NWM_CNN_California_AR_2023

This dataset supports the analysis presented in our study 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.

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模型-卷积神经网络（NWM-CNN）进行洪水预测的研究成果分析。旨在提升洪水预测的准确性和应对能力，本集合汇聚了加利福尼亚州在2023年水年度大气河流事件期间发生的洪水事件的全面资料。其中包含NWM-CNN预测、Sentinel-1洪水观测以及加利福尼亚州萨克拉门托县在2023年显著大气河流事件期间及过去数十年的洪水损害历史报告。

数据集的结构旨在便于对NWM-CNN模型在预测地表水域方面的高时间分辨率和准确性进行深入分析。通过整合卫星图像与水动力学模型，NWM-CNN模型在洪水建模领域取得了显著进展，为损害评估、洪水预测以及参数化保险解决方案的支撑提供了有效工具。

数据集的关键组成部分包括：
* NWM-CNN预测：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比较的卫星衍生洪水范围。
* 洪水损害报告：萨克拉门托县内洪水损害的历史记录，为模型效能提供真实对比。

通过公开这些数据，我们旨在通过提高科学信息和资源的获取途径，为减少气候灾害的影响贡献力量。