See also below our considerations in relation to the other points raised by the reviewer:
Discharge measurements: As reported in Section 2.2.1 (lines 149-150), we mainly used continuous discharge data collected from two stream sections with reliable rating curves. We have chosen the spot stream discharge measurements dates in periods mostly falling within the recession curve (cf., red dots in Fig. 5): it was possible by checking remotely the stream-level dataloggers (placed in stream sections S2 and S5 in Fig. 1). Spot measuremets by the OTT MF Pro also allowed us to assess the performance of the rating curves during the recession phase, where baseflow contribution is dominant. Combining continuous and discrete monitoring enabled us to develop robust conclusions, which help us understand stream segments primarily fed by groundwater moving from the stream headwaters toward section S5.
Drone flight: Regarding the use of a single thermal drone flight, it should not be seen as a lack of information, but rather as an optimization of the cost/benefit ratio to achieve the best possible result, given the multiple factors involved in a complex, mountainous region. We have a new drone flight conducted in summer 2025, the results of which will be included in the revised version of the manuscript. In any case, the thermal image we presented supported the findings of the discharge data analysis, enabling the identification of the main inflow on both the right and left banks of the stream. It should be noted that the drone flight was carried out in winter 2025, when vegetation effects were negligible. Since groundwater temperatures are often more stable than those of the surrounding land (including stream water), the absence of vegetation makes this contrast easier to see. The main groundwater inflow points into the stream were carefully identified using a drone survey and subsequently confirmed by visual inspections. In these conditions, stream discharge data, coupled with thermal drone imagery, enabled robust conclusions about the location of stretches characterized by groundwater inflow, thereby defining geochemical sampling points. In the discussion section, we will add new text to emphasize that, in the mountain region, the best practice is to acquire basic, insightful information to understand the hydrogeological system. Overall, we believe that the data analyses are not oversimplified and yield robust conclusions that can be easily incorporated into water management systems.
Comment 1: a critical uncertainty quantification and propagation to the derived quantities.
Answer: We thank the Reviewer for this valuable comment. We fully agree that an uncertainty quantification and its propagation to the derived quantities would provide a more robust assessment of the water balance components. In the revised version of the manuscript, we will better highlight and discuss this aspect, providing possible directions for how such analyses could be implemented in future studies.
However, a comprehensive uncertainty analysis is beyond the scope of the present work, which primarily aims to present an integrated and preliminary quantitative framework for investigating surface–groundwater interactions in complex basins. The manuscript focuses on demonstrating which instruments, methods, and datasets can support this type of analysis, and what kind of insights can be derived.
To provide at least a preliminary quantification of uncertainty, we will add one or more additional datasets in the revised version, which expand the ensemble of possible water balance estimates and yield a more robust characterization of the overall uncertainty. We emphasize that this represents a basic approach, while more sophisticated methods could further improve the quantification. Nonetheless, maintaining this level of simplicity is appropriate given the length and scope of the manuscript.
Comment 2: Although the authors’ conclusions may be correct, the fact that many data are collected at a relatively coarse spatial and temporal scale is an issue that should be discussed more critically and taken into consideration.
Answer: We thank the Reviewer for this constructive comment. We agree that the spatial and temporal resolution of some datasets represents a limitation that should be discussed more critically. In the revised version of the manuscript, we will explicitly address this aspect in the Discussion section.
Note that most of the datasets used in our analysis (e.g., MCM, IT-SNOW, and MOD16A2) have relatively fine spatial resolutions of about 1 km (MCM) and 500 m (IT-SNOW and MOD16A2), which are among the most accurate and detailed currently available for the study area. Only a few datasets, such as MERIDA, IMERG, ERA5, and EUMETSAT LSA SAF have coarser resolutions exceeding 5 km, which may introduce additional uncertainty and smoothing effects in the spatial variability of hydrological processes.
We will also verify, during the revision process, whether more recent or higher-resolution datasets have become available and could be incorporated or compared to improve the analysis.
Comment 3: It is somewhat disappointing to see a study focusing on groundwater–surface water interactions that does not actually include data (particularly high-resolution time series) from piezometers. It is also unclear how the spring water was sampled and how the springs were gauged. I do not see a dataset that truly captures the spatio-temporal dynamics of groundwater–surface water interactions; rather, it seems to describe the contribution of the springs to the river. But what about the contribution of the river to the groundwater system? Where is the interaction?.
Answer: We respectfully disagree with the Reviewer on this point. As clearly stated in the manuscript, the Ussita stream is primarily sustained by groundwater in specific stream stretches, as confirmed by the BFI computed from streamflow data recorded at stream sections S2 and S5 (BFI values of 80% and 90%, respectively). It is a key point because the analysis of stream discharge during no-recharge periods provides information on the groundwater system without the need for piezometric data, which, as stressed, are challenging to collect in mountainous areas since piezometers must be drilled at high depth to reach the piezometric surface. Since there are no piezometers in the area, this does not mean that groundwater cannot be studied. During the no-recharge period, the stream water is spring water; thus, monitoring the stream discharge effectively means monitoring groundwater (e.g., . Thanks to drone flight, we also identified some punctual springs between sections S3 and S5, the water of which has been sampled together with stream water during the measurement campaigns.
