Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources
This work addresses the need for accurate snowpack estimation for water resource managers, offering a more cost-effective and less biased alternative to expensive LiDAR flights or in situ measurements, though it is incremental as it builds on existing data fusion methods.
The paper tackled the problem of estimating snowpack in mountainous water basins by fusing spatial and temporal information from openly-available satellite and weather data sources, resulting in a multisource model that outperformed single-source estimation by 5.0 inches RMSE and sparse in situ measurements by 1.2 inches RMSE.
Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy. Currently, this estimation requires multiple LiDAR-equipped plane flights or in situ measurements, both of which are expensive, sparse, and biased towards accessible regions. In this paper, we demonstrate that fusing spatial and temporal information from multiple, openly-available satellite and weather data sources enables estimation of snowpack in key mountainous regions. Our multisource model outperforms single-source estimation by 5.0 inches RMSE, as well as outperforms sparse in situ measurements by 1.2 inches RMSE.