Leveraging emerging sensing technology and machine learning to improve and expand hydrological forecasting to hyper-local scales with NWM-coupled adaptive sensor networks

Much of the northeastern U.S. is dominated by montane headwater catchments, and recent flooding events illustrate the need for accurate and timely forecasts for such systems. However, model forecast accuracy is often reduced in mountainous regions due to sparse gaging, complex topography, and spatially heterogeneous rainfall/runoff patterns. This project aims to improve the performance and expand the capacity of the National Water Model (NWM) forecasting in montane headwater catchments by achieving three main objectives: 1) assess the NWM performance in montane headwaters, which will improve our understanding of the combinations of geophysical and hydro-climatic forcings that govern streamflow at the subwatershed scale during different events and across seasons, and develop a machine learning correction algorithm that improves flow forecasts in these systems that could be operationalized, 2) deploy a distributed water level sensing network in focal Vermont watersheds in different river corridor environments upstream and downstream of gages that will allow us to use the NWM to forecast water level across different reach environments, and 3) predict water levels at different locations within each focal watershed based on the high-frequency water level data and NWM flow forecasts using machine learning. Upon completion of this project, users and managers of streams in montane watersheds will be able to easily access hyper-localized water level forecasts based on short-range NWM discharge predictions that are post-processed via adaptive sensing and machine learning models. Ultimately, we intend to develop an operational workflow that would allow other communities across the country to improve the performance of these forecasts and leverage relatively low-cost sensor technologies to provide distributed NWM-derived water level forecasts across environments and infrastructures of concern. Improving (through correction algorithms) and expanding (through distributed water level forecasting) the forecast capacity at these sites and providing a template for others to do so will improve operational workflows and extend water resources predictions, capabilities, and applications. Furthermore, the approaches developed here will be particularly suitable for the modular and model-agnostic environment envisioned for the NWM Next Generation Water Resources Modeling Framework (NextGen).