Elsaidy et al. (2025) Groundwater drought assessment in a Mediterranean coastal catchment through a multi-index approach

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-12-15
• Authors: Abedulla Elsaidy, Lorenzo Villani, Estifanos Addisu Yimer, Miguel Moreno Gómez, Marijke Huysmans, Yunes Mogheir, Ann van Griensven
• DOI: 10.1016/j.ejrh.2025.103043

Research Groups

• Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium
• Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy
• Department of Water Science and Engineering, IHE Delft Institute for Water Education, Delft, the Netherlands
• Civil and Environmental Engineering Department, Islamic University of Gaza, Gaza, Palestine

Short Summary

This study assesses spatio-temporal groundwater drought dynamics in the Bruna River catchment, Central Italy, using a multi-index approach and a regional hydrological model, finding that groundwater droughts are detectable even with short records and are strongly linked to prolonged meteorological deficits.

Objective

• To investigate the spatio-temporal patterns of groundwater drought (GWDr) in the Bruna catchment using calculated indices (Standardized Groundwater Index (SGI), Standardized Precipitation Index (SPI), Q20 thresholds method) and existing indicators from Combined Drought Indicator (CDI) and BIGBANG datasets (Standardized Precipitation Evapotranspiration Index (SPEI), recharge, soil moisture).
• To develop a framework that overcomes the constraints of short time series and limited data coverage for groundwater drought assessment.

Study Configuration

• Spatial Scale: Bruna River catchment, Tuscany, Central Italy (catchment area: 441 square kilometers). Model outputs (BIGBANG) at 1 square kilometer spatial resolution, CDI at 5 square kilometers spatial resolution.
• Temporal Scale: Groundwater observations: September 2014–February 2023. Precipitation data: April 2002–December 2024. CDI data: 2014–2023. BIGBANG model data: 1951–2024. All data aggregated to monthly resolution for analysis.

Methodology and Data

• Models used:
  • BIGBANG 8.0 (GIS-based national-scale hydrological budget model)
  • LISFLOOD hydrological model (used for soil moisture anomalies in CDI)
• Data sources:
  • Groundwater levels: Italian regional hydrological service (7 monitoring wells in the Grosseto Plain Aquifer).
  • Precipitation data: Casteani station (TOS03002515) from SIR Toscana.
  • Combined Drought Indicator (CDI) v4.0: Copernicus European Drought Observatory (EDO) (10-day, 5 km × 5 km resolution).
  • BIGBANG 8.0 dataset: (1 km × 1 km grid, monthly resolution) providing SPEI, SPI, potential and actual evapotranspiration, aquifer recharge, runoff, hydroclimatic budget, internal flow, surplus, temperature, precipitation, and soil water content.
  • Satellite data: Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) (for CDI).
  • Drought indices calculated: Standardized Groundwater Index (SGI), Standardized Precipitation Index (SPI) at 3, 6, 9, 12, and 24-month aggregation periods, and Q20 thresholds method for groundwater levels.

Main Results

• Groundwater levels exhibited clear seasonal patterns (decline in summer, recovery in winter) and spatial variability, with some wells showing high fluctuations (e.g., S21, S72 with >5 meters drop) and others low (e.g., S13, S12, S73 with ~1 meter fluctuation).
• Two significant groundwater drought episodes were consistently identified across multiple indices: 2017–2018 and 2021–2022.
• SGI values ranged from -1.28 (dry) to +1.28 (wet). Persistent groundwater drought (SGI < -1 for >1 month) was most frequent in wells S12 and S21 (four events), followed by S13 (three events).
• The central and southern parts of the catchment were identified as hotspots for persistent and prolonged groundwater droughts, aligning with CDI analysis.
• SGI correlated strongly with the Q20 thresholds method (e.g., an SGI threshold of -0.76 captured 100% of Q20 drought events across all wells).
• Correlations between SPI and SGI increased with longer aggregation periods, with the highest correlation of 0.84 observed between SPI12 and SGI at well S12, indicating the aquifer's slow response to precipitation.
• CDI analysis showed the Warning stage as most prevalent (occurring 64–131 times), followed by Watch (6–24 times), and Alert (up to 32 times in hotspots). Up to 50% of the catchment area experienced severe drought (Alert stage) in 2017, 2018, and 2022.
• Aquifer recharge, as estimated by the BIGBANG model, was identified as a key driver for both the onset and termination of groundwater drought, with varying lag times.
• Some wells (e.g., S21, S73) exhibited anomalous behavior, deviating from regional meteorological drought signals, suggesting localized anthropogenic impacts (e.g., abstraction, Managed Aquifer Recharge) or specific hydrogeological interactions (e.g., Ombrone River).

Contributions

• Developed and validated an integrated drought monitoring framework combining standardized, threshold-based, and combined indices with regional hydrological model outputs for groundwater drought assessment.
• Demonstrated the effectiveness of this multi-index framework for reliable groundwater drought detection and monitoring, even in data-limited regions with short time series (<30 years) and limited well coverage.
• Quantified the strong correlation between groundwater drought indices (SGI) and longer-term meteorological drought indices (SPI9, SPI12, SPEI9, SPEI12), highlighting the aquifer's sensitivity to prolonged hydroclimatic deficits.
• Identified specific groundwater drought hotspots in the central and southern parts of the Bruna River catchment and distinguished areas where local anthropogenic impacts or hydrogeological factors override broader climatic signals.
• Provided a robust framework that can serve as a basis for developing and validating models to generalize and project groundwater drought dynamics, supporting operational water management and climate adaptation strategies in Mediterranean coastal aquifers.

Funding

• Research Foundation – Flanders (FWO) for the International Coordination Action (ICA) "Open Water Network: impacts of global change on water quality" (project code G0ADS24N).
• AXA Research Chair fund on Water Quality and Global change.
• Research Foundation Flanders (FWO, PhD grant no. 1S11022N).
• VLIR-VUB Global Minds.

Citation

@article{Elsaidy2025Groundwater,
  author = {Elsaidy, Abedulla and Villani, Lorenzo and Yimer, Estifanos Addisu and Gómez, Miguel Moreno and Huysmans, Marijke and Mogheir, Yunes and Griensven, Ann van},
  title = {Groundwater drought assessment in a Mediterranean coastal catchment through a multi-index approach},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.103043},
  url = {https://doi.org/10.1016/j.ejrh.2025.103043}
}