Aboelnour et al. (2026) Mapping tomorrow’s flood: a probabilistic, equity-centered risk assessment for the Indianapolis Metropolitan Area

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-03-09
• Authors: Mohamed Aboelnour, Diogo Bolster
• DOI: 10.1007/s11069-026-08064-2

Research Groups

• Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, USA
• Environmental Change Initiative, University of Notre Dame, USA
• Geology Department, Faculty of Science, Suez Canal University, Egypt

Short Summary

This study develops a high-resolution, probabilistic framework to map current and future urban flood risk in the Indianapolis Metropolitan Area by integrating stochastic precipitation, surface runoff, and a Composite Flood Risk Index (CFRI) that includes social poverty vulnerability and exposure. It finds that climate change will significantly intensify and redistribute flood risk, increasing very-high CFRI zones sevenfold by century's end, especially in previously low-risk suburban areas.

Objective

• To understand how spatial patterns of flood hazard, population exposure, and poverty-based vulnerability interact under present and future climate–socioeconomic scenarios.
• To provide a screening-level, spatially explicit perspective on how socioeconomic assumptions influence the distribution of relative flood risk.

Study Configuration

• Spatial Scale: Indianapolis Metropolitan Area (IMA), approximately 9,200 square kilometers, with all spatial datasets resampled to 200 meters × 200 meters grids.
• Temporal Scale:
  • Historical baseline: Daily precipitation records from 1990 to 2023.
  • Future projections: Three 30-year periods centered on the 2020s, 2050s, and 2080s.
  • Climate scenarios: Representative Concentration Pathway (RCP) 4.5 (moderate emissions) and RCP 8.5 (high emissions).
  • Socioeconomic scenarios: Shared Socioeconomic Pathway (SSP) 2 and SSP5.

Methodology and Data

• Models used:
  • Probabilistic hazard modeling: Monte Carlo simulation (10,000 realizations of daily precipitation per grid cell).
  • Precipitation modeling: Gamma distribution for daily precipitation, Generalized Extreme Value (GEV) distribution for annual maximum daily precipitation, and nonparametric bootstrapping for uncertainty quantification.
  • Surface runoff estimation: Soil Conservation Service Curve Number (SCS-CN) method.
  • Drainage capacity: Modified soil-based formulation incorporating saturated hydraulic conductivity and infrastructure density (road/sewer).
  • Flood risk index: Composite Flood Risk Index (CFRI), adapting the Vulnerability-Adjusted Risk Index (VARI) framework.
  • Social vulnerability: Inverse per capita Gross Domestic Product (GDP), classified into national income quintiles.
  • Exposure: Flood Exposure Population (FEP) calculated as population multiplied by flood exceedance probability.
• Data sources:
  • Daily precipitation records: Ten NOAA weather stations (1990-2023).
  • Future daily precipitation projections: Statistically downscaled (Hybrid Delta method) from ten CMIP5 Global Climate Models (GCMs) for RCP 4.5 and RCP 8.5.
  • Land use data: 2023 National Land Cover Database (NLCD).
  • Road network features: OpenStreetMap.
  • Storm sewer infrastructure: City of Indianapolis open data portal (OpenIndy).
  • Population data: 2020 building-constrained WorldPop dataset, and SSP2 and SSP5 population projections.
  • GDP data: Gridded GDP per capita for current and projected periods (aligned with SSP2–4.5 and SSP5–8.5 scenarios).
  • Soil data: USDA’s SSURGO database.
  • Topography: 10-meter Digital Elevation Model (DEM) from USDA Geospatial Data Gateway.

Main Results

• Extreme Precipitation Uncertainty: Under RCP 4.5, 100-year return levels (RL100) ranged from 114–221 millimeters, with 95% bootstrap confidence intervals of ±20–35%. Under RCP 8.5, RL100 ranged from 130 to over 209 millimeters, with confidence intervals broadening to ±25–40%.
• Flood Hazard Exceedance Probability (PF):
  • Under current conditions, the mean PF was 0.24, with only 2.1% of areas exceeding the very-high threshold (≥ 10 centimeters ponding depth).
  • Under future scenarios, mean PF rose to 0.29 (RCP 4.5) and 0.32 (RCP 8.5), with very-high/high-hazard areas expanding to 12–17%. Highly developed areas reached a PF of 0.636 under RCP 8.5.
  • Climate change increased PF by an average of 30–37% across land types, with some areas experiencing over 60% increase under RCP 8.5, particularly in previously low-risk forested and pasture lands.
• Social Poverty Vulnerability (SPV): Currently, 59% of the IMA falls into the very-high vulnerability zone, primarily in peripheral and suburban counties. Under the RCP 8.5–SSP5 scenario, the proportion of very-high vulnerability areas is projected to rise from 18% to 33%.
• Population Exposure (FEP): Currently, 23% of residents live in high or very-high exposure zones. Under RCP 8.5–SSP5, the share of the population in very-high exposure zones more than doubles from 11% to 27%, and very-low exposure areas plummet from 51% to 5%.
• Composite Flood Risk Index (CFRI):
  • Under current conditions, 8% of the IMA was classified as high risk, concentrated in the northeast and northwest.
  • Under the RCP 8.5–SSP5 scenario, very-high CFRI zones are projected to increase sevenfold (from less than 1% to 7%), and high CFRI areas grow from 8% to 21%. Low-risk zones shrink from 59% to 23%, while medium-risk zones become dominant (33% to 48%).
  • Transition analysis shows that 72% of the IMA experienced shifts in CFRI categories, with 34% moving from low to moderate risk and 10% from low to high risk under the RCP 8.5–SSP5 scenario.

Contributions

• Developed an integrated, high-resolution framework for urban flood risk assessment that combines probabilistic hazard modeling, physically-informed surface runoff estimation, and a Composite Flood Risk Index (CFRI) incorporating exposure and social poverty vulnerability.
• Introduced a novel approach to generate a probabilistic hazard field using Monte Carlo precipitation simulations, capturing the full range of plausible rainfall scenarios and propagating uncertainty.
• Integrated future climate (RCPs) and socioeconomic pathways (SSPs) to analyze the dynamic evolution of hazard–exposure–vulnerability patterns over time.
• Provided a spatially explicit, comparative assessment that highlights the limitations of hazard-only assessments and underscores the importance of integrating socioeconomic dimensions and uncertainty into urban flood risk analyses.
• Generated high-resolution (200 m × 200 m) risk maps that can guide policymakers in targeting adaptation investments, prioritizing vulnerable populations, and designing equitable resilience strategies.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Citation

@article{Aboelnour2026Mapping,
  author = {Aboelnour, Mohamed and Bolster, Diogo},
  title = {Mapping tomorrow’s flood: a probabilistic, equity-centered risk assessment for the Indianapolis Metropolitan Area},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-026-08064-2},
  url = {https://doi.org/10.1007/s11069-026-08064-2}
}