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Rapid inundation mapping using NWM, satellite observations, and a convolutional neural network - Demonstrated on California Atmospheric Rivers 2023


An older version of this resource http://www.hydroshare.org/resource/dbf8e4c2a39a4c228db867b04f9c21ed is available.
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Created: Aug 01, 2024 at 1:14 p.m.
Last updated: Sep 25, 2024 at 4:33 p.m. (Metadata update)
Published date: Aug 01, 2024 at 8:01 p.m.
DOI: 10.4211/hs.8b76906c4b604c458fbcb5ea7c8c0be7
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Sharing Status: Published
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Abstract

This dataset supports the analysis presented in our paper (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL109424) on flood prediction using the Novel Water Model-Convolutional Neural Network (NWM-CNN). Aimed at enhancing flood forecasting and resilience, this collection encompasses a comprehensive compilation of flood events across California during the Atmospheric River events during the water year 2023. Included are NWM-CNN predictions, Sentinel-1 flood observations, and historical flood damage reports for Sacramento County, California, during the significant 2023 atmospheric river event and over the past decades.

The dataset is structured to facilitate a detailed examination of the NWM-CNN model's performance in predicting surface water area with high temporal resolution and accuracy. By integrating satellite imagery with hydrodynamic modeling, the NWM-CNN model represents a significant advancement in flood modeling, offering an effective tool for damage assessment, flood forecasting, and supporting parametric insurance solutions.

Key components of the dataset include:
* NWM-CNN Predictions: Model predictions at a 250m grid cell resolution.
* Sentinel-1 Flood Observations: Satellite-derived flood extents used for comparison against model predictions.
* Flood Damage Reports: Historical records of flood damage within Sacramento County, providing a ground-truth comparison for model efficacy.

Code to analyze this data is available here:
Jonathan Frame. (2024). jmframe/NWM_CNN_california_AR_2023: GRL Paper Proof 1 (1.0.0). Zenodo. https://zenodo.org/records/13153247. DOI: 10.5281/zenodo.13153247

By making this data publicly available, we aim to contribute to the collective efforts in reducing the impacts of climate disasters through improved access to scientific information and resources.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
42.3780°
East Longitude
-113.8184°
South Latitude
32.2723°
West Longitude
-125.2441°

Temporal

Start Date:
End Date:

Content

Related Resources

This resource updates and replaces a previous version Frame, J. M. (2024). NWM_CNN_California_AR_2023, HydroShare, http://www.hydroshare.org/resource/dbf8e4c2a39a4c228db867b04f9c21ed

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Floodbase

How to Cite

Frame, J. M. (2024). Rapid inundation mapping using NWM, satellite observations, and a convolutional neural network - Demonstrated on California Atmospheric Rivers 2023, HydroShare, https://doi.org/10.4211/hs.8b76906c4b604c458fbcb5ea7c8c0be7

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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