Thanks to the integrated approach, we captured the spatio-temporal dynamics of groundwater–surface water interactions. Gaining stream is one of the possible scenarios of the GW-SW interactions (Irvine et al., 2024), which are highly dependent on the spatial scale through which their interaction occurs. Using streamflow data and other techniques, we demonstrated where and how GW-SW interactions occur. We carefully located stream stretches where groundwater feeds the stream, and highlighted stretches where no interactions occur (e.g., the stream flow remains almost constant between sections S1 and S3). The Ussita stream is, especially in the headwaters (section S2) and at the catchment closure (between sections S3 and S5), a gaining stream, where the surface water body increases in volume through seepage, fracture flow, and macropore discharge (e.g., Irvine et al., 2024).Comment 4: The work would greatly benefit from a model-based interpretation of the results. Would a hydrological model support these conclusions?.
Answer: We thank the Reviewer for this important comment. We agree that a model-based interpretation could provide additional insight into the processes discussed in this study and how to build a model suitable for reproducing hydrological processes in the area (see, for example, Kavetski and Fenicia, 2011). However, including a full modeling experiment would substantially lengthen the manuscript and fall outside the current scope, which focuses on developing an integrated and preliminary framework based on the available data.
This study, nevertheless, represents a fundamental step toward any future modeling effort (see, for example, Anderson et al., 2015; Enemark et al., 2019). Our results already highlight the challenges of building and calibrating a hydrological model in such a complex basin, given the heterogeneity of data sources, scales, and hydrological conditions (see, for example, Azimi et al., 2022). In the revised version, we will include a discussion that emphasizes these aspects and the potential role of modeling as a natural extension of this work.
References
Anderson, M.P., Woessner, W.W., & Hunt, R.J.: Modeling Purpose and Conceptual Model, Applied Groundwater Modeling, Elsevier Inc, pp. 27-67, doi: 10.1016/B978-0-08-091638-5.00002-X, 2015.
Azimi, S., Massari, C., Formetta, G., Barbetta, S., Tazioli, A., Fronzi, D., Modanesi, S., Tarpanelli, A., and Rigon, R.: On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions, HESS, 27(24), 4485-4503, doi:10.5194/hess-27-4485-2023, 2023.
Enemark, T., Peeters, L. J., Mallants, D., & Batelaan, O.: Hydrogeological conceptual model building and testing: A review. Journal of Hydrology, 569, 310-329, doi: 10.1016/j.jhydrol.2018.12.007,2019.
Irvine, D.J., Singha, K., Kurylyk, B.L., Briggs, M.A., Sebastian, Y., Tait, D.R., & Helton, A.M.: Groundwater-Surface water interactions research: Past trends and future directions. Journal of Hydrology, 644, 132061, doi: 10.1016/j.jhydrol.2024.132061, 2024.
Kavetski, D., & Fenicia, F.: Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights. Water Resources Research, 47(11), doi: 10.1029/2010WR010174, 2011.Citation: https://doi.org/10.5194/egusphere-2025-4368-AC1
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AC1: 'Reply on RC1', Lucio Di Matteo, 06 Nov 2025
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The work of Ortenzi et al. presents an interesting dataset for studying mountain catchments in the Mediterranean area. I personally found the introduction and methodology sections rather long, and some basic concepts could easily be moved to the Supporting Information for less experienced readers.
Conversely, the description of the monitoring and measurement concepts should be expanded, focusing on important aspects such as the use of the data in the study and their associated uncertainty. In particular, it should be explained why discrete monitoring was performed on those specific dates, why only one thermal image was taken, and whether the temporal and spatial resolution are sufficient to derive robust conclusions. This should be a key point in explaining why other researchers should follow the proposed approach when conducting similar investigations in other catchments.
The presentation of the results and the discussion could benefit from more detailed analyses and a more critical assessment of the authors’ conclusions. In my view, the data analysis is somewhat questionable: on one hand, I appreciate its simplicity, but on the other, it should not be oversimplified. The study could and should be improved by considering the following points:
A critical uncertainty quantification and propagation to the derived quantities.
Although the authors’ conclusions may be correct, the fact that many data are collected at a relatively coarse spatial and temporal scale is an issue that should be discussed more critically and taken into consideration.
It is somewhat disappointing to see a study focusing on groundwater–surface water interactions that does not actually include data (particularly high-resolution time series) from piezometers. It is also unclear how the spring water was sampled and how the springs were gauged. I do not see a dataset that truly captures the spatio-temporal dynamics of groundwater–surface water interactions; rather, it seems to describe the contribution of the springs to the river. But what about the contribution of the river to the groundwater system? Where is the interaction?
The work would greatly benefit from a model-based interpretation of the results. Would a hydrological model support these conclusions?