A framework for the evaluation of flood inundation predictions over extensive benchmark databases

Devi, Dipsikha; Dhital, Supath; Munasinghe, Dinuke; Cohen, Sagy; Baruah, Anupal; Chen, Yixian; Tian, Dan; Pruitt, Carson

doi:10.1016/j.envsoft.2025.106786

i

A framework for the evaluation of flood inundation predictions over extensive benchmark databases

2026
By Devi, Dipsikha ; Dhital, Supath ; Munasinghe, Dinuke ; ...

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Journal Title:

Environmental Modelling & Software
Personal Author:

Devi, Dipsikha ; Dhital, Supath ; Munasinghe, Dinuke ; Cohen, Sagy ; Baruah, Anupal ; Chen, Yixian ; Tian, Dan ; Pruitt, Carson
NOAA Program & Office:

NWS (National Weather Service) ; CIROH (Cooperative Institute for Research to Operations in Hydrology)
Description:

Accurate Flood Inundation Mapping (FIM) is essential for forecasting and evaluation. Traditional pixel-based approaches can be time-intensive and error-prone. Here, we introduced the Flood Inundation Mapping Evaluation Framework (FIMeval), an open-source toolset for large-scale FIM evaluation. FIMeval links to a benchmarking database that includes high-quality FIM benchmarks across the Contiguous United States, derived from remote sensing and high-fidelity model-predicted datasets. FIMeval supports pixel-based metrics and integrates impact-based assessments using building footprint data. We demonstrated its application using (a) high-resolution aerial imagery FIM for 2016 Midwest Flood (b) remote sensing-derived benchmarks from Hurricane Matthew (2016), and (b) simulated 100-year and 500-year FIM across 45 Hydrologic Unit Code-8 watersheds using the Federal Emergency Management Agency's Base Level Engineering dataset. The NOAA Office of Water Prediction Height Above Nearest Drainage (OWP HAND-FIM) was the model-predicted FIM for all case studies. We tested the influence of data-imbalance on the scores using two inbuilt methods.
Source:

Environmental Modelling & Software, 196, 106786
DOI:

https://doi.org/10.1016/j.envsoft.2025.106786
ISSN:

1364-8152
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
Funding:

Grant no. NA22NWS4320003
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:bd5b5b848c72ca4c56b4957c2ff1bbbb4c123f2041735ced5f3ee369a11ecadd7d1781c6864976a39c99b87312c04b5fb10d9b7af6ff9f0550f9182c35cc893b
Download URL:

https://repository.library.noaa.gov/view/noaa/72747/noaa_72747_DS1.pdf
File Type:

[PDF - 12.66 MB ]

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