We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning could be for the future of hydrological modeling. Given the recent success of machine learning applied to modeling problems, it is unclear what the role of hydrological theory might be in the future. We suggest that a central challenge in hydrology right now should be to clearly delineate where and when hydrological theory adds value to prediction systems. Lessons learned from the history of hydrological modeling motivate several clear next steps toward integrating machine learning into hydrological modeling workflows.
Natel Energy is recognized for the creation of a first-of-its kind fish passage test lab, designed and built at the hydropower company’s Alameda, CA headquarters.
A great video explanation of the Center Sender, featuring Sterling Watson.