Prasain et al. (2026) A comprehensive review on impact of climate change and land use change on groundwater

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-02-12
• Authors: Suresh Prasain, Tek Maraseni, Xiaoye Liu, Zhenyu Zhang, Michael Scobie, Manish Shrivastav, Archana Bhandari
• DOI: 10.1016/j.ejrh.2026.103213

Research Groups

• Department of Soil Science and Agricultural Engineering, Agriculture and Forestry University, Chitwan, Nepal
• Centre for Sustainable Agricultural Systems (CSAS), University of Southern Queensland, Toowoomba, Queensland, Australia
• School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, Queensland, Australia
• Institute of Life Sciences and the Environment (ILSE), University of Southern Queensland, Toowoomba, Queensland, Australia
• Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
• School of Agriculture and Environmental Science, University of Southern Queensland, Toowoomba, Queensland, Australia
• Department of Agricultural and Biosystems Engineering, Iowa State University College of Engineering, Ames, Iowa, United States
• Centre for Agricultural Engineering, University of Southern Queensland, Toowoomba, Queensland, Australia

Short Summary

This systematic review of 575 studies (1990-2024) comprehensively assesses the combined impacts of climate and land use change (CLUC) on groundwater quantity, quality, and system vulnerability, revealing a rapid expansion of research since 2016 but persistent geographic and thematic imbalances.

Objective

• To provide a comprehensive evaluation of global research on CLUC impacts on groundwater and its resource nexus by analyzing its status, temporal trends, bibliometric structures, thematic developments, and methodological approaches.
• To assess the status, trend, and temporal evolution of CLUC–groundwater research.
• To map bibliometric networks (keyword co-occurrence, co-authorship, citation, and co-citation) to identify influential authors, journals, institutions, and geographic research patterns.
• To identify and classify major thematic areas and research clusters.
• To review and evaluate methodological and modeling frameworks used in previous studies.

Study Configuration

• Spatial Scale: Global, covering 90 countries, with a focus on country-level studies (90%) and some global (6%) and regional (4%) analyses. Key regions of study concentration include China, India, the United States, Iran, and Australia.
• Temporal Scale: The systematic review covers studies published between 1990 and 2024.

Methodology and Data

• Models used:
  • Systematic Literature Review (SLR) with Double Diamond Approach (DDA) and PRISMA-P framework.
  • Bibliometric analysis (VOSviewer, Python scripts).
  • Climate Models: Global Climate Models (GCMs), Regional Climate Models (RCMs) (CMIP1-6 generations), integrated GCM–RCM frameworks.
  • Downscaling Models: Statistical Downscaling Models (SDMs) (e.g., Bias-Correction and Spatial Disaggregation (BCSD), Delta Change Method (DCM), Quantile Mapping (QM), LARG-WG), Dynamical Downscaling Models (DDMs) (e.g., Weather Research and Forecasting (WRF), PRECIS), Hybrid Downscaling Models (HDMs) (e.g., Bias-Corrected Constructed Analogue (BCDD), CORDEX, ANN–MLP).
  • Land Use Change (LUC) Models: MLC using Inverse Distance Weighting (MLC-IDW), LRA/MLR, CA-Markov, Dyna-CLUE, PLUS, supervised image classifiers (e.g., Maximum Likelihood, SVM), unsupervised methods (e.g., K-means), Object-Based Image Analysis (OBIA).
  • Groundwater Models: Statistical Analysis Models (SAMs) (e.g., LoRA, LRA, MLA, Mann–Kendall tests, PCA/FA, WQI), Soil and Water Balance Models (SWBMs) (e.g., SWAT/SWAT+, WETSPASS, HELP), Numerical and Hydrogeological Models (NHMs) (e.g., MODFLOW, MIKE-SHE), Groundwater Quality Based Models (GQBMs) (e.g., MODFLOW-MT3DMS, SWAT/HELP/WETSPASS-SEAWAT, SWAT-SUTRA).
  • Decision Support & Vulnerability Models: Multi-Criteria Decision-Making (MCDM) methods (e.g., AHP, TOPSIS, Entropy Weight Method (EWM), VIKOR), integrated with Vulnerability Models (VMs) to form Groundwater Sustainability Models (GWSMs) (e.g., DRASTIC, GRACE, GLADI).
  • Artificial Intelligence (AI)/Machine Learning (ML)/Deep Learning (DL) Models: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear Autoregressive Exogenous model (NARX).
• Data sources:
  • Scientific databases: Web of Science, Scopus, ScienceDirect, Google Scholar.
  • Satellite imagery: Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Google Earth Engine (GEE), Sentinel.
  • Geospatial software: ArcGIS, QGIS, ENVI, ERDAS IMAGINE.
  • Data types: Secondary data (85% of studies), primary data, or a combination (7%).

