Rapid Agricultural Impact Mapping of Hurricanes Using Open-Source Tools

Hurricanes pose significant threats to agriculture, often resulting in substantial damage to crops, soil, and infrastructure. Efficient assessment methods are crucial for mitigating these impacts and enhancing disaster preparedness. This study presents an open-source workflow that leverages QGIS and Jupyter Notebooks to rapidly assess hurricane impacts on agricultural areas. The workflow automates the integration of publicly accessible datasets from the National Hurricane Center (NHC), observed or forecasted precipitation data from the Office of Water Prediction of NOAA, Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) precipitation data from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), USDA cropland layers, and socioeconomic data from the US Census Bureau. By combining these diverse data sources, the workflow maps wind patterns of hurricanes, potential flood risks or estimated flood inundation depth, cumulative precipitation (using both IMERG data and NOAA estimation), modeled soil erosion, and estimated crop loss using combined hurricane damage indices (e.g., Hurricane Composite Intensity Index), providing a comprehensive analysis and estimation of hurricane-induced damage. The integration of ground observations, model outputs, satellite imagery, and advanced geographic information systems (GIS) capabilities enables timely and accurate identification of affected regions. The inclusion of socioeconomic variables is a critical aspect of the workflow, as it helps identify and prioritize vulnerable rural communities for targeted disaster response. Decision-ready infographics can be created with spatial joins and overlay analysis of all the aligned layers. These infographics can be used by decision-makers to ensure that resources are allocated effectively, addressing the needs of those most impacted by the hurricane. The workflow is designed to be reproducible and replicable. Demonstrated through case studies, the methodology showcases its practical application in real-world scenarios. These case studies highlight the workflow's ability to provide timely insights that are essential for disaster management and recovery efforts. By offering a streamlined and accessible approach, the workflow supports collaboration and innovation among researchers, practitioners, and policymakers. The complete workflow, including reproducible notebooks and tutorials, will be released on GitHub. This repository will serve as a valuable resource for the community, enhancing the accessibility and usability of the tools. By providing detailed documentation and step-by-step guides, the project aims to empower users to implement the workflow in their own contexts. The open-source approach not only facilitates collaboration but also encourages continuous improvement and innovation. This study contributes to agricultural resilience and national disaster preparedness by offering a robust and adaptable solution for assessing hurricane impacts. It demonstrates the capabilities of hurricane impact analysis by leveraging open geospatial web services and open data. The integration of diverse datasets, advanced GIS capabilities, and automated data processing ensures that the workflow can provide comprehensive and timely analyses. The resulting data can be presented as queryable dynamic maps or decision-ready information. By prioritizing vulnerable communities and supporting effective disaster response, the project aims to mitigate the adverse effects of hurricanes on agriculture and promote sustainable recovery efforts.