HydroChronos: Forecasting Decades of Surface Water Change
This work addresses a gap in water resource management and climate change adaptation by providing standardized data and models for forecasting surface water changes, though it appears incremental in advancing existing methods.
The authors tackled the lack of comprehensive datasets and benchmarks for forecasting surface water dynamics by introducing HydroChronos, a large-scale multi-modal dataset spanning over three decades, and AquaClimaTempo UNet, a novel architecture that outperformed a persistence baseline by +14% and +11% F1 on classification tasks and +0.1 MAE on regression.
Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmarks. In this paper, we introduce HydroChronos, a large-scale, multi-modal spatiotemporal dataset for surface water dynamics forecasting designed to address this gap. We couple the dataset with three forecasting tasks. The dataset includes over three decades of aligned Landsat 5 and Sentinel-2 imagery, climate data, and Digital Elevation Models for diverse lakes and rivers across Europe, North America, and South America. We also propose AquaClimaTempo UNet, a novel spatiotemporal architecture with a dedicated climate data branch, as a strong benchmark baseline. Our model significantly outperforms a Persistence baseline for forecasting future water dynamics by +14% and +11% F1 across change detection and direction of change classification tasks, and by +0.1 MAE on the magnitude of change regression. Finally, we conduct an Explainable AI analysis to identify the key climate variables and input channels that influence surface water change, providing insights to inform and guide future modeling efforts.