HydroChronos: Forecasting Decades of Surface Water Change

• URL: http://arxiv.org/abs/2506.14362v2
• Date: Thu, 07 Aug 2025 11:46:02 GMT
• Title: HydroChronos: Forecasting Decades of Surface Water Change
• Authors: Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor, Isaac Corley, Tania Cerquitelli, Elena Baralis, Paolo Garza,
• Abstract summary: We introduce Hydroron, a large-scale, multi- standardized Digital water dynamics dataset for surface water dynamics forecasting.<n>The dataset includes over three decades of Landsat 5 and Sentinel-2 imagery, climate, aligned and Elevation Models for diverse lakes and rivers across Europe, North America, and South America.<n>We also conduct an Explainable AI analysis to identify the key climate variables that influence surface water change, providing insights to inform and guide future modeling efforts.
• Score: 15.577692117754015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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