We sincerely thank Referee #1 (RC1) for the careful and constructive assessment of our manuscript. We appreciate the reviewer’s recognition of the relevance of the topic and of the potential value of the SPI-SGI analysis for understanding groundwater drought propagation across different aquifer systems.

We agree that the hydrometeorological interpretation of drought propagation should be developed more explicitly and that the DIPI framework should be presented more cautiously. In the revised manuscript, we will place greater emphasis on the SPI-SGI relationship, lagged response, attenuation, storage memory, and recovery asymmetry as the central hydrometeorological components of the study. We will also clarify that SPI is used as an indicator of precipitation-deficit propagation into groundwater levels, not as a complete representation of all meteorological and recharge-related controls.

At the same time, we would like to clarify the manuscript's intended scope. Our aim is not to develop a comprehensive recharge model or a complete hydrometeorological water-balance attribution framework. Rather, the study quantifies how precipitation deficits translate into groundwater drought signals across different hydrogeological conditions and examines how these observed response metrics can support a spatial, index-based assessment of groundwater drought impact potential.

In response to the reviewer’s comments, we will revise the manuscript to: (1) expand the interpretation of the SPI-SGI coupling process; (2) justify the use of SPI and explicitly discuss the absence of PET, evapotranspiration, soil moisture, and recharge indicators as a limitation; (3) soften conclusions based on the two deep confined wells; and (iv) present DIPI as a diagnostic screening framework rather than a fully causal or predictive model. We have addressed the reviewer’s comments point by point below.

1. We appreciate the reviewer's insightful comment. We concur that the relationship between SPI and SGI, along with the multi-timescale analysis of drought propagation and recovery, constitutes the primary hydrometeorological contribution of this study. Additionally, we acknowledge that DIPI, including components such as Exposure-Pressure-Sensitivity, GDEs, groundwater quality, abstraction, and mining pressure, is more directly linked to assessing groundwater drought vulnerability and water resource screening than to hydrometeorological process analysis alone.

In the revised manuscript, we will rebalance the structure and interpretation. The SPI-SGI analysis will be presented as the core process-oriented component of the study, whereas DIPI will be described as an index-based extension that translates observed groundwater drought-response metrics into a spatial assessment of drought-impact potential.

We will also expand the discussion of how precipitation anomalies are transformed into groundwater drought signals through recharge connectivity, storage memory, lag, and attenuation. The explanatory power of SPI will be discussed as scale- and aquifer-dependent: stronger, faster coupling is expected in shallow unconfined systems, whereas weaker, delayed, and more persistent responses are expected in confined systems with greater storage and longer memory.

Concerning the difference between meteorological and human-induced signals, we acknowledge that this distinction cannot be strictly causal when relying solely on SPI-SGI correlations and SGI-based metrics. Groundwater levels likely reflect the combined influence of climate, extraction, and mining-related dewatering. Therefore, we will not suggest that the E-P contrast directly separates intrinsic aquifer behavior from human activity.

2. We agree that the SPI-SGI analysis should be interpreted more explicitly in terms of hydrometeorological processes. The manuscript already includes a lagged SPI-SGI correlation analysis across 3-, 6-, and 12-month aggregation windows, with SGI shifted relative to SPI over lags of 0-12 months. The analysis was also repeated using detrended indices to reduce the risk that correlations primarily reflect common long-term trends. However, we agree that these results are not yet sufficiently discussed from a process perspective.

In the revised version, we will interpret the SPI-SGI relationships through four linked mechanisms: (1) attenuation of short-term precipitation variability, (2) lagged groundwater response, (3) increased system memory with aquifer depth and confinement, and (4) asymmetric recovery following groundwater drought events. We will frame the SPI-SGI analysis as a diagnostic tool for identifying dominant response timescales and groundwater memory, rather than as a complete recharge process model.

3. We thank the reviewer for raising this point. We agree that groundwater drought development and recovery may be influenced by evapotranspiration, temperature, soil moisture, recharge timing, snow processes, and groundwater abstraction. We also agree that a complete hydrometeorological attribution would require additional variables, such as PET, actual evapotranspiration, soil moisture, or independently estimated recharge.

