A historical data curation (HDC) framework integrated with GIS-based multi-criteria decision analysis to inform flood risk prioritization in data-scarce regions
Abstract
Data-scarce regions lack reliable long-term gauging records and standardized flood databases which constrains classical flood risk assessments and fail to adequately reflect local contexts. Global initiatives emphasize investments in non-traditional data curation to improve the representation of vulnerability and to transform how disaster risk is addressed, complementing systematic flood risk assessment strategies. In this study, we present the outcomes of past flood information curation from newspapers and archives to build a national flood database and evaluate the basin-scale flood risk. We particularly propose a reproducible Historical Data Curation-Multi-Criteria Decision Analysis (HDC-MCDA) framework that transforms qualitative historical archives into standardized quantitative Curated Historical Flood Descriptors (CFD) and integrates them with conventional GISbased MCDA flood risk analysis. We curated a database of 700 flood events (1955-2020) in Lebanon with documented description and impacts. Two CFD, Historical Flood Occurrence (Ne) and Historical Flood Severity (Sev), were developed through text processing and integrated in the MCDA analysis. The study was applied for 16 basins, where hazard and vulnerability are represented by conventional morphological, hydrological and landuse descriptors extracted from available data. Across the studied scenarios, HDC-MCDA led to notable shift in flood risk level, suggesting that HDC captures the realized impact and recalibrates flood risk prioritization by perceived impact information that is often not reflected by scarce hazard and vulnerability data. The study provides a reproducible framework for "soft" documentary data curation and integration into risk assessments, informing prioritization of mitigation and early warning efforts in data-scarce countries.
Related
- Integrating land-use dynamics, water quality, and multi-criteria ecological suitability for fish sanctuary prioritization in three major river basins of Bangladesh
- Feedstock-Informed Technology Selection for Chemical Recycling of Plastic Rejects: An Integrated Physicochemical Characterization and Multi-Criteria Decision Framework
- A novel integrated objective-subjective weighting method using principal component analysis to correct overrepresentation of correlated criteria in sustainability decision-making
- Optimization of UV-C irradiation time for biologically treated saline wastewater using artificial neural networks and a hybrid multi-criteria decision-making framework
- Development of a MOORA multi-criteria decision-making framework to reduce chloride concentrations in urban aquifers using MT3DMS groundwater transport modelling
BibTeX
@article{hdeib2026historical,
title = {{A historical data curation (HDC) framework integrated with GIS-based multi-criteria decision analysis to inform flood risk prioritization in data-scarce regions}},
author = {Rouya Hdeib and Chadi Abdallah and François Colin and Hassan Sabeh and Roger Moussa},
journal = {Journal of Hydrology},
year = {2026},
month = oct,
doi = {10.1016/j.jhydrol.2026.136407},
url = {https://hal.inrae.fr/hal-05785693v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A historical data curation (HDC) framework integrated with GIS-based multi-criteria decision analysis to inform flood risk prioritization in data-scarce regions
AU - Rouya Hdeib
AU - Chadi Abdallah
AU - François Colin
AU - Hassan Sabeh
AU - Roger Moussa
PY - 2026
DA - 2026/10/08
JO - Journal of Hydrology
DO - 10.1016/j.jhydrol.2026.136407
UR - https://hal.inrae.fr/hal-05785693v1
AB - Data-scarce regions lack reliable long-term gauging records and standardized flood databases which constrains classical flood risk assessments and fail to adequately reflect local contexts. Global initiatives emphasize investments in non-traditional data curation to improve the representation of vulnerability and to transform how disaster risk is addressed, complementing systematic flood risk assessment strategies. In this study, we present the outcomes of past flood information curation from newspapers and archives to build a national flood database and evaluate the basin-scale flood risk. We particularly propose a reproducible Historical Data Curation-Multi-Criteria Decision Analysis (HDC-MCDA) framework that transforms qualitative historical archives into standardized quantitative Curated Historical Flood Descriptors (CFD) and integrates them with conventional GISbased MCDA flood risk analysis. We curated a database of 700 flood events (1955-2020) in Lebanon with documented description and impacts. Two CFD, Historical Flood Occurrence (Ne) and Historical Flood Severity (Sev), were developed through text processing and integrated in the MCDA analysis. The study was applied for 16 basins, where hazard and vulnerability are represented by conventional morphological, hydrological and landuse descriptors extracted from available data. Across the studied scenarios, HDC-MCDA led to notable shift in flood risk level, suggesting that HDC captures the realized impact and recalibrates flood risk prioritization by perceived impact information that is often not reflected by scarce hazard and vulnerability data. The study provides a reproducible framework for "soft" documentary data curation and integration into risk assessments, informing prioritization of mitigation and early warning efforts in data-scarce countries.
SN - 0022-1694
SN - 1879-2707
ER -