Architecting an end-to-end observing system for understanding water dynamics

Understanding and forecasting water dynamics is critical to addressing a number of scientific and societal challenges including climate change, disaster mitigation, water resources management, agriculture, food supply, and associated economic impacts. Predicting hydrological flows and stocks using data from space-based, airborne, and in situ measurements, along with assimilation capabilities, are critical for developing hydrological models which can help with increased understanding and enabling response to specific hydrological events such as flooding. Both observing systems and ground-based capabilities need to shift to become more event-driven. To get there, computational capabilities and artificial intelligence both onboard and on the ground require new end-to-end data and analytical architectures which can integrate major capabilities spanning scientific modeling, onboard analytics, automated planning and scheduling, and science data archives.

We will present an architectural approach and testbed under development for NASA's New Observing System that is developing a use case based on understanding hydrological events, particularly river flow, in the western United States. We will discuss the technical data system and data science capabilities under development and the integration of hydrological models with a data and analytics architecture aimed at being able to dynamically task a multi-sensor observing system targeting a future NASA capability. Finally, we will discuss potential partnerships with other agencies in exploring how machine learning models can be integrated to enhance the use of data-driven methods for identifying key events for planning and acquiring new observations.