Leveraging Large Language Models for Global Assessment of National Flood Adaptation Plans

With millions of people exposed globally, riverine floods are one of the major natural hazards worldwide, resulting in a direct average annual loss of US$ 104 billion and 7 million fatalities in the twentieth century. Amidst increasing calls for accelerating climate adaptation, including the recent UNEP report, a pivotal question remains: what are the status, effectiveness, and potential of adaptation efforts to reduce future flood risks? National adaptation plans play a central role in climate risk governance by driving adaptation, yet their length and heterogeneity in language, content organization, and format pose challenges to a systematic and scalable comparison across countries. Extracting structured information from these plans requires advanced methods from natural language processing (NLP) and machine learning.

We first compile a dataset including national flood plans from different countries worldwide using a hybrid information retrieval strategy that integrate manual keyword search, GPT-5.1–assisted queries, community engagement through surveys and direct outreach, and manual validation. Building on this dataset, we implement a language model-based workflow for topic modelling and content analysis. Our workflow combines text preprocessing, embedding, and a guided topic modelling step that incorporates 18 predefined categories of flood adaptation measures from the EU Floods Directive, such as emergency response planning and water flow regulation. Our approach enables structured analysis of flood adaptation plans, mapping of measure diversity and prevalence across countries and regions, and identification of correlations with hazard characteristics, damages, and economic indicators. In addition, our workflow supports the detection of emerging or overlooked adaptation measures.