| 10 Jun 2026
Integrating propagation and recovery dynamics into groundwater drought vulnerability assessment through exposure, pressure, and aquifer system response
Abstract. Groundwater drought is influenced more by system-specific response dynamics than by meteorological forcing alone. We introduce a multi-scale framework that combines exposure, pressure, and sensitivity with process-based metrics of drought propagation and recovery to assess groundwater drought vulnerability. The Drought Impact Potential Index (DIPI) is developed and tested across a regional aquifer system. Propagation probability, median recovery time, and resilience metrics are examined across temporal scales and in groundwater systems at different depths. The findings reveal that spatial vulnerability patterns are driven by variations in system memory and response time. Deeper aquifer systems tend to have higher propagation probability, longer recovery periods, and increased vulnerability, indicating delayed responses and persistent drought signals. Conversely, shallower systems respond more quickly and recover faster, leading to lower drought persistence. The spatial distribution of DIPI remains consistent whether using weighted or unweighted versions, confirming that the identified patterns are robust and reflect fundamental hydrogeological controls. These results demonstrate that groundwater drought vulnerability arises from interactions between external forcing and internal system dynamics and cannot be understood solely through static indicators. An area-based analysis of exposure–pressure contrast shows that 60.4 % of the study area is dominated by the intrinsic system response, compared to 21.8 % driven primarily by human pressure. The proposed framework offers a process-based approach for groundwater drought assessment and can be applied to other diverse aquifer systems.
The manuscript investigates groundwater drought by combining meteorological drought information, represented by SPI, with groundwater response, represented by SGI. It quantifies drought propagation, lag, recovery, and vulnerability across multiple timescales and compares these processes among shallow, intermediate, and deep aquifer systems. The study further attempts to link these temporal drought-response metrics with spatial vulnerability assessment through the composite DIPI framework.
While the topic is relevant and the SPI-SGI analysis has potential value for understanding groundwater drought propagation. The main limitation is that the hydrometeorological process mechanisms are not developed in enough depth. Instead, the paper places substantial emphasis on hydrogeological interpretation, vulnerability mapping, and the composite DIPI index.
Second, the Exposure component of DIPI is derived from SGI, but SGI itself may already be affected by groundwater abstraction and mining-related dewatering. At the same time, Pressure separately includes abstraction and mining effects. Therefore, E and P are not necessarily independent. The E−P spatial contrast should not be interpreted directly as a causal separation between “intrinsic system response” and “human pressure.”
Third, the spatial interpolation of DIPI is strongly constrained by the limited number of monitoring wells. Reviewers are likely to expect spatial uncertainty analysis, interpolation cross-validation, justification of the interpolation method, and error maps. Without these additions, statements such as “the hotspot configuration is robust” are too strong.
Minor comments
The name of DIPI is inconsistent. The abstract and methods refer to the Drought Impact Potential Index, whereas around line 135 the manuscript refers to the Drought Impact and Pressure Index. The terminology should be made consistent throughout the manuscript.
Line 115 states that the pressure component is shown in Fig. 6, but Fig. 6 actually presents the propagation–recovery trajectories. The DIPI spatial map appears to correspond to Fig. 7 or Appendix A. This figure reference should be corrected.