Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States

The CONtiguous United States scale (CONUS) Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope (FIM HF IRIS) dataset integrates satellite-derived global IRIS river slopes for 117,357 spatially corresponding main-stream reaches within National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction operational FIM forecasting system (OWP HAND-FIM). A spatial joining approach was first developed to align FIM HF and IRIS reaches, addressing differences in reach flowline sources. Original FIM HF slopes had an average bias of 76 ± 168% relative to IRIS slopes. Applying to OWP HAND-FIM, FIM HF IRIS improved FIM accuracy by average 31 ± 25% (CSI) across eight flood events compared to the FIM HF slopes. Using a common attribute, IRIS data were transferred from FIM HF IRIS to the CONUS Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. Leveraging a common attribute, the resulting datasets enable using SWOT vector data within OWP HAND-FIM and NextGen. The spatial joining approach enabling integrating the hydrofabrics with other hydrologic datasets via flowlines is provided.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-10-20
DOI
https://doi.org/10.1038/s41597-025-05941-6
Citations
2
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
2.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States

Dinuke Munasinghe, Sagy Cohen, Dipsikha Devi, Anupal Baruah et al.
2 citations
Scientific Data
Flood Risk Assessment and Management
2.21
article

Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States

Dinuke Munasinghe, Sagy Cohen, Dipsikha Devi, Anupal Baruah, Yixian Chen, Dan Tian, Supath Dhital
article en
2 citations

Abstract

The CONtiguous United States scale (CONUS) Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope (FIM HF IRIS) dataset integrates satellite-derived global IRIS river slopes for 117,357 spatially corresponding main-stream reaches within National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction operational FIM forecasting system (OWP HAND-FIM). A spatial joining approach was first developed to align FIM HF and IRIS reaches, addressing differences in reach flowline sources. Original FIM HF slopes had an average bias of 76 ± 168% relative to IRIS slopes. Applying to OWP HAND-FIM, FIM HF IRIS improved FIM accuracy by average 31 ± 25% (CSI) across eight flood events compared to the FIM HF slopes. Using a common attribute, IRIS data were transferred from FIM HF IRIS to the CONUS Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. Leveraging a common attribute, the resulting datasets enable using SWOT vector data within OWP HAND-FIM and NextGen. The spatial joining approach enabling integrating the hydrofabrics with other hydrologic datasets via flowlines is provided.

Scientific DataVol. 12(1)
University of Alabama (US)
National Oceanic and Atmospheric Administration
Openalex Percentile: Top 11%
Flood Risk Assessment and Management
2.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States — Dinuke Munasinghe, Sagy Cohen, et al. · Scientific Data (2025) | TGRS Research Map | TGRS