Zhang et al. (2026) Global mapping of potential coastal compound flood risk at 0.1∘ resolution

Identification

• Journal: Communications Earth & Environment
• Year: 2026
• Date: 2026-01-08
• Authors: Jiaqi Zhang, Matteo Convertino
• DOI: 10.1038/s43247-025-03155-7

Research Groups

• Ecosystem Intelligence & Design Center (TREES), and Nature-Positive Design Hub (N+D), Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
• Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Short Summary

This study quantifies global potential coastal compound flood risk at 0.1° resolution by integrating a novel compound flood hazard metric, population exposure, and an empirical vulnerability function. It identifies Asia as having the highest proportion of high-risk areas, with river deltas and low-lying coasts emerging as global hotspots.

Objective

• To quantify global potential coastal compound flood risk (PCCFR) at 0.1° resolution by developing a probability-adjusted compound flood volume metric, a data-driven empirical vulnerability function, and integrating these with population exposure.

Study Configuration

• Spatial Scale: Global mapping at 0.1° resolution (approximately 10 km at the equator), with initial flood inundation data at 30 arc-seconds (approximately 1 km at the equator) aggregated to 0.1° for vulnerability and risk calculations.
• Temporal Scale: Riverine flood hazard data from 1960–1999; oceanic flood hazard data from 1979–2014; population and GDP data for 2010. Flood hazard is assessed across multiple return periods (5, 10, 25, 50, 100, 250, 500, and 1000 years).

Methodology and Data

• Models used:
  • PCR-GLOBWB hydrological model (for riverine flood simulations).
  • Gumbel Copula (for comparison of joint exceedance probability).
  • Quadratic quantile regression (for fitting the upper-envelope function in empirical vulnerability).
• Data sources:
  • World Resources Institute’s Aqueduct Floods Hazard Dataset (2020 version) for riverine and oceanic flood inundation (depth and extent).
  • WorldPop gridded population data (2010) at 30 arc-seconds resolution.
  • Gridded Gross Domestic Product (GDP) data (2010) at 1 km resolution, derived from calibrated nighttime light observations.
  • Global Tide and Surge Reanalysis (GTSR) dataset (for oceanic flood levels).
  • HydroSHED DEM dataset at 30 arc-seconds.
  • World Bank country income classification.
  • Time series daily precipitation data (for sensitivity analysis).

Main Results

• Under the empirical vulnerability scenario, Asia exhibits the highest 35.22% internal high-risk grid cells, followed by Africa (20.21%), Europe (17.02%), South America (9.89%), and North America (2.31%).
• River deltas (e.g., Ganges, Pearl, Niger, Mississippi, Rhine) and low-lying coasts are identified as global compound flood risk hotspots.
• Globally, approximately 117.19 million people and 1.63 trillion USD in assets are exposed to potential coastal compound flood hazards.
• Bangladesh accounts for the largest share of high-hazard grid cells (14.85%) and the highest population exposure (25.71%) to high compound flood hazard, while the Netherlands shows the highest share of asset exposure (37.40%).
• The relationship between compound flood hazard (Vpc) and GDP demonstrates a power-law trend at the subnational level and an inverted-U pattern at the grid scale.
• Different vulnerability assumptions (raw GDP, inverted GDP, empirical vulnerability) significantly alter the spatial distribution of perceived high-risk areas, highlighting the importance of data-driven approaches.
• High-risk subnational administrative units are concentrated in Southeast Asia, including regions in Vietnam, Bangladesh, Thailand, China (Macao), India (West Bengal), Japan (Gifu), and North Korea (Pyongyang).

Contributions

• Introduces a novel probability-adjusted compound flood volume (Vpc) metric that integrates riverine and oceanic flood volumes across multiple return periods under a physically plausible co-occurrence assumption, offering a conservative estimate for design-oriented risk assessment.
• Develops a data-driven empirical vulnerability metric based on the Vpc-GDP relationship, reflecting real-world adaptive responses and providing a more robust and context-sensitive measure than traditional GDP-based proxies.
• Provides the first global mapping of potential coastal compound flood risk (PCCFR) at 0.1° resolution, integrating hazard, population exposure, and empirical vulnerability.
• Offers a strategic screening tool to identify global compound flood risk hotspots, supporting prioritization for detailed local assessments and targeted adaptation efforts.
• Emphasizes the critical need to account for compound flood interactions and the non-linear, context-specific nature of vulnerability for accurate risk assessment and informed policy decisions.

Funding

• Shenzhen Pengcheng Peacock Pengcheng Talents funding (B class, 0020210320) for "ECOgeomorphic and Hydroclimatic ImpactS on Biodiversity and Ecosystem FuNction: Mapping Critical Shifts and ConnEctions" (ECOSENSE).
• Shenzhen Stability Support Grant (WDZC20231128160214001) for "Optimizing Deltas’ Natural Engineers: Systemic Restoration to Counter EcoHydroClimate Extremes".
• Shenzhen Science and Technology Program (ZDSYS20220606100806014) for the Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration at Tsinghua SIGS.
• Tsinghua SIGS Cross-disciplinary Research and Innovation Fund Research Plan "The Fibers of Nature: Ecohydrological Flows Assessment via Distributed Fiber-optic Sensing Networks" (JC2024011).

Citation

@article{Zhang2026Global,
  author = {Zhang, Jiaqi and Convertino, Matteo},
  title = {Global mapping of potential coastal compound flood risk at 0.1∘ resolution},
  journal = {Communications Earth & Environment},
  year = {2026},
  doi = {10.1038/s43247-025-03155-7},
  url = {https://doi.org/10.1038/s43247-025-03155-7}
}