Main Results

• The review synthesized 575 studies published between 1990 and 2024, identifying a significant increase in CLUC–groundwater research (74%) after 2016.
• Research is geographically concentrated in China (11%), India (9%), the United States (7%), Iran (6%), and Australia (5%), with notable gaps in data-scarce and high-risk regions like Sub-Saharan Africa, Southeast Asia, and Latin America.
• Thematic focus is heavily skewed towards groundwater quantity (66%), particularly recharge dynamics (57%), while groundwater quality (34%) remains underrepresented, with most quality studies focusing on mineral-contaminated pollution (39%) and seawater intrusion/salinization (36%).
• Most studies (87%) concentrate on unconfined aquifers, especially alluvial (50%) and coastal (27%) types, indicating a physiographical bias.
• Agricultural use (90%) dominates groundwater utilization research, reflecting its high socioeconomic relevance.
• Methodological approaches have evolved from basic hydrological models to integrated hybrid systems, including AI/ML-enhanced tools and multi-criteria decision frameworks, with a notable increase in AI/ML/DL applications since 2021.
• Statistical tools (50%) and Soil and Water Balance Models (SWBMs, 38%) are the dominant modeling approaches.
• Climate change scenario analysis predominantly uses GCMs (42%) and time-series analysis (36%), with RCPs (35%) and SSP–RCPs (11%) being common, favoring moderate-to-high emissions pathways (SSP2–4.5, SSP5–8.5).
• Statistical Downscaling Models (SDMs) (62%), particularly BCSD (50%), are the most frequently used downscaling methods.
• Landsat (55%) and ArcGIS (77%) are the primary data source and processing tool, respectively, for Land Use Change (LUC) scenario analysis, with MLC-IDW (36%) being a leading modeling technique.
• Key limitations identified include limited incorporation of socio-economic drivers, poor transparency in AI-based modeling, weak policy linkages, and challenges in integrating heterogeneous datasets.

Contributions

• Provides the most comprehensive assessment to date (575 publications) of combined climate and land use change impacts on both groundwater quantity and quality.
• Employs a rigorous Double Diamond Approach (DDA) within the PRISMA-P framework for systematic literature selection and evaluation, enhancing methodological robustness over traditional narrative reviews.
• Incorporates advanced bibliometric network analysis (keyword co-occurrence, co-authorship, citation, co-citation) to identify influential entities and global collaboration patterns.
• Highlights critical thematic gaps, geographic imbalances, and emerging research hotspots, offering insights for future research priorities.
• Traces the evolution of methodological and modeling frameworks, from basic hydrological models to integrated hybrid and AI/ML-enhanced systems, providing a roadmap for sustainable groundwater governance under CLUC pressures.

Funding

• In-kind institutional support was provided by the University of Southern Queensland to Prof. Tek Maraseni, Dr. Xiaoye Liu, Dr. Zhenyu Zhang, Michael Scobie, and Er. Suresh Prasain.
• Er. Suresh Prasain also received non-financial support from the University of Southern Queensland.
• Archana Bhandari received in-kind support from Agriculture and Forestry University.
• Dr. Manish Shrivastav received in-kind statistical support from Iowa State University of Science and Technology.
• No article publication charges were funded.

Citation

@article{Prasain2026comprehensive,
  author = {Prasain, Suresh and Maraseni, Tek and Liu, Xiaoye and Zhang, Zhenyu and Scobie, Michael and Shrivastav, Manish and Bhandari, Archana},
  title = {A comprehensive review on impact of climate change and land use change on groundwater},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103213},
  url = {https://doi.org/10.1016/j.ejrh.2026.103213}
}