GHAZZAR (2025) Hydro-climatic Projections and Computational Framework for the Maamora Aquifer: SPI, SPEI, GRDI, and De Martonne Aridity Index Datasets using CORDEX and WRF

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-12-30
• Authors: Ayoub GHAZZAR
• DOI: 10.17632/s7zs8rhvtn.1

Research Groups

• Universite Mohammed V de Rabat
• Ecole Mohammadia d'Ingenieurs

Short Summary

This study presents a computational framework and generates hydro-climatic projection datasets for the Maamora Aquifer, Morocco, utilizing CORDEX and WRF models to assess future meteorological and hydrological drought conditions through multiple indices under RCP4.5 and RCP8.5 scenarios.

Objective

• To develop a computational framework and generate comprehensive hydro-climatic projection datasets for the Maamora Aquifer, Morocco, enabling a multi-index assessment of future meteorological and hydrological drought under RCP4.5 and RCP8.5 climate change scenarios.

Study Configuration

• Spatial Scale: Maamora Aquifer, Morocco, with specific analysis for three distinct aquifer zones (Coastal, Central, and Eastern).
• Temporal Scale: Long-term future projections, with drought indices calculated using a 12-month lead time to assess long-term trends.

Methodology and Data

• Models used:
  • Multi-model ensemble of four downscaled and bias-adjusted regional climate models from the CORDEX framework: CNRM-CM5, EC-EARTH, IPSL-CM5A-MR, and MPI-ESM-LR.
  • Regionally-configured Weather Research and Forecasting (WRF) model.
• Data sources:
  • Climate data retrieved from the ESGF MetaGrid (raw CORDEX and WRF datasets).
  • Extracted variables: monthly precipitation (in millimeters) and minimum/maximum temperatures (in degrees Celsius).
  • Meteorological drought indices calculated: Standardized Precipitation Index (SPI) using Gamma distribution fitting, Standardized Precipitation-Evapotranspiration Index (SPEI) using the Hargreaves method for Potential Evapotranspiration (PET), and De Martonne Aridity Index.
  • Hydrological drought assessment: Groundwater recharge estimated using zone-specific infiltration coefficients applied to precipitation data, and Groundwater Recharge Drought Index (GRDI) calculated by fitting recharge data to six candidate probability distributions (Gamma, Lognormal, Weibull, Extreme Value, Rician, and Nakagami) with the Kolmogorov–Smirnov (K–S) test for best fit.

Main Results

• Generation of comprehensive datasets for Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Groundwater Recharge Drought Index (GRDI), and De Martonne Aridity Index for the Maamora Aquifer.
• Development of a computational framework comprising MATLAB and R-studio scripts for automated calculation of these indices, including specific statistical fitting methods (e.g., Gamma for SPI, Hargreaves for SPEI, and K-S test for GRDI).
• Provision of Excel templates for manual calculation of SPI (simple standardization) and De Martonne Aridity Index.
• The framework includes scripts for visualizing temporal trends of temperature and precipitation projections and estimating groundwater recharge volume using zone-specific infiltration coefficients.

Contributions

• Provides a unique, multi-index hydro-climatic dataset and a reproducible computational framework specifically tailored for assessing future meteorological and hydrological drought in the Maamora Aquifer, Morocco.
• Integrates a multi-model ensemble of CORDEX regional climate models with a regionally-configured WRF model, enhancing the robustness of climate projections for the region.
• Offers a comprehensive suite of standardized and non-standardized drought indices, including a statistically rigorous approach for GRDI, to evaluate both atmospheric water deficits and groundwater stress.
• Facilitates informed water resource management and future research in the Maamora Aquifer by providing processed data and transparent methodologies.

Funding

Not specified in the provided text.

Citation

@article{GHAZZAR2025Hydroclimatic,
  author = {GHAZZAR, Ayoub},
  title = {Hydro-climatic Projections and Computational Framework for the Maamora Aquifer: SPI, SPEI, GRDI, and De Martonne Aridity Index Datasets using CORDEX and WRF},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/s7zs8rhvtn.1},
  url = {https://doi.org/10.17632/s7zs8rhvtn.1}
}