洪水灾害制图对防灾减灾至关重要，但在数据匮乏地区仍面临挑战，因传统水动力模型需大量地球物理输入。本文提出\textit{ZeroFlood}框架，利用地理基础模型（GeoFMs）仅依靠单模态遥感观测（EO）数据——特别是合成孔径雷达（SAR）影像——预测洪水灾害图。我们构建了一个覆盖欧洲大陆的数据集，将EO数据与洪水灾害模拟结果配对。基于该数据集，我们评估了若干近期GeoFMs在洪水灾害分割任务上的性能。实验结果表明，表现最优的模型TerraMind达到88.36\%的F1分数，较监督学习基线提升逾3个百分点。我们进一步证明，引入模态内思考（Thinking-in-Modality, TiM）机制可进一步提升性能。这些结果验证了地理基础模型在仅依赖有限观测输入的数据驱动型洪水灾害制图中的潜力。该数据集与实验代码已公开发布于https://github.com/khyeongkyun/zeroflood。

Flood hazard mapping is essential for disaster prevention but remains challenging in data-scarce regions, where traditional hydrodynamic models require extensive geophysical inputs. This paper introduces \textit{ZeroFlood}, a framework that leverages Geo-Foundation Models (GeoFMs) to predict flood hazard maps using single-modality Earth Observation (EO) data, specifically SAR imagery. We construct a dataset that pairs EO data with flood hazard simulations across the European continent. Using this dataset, we evaluate several recent GeoFMs for the flood hazard segmentation task. Experimental results show that the best-performing model, TerraMind, achieves an F1-score of 88.36\%, outperforming supervised learning baselines by more than 3 percentage points. We shows the performance can be further improved by applying the Thinking-in-Modality (TiM) mechanism. These results demonstrate the potential of Geo-Foundation Models for data-driven flood hazard mapping using limited observational inputs. The dataset and experiment code are publicly available at https://github.com/khyeongkyun/zeroflood.