The choice to focus on SPI and SGI was a deliberate methodological decision. This study aims to quantify how precipitation deficits influence groundwater-level anomalies across aquifer systems with varying storage and connectivity, rather than developing a comprehensive water-balance or recharge model. SPI was chosen because it is a straightforward, widely adopted, and reliable indicator of precipitation deficit, available consistently throughout the entire study period and across all meteorological stations involved.

We therefore do not intend to add SPEI or PET-based indicators as a new analytical component to the revised manuscript. Including them would broaden the paper's scope toward climatic water-balance modeling and would require additional assumptions about PET estimation, spatial representativeness, soil-water storage, and recharge partitioning. Instead, we will clarify the scope: SGI is used as the integrated groundwater-system response, whereas SPI represents precipitation-deficit forcing.

We will explicitly acknowledge this limitation in the revised manuscript. It will clarify that the SPI-SGI analysis indicates the propagation of precipitation-deficit signals into groundwater levels, rather than fully attributing groundwater drought to all hydrometeorological factors. Additionally, suggestions for future research will include incorporating SPEI-based analysis, PET/recharge modeling, and soil-moisture data.

4. We agree with the reviewer. The limited number of monitoring wells, especially in the deep confined aquifer, does not allow us to draw strong statistical or spatially representative conclusions about the entire aquifer system. The two deep confined piezometers provide useful evidence from the available monitoring network, but they should not be interpreted as a statistically robust sample of all deep confined groundwater conditions in GWB 43.

In the revised manuscript, we will temper the language used when discussing conclusions about aquifer depth and confinement. We will steer clear of claims suggesting that deeper aquifers typically show higher propagation probability, longer recovery times, or increased vulnerability as statistically confirmed patterns. Instead, we will present these findings as observations derived from the monitoring network that align with the conceptual hydrogeological model.

For example, statements such as “deeper aquifer systems have higher propagation probability, longer recovery periods, and increased vulnerability” will be revised to “within the available monitoring network, the deep confined wells showed longer drought persistence and slower recovery, consistent with the expected influence of storage, confinement, and delayed recharge.” We will also strengthen the limitation statement regarding the expert-based aquifer grouping and the limited number of confined and deep confined wells.

5. We thank the reviewer for this detailed and helpful comment. We agree that DIPI is more closely aligned with groundwater drought vulnerability screening and spatial assessment than with hydrometeorological process analysis alone. In the revised manuscript, we will present DIPI as an index-based diagnostic framework rather than a fully causal or predictive model.

We also agree that the weighting scheme is subjective. The comparison between the weighted and equal-weighted DIPI variants will be presented only as a preliminary sensitivity check, not as a systematic uncertainty analysis. We will soften statements such as “the hotspot configuration is robust” and instead note that the broad spatial pattern was similar under the two tested aggregation schemes, while a more complete sensitivity analysis would be required to support stronger conclusions.

The reviewer is also correct that Exposure and Pressure are not fully independent. Because Exposure is derived from SGI-based drought metrics, it may already reflect the influence of abstraction, mining-related dewatering, or other long-term human modifications to groundwater levels. Therefore, the E-P contrast will no longer be interpreted as a causal separation between “intrinsic aquifer response” and “human pressure.” We will present it as a diagnostic contrast between SGI-derived drought response and mapped anthropogenic pressure.

We also acknowledge that the spatial interpolation of DIPI is limited by the sparse monitoring network. Consequently, the DIPI maps should be viewed as preliminary screening tools highlighting general regional trends rather than precise high-resolution predictions. We will include a clearer statement emphasizing that thorough spatial uncertainty analysis, interpolation cross-validation, and error mapping are dependent on a denser, more evenly distributed monitoring network, particularly for confined and deep confined aquifers.

Minor comments

We thank the reviewer for identifying these inconsistencies. We will use the name Drought Impact Potential Index (DIPI) consistently throughout the manuscript. The incorrect phrase “Drought Impact and Pressure Index” will be replaced.

We will also correct the figure reference in Sect. 2.3. The pressure component is not shown in Fig. 6, which presents propagation-recovery trajectories, but appears in the DIPI spatial outputs. We will revise the sentence to refer to Appendix A, Fig. A1, and, where appropriate, to Fig. 7.

