A forecasting scheme for accelerated harmful algal bloom monitoring (FASTHAB)

About half of the world's population lives within ~ 200 kilometers of coastlines near oceans or fresh waters. These coastal zones and their ecosystem services massively contribute to the well-being and economy of the immediate residents. The changing climate that bears extreme weather patterns (e.g., heatwaves, extended wet/dry periods), along with human developments (e.g., urbanization, intensified agriculture/aquaculture), pose significant risks to human settlements and coastal environments. One cascading effect of climate change and anthropogenic activities is the increased frequency, intensity, and extent of harmful algal blooms (HABs) in coastal oceans, estuaries, and freshwater ecosystems. Forecasting these HAB characteristics is crucial for effective resource management and decision-making. A reliable and advanced early warning system for water quality (WQ) conditions and HABs is a desired functionality of any forecasting framework regardless of its underlying mechanisms/observations. For instance, existing coupled hydrodynamic-biogeochemical models (i.e., process-based models; PBMs) are complex, computationally demanding, and require extensive calibration/tuning; hence, they are not commonly utilized for operational short-term (monthly) forecasting. On the other hand, satellite-based prediction tools are hampered by cloud coverage, sometimes leaving large swaths of an ecosystem unmeasured for extended periods and returning poor forecasting skills. By leveraging NASA's invaluable historical ocean color (OC) products, simulated PBM outputs (e.g., salinity), and physical forcing data (e.g., wind speed, air temperature), we propose to develop a fast and efficient machine learning (ML) scheme for predicting WQ variables and potential HAB events. These WQ variables include chlorophyll-a (Chla), Total Suspended Solids (TSS), Secchi disk depth (Zsd), temperature (T), and salinity (S), all of which are essential indicators and drivers of HABs. Our spatiotemporal core ML model (i.e., Convolutional Long Short-Term Memory) that uses the Monte Carlo Dropout technique will enable the forecasting of WQ conditions and their associated uncertainties 1-30 days in advance. We will initially train our scheme (FASTHAB) with data in the 2000-2021 timespan and plan to thoroughly validate its forecasting skill in the 2022-2024 timeframe using in situ and/or OC-derived WQ data for any chosen 30-day period, for which only physical forcing data is required/used for prediction. Our forecasting scheme will be prototyped for the Chesapeake Bay ecosystem -- the largest estuary in North America -- for two reasons: a) its significant regional/national socioeconomic importance because of its fisheries, aquaculture, and recreation/tourism, and b) its long-term history of in situ WQ monitoring program rendering it a suitable testbed for research and developments. We will ultimately integrate FASTHAB into an existing (AIST-funded) Artificial intelligence (AI) framework to ensure its future use in compliance with Earth System Digital Twins (ESDT). Our team, composed of remote sensing experts, computer scientists, modelers, HAB ecologists, and aquaculture specialists, will address the challenges of this Early-Stage Technology (EST) project to advance the nation's WQ and HAB forecasting skills by incorporating past, current, and future OC observations and products. Our readily generalizable and scalable FASTHAB scheme will allow for early identifications of hotspots across the Chesapeake Bay ecosystem in support of resource management and decision-making, enabling apt mitigation actions essential to public health, ecosystem recovery, fisheries, and aquaculture operations.

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.