Transient Events: A Rapid-Response, Multi-Platform Observing Strategy for Detecting and Understanding Abrupt Change in Aquatic Ecosystems

Abstract

Extremes, not average conditions, increasingly govern the state of aquatic ecosystems and the risks they carry for people. Events far outside normal variability can reshape ecosystem structure and function within hours to weeks, and both the observational record and climate projections indicate such extremes are growing more frequent, more intense, and longer lasting. Whether an extreme becomes a disaster is largely a question of anticipation: whether it is detected early, whether its trajectory can be predicted, and whether that prediction becomes a warning in time to act. These Transient Events take many forms, including harmful algal and bacterial bloom outbreaks, marine heatwaves, oil and industrial chemical spills, volcanic ash deposition, wildfires, hurricanes, storms, river discharge and combined sewer outflows, rapid coastal flooding, erosion, coral bleaching, or intense acidification pulses. Despite their diversity, these events share a common observational challenge: their episodic and often unpredictable timing, location and duration are poorly matched to the fixed sampling intervals and revisit frequencies of most operational observing systems. This white paper presents the science case for an integrated observing and modeling strategy capable of detecting, quantifying, and predicting marine responses to Transient Events, and for translating those predictions into timely warnings. No single platform can meet the combined demands of fine spatial detail, rapid revisit frequency, adequate spectral discrimination, and all-weather capability that these events require. Addressing this challenge within NASA’s Hydrosphere and Biosphere will require an integrated observing and prediction framework that combines: sustained and expanded satellite ocean color observations, including geostationary continuity; appropriately calibrated low-cost CubeSat constellations capable of increasing spatial and temporal sampling; a coordinated, rapidly deployable suite of suborbital airborne (crewed and uncrewed), underwater autonomous and in-situ assets; edge-computing AI to automatically detect anomalies and trigger downstream tasking; targeted disturbance-ecology field campaigns paired with standby rapid response capability; and integrated modeling and data assimilation to predict event evolution. We recommend priority investments in each of these elements, together with the data infrastructure, algorithm development, and interagency coordination needed to convert observations into timely and actionable intelligence for coastal communities, resource managers, and emergency responders.