Moumen et al. (2026) Riverine Flood Mapping Methods and Criteria: A Meta-Analysis Review and Synthesized Guidelines

Identification

• Journal: Natural Hazards Research
• Year: 2026
• Date: 2026-03-01
• Authors: Rachid El Moumen, Mohammed Hssaisoune, Yassine Ait-Brahim, Sofyan Sbahi, Estanislao Pujades Garnés, Ismail Aït Lahssaine, Lhoussaine Bouchaou
• DOI: 10.1016/j.nhres.2026.03.004

Research Groups

• Applied Geology and Geo-Environment Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco.
• Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul, Morocco.
• International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir, Morocco.
• National Center for Studies and Research on Water and Energy (CNEREE), Cadi Ayyad University, Marrakech, Morocco.
• Institute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), Barcelona, Spain.

Short Summary

This meta-analysis statistically evaluates the influence of methods, topography, area extent, reference dataset size, and criteria on riverine flood mapping (RFM) accuracy across 142 studies, synthesizing guidelines for objective and context-appropriate method and criterion selection. It finds remote sensing and machine/deep learning methods generally most accurate, with performance varying significantly by topography and area extent, and identifies distance from river, elevation, and slope as the most influential criteria.

Objective

• To statistically evaluate the influence of five variables (applied method, topographic type, selected criteria in multi-criteria methods (MCM), area extent, and reference dataset size) on the accuracy of riverine flood mapping (RFM).
• To propose general guidelines for RFM to support objective, context-appropriate method and criterion selection, and address key challenges.

Study Configuration

• Spatial Scale: Meta-analysis of 142 riverine flood mapping research articles from worldwide studies.
• Temporal Scale: Review of scientific publications up to December 2024.

Methodology and Data

• Models used: Statistical analysis methods including One-way ANOVA, Tukey's HSD test, coefficient of determination (R²), and Mann–Whitney U test were used to analyze the collected data. The study reviews various RFM methods: Subjective (e.g., AHP, SAW, TOPSIS), Physically-based (e.g., 1D, 2D, 3D hydrodynamic models), and Empirical (Remote Sensing, Statistical methods, Machine Learning and Deep Learning).
• Data sources: Accuracy metrics and associated variables (applied method, topography type, area extent, reference dataset size, and selected criteria) extracted from 142 peer-reviewed research articles on riverine flood mapping, identified through bibliometric searches in Web of Science and Scopus.

Main Results

• Overall method rankings show Remote Sensing (RS) as the most accurate approach, followed by Machine Learning and Deep Learning (MLDL) and Physically-based (Ph-B) methods, then statistical approaches, with subjective methods lowest.
• MLDL and statistical methods perform best in mountainous regions, while RS and Ph-B are more appropriate for lowlands.
• Ph-B accuracy is uniquely sensitive to area extent, performing highest in small areas.
• The reference dataset size (RDS) and area extent generally showed a weak and statistically insignificant influence on overall RFM accuracy (R² = 0.35, p = 0.4 for RDS; R² = 0.19, p = 0.46 for area extent).
• Distance from the river (DFR), elevation, and slope are the most influential criteria in Multi-Criteria Methods (MCM), with high Criterion Influence Ratios (CIR) of 64%, 58%, and 53% respectively. Prioritizing these three criteria is associated with significantly higher accuracy (mean accuracy 93.61% vs. 83.49% for studies dominated by other criteria, p < 0.05).

Contributions

• Provides a state-of-the-art meta-analysis of riverine flood mapping accuracy across method type, topography, area extent, reference dataset size, and selected criteria, addressing gaps in quantitative assessments and benchmarking.
• Synthesizes general, evidence-based guidelines for riverine flood mapping, emphasizing the most relevant methods and criteria with respect to study area characteristics, to support objective and context-appropriate selection.
• Identifies key challenges and inconsistencies in existing literature, particularly regarding subjective method/criteria selection and conflicting case-study results.

Funding

No funding was received for this research. However, the paper acknowledges that it presents part of the outputs of the Water4Med and AgreeMed Projects (financed by MESRSRI within the PRIMA-S2 program, EU) and the APRD research program (GEANTech project) from the Moroccan Ministry of Higher Education, Scientific Research and Innovation, and the OCP Foundation.

Citation

@article{Moumen2026Riverine,
  author = {Moumen, Rachid El and Hssaisoune, Mohammed and Ait-Brahim, Yassine and Sbahi, Sofyan and Garnés, Estanislao Pujades and Lahssaine, Ismail Aït and Bouchaou, Lhoussaine},
  title = {Riverine Flood Mapping Methods and Criteria: A Meta-Analysis Review and Synthesized Guidelines},
  journal = {Natural Hazards Research},
  year = {2026},
  doi = {10.1016/j.nhres.2026.03.004},
  url = {https://doi.org/10.1016/j.nhres.2026.03.004}
}