Popat et al. (2026) Quantifying groundwater drought hazards with the groundwater level deficit anomaly index (GLDAI)

Identification

• Journal: Hydrogeology Journal
• Year: 2026
• Date: 2026-02-16
• Authors: Eklavyya Popat, Robert Reinecke, Andreas Hartmann
• DOI: 10.1007/s10040-026-03021-6

Research Groups

• Institute of Groundwater Management, Technical University of Dresden, Germany
• Institute of Geographie, Johannes Gutenberg-University Mainz, Germany
• CDM Smith SE, Germany

Short Summary

This study introduces the Groundwater Level Deficit Anomaly Index (GLDAI), a novel metric that integrates both groundwater anomalies and their associated deficits to provide a more accurate characterization of groundwater drought severity. Using 25 years of data from 1,847 monitoring wells across Germany, GLDAI successfully identifies major droughts while reducing the overestimation of severity compared to anomaly-only indices.

Objective

• To introduce and validate the Groundwater Level Deficit Anomaly Index (GLDAI) as a comprehensive indicator for quantifying groundwater drought hazards by jointly representing groundwater anomalies and their associated deficits.

Study Configuration

• Spatial Scale: 1,847 groundwater monitoring wells across Germany, covering 13 states and various aquifer types (porous, porous and fractured, porous aquitards, fractured aquitards).
• Temporal Scale: Monthly groundwater level records spanning 1992 to 2021 (30-year period for demand calculation).

Methodology and Data

• Models used:
  • Groundwater Level Deficit Anomaly Index (GLDAI)
  • Groundwater Level Deficit Index (dgw)
  • Drought Anomaly Probability Index for Groundwater Level (pgw)
  • Standardized Groundwater Level Index (SGLI) for comparative analysis
  • Statistical methods: Cumulative Distribution Functions (CDFs), Kolmogorov–Smirnov (KS) test, Akaike Information Criterion (AIC), Kullback–Leibler (KL) divergence, z-score, Pearson correlation coefficient.
  • R packages: gamlss, gamlss.dist, gamlss.family, extremeStat, fitdistrplus.
• Data sources:
  • Monthly groundwater level records from 1,847 monitoring wells across Germany (curated by Correctiv, 2022).
  • Aquifer characteristics data from the German Federal Institute for Geosciences and Natural Resources.

Main Results

• GLDAI successfully identified major historical groundwater droughts in Germany (1992, 2003, 2018–2020, 2021).
• The index reduced drought severity estimates by 22.75% compared to anomaly-based indices, providing a more realistic representation of groundwater stress.
• From 1992 to 2021, the proportion of months classified as drought decreased by 4.4% when using GLDAI (15.3%) compared to SGLI (19.7%).
• GLDAI demonstrates adaptability to different hydrogeological settings, reflecting gradual drought impacts in porous-fractured systems and rapid fluctuations in fractured aquitards.
• State-wise analysis indicated Lower Saxony experiences the highest frequency of groundwater droughts, while Thuringia exhibits the lowest.
• GLDAI consistently lies between the deficit (dgw) and anomaly (pgw) components, reflecting its balanced approach.

Contributions

• Introduces GLDAI, a novel groundwater drought indicator that uniquely integrates both deficit (shortfall relative to demand) and anomaly (deviation from historical norms) components.
• Fills a critical gap in groundwater drought monitoring by providing a more nuanced and realistic assessment of drought severity, addressing the overestimation inherent in purely anomaly-based indices.
• Offers a robust framework for regional-to-global drought risk assessments and supports more effective groundwater management and mitigation planning.
• Enhances the ability to discern the severity and nature of drought hazards, informing stakeholders about impacts on both human and ecosystem water needs.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Open Access funding was enabled and organized by Projekt DEAL.

Citation

@article{Popat2026Quantifying,
  author = {Popat, Eklavyya and Reinecke, Robert and Hartmann, Andreas},
  title = {Quantifying groundwater drought hazards with the groundwater level deficit anomaly index (GLDAI)},
  journal = {Hydrogeology Journal},
  year = {2026},
  doi = {10.1007/s10040-026-03021-6},
  url = {https://doi.org/10.1007/s10040-026-03021-6}
}