Syarifuddin et al. (2026) Integrating rainfall return periods in MCDA-based flood risk mapping: a fuzzy-AHP case study in an ungauged watershed

Identification

• Journal: Stochastic Environmental Research and Risk Assessment
• Year: 2026
• Date: 2026-04-01
• Authors: Magfira Syarifuddin, Satoru Oishi, Haryati M. Sengadji, Chris Natali Namah, Masria Masria, Atsushi Hamada
• DOI: 10.1007/s00477-026-03211-4

Research Groups

• Kupang State Agricultural Polytechnic, Kupang, East Nusa Tenggara, Indonesia
• Faculty of Sustainable Design, University of Toyama, Toyama, Japan
• Graduate School of Engineering, Kobe University, Kobe, Japan
• RIKEN Center for Computational Science (R-CCS), Kobe, Japan
• Center for Environmental Remote Sensing, Chiba University, Chiba, Japan

Short Summary

This study developed a Fuzzy Analytic Hierarchical Process (Fuzzy AHP) framework integrated with GIS and Multi-Criteria Decision Analysis (MCDA) to map flood risk in an ungauged watershed, explicitly incorporating rainfall return periods. The framework significantly improved flood risk assessment accuracy, correctly classifying over 90% of observed flooded areas into high-risk categories, demonstrating the critical value of probabilistic rainfall data.

Objective

• To evaluate the impact of incorporating rainfall return periods into the Fuzzy Analytic Hierarchical Process (Fuzzy AHP) for flood risk mapping.
• To capture flood risk variability across different hazard scenarios by considering probabilistic rainfall events.
• To account for the stochastic nature of rare, high-intensity events, which are often ignored in conventional GIS-MCDA studies.
• To validate the resulting flood risk maps using Sentinel-1-based flood mapping, providing empirical evidence of the framework’s practical applicability and reliability.

Study Configuration

• Spatial Scale: Nunkurus Watershed, Timor Island, Indonesia, covering 711 square kilometers. All raster data were processed and resampled to a 15 meter resolution.
• Temporal Scale: Rainfall return periods were estimated using 22 years of daily rainfall data. Flood risk maps were validated against two historical flood events: Tropical Cyclone Seroja (April 4, 2021) and the Christmas Flood (December 25–27, 2022). Sentinel-1 SAR imagery was used for pre- and post-flood event analysis.

Methodology and Data

• Models used:
  • Fuzzy Analytic Hierarchical Process (Fuzzy AHP)
  • Multi-Criteria Decision Analysis (MCDA)
  • Geographic Information Systems (GIS)
  • Gumbel Distribution (for rainfall return period estimation)
  • Mononobe formula (for Intensity-Duration-Frequency curves)
  • Inverse Distance Weighting (IDW) for rainfall interpolation
  • Weighted overlay model for flood risk calculation
  • Pearson correlation analysis for sub-criteria and flood risk relationships
• Data sources:
  • Digital Surface Model (DSM) from Digital Elevation Model Nasional (DEMNAS) (approx. 8 m resolution) for elevation, slope, and Topographic Wetness Index (TWI).
  • Land-use/land-cover (LULC), watershed, and soil maps from Indonesia’s Ministry of Forestry.
  • Road and river network data from the Geospatial Information Agency of Indonesia (BIG).
  • Population and poverty ratios from the Central Bureau of Statistics (BPS) of Kupang Regency.
  • Daily rainfall data from the Regional Office of the Meteorology, Climatology, and Geophysical Agency (BMKG).
  • Sentinel-1 SAR imagery (Copernicus Sentinel Data) for observed flood inundation maps.

Main Results

• The integration of six rainfall return periods (2, 5, 10, 25, 50, and 100 years) with three intensity-duration patterns (18 scenarios) provided a more balanced assessment of flood risk factors.
• Validation with Sentinel-1 SAR data demonstrated that the Fuzzy AHP framework incorporating rainfall return periods produced robust results, correctly classifying over 90% of flooded pixels into the Moderate-to-high, High, and Very-high risk classes.
• In contrast, models that did not embed rainfall return periods misclassified more than 70% of flooded pixels into lower-risk classes.
• Elevation, rainfall intensity, and slope exhibited the strongest correlations with flood risk (r ≈ ± 0.7). Soil type and LULC showed moderate positive correlations (r ≈ 0.5).
• Longer-duration, less frequent rainfall events (e.g., 100-year return period, 12-hour duration) resulted in a wider spatial extent of High to Very-high risk zones, particularly along floodplains.
• Short-duration, high-intensity rainfall events produced wider Very-high risk class areas than moderate-intensity, moderate-duration events, indicating a non-linear relationship between duration and flood risk.

Contributions

• This study introduces a novel Fuzzy AHP framework that explicitly integrates rainfall return periods into GIS-based MCDA for flood risk mapping, addressing a critical gap in previous studies that often overlooked the probabilistic nature of extreme events.
• It provides an improved and more robust approach for regional flood risk assessment, particularly valuable for ungauged and data-scarce basins, by capturing flood risk variability across different hazard scenarios and accounting for the stochastic characteristics of rare, high-intensity events.
• The framework offers a transparent, reproducible, and transferable methodology for flood-risk prioritization and mitigation planning, contributing directly to Sustainable Development Goals (SDG 11, 6, 13, and 9).
• Empirical validation using Sentinel-1 SAR data from actual flood events demonstrates the practical applicability and reliability of the proposed framework in real-world scenarios.

Funding

• Research on International Cooperation and Partnerships (Penelitian Kerjasama dan Kemitraan Luar Negeri) grant funded by Kupang State Agricultural Polytechnic (2024).
• Japan Science and Technology (JST) Moonshot R&D Program (Grant No. JPMJMS2389).

Citation

@article{Syarifuddin2026Integrating,
  author = {Syarifuddin, Magfira and Oishi, Satoru and Sengadji, Haryati M. and Namah, Chris Natali and Masria, Masria and Hamada, Atsushi},
  title = {Integrating rainfall return periods in MCDA-based flood risk mapping: a fuzzy-AHP case study in an ungauged watershed},
  journal = {Stochastic Environmental Research and Risk Assessment},
  year = {2026},
  doi = {10.1007/s00477-026-03211-4},
  url = {https://doi.org/10.1007/s00477-026-03211-4}
}