466 citations · 1.6k across the 11 of their papers we have counts for
12 papers
Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models
Wencong Yang, Leo Lonzarich, Yalan Song +4
As increasingly-damaging floods can strike within hours of a storm and in ungauged reaches, hourly network-wide simulation has become critical societal infrastructure. Here we demo…
Sensitivity-Constrained Neural Operators for Data-Efficient Forward and Inverse Modeling of Partial Differential Equation Systems
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer +1
Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial inputs and inverse or repea…
Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Wencong Yang, Haoyu Ji, Leo Lonzarich +2
Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular ho…
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Haoyu Ji, Yalan Song, Tadd Bindas +9
To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but a…
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models
Amirmoez Jamaat, Yalan Song, Farshid Rahmani +3
Data assimilation (DA) enables hydrologic models to update their internal states using near-real-time observations for more accurate forecasts. With deep neural networks like long…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility
Olukunle O. Owolabi, Kathryn Lawson, Sanhita Sengupta +5
Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing e…
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…
The data synergy effects of time-series deep learning models in hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson +2
When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multi…
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
Wenyu Ouyang, Kathryn Lawson, Dapeng Feng +3
A large fraction of major waterways have dams influencing streamflow, which must be accounted for in large-scale hydrologic modeling. However, daily streamflow prediction for basin…
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
Dapeng Feng, Kathryn Lawson, Chaopeng Shen
While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions w…
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…