320 citations · 626 across the 6 of their papers we have counts for
4 papers · 1 filter
StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Nicholas Kraabel, Jiangtao Liu, Yuchen Bian +2
Managing natural resources and mitigating risks from floods, droughts, wildfires, and landslides require models that can accurately predict climate-driven land-surface responses. T…
Probing the limit of hydrologic predictability with the Transformer network
Jiangtao Liu, Yuchen Bian, Chaopeng Shen
For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of dail…
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Dapeng Feng, Jiangtao Liu, Kathryn Lawson +1
Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and wate…
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Wen-Ping Tsai, Dapeng Feng, Ming Pan +5
The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibr…