On the advancing frontier of deep learning in hydrology: a hydrologic applications perspective

In the last decade, the realization that certain deep learning (DL) architectures are particularly well-suited to the simulation and prediction of hydrologic systems and their characteristic memory-influenced dynamics has led to remarkable rise in DL-centered hydrologic research and applications.  Numerous new datasets, computational and open software resources, and progress in related fields such as numerical weather prediction have also bolstered this growth.  Advances in DL for hydrologic forecasting research and operations is likely the most eye-catching and intuitive use case, but DL methods are now also making inroads into more process-intensive hydrologic modeling contexts, and among groups that have been skeptical of their potential suitability despite performance-related headlines. Nevertheless, even in the forecasting context, and despite offering new strategies and concepts to resolve long-standing hurdles in hydrologic process-based modeling efforts, the uptake of DL-based systems in many public-facing services and applications has been slow. 

This presentation provides perspective on the ways in which DL techniques are garnering interest in traditionally process-oriented modeling arenas -- from flood and drought forecasting to watershed studies to hydroclimate risk modeling – and on sources of hesitancy.  Clear pathways, momentum and motivations for DL approaches to supplant process-based models exist in some applications, whereas in others, governing interests and constraints appear likely to restrict DL innovations to narrower niches.  Concerns over explainability have been a common topic, but less discussed questions about fitness or adequacy for purpose and institutional requirements can also be influential.  Drawing from relevant hydrologic modeling programs, projects and initiatives in the US and elsewhere, we aim to provide a real-world status update on the advancing frontier of deep learning in applied hydrologic science and practice.