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

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.

本数据集旨在支持我们关于利用新型水文模型-卷积神经网络（NWM-CNN）进行洪水预测的论文分析（https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL109424）。旨在提升洪水预报能力和抗灾韧性，本集合全面收录了2023年水年度大气河流事件期间加利福尼亚州发生的洪水事件。其中包括NWM-CNN预测、Sentinel-1洪水观测以及加利福尼亚州萨克拉门托郡在2023年重大大气河流事件期间以及过去几十年内的历史洪水损害报告。

该数据集的结构旨在促进对NWM-CNN模型在预测地表水域方面高时间分辨率和准确性的详细分析。通过整合卫星图像与水动力学建模，NWM-CNN模型在洪水建模领域取得了重大进步，为灾害评估、洪水预报以及参数化保险解决方案的支撑提供了一种有效工具。

数据集的关键组成部分包括：
* NWM-CNN预测：以250米网格单元分辨率的模型预测。
* Sentinel-1洪水观测：用于与模型预测进行对比的卫星衍生洪水范围。
* 洪水损害报告：提供模型有效性对比的萨克拉门托郡历史洪水损害记录。

分析此数据的相关代码可在此处获取：
Jonathan Frame. (2024). jmframe/NWM_CNN_california_AR_2023: GRL论文校样1（1.0.0）. Zenodo. https://zenodo.org/records/13153247. DOI: 10.5281/zenodo.13153247

通过公开提供这些数据，我们旨在通过提升对科学信息和资源的获取，为减少气候灾害的影响做出集体努力。