“Our neural network approaches calibration by learning from the large datasets we have from past readings, while simultaneously considering the physics-based information from the NWM,” Song said. “This allows us to process large datasets very efficiently, without losing the level of detail a physics-based model provides, and at a higher level of consistency and reliability.”
Shen said this approach to calibration is not just efficient, but highly consistent, regardless of the region being simulated.
“The old approach is not only highly inefficient, but quite inconsistent,” Shen said. “With our new approach, we can create simulations using the same process, regardless of the region we are trying to simulate. As we process more data and create more predictions, our neural network will continue to improve. With a trained neural network, we can generate parameters for the entire U.S. within minutes.”
According to Shen, their model is a candidate for use in the next generation framework of NWM that NOAA is developing to improve the standards of flood forecasting around the country. While not yet selected, Shen said their model is “highly competitive” as it is already coupled to this operational framework. However, it may still take time for model users to get comfortable with the AI component of the model, according to Shen, who explained that careful independent evaluations are required to demonstrate the model accuracy can be trusted even in untrained scenarios. The team is working to close the final gap — improving the model’s prediction capability from daily to hourly — to make it more useful for operational applications, like hourly flood watches and warnings. Shen credited the research-to-operation work to civil engineering doctoral candidate Leo Lonzarich, noting that developing a framework other researchers can expand will be key to solving problems and evolving the model as a community.
“Once the model is trained, we can generate predictions at unprecedented speed,” Shen explained. “In the past, generating 40 years of high-resolution data through the NWM could take weeks, and required many different super computers working together. Now, we can do it on one system, within hours, so this research could develop extremely rapidly and massively save costs.”
Although these models are primarily used for flood prediction, simulations provide hydrologists with information that can be used to predict other major events, such as droughts. Such predictions could be used to inform water resource management, which Shen said could have implications for agriculture and sustainability research.
“Because our model is physically interpretable, it can describe river basin features like soil moisture, the baseflow rate of rivers, and groundwater recharge, which is very useful for agriculture and much harder for purely data-driven machine learning to produce,” Shen explained. “We can better understand natural systems that play critical roles in supporting ecosystems and the organisms within them all over the country.”
Alongside Shen and Song, the paper’s co-authors from Penn State include Tadd Bindas, who recently earned a doctorate in civil and environmental engineering; Kathryn Lawson, a research associate of deep learning in hydrology; and Penn State civil and environmental engineering doctoral candidates Haoyu Ji, Leo Lonzarich, Jiangtao Liu, Farshid Rahmani and Kamlesh Arun Sawadekar.
Additional co-authors include Wouter J.M. Knoben Cyril Thébault and Martyn P. Clark, University of Calgary; Katie van Werkhoven, Sam Lamont and Matthew Denno, Research Triangle Institute; Ming Pan and Yuan Yang, Scripps Institution of Oceanography, University of California, San Diego; Jeremy Rapp, Michigan State University; Mukesh Kumar, Richard Adkins, James Halgren, Trupesh Patel and Arpita Patel, University of Alabama. The Penn State team thanked the computing support offered by University of Alabama.
The Cooperative Institute for Research to Operations in Hydrology’s National Oceanic and Atmospheric Administration’s Cooperative Agreement, as well as the U.S. Department of Energy, National Energy Research Scientific Computing Center and the California Department of Water Resources Atmospheric River Program supported this research.
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