Community Water Resources Modeling

This project will build advanced cyberinfrastructure resources necessary to fuse a diverse array of existing and new heterogeneous data sources for supporting expanded evaluation data testbeds in the Northeast. The testbeds will be derived from the following three use-cases to be investigated by the UVM team: (1) Improving the representation of floodplain effects on hydraulic routing and flood inundation modeling and mapping (flood forecasting); (2) Coupling novel low cost spatially distributed nutrient sensors and National Water Model output to forecast nutrient loading and inform state implementation of EPA mandated nutrient reduction targets (nutrient load forecasting); and (3) Forecasting the incidence and duration of harmful algal blooms (HABs) at daily, weekly, and seasonal scales (HABs forecasting). These three Northeast CIROH testbeds will generate and consume a wide array of eco-hydrological observations and field data, such as watershed/stream flows, nutrient concentrations, and lake water quality sensors as well as remote sensing data from drones and satellites. The proposed cyberinfrastructure will provide a centralized location for the CIROH team to find data and code for evaluation, utilities and containers to analyze and reformat data, documentation for the various data sources, and operational user outreach and facilitation to help the CIROH forecast teams use the data for continuously improving the forecast accuracy. The primary result of this work will be a Northeast testbed data portal and the underlying cyberinfrastructure with links and guided instructions to external data sources, direct access to data generated from the Northeast testbeds and legacy data from previous related projects in the Northeast, supporting code and documentation, and technical support for the use of the resources provided in the portal. Collecting all data, software tools, and documentation in a single location will streamline access to evaluation data for researchers, provide valuable resources for the use of the data, and can serve as a point of contact for external collaborators and operational teams at NOAA.

Despite significant advancements in satellite monitoring of HABs, the "accurate" forecasting of HABs and development of "real-time" HABs Early Warning Systems (HABEWS) at finer spatial (200 m to 500 m) and temporal (daily to seasonal) scales still requires a considerable amount of basic and applied research. In this sub-project, we will advance the National Water Model (NWM’s) predictive intelligence for early warnings of HABs at daily, weekly, and seasonal lead time scales by applying machine learning, process-based modeling, and hybrid frameworks that couple the two. This project will scale and test an approach to forecasting HABs in freshwater lakes and estuaries that leverages hydrological forecasts derived from NWM and existing Earth Observation datasets currently being produced in real time through satellites and in situ monitoring systems and sensors. In year 1, WRF-Hydro derived NWM hydrological hindcasts and forecasts will be embedded in an existing Integrated Assessment Model (IAM) computational workflow to drive an already calibrated and validated process-based lake model (AEM3D). The IAM also uses weather data that can be derived from National Weather Model and/or IBM weather forecast products. This workflow will produce high resolution HAB hindcasts and forecasts in two bays of Lake Champlain (Missisquoi Bay and St. Albans Bay). In year 2, we will develop and test a self-learning AI-HABEWS by identifying a best-fitting deep neural network to update AEM3D by validating HAB forecasts through community science monitors, and in situ & satellite sensors. In year 3, we will conduct a sensitivity analysis of HAB forecast accuracy (as predicted by machine-learning, process based, and hybrid forecast models) to hydrological forecasts based on NWM (both WRF-Hydro and Topmodel versions), SWAT, and RHESSys models. This sub-project will advance NOAA's mission to understand and predict the effects of changing climate, weather, and socio-environmental factors on marine ecosystems. This may, in turn, help conserve marine ecosystems and further advance NOAA's vision of building resilient and healthy ecosystems. We expect to publish six peer reviewed articles in high impact journals, along with the publication of open science code and documentation of the combined IAM, NWM, and AI-HABEWS workflow; deliver nine national/international conference presentations; and mentor two interdisciplinary post-docs and one Computer Science PhD student in this 3-year project.