5 papers
Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
Yuan-Heng Wang, Yang Yang, Fabio Ciulla +2
While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded i…
Subsurface Property Mapping using Google AlphaEarth Foundations
Nori Nakata, Jingxiao Liu, Guodong Chen +2
Subsurface properties are essential for hazard assessment, energy and environmental management, and infrastructure resilience, but direct observations are sparse and uneven, motiva…
HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Yihan Wang, Annan Yu, Lujun Zhang +2
Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on…
Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models
Jared D. Willard, Fabio Ciulla, Helen Weierbach +2
The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datase…
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia +1
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources h…