Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
arXiv:2601.01558v2 Announce Type: replace Abstract: Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils. Traditional basin attributes describe some of these differences, but they cannot fully represent the complexity of natural environments. This study examines whether AlphaEarth Foundation embeddings, which are learned from large collections of satellite images rather than designed by experts, offer a more informative way to describe basin characteristics. These embeddings summarize patterns in
The increasing maturity of foundation models for earth observation (like AlphaEarth) coincides with growing global hydrological stress, enabling AI to tackle complex environmental challenges more effectively.
Improved hydrological models directly address water scarcity issues and enhance resource management, critical for economic stability and adaptation to climate change.
The reliance on expert-designed basin attributes is shifting towards AI-learned embeddings, offering a more nuanced and potentially more accurate understanding of complex natural environments for predictive modeling.
- · Hydrological modeling agencies
- · Water resource management organizations
- · Agricultural sector
- · AI-driven earth observation companies
- · Traditional hydrological modeling approaches
- · Sectors reliant on outdated water forecasting methods
More accurate river flow predictions, particularly in poorly-gauged or ungauged basins.
Enhanced resilience to droughts and floods through better planning and mitigation strategies.
Potential for optimized agricultural yields and industrial water usage, reducing geopolitical tension over shared water resources.
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Read at arXiv cs.LG