Summary of planned manuscript changes

• Revise the Introduction to foreground the SPI-SGI propagation and recovery analysis as the primary hydrometeorological component and to present DIPI as a vulnerability-oriented extension.
• Revise the Methods to justify the use of SPI as a precipitation-deficit indicator and to clarify that SPI does not represent PET, actual evapotranspiration, soil moisture, snow processes, or recharge timing.
• Expand Sect. 5.4 and the Discussion to interpret lagged SPI-SGI relationships in terms of attenuation, lag, storage memory, recharge connectivity, and recovery asymmetry.
• Soften statements in the Abstract, Results, Discussion, and Conclusions regarding the deep confined aquifer, emphasizing that the two deep confined piezometers provide indicative evidence from the available monitoring network rather than population-wide statistical support.
• Revise DIPI-related sections to present the index as an exploratory screening and diagnostic framework. Replace strong claims about robustness with cautious language and present the weighted/equal-weighted comparison as a preliminary sensitivity check.
• Clarify that Exposure and Pressure are not fully independent because SGI-derived metrics may already incorporate the effects of abstraction and mining-related dewatering. Interpret the E-P contrast as a diagnostic index contrast, not as a causal separation between natural and anthropogenic controls.
• Strengthen limitations related to spatial interpolation, monitoring density, and the lack of full uncertainty analysis, cross-validation, and interpolation error maps.
• Use the name Drought Impact Potential Index (DIPI) consistently and correct the erroneous reference to Fig. 6 in Sect. 2.3.

We thank the commenter for the careful and constructive assessment of our manuscript. The comment raises important hydrogeological issues regarding the terminology used to describe the framework, the interpretation of SGI-based drought indicators in a mining-affected groundwater body, and the limitations of the monitoring network. We agree that these points should be clarified more explicitly in the revised manuscript.

Process-based terminology

We agree that describing our approach as “process-based” may be too strong if interpreted strictly in the groundwater modeling sense, i.e., as an approach based on solving groundwater-flow equations using parameters such as hydraulic conductivity, storage coefficient, recharge, and boundary conditions. Our approach is not a numerical groundwater-flow model. It is an indicator-based, statistical framework that integrates hydrogeologically relevant drought-response metrics, including propagation probability, recovery time, vulnerability, and resilience.

In the revised manuscript, we will replace or qualify the term “process-based” throughout the text. We will instead use terms such as “process-informed”, “hydrogeologically informed statistical framework”, or “indicator-based framework incorporating propagation and recovery metrics”. We will also revise statements that imply a “mechanistic” or “physically grounded” interpretation to better distinguish observed drought-response patterns from physically simulated groundwater-flow processes.

Mining-induced drawdown and SGI interpretation

We agree that SGI-based analyses cannot, by themselves, distinguish groundwater-level decline caused by meteorological drought from decline caused by anthropogenic drawdown. This is particularly important in GWB 43, where open-pit lignite mining and associated dewatering alter hydraulic gradients and groundwater storage conditions.

Our aim was not to classify all negative SGI values as purely natural drought. The manuscript already includes mining-related pressure as a separate component of the DIPI framework, but we agree that the interpretation of drought duration and recovery, especially in deep aquifers, should be more cautious. In the revised Discussion, we will clarify that prolonged or non-recovering SGI drought episodes in deeper aquifers may reflect a combined signal of climatic drought, aquifer memory, and persistent mining-induced drawdown. We will avoid attributing the absence of recovery solely to natural system memory.

While we do not fully agree that wells affected by mining should be excluded, it's important to note that this study aims not to reconstruct a purely natural groundwater drought signal but to evaluate the potential impact of groundwater drought in a human-modified groundwater system. In GWB 43, mining-related dewatering is a key aspect of the hydrogeological setting and a major factor influencing groundwater conditions. Omitting these piezometers would eliminate the areas where drought effects and human activities interact most heavily.

However, we agree that SGI signals from mining-affected wells must be interpreted cautiously. In the revised manuscript, we will explicitly state that negative SGI values in such wells may reflect a combined signal of meteorological drought, aquifer memory, abstraction, mining-induced drawdown, and possible vertical leakage. Therefore, these results will be interpreted as groundwater-level drought signals under combined climatic and anthropogenic forcing, rather than as purely natural drought responses.

Vertical leakage and hydraulic gradients

We agree that mining-induced drawdown may create or intensify downward hydraulic gradients, potentially inducing leakage from shallow aquifers into deeper drained zones. We will add a dedicated paragraph to the Discussion explaining that dewatering of deeper horizons in a multilayered aquifer system can alter vertical gradients and cause downward leakage. Consequently, groundwater-level declines recorded in shallow monitoring wells may reflect both the propagation of meteorological drought and leakage toward the mining drainage system.

We also note that separating these mechanisms would require paired shallow and deep piezometers at the same sites to calculate vertical hydraulic gradients. Because our dataset consists of spatially distributed monitoring wells rather than nested multilevel piezometers, this separation cannot be performed robustly with the available data.

Limitations of SGI

We agree that this limitation should be stated earlier in the manuscript. We will add a sentence to the Methods section noting that standardized groundwater indices aggregate all groundwater-level anomalies into a single standardized signal. Therefore, SGI identifies deviations from normal groundwater-level conditions but does not independently diagnose their cause. In human-modified systems, SGI may capture signals from meteorological drought, groundwater abstraction, mining dewatering, and induced vertical leakage.

Deep aquifer cluster and spatial interpolation

We concur that the deep-confined group, represented by only two wells, cannot be used to draw broad regional generalizations. Although the manuscript mentions that confined groups have few wells and should not be regarded as population-wide data, we believe this limitation should be highlighted more prominently.

We will revise the text and figure captions to clarify that interpolated surfaces based on monitoring points are exploratory visualizations intended to support comparison with pressure and sensitivity layers, not precise regional maps of deep-aquifer conditions. Conclusions about the deep confined aquifer will be limited to the monitored locations and will not be generalized to the entire regional aquifer system.

Pressure component and well density

We agree that well density is an imperfect proxy for anthropogenic pressure because it does not reflect actual abstraction volumes. A household well and a high-capacity dewatering system do not impose the same hydraulic stress. In our framework, mining-related pressure was included as a separate layer, but we agree that the abstraction-density component should be described more cautiously.

We will revise the Methods and Limitations sections to clarify that well density reflects the spatial occurrence of abstraction pressure rather than actual withdrawal intensity. Where abstraction rate or dewatering volume data are unavailable or incomplete, this proxy provides only an approximate indicator of pressure. We will also avoid wording implying that borehole density directly quantifies hydraulic stress.

Shallow aquifer interpretation

We concur that the interpretation of the shallow aquifer should be more tentative. Therefore, we will revise the relevant sentences to indicate that observed declines in shallow groundwater levels likely result from a combination of meteorological drought effects, local water extraction, hydraulic alterations from mining, and potential vertical leakage into deeper drained zones. This update will align the interpretation more closely with GWB 43's hydrogeological context.

Weights and expert-based grouping

We acknowledge that both the expert-based grouping and the selected DIPI weights introduce uncertainty. The revised manuscript will better justify these choices and explicitly refer to the unweighted DIPI variant as a sensitivity test. We will also clarify that the purpose of the weighting scheme is not to provide a unique physical solution but to construct a transparent diagnostic index that integrates exposure, pressure, and sensitivity.

Summary of planned revisions

In response to this comment, we will revise the manuscript by:

1. replacing or qualifying “process-based” terminology;
2. clarifying that DIPI is a hydrogeologically informed statistical/index-based framework;
3. adding a limitation that SGI cannot distinguish climatic drought from anthropogenic drawdown;
4. expanding the Discussion on mining-induced drawdown, vertical gradients, and possible downward leakage;
5. softening the interpretation of deep-aquifer memory and recovery;
6. limiting conclusions from the two deep wells to monitored conditions rather than the whole regional aquifer;
7. clarifying that interpolated maps are exploratory visualizations;
8. describing well density as a proxy for pressure, not a direct measure of abstraction intensity.

Overall, we believe these revisions will improve the hydrogeological precision of the manuscript while preserving the study’s main contribution: a transferable diagnostic framework for identifying where groundwater drought signals, anthropogenic pressure, and aquifer sensitivity